20-supplementary-files ================ Kathleen Durkin 2026-06-15 - [1 Machinery.fasta reference table](#1-machineryfasta-reference-table) - [2 Epimachinery category labels for Machinery.fasta table](#2-epimachinery-category-labels-for-machineryfasta-table) - [3 Alignment summary tables](#3-alignment-summary-tables) - [3.1 RNA-seq](#31-rna-seq) - [3.1.1 1.1 Raw reads](#311-11-raw-reads) - [3.1.2 1.2 Trimmed reads](#312-12-trimmed-reads) - [3.1.3 1.3 Retained after trimming (%)](#313-13-retained-after-trimming-) - [3.1.4 1.4 Alignment rate, unique %, multi %](#314-14-alignment-rate-unique--multi-) - [3.1.5 1.5 Mismatch %](#315-15-mismatch-) - [3.1.6 1.6 Number of lncRNAs identified](#316-16-number-of-lncrnas-identified) - [3.2 sRNA-seq alignment statistics](#32-srna-seq-alignment-statistics) - [3.2.1 2.1 Species metadata (genome)](#321-21-species-metadata-genome) - [3.2.2 2.1 Raw reads](#322-21-raw-reads) - [3.2.3 2.2 Trimmed reads](#323-22-trimmed-reads) - [3.2.4 2.3 Retained after trimming (%)](#324-23-retained-after-trimming-) - [3.2.5 2.4 ShortStack alignment stats](#325-24-shortstack-alignment-stats) - [3.2.6 2.5 Mapped miRNA reads](#326-25-mapped-mirna-reads) - [3.2.7 2.6 Number of miRNAs identified](#327-26-number-of-mirnas-identified) - [4 Mature miRNA names and sequences for all species](#4-mature-mirna-names-and-sequences-for-all-species) - [5 miRanda binding information + PCC coexpression (miRNA-mRNA and miRNA-lncRNA)](#5-miranda-binding-information--pcc-coexpression-mirna-mrna-and-mirna-lncrna) - [5.1 miRNA-mRNA](#51-mirna-mrna) - [5.2 miRNA-lncRNA](#52-mirna-lncrna) Code for any compiling/formating of supplementary figures/tables for the DDE manuscript. # 1 Machinery.fasta reference table ``` r protein_names <- c( # DNA methylation "DNMT1" = "DNA (cytosine-5-)-methyltransferase 1", "DNMT3A" = "DNA (cytosine-5-)-methyltransferase 3 alpha", "DNMT3B" = "DNA (cytosine-5-)-methyltransferase 3 beta", "DNMT3L" = "DNA (cytosine-5-)-methyltransferase 3 like", # TET dioxygenases "TET1" = "Methylcytosine dioxygenase TET1", "TET2" = "Methylcytosine dioxygenase TET2", "TET3" = "Methylcytosine dioxygenase TET3", # Methyl-CpG binding domain "MBD1" = "Methyl-CpG-binding domain protein 1", "MBD2" = "Methyl-CpG-binding domain protein 2", "MBD3" = "Methyl-CpG-binding domain protein 3", "MBD4" = "Methyl-CpG-binding domain protein 4", "MBD5" = "Methyl-CpG-binding domain protein 5", "MBD6" = "Methyl-CpG-binding domain protein 6", # UHRF "UHRF1" = "E3 ubiquitin-protein ligase UHRF1", "UHRF2" = "E3 ubiquitin-protein ligase UHRF2", # ZBTB readers "ZBTB33" = "Zinc finger and BTB domain-containing protein 33 (Kaiso)", "ZBTB4" = "Zinc finger and BTB domain-containing protein 4", "ZBTB38" = "Zinc finger and BTB domain-containing protein 38", "ZFP57" = "Zinc finger protein 57", "KLF4" = "Krueppel-like factor 4", "EGR1" = "Early growth response protein 1", "WT1" = "Wilms tumor protein", "CTCF" = "CCCTC-binding factor", # HDACs "HDAC1" = "Histone deacetylase 1", "HDAC2" = "Histone deacetylase 2", "HDAC3" = "Histone deacetylase 3", "HDAC4" = "Histone deacetylase 4", "HDAC5" = "Histone deacetylase 5", "HDAC6" = "Histone deacetylase 6", "HDAC7" = "Histone deacetylase 7", "HDAC8" = "Histone deacetylase 8", "HDAC9" = "Histone deacetylase 9", "HDAC10" = "Histone deacetylase 10", "HDAC11" = "Histone deacetylase 11", # Sirtuins "SIRT1" = "NAD-dependent protein deacetylase sirtuin-1", "SIRT2" = "NAD-dependent protein deacetylase sirtuin-2", "SIRT3" = "NAD-dependent protein deacetylase sirtuin-3", "SIRT4" = "NAD-dependent protein lysine acylase sirtuin-4", "SIRT5" = "NAD-dependent protein lysine desuccinylase sirtuin-5", "SIRT6" = "NAD-dependent protein deacylase sirtuin-6", "SIRT7" = "NAD-dependent protein desuccinylase sirtuin-7", # KATs "KAT1-HAT1" = "Histone acetyltransferase type B catalytic subunit (HAT1/KAT1)", "KAT2A" = "Histone acetyltransferase KAT2A (GCN5)", "KAT2B" = "Histone acetyltransferase KAT2B (PCAF)", "KAT3A-CREBBP" = "CREB-binding protein (CREBBP/KAT3A)", "KAT3B-EP300" = "Histone acetyltransferase p300 (EP300/KAT3B)", "KAT4-TAF1" = "Transcription initiation factor TFIID subunit 1 (TAF1/KAT4)", "KAT5" = "Histone acetyltransferase KAT5 (Tip60)", "KAT6A" = "Histone acetyltransferase KAT6A (MOZ)", "KAT6B" = "Histone acetyltransferase KAT6B (MORF)", "KAT7" = "Histone acetyltransferase KAT7 (HBO1)", "KAT8" = "Histone acetyltransferase KAT8 (MOF)", "KAT9-ELP3" = "Elongator complex protein 3 (ELP3/KAT9)", "KAT12-GTF3C4" = "Transcription factor IIIC subunit 4 (GTF3C4/KAT12)", "KAT13A-NCOA1" = "Nuclear receptor coactivator 1 (NCOA1/KAT13A)", "KAT13B-NCOA3" = "Nuclear receptor coactivator 3 (NCOA3/KAT13B)", "KAT13C-NCOA2" = "Nuclear receptor coactivator 2 (NCOA2/KAT13C)", "KAT13D-CLOCK" = "Circadian locomoter output cycles protein kaput (CLOCK/KAT13D)", "KAT14" = "Histone acetyltransferase KAT14 (CSRP2BP)", # KMTs "KMT1C-EHMT2" = "Histone-lysine N-methyltransferase EHMT2 (G9a/KMT1C)", "KMT1D-EHMT1" = "Histone-lysine N-methyltransferase EHMT1 (GLP/KMT1D)", "KMT1E-SETDB1" = "Histone-lysine N-methyltransferase SETDB1 (KMT1E)", "KMT1F-SETDB2" = "Histone-lysine N-methyltransferase SETDB2 (KMT1F)", "SUV39H1-KMT1A" = "Histone-lysine N-methyltransferase SUV39H1 (KMT1A)", "SUV39H2-KMT1B" = "Histone-lysine N-methyltransferase SUV39H2 (KMT1B)", "KMT2A" = "Histone-lysine N-methyltransferase 2A (MLL1)", "KMT2B" = "Histone-lysine N-methyltransferase 2B (MLL4)", "KMT2C" = "Histone-lysine N-methyltransferase 2C (MLL3)", "KMT2D" = "Histone-lysine N-methyltransferase 2D (MLL2)", "KMT2E" = "Histone-lysine N-methyltransferase 2E (MLL5)", "KMT2F-SETD1A" = "Histone-lysine N-methyltransferase SETD1A (KMT2F)", "KMT2G-SETD1B" = "Histone-lysine N-methyltransferase SETD1B (KMT2G)", "KMT2H-ASH1L" = "Histone-lysine N-methyltransferase ASH1L (KMT2H)", "KMT3A-SETD2" = "Histone-lysine N-methyltransferase SETD2 (KMT3A)", "KMT3B-NSD1" = "Histone-lysine N-methyltransferase NSD1 (KMT3B)", "KMT3C-SMYD2" = "SET and MYND domain-containing protein 2 (SMYD2/KMT3C)", "KMT4-DOT1L" = "Histone-lysine N-methyltransferase DOT1L (KMT4)", "KMT5A\u2014SETD8" = "Histone-lysine N-methyltransferase SETD8 (KMT5A)", "KMT5B" = "Histone-lysine N-methyltransferase KMT5B (SUV420H1)", "KMT5C" = "Histone-lysine N-methyltransferase KMT5C (SUV420H2)", "KMT6-EZH2" = "Histone-lysine N-methyltransferase EZH2 (KMT6)", "KMT7-SETD7/9" = "Histone-lysine N-methyltransferase SETD7 (KMT7)", "KMT8-PRDM2" = "PR domain zinc finger protein 2 (PRDM2/KMT8)", "ASH2L" = "Set1/Ash2 histone methyltransferase complex subunit ASH2", "WHSC1-NSD2" = "Histone-lysine N-methyltransferase NSD2 (WHSC1/MMSET)", "WHSC1L-NSD3" = "Histone-lysine N-methyltransferase NSD3 (WHSC1L1)", "EZH1" = "Histone-lysine N-methyltransferase EZH1", "SETD3" = "Histone methyltransferase SETD3", "SETD4" = "SET domain-containing protein 4", "SETD5" = "SET domain-containing protein 5", "SETD6" = "N-lysine methyltransferase SETD6", "SETMAR" = "Histone-lysine N-methyltransferase SETMAR (Metnase)", "SMYD1" = "SET and MYND domain-containing protein 1", "SMYD3" = "SET and MYND domain-containing protein 3", "SMYD4" = "SET and MYND domain-containing protein 4", "SMYD5" = "SET and MYND domain-containing protein 5", # KDMs "KDM1A" = "Lysine-specific histone demethylase 1A (LSD1)", "KDM1B" = "Lysine-specific histone demethylase 1B (LSD2)", "KDM2A" = "Lysine-specific demethylase 2A", "KDM2B" = "Lysine-specific demethylase 2B", "KDM3A" = "Lysine-specific demethylase 3A", "KDM3B" = "Lysine-specific demethylase 3B", "KDM3C-JMJD1C" = "Lysine-specific demethylase JMJD1C (KDM3C)", "KDM4A" = "Lysine-specific demethylase 4A", "KDM4B" = "Lysine-specific demethylase 4B", "KDM4C" = "Lysine-specific demethylase 4C", "KDM4D" = "Lysine-specific demethylase 4D", "KDM5A" = "Lysine-specific demethylase 5A (JARID1A)", "KDM5B" = "Lysine-specific demethylase 5B (JARID1B)", "KDM5C" = "Lysine-specific demethylase 5C (JARID1C)", "KDM5D" = "Lysine-specific demethylase 5D (JARID1D)", "KDM6A" = "Lysine-specific demethylase 6A (UTX)", "KDM6B" = "Lysine-specific demethylase 6B (JMJD3)", "KDM7A" = "Lysine-specific demethylase 7A (JHDM1D/PHF8-related)", "KDM8" = "Lysine-specific demethylase 8 (JMJD5)", "UTY" = "Inactive histone demethylase UTY", "PHF2" = "PHD finger protein 2 (histone demethylase)", "PHF8" = "Histone lysine demethylase PHF8", "JMJD6" = "Bifunctional arginine demethylase and lysyl-hydroxylase JMJD6", "JMJD7" = "Lysine demethylase JMJD7", "JMJD8" = "JMJD8 protein", "JARID2" = "Protein Jumonji (JARID2)", "HR" = "Lysine demethylase and nuclear receptor corepressor HR", "RIOX1" = "Ribosomal oxygenase 1 (NO66)", "RIOX2" = "Ribosomal oxygenase 2 (MINA53)", "TYW5" = "tRNA wybutosine-synthesizing protein 5", # PRDM "PRDM1" = "PR domain zinc finger protein 1 (Blimp1)", "MECOM-PRDM3" = "EVI1/MDS1 and EVI1 complex locus protein (MECOM/PRDM3)", "PRDM4" = "PR domain zinc finger protein 4", "PRDM5" = "PR domain zinc finger protein 5", "PRDM6" = "PR domain zinc finger protein 6", "PRDM7" = "PR domain zinc finger protein 7", "PRDM8" = "PR domain zinc finger protein 8", "PRDM9" = "PR domain zinc finger protein 9", "PRDM10" = "PR domain zinc finger protein 10", "PRDM11" = "PR domain zinc finger protein 11", "PRDM12" = "PR domain zinc finger protein 12", "PRDM13" = "PR domain zinc finger protein 13", "PRDM14" = "PR domain zinc finger protein 14", "PRDM15" = "PR domain zinc finger protein 15", "PRDM16" = "PR domain zinc finger protein 16", # PRMTs "PRMT2" = "Protein arginine N-methyltransferase 2", "PRMT3" = "Protein arginine N-methyltransferase 3", "PRMT5" = "Protein arginine N-methyltransferase 5", "PRMT6" = "Protein arginine N-methyltransferase 6", "PRMT7" = "Protein arginine N-methyltransferase 7", "PRMT8" = "Protein arginine N-methyltransferase 8", "PRMT9" = "Protein arginine N-methyltransferase 9", # PARPs "PARP2" = "Poly [ADP-ribose] polymerase 2", "PARP3" = "Poly [ADP-ribose] polymerase 3", "PARP4" = "Poly [ADP-ribose] polymerase 4", "PARP5A-TNKS" = "Tankyrase-1 (TNKS/PARP5A)", "PARP5B-TNKS2" = "Tankyrase-2 (TNKS2/PARP5B)", "PARP6" = "Poly [ADP-ribose] polymerase 6", "PARP7-TIPARP" = "TCDD-inducible poly [ADP-ribose] polymerase (TIPARP/PARP7)", "PARP8" = "Poly [ADP-ribose] polymerase 8", "PARP9" = "Poly [ADP-ribose] polymerase 9 (BAL1)", "PARP10" = "Poly [ADP-ribose] polymerase 10", "PARP11" = "Poly [ADP-ribose] polymerase 11", "PARP12" = "Poly [ADP-ribose] polymerase 12", "PARP13-ZC3HAV1" = "Zinc finger CCCH-type antiviral protein 1 (ZAP/PARP13)", "PARP14" = "Poly [ADP-ribose] polymerase 14 (BAL2)", "PARP15" = "Poly [ADP-ribose] polymerase 15 (BAL3)", "PARP16" = "Poly [ADP-ribose] polymerase 16", "PARG" = "Poly(ADP-ribose) glycohydrolase", "ARH1-ADPRH" = "ADP-ribosylhydrolase ARH1", "ARH3-ADPRS" = "ADP-ribosylhydrolase ARH3", "OGT" = "UDP-N-acetylglucosamine--peptide N-acetylglucosaminyltransferase (OGT)", # SUMO "SUMO2" = "Small ubiquitin-related modifier 2", "SUMO3" = "Small ubiquitin-related modifier 3", "SENP1" = "Sentrin-specific protease 1", "SENP2" = "Sentrin-specific protease 2", "SENP3" = "Sentrin-specific protease 3", "SENP5" = "Sentrin-specific protease 5", "SENP6" = "Sentrin-specific protease 6", "SENP7" = "Sentrin-specific protease 7", "SENP8" = "Sentrin-specific protease 8 (NEDD8 deSUMOylation)", # Ubiquitin E1/E2/E3 "UBA1" = "Ubiquitin-activating enzyme E1", "UBA2" = "SUMO-activating enzyme subunit 2", "UBA3" = "NEDD8-activating enzyme E1 catalytic subunit", "UBA5" = "Ubiquitin-like modifier-activating enzyme 5", "UBA6" = "Ubiquitin-activating enzyme E1-like protein", "UBA7" = "Ubiquitin-activating enzyme E1-like protein 2 (UBE1L)", "UBE2A" = "Ubiquitin-conjugating enzyme E2 A", "UBE2D1" = "Ubiquitin-conjugating enzyme E2 D1", "UBE2D2" = "Ubiquitin-conjugating enzyme E2 D2", "UBE2D3" = "Ubiquitin-conjugating enzyme E2 D3", "UBE2V1" = "Ubiquitin-conjugating enzyme E2 variant 1", "UBE2Z" = "Ubiquitin-conjugating enzyme E2 Z", "UBE3A" = "Ubiquitin-protein ligase E3A", "UBE4A" = "Ubiquitin conjugation factor E4 A", "UBR2" = "E3 ubiquitin-protein ligase UBR2", "BRCA1" = "Breast cancer type 1 susceptibility protein (BRCA1)", "HUWE1" = "E3 ubiquitin-protein ligase HUWE1", "RNF8" = "E3 ubiquitin-protein ligase RNF8", "RNF20" = "E3 ubiquitin-protein ligase BRE1A (RNF20)", "RNF40" = "E3 ubiquitin-protein ligase BRE1B (RNF40)", "RNF168" = "E3 ubiquitin-protein ligase RNF168", "TRIM37" = "E3 ubiquitin-protein ligase TRIM37", "DTX3L" = "Deltex E3 ubiquitin ligase 3L (DTX3L)", "CUL4A" = "Cullin-4A", "DDB1" = "DNA damage-binding protein 1", "TOM1" = "Target of Myb protein 1 (TOM1)", # USPs "USP1" = "Ubiquitin carboxyl-terminal hydrolase 1", "USP2" = "Ubiquitin carboxyl-terminal hydrolase 2", "USP3" = "Ubiquitin carboxyl-terminal hydrolase 3", "USP4" = "Ubiquitin carboxyl-terminal hydrolase 4", "USP5" = "Ubiquitin carboxyl-terminal hydrolase 5", "USP6" = "Ubiquitin carboxyl-terminal hydrolase 6", "USP7" = "Ubiquitin carboxyl-terminal hydrolase 7 (HAUSP)", "USP8" = "Ubiquitin carboxyl-terminal hydrolase 8", "USP9Y" = "Ubiquitin carboxyl-terminal hydrolase 9, Y-linked", "USP10" = "Ubiquitin carboxyl-terminal hydrolase 10", "USP11" = "Ubiquitin carboxyl-terminal hydrolase 11", "USP12" = "Ubiquitin carboxyl-terminal hydrolase 12", "USP13" = "Ubiquitin carboxyl-terminal hydrolase 13", "USP14" = "Ubiquitin carboxyl-terminal hydrolase 14", "USP15" = "Ubiquitin carboxyl-terminal hydrolase 15", "USP16" = "Ubiquitin carboxyl-terminal hydrolase 16", "USP18" = "Ubiquitin carboxyl-terminal hydrolase 18", "USP19" = "Ubiquitin carboxyl-terminal hydrolase 19", "USP20" = "Ubiquitin carboxyl-terminal hydrolase 20", "USP21" = "Ubiquitin carboxyl-terminal hydrolase 21", "USP22" = "Ubiquitin carboxyl-terminal hydrolase 22", "USP24" = "Ubiquitin carboxyl-terminal hydrolase 24", "USP25" = "Ubiquitin carboxyl-terminal hydrolase 25", "USP26" = "Ubiquitin carboxyl-terminal hydrolase 26", "USP27X" = "Ubiquitin carboxyl-terminal hydrolase 27, X-linked", "USP28" = "Ubiquitin carboxyl-terminal hydrolase 28", "USP29" = "Ubiquitin carboxyl-terminal hydrolase 29", "USP30" = "Ubiquitin carboxyl-terminal hydrolase 30", "USP31" = "Ubiquitin carboxyl-terminal hydrolase 31", "USP32" = "Ubiquitin carboxyl-terminal hydrolase 32", "USP33" = "Ubiquitin carboxyl-terminal hydrolase 33", "USP34" = "Ubiquitin carboxyl-terminal hydrolase 34", "USP35" = "Ubiquitin carboxyl-terminal hydrolase 35", "USP36" = "Ubiquitin carboxyl-terminal hydrolase 36", "USP37" = "Ubiquitin carboxyl-terminal hydrolase 37", "USP38" = "Ubiquitin carboxyl-terminal hydrolase 38", "USP39" = "Ubiquitin carboxyl-terminal hydrolase 39", "USP40" = "Ubiquitin carboxyl-terminal hydrolase 40", "USP41" = "Ubiquitin carboxyl-terminal hydrolase 41", "USP42" = "Ubiquitin carboxyl-terminal hydrolase 42", "USP43" = "Ubiquitin carboxyl-terminal hydrolase 43", "USP44" = "Ubiquitin carboxyl-terminal hydrolase 44", "USP45" = "Ubiquitin carboxyl-terminal hydrolase 45", "USP46" = "Ubiquitin carboxyl-terminal hydrolase 46", "USP47" = "Ubiquitin carboxyl-terminal hydrolase 47", "USP48" = "Ubiquitin carboxyl-terminal hydrolase 48", "USP49" = "Ubiquitin carboxyl-terminal hydrolase 49", "USP50" = "Ubiquitin carboxyl-terminal hydrolase 50", "USP51" = "Ubiquitin carboxyl-terminal hydrolase 51", "USP53" = "Ubiquitin carboxyl-terminal hydrolase 53", "USP54" = "Ubiquitin carboxyl-terminal hydrolase 54", "PAN2" = "Poly(A)-specific ribonuclease subunit PAN2 (USP52)", # UCH / OTU / other DUBs "UCHL1" = "Ubiquitin carboxyl-terminal hydrolase isozyme L1", "UCHL3" = "Ubiquitin carboxyl-terminal hydrolase isozyme L3", "UCHL4" = "Ubiquitin carboxyl-terminal hydrolase isozyme L4", "UCHL5" = "Ubiquitin carboxyl-terminal hydrolase isozyme L5 (UCH37)", "OTUD1" = "OTU domain-containing protein 1", "OTUD3" = "OTU domain-containing protein 3", "OTUD4" = "OTU domain-containing protein 4", "OTUD5" = "OTU domain-containing protein 5 (DUBA)", "OTUD6A" = "OTU domain-containing protein 6A", "OTUD6B" = "OTU domain-containing protein 6B", "OTUD7A" = "OTU domain-containing protein 7A (CEZANNE2)", "OTUD7B" = "OTU domain-containing protein 7B (CEZANNE)", "BAP1" = "Ubiquitin carboxyl-terminal hydrolase BAP1", "BRCC3" = "Lys-63-specific deubiquitinase BRCC3", "MYSM1" = "Histone H2A deubiquitinase MYSM1", "ATXN3" = "Ataxin-3 (Josephin domain deubiquitinase)", "ATXN3L" = "Ataxin-3-like protein", "JOSD1" = "Josephin domain-containing protein 1", "JOSD2" = "Josephin domain-containing protein 2", "PSMD14" = "26S proteasome non-ATPase regulatory subunit 14 (POH1/Rpn11)", "PSMD7" = "26S proteasome non-ATPase regulatory subunit 7 (Rpn8)", "COPS5" = "COP9 signalosome complex subunit 5 (JAB1/CSN5)", "COPS6" = "COP9 signalosome complex subunit 6 (CSN6)", "STAMBPL1"= "Stam-binding protein-like 1 (AMSH-LP)", "ZRANB1" = "Zinc finger RANBP2-type and RING finger-containing protein 1 (TRABID)", "VCPIP1" = "Deubiquitinating protein VCPIP1", "EIF3H" = "Eukaryotic translation initiation factor 3 subunit H", "EIF3F" = "Eukaryotic translation initiation factor 3 subunit F", "MPND" = "MPN domain-containing protein", "PRPF8" = "Pre-mRNA-processing-splicing factor 8 (PRPF8/SNRNP220)", "ARID1A" = "AT-rich interactive domain-containing protein 1A", "ARID1B" = "AT-rich interactive domain-containing protein 1B", # ADAR "ADAR" = "Double-stranded RNA-specific adenosine deaminase (ADAR1)", "ADARB1" = "Double-stranded RNA-specific editase B1 (ADAR2)", "ADARB2" = "Double-stranded RNA-specific editase B2 (ADAR3)", # m6A writers / readers / erasers "METTL14" = "N6-adenosine-methyltransferase non-catalytic subunit (METTL14)", "METTL16" = "RNA N6-methyladenosine methyltransferase METTL16", "METTL1" = "tRNA (guanine(46)-N(7))-methyltransferase METTL1", "METTL2A" = "tRNA (cytosine(34)-N(4))-methyltransferase METTL2A", "METTL2B" = "tRNA (cytosine(34)-N(4))-methyltransferase METTL2B", "METTL4" = "N6-adenosine-methyltransferase METTL4", "METTL5" = "rRNA adenine N(6)-methyltransferase (METTL5)", "METTL6" = "Methyltransferase-like protein 6", "METTL8" = "Methyltransferase-like protein 8", "WTAP" = "pre-mRNA-splicing regulator WTAP (m6A writer complex)", "VIRMA" = "Vir-like m6A methyltransferase associated (KIAA1429)", "CBLL1" = "Hakai (CBLL1); E3 ubiquitin ligase, m6A writer complex", "ZC3H13" = "Zinc finger CCCH domain-containing protein 13 (m6A writer complex)", "RBM15" = "RNA-binding protein 15", "RBM15B" = "RNA-binding protein 15B", "PCIF1" = "mRNA cap-specific m6Am methyltransferase PCIF1", "FTO" = "Alpha-ketoglutarate-dependent dioxygenase FTO (m6A eraser)", "ALKBH5" = "mRNA demethylase ALKBH5 (m6A eraser)", "ALKBH1" = "tRNA N(1)-methyl adenine demethylase ALKBH1", "ALKBH3" = "tRNA demethylase ALKBH3", "YTHDC1" = "YTH domain-containing protein 1 (m6A nuclear reader)", "YTHDC2" = "YTH domain-containing protein 2 (m6A reader/helicase)", "YTHDF1" = "YTH domain-containing family protein 1 (m6A cytoplasmic reader)", "YTHDF2" = "YTH domain-containing family protein 2 (m6A decay reader)", "YTHDF3" = "YTH domain-containing family protein 3 (m6A reader)", "HNRNPC" = "Heterogeneous nuclear ribonucleoprotein C", "HNRNPA2B1" = "Heterogeneous nuclear ribonucleoproteins A2/B1", "RBMX" = "RNA-binding motif protein, X-linked (HNRNPG)", "IGF2BP1" = "Insulin-like growth factor 2 mRNA-binding protein 1", "IGF2BP2" = "Insulin-like growth factor 2 mRNA-binding protein 2", "IGF2BP3" = "Insulin-like growth factor 2 mRNA-binding protein 3", "FMR1" = "Fragile X mental retardation protein 1", "PRRC2A" = "Proline-rich coiled-coil-containing protein 2A (m6A reader)", "ELF3" = "ETS-related transcription factor ELF3", # RNA methylation (non-m6A) "NSUN2" = "tRNA (cytosine(34)-C(5))-methyltransferase NSUN2", "NSUN3" = "Methyltransferase-like protein NSUN3 (mitochondrial tRNA)", "NSUN4" = "Bifunctional methyltransferase/rRNA maturation factor NSUN4", "NSUN5" = "rRNA methyltransferase NSUN5", "NSUN6" = "tRNA methyltransferase NSUN6", "TRDMT1" = "tRNA (cytosine-5-)-methyltransferase TRDMT1 (DNMT2)", "NOP2" = "Ribosome biogenesis protein NOP2 (NSUN1)", "DKC1" = "H/ACA ribonucleoprotein complex subunit 4 (Dyskerin/DKC1)", "TRMT1" = "tRNA (guanine(26)-N(2))-methyltransferase TRMT1", "TRMT61A" = "tRNA (adenine(58)-N(1))-methyltransferase TRMT61A", "TRMT61B" = "rRNA (adenine(58)-N(1))-methyltransferase TRMT61B", "TRMT10A" = "tRNA (guanine(9)-N(1))-methyltransferase TRMT10A", "TRMT10C" = "Mitochondrial tRNA methyltransferase TRMT10C", "TRMT112" = "tRNA methyltransferase subunit TRMT112", "ALYREF" = "THO complex subunit ALYREF (Aly/REF export factor)", "ZCCHC4" = "Zinc finger CCHC domain-containing protein 4 (rRNA m6A writer)", "NAT10" = "N-acetyltransferase 10 (RNA cytidine acetyltransferase)", "DUS1L" = "tRNA-dihydrouridine synthase 1-like", # Pseudouridine synthases "PUSL1" = "Pseudouridine synthase 1-like", "PUS1" = "tRNA pseudouridine synthase A (PUS1)", "PUS3" = "tRNA pseudouridine synthase C (PUS3)", "PUS7" = "tRNA pseudouridine synthase G (PUS7)", "PUS7L" = "tRNA pseudouridine synthase 7-like", "PUS10" = "tRNA pseudouridine synthase 10 (RPUSD2-related)", "RPUSD1" = "RNA pseudouridylate synthase domain-containing 1", "RPUSD2" = "RNA pseudouridylate synthase domain-containing 2", "RPUSD3" = "RNA pseudouridylate synthase domain-containing 3", "RPUSD4" = "RNA pseudouridylate synthase domain-containing 4", "TRUB1" = "Probable tRNA pseudouridine synthase 1 (TRUB1)", "TRUB2" = "Probable tRNA pseudouridine synthase 2 (TRUB2)", # NATs / acetyltransferases "NAA10" = "N-alpha-acetyltransferase 10 (NatA catalytic subunit)", "NAA50" = "N-alpha-acetyltransferase 50 (NatE catalytic subunit)", "NAA60" = "N-alpha-acetyltransferase 60 (NatF catalytic subunit)", "NAT8" = "N-acetyltransferase 8 (camello-like 1)", "NAT8B" = "N-acetyltransferase 8B (camello-like 2)", "NAT8L" = "N-acetyltransferase 8-like", "ATAT1" = "Alpha-tubulin acetyltransferase 1", "GTF3C2" = "Transcription factor IIIC 110 kDa subunit (TFIIIC110/GTF3C2)", "GTF3C1" = "Transcription factor IIIC 220 kDa subunit (TFIIIC220/GTF3C1)", "DLAT" = "Dihydrolipoyllysine-residue acetyltransferase (PDC-E2)", "ATF2" = "Cyclic AMP-dependent transcription factor ATF-2", "MCM3AP" = "Germinal-center associated nuclear protein (GANP/MCM3AP)", "MAPT" = "Microtubule-associated protein tau (MAPT)", "BRD4" = "Bromodomain-containing protein 4", "ESCO1" = "N-acetyltransferase ESCO1 (establishment of cohesion)", "ESCO2" = "N-acetyltransferase ESCO2", "BLOC1S1" = "Biogenesis of lysosome-related organelles complex 1 subunit 1", "ACAT1" = "Acetyl-CoA acetyltransferase 1 (mitochondrial)", "ACAT2" = "Acetyl-CoA acetyltransferase 2 (cytoplasmic)", # Histone variants / linker histones "H1-0" = "Histone H1.0", "H1-1" = "Histone H1.1 (HIST1H1A)", "H1-2" = "Histone H1.2 (HIST1H1C)", "H1-3" = "Histone H1.3 (HIST1H1D)", "H1-4" = "Histone H1.4 (HIST1H1E)", "H1-5" = "Histone H1.5 (HIST1H1B)", "H1-6" = "Histone H1t", "H1-7" = "Histone H1oo (oocyte-specific)", "H1-8" = "Histone H1.8 (H1foo)", "H1-10" = "Histone H1.10", "H2AX" = "Histone H2A.x", "H2AZ1" = "Histone H2A.z.1", "H2AZ2" = "Histone H2A.z.2", "H2AW" = "Histone H2A.W", "H2AP" = "Histone H2A.P", "H2AJ" = "Histone H2A.J", "H2AB1" = "Histone H2A-Bbd type 1 (H2A.Bbd)", "H2AB2" = "Histone H2A-Bbd type 2", "H2AB3" = "Histone H2A-Bbd type 3", "MACROH2A1" = "Core histone macro-H2A.1 (macroH2A1/H2AFY)", # Chromatin regulators "BAZ1B" = "Bromodomain adjacent to zinc finger domain protein 1B (WSTF)", "BAZ2B" = "Bromodomain adjacent to zinc finger domain protein 2B", "HIRA" = "Histone cell cycle regulator (HIRA)", "WDR5" = "WD repeat-containing protein 5 (H3K4 methyltransferase complex subunit)", # EYA phosphatases "EYA1" = "Eyes absent homolog 1 (H2AX phosphatase)", "EYA2" = "Eyes absent homolog 2", "EYA3" = "Eyes absent homolog 3", "EYA4" = "Eyes absent homolog 4", # Protein phosphatases "PPM1D" = "Protein phosphatase 1D (WIP1)", "PPP6C" = "Serine/threonine-protein phosphatase 6 catalytic subunit", "PPP4C" = "Serine/threonine-protein phosphatase 4 catalytic subunit", "CDCA2" = "Cell division cycle associated 2 (Repo-Man; PP1 targeting)", "PPP1CA" = "Serine/threonine-protein phosphatase PP1-alpha catalytic subunit", "PPP1CB" = "Serine/threonine-protein phosphatase PP1-beta catalytic subunit", "PPP1CC" = "Serine/threonine-protein phosphatase PP1-gamma catalytic subunit", "PPP1R1B" = "Protein phosphatase 1 regulatory subunit 1B (DARPP-32)", "PPP1R1C" = "Protein phosphatase 1 regulatory subunit 1C (I-3)", "PPP1R2" = "Protein phosphatase inhibitor 2", "PPP1R3A" = "Protein phosphatase 1 regulatory subunit 3A (GM)", "PPP1R3B" = "Protein phosphatase 1 regulatory subunit 3B (GL)", "PPP1R3C" = "Protein phosphatase 1 regulatory subunit 3C (PTG)", "PPP1R3D" = "Protein phosphatase 1 regulatory subunit 3D (R6)", "PPP1R3E" = "Protein phosphatase 1 regulatory subunit 3E", "PPP1R3F" = "Protein phosphatase 1 regulatory subunit 3F", "PPP1R3G" = "Protein phosphatase 1 regulatory subunit 3G", "PPP1R7" = "Protein phosphatase 1 regulatory subunit 7", "PPP1R8" = "Protein phosphatase 1 regulatory subunit 8 (NIPP1)", "PPP1R9A" = "Protein phosphatase 1 regulatory subunit 9A (neurabin-1)", "PPP1R9B" = "Protein phosphatase 1 regulatory subunit 9B (neurabin-2/spinophilin)", "PPP1R10" = "Protein phosphatase 1 regulatory subunit 10 (PNUTS)", "PPP1R11" = "Protein phosphatase 1 regulatory subunit 11 (PNUTS-like)", "PPP1R12A" = "Protein phosphatase 1 regulatory subunit 12A (MYPT1)", "PPP1R12B" = "Protein phosphatase 1 regulatory subunit 12B (MYPT2)", "PPP1R12C" = "Protein phosphatase 1 regulatory subunit 12C (MBS85)", "PPP1R13B" = "Protein phosphatase 1 regulatory subunit 13B (ASPP1)", "PPP1R14A" = "Protein phosphatase 1 regulatory subunit 14A (CPI-17)", "PPP1R14B" = "Protein phosphatase 1 regulatory subunit 14B (PHI-1)", "PPP1R14C" = "Protein phosphatase 1 regulatory subunit 14C", "PPP1R14D" = "Protein phosphatase 1 regulatory subunit 14D", "PPP1R15B" = "Protein phosphatase 1 regulatory subunit 15B (GADD34-related)", "PPP1R16A" = "Protein phosphatase 1 regulatory subunit 16A (MYPT3)", "PPP1R16B" = "Protein phosphatase 1 regulatory subunit 16B (TA-MYPT)", # Kinases "AURKB" = "Aurora kinase B", "AURKA" = "Aurora kinase A", "AURKC" = "Aurora kinase C", "JAK2" = "Tyrosine-protein kinase JAK2", "HASPIN" = "Serine/threonine-protein kinase haspin (GSG2)", "BUB1" = "Mitotic checkpoint serine/threonine-protein kinase BUB1", "WEE1" = "Wee1-like protein kinase", "SGO2" = "Shugoshin-like 2 (SGO2)", "SGO1" = "Shugoshin-like 1 (SGO1)", "DLK1" = "Protein delta homolog 1 (Preadipocyte factor 1/Pref-1)", "DAPK3" = "Death-associated protein kinase 3 (ZIPK)", "PKN1" = "Serine/threonine-protein kinase N1 (PKN1/PRK1)", "PKM" = "Pyruvate kinase PKM", "STK4" = "Serine/threonine-protein kinase 4 (MST1/STK4)", "MST1" = "Hepatocyte growth factor-like protein (MST1)", "SLK" = "STE20-like serine/threonine-protein kinase SLK", "PRKAA1" = "5'-AMP-activated protein kinase catalytic subunit alpha-1 (AMPKa1)", "PRKAA2" = "5'-AMP-activated protein kinase catalytic subunit alpha-2 (AMPKa2)", "DBF4" = "Protein DBF4 homolog A (Cdc7 kinase regulatory subunit)", "DYRK1A" = "Dual specificity tyrosine-phosphorylation-regulated kinase 1A", "RPS6KA4" = "Ribosomal protein S6 kinase alpha-4 (MSK2)", "RPS6KA5" = "Ribosomal protein S6 kinase alpha-5 (MSK1)", "PRKCB" = "Protein kinase C beta type (PKCb)", "JKAMP" = "JMJD8-associated regulatory complex component (JKAMP)", "MAPK8" = "Mitogen-activated protein kinase 8 (JNK1)", "MAPK9" = "Mitogen-activated protein kinase 9 (JNK2)", "MDC1" = "Mediator of DNA damage checkpoint protein 1", "MCPH1" = "Microcephalin (MCPH1; DNA damage sensor)", "PAK2" = "Serine/threonine-protein kinase PAK2", "ATM" = "Serine-protein kinase ATM (DNA damage checkpoint)", "ATR" = "Serine/threonine-protein kinase ATR", "CSNK2B" = "Casein kinase II subunit beta", "PRKCD" = "Protein kinase C delta type", "MAP2K1" = "Dual specificity mitogen-activated protein kinase kinase 1 (MEK1)", "MAP3K12" = "Mitogen-activated protein kinase kinase kinase 12 (DLK/MUK)", "MAP3K22" = "Mitogen-activated protein kinase kinase kinase 22", "MAPK1" = "Mitogen-activated protein kinase 1 (ERK2)", "MAPK3" = "Mitogen-activated protein kinase 3 (ERK1)", "MAPK8B" = "Mitogen-activated protein kinase 8b (JNK1b, zebrafish)", "MAPK11" = "Mitogen-activated protein kinase 11 (p38b)", "MAPK12" = "Mitogen-activated protein kinase 12 (p38g/ERK6)", "MAPK13" = "Mitogen-activated protein kinase 13 (p38d/SAPK4)", "MAPK14" = "Mitogen-activated protein kinase 14 (p38a)", "PRKDC" = "DNA-dependent protein kinase catalytic subunit (DNA-PKcs)", "PIM1" = "Proto-oncogene serine/threonine-protein kinase Pim-1", "CDK8" = "Cyclin-dependent kinase 8 (Mediator kinase)", "CHUK" = "Inhibitor of nuclear factor kappa-B kinase subunit alpha (IKKa)", "CDK1" = "Cyclin-dependent kinase 1 (CDC2)", "CHEK1" = "Serine/threonine-protein kinase Chk1", "APBB1" = "Amyloid precursor protein-binding family B member 1 (Fe65)", "YWHAQ" = "14-3-3 protein theta", # ZC3H12 ribonucleases "ZC3H12A" = "Endoribonuclease ZC3H12A (MCPIP1/Regnase-1)", "ZC3H12B" = "Endoribonuclease ZC3H12B (Regnase-2)", "ZC3H12C" = "Endoribonuclease ZC3H12C (Regnase-3)", "ZC3H12D" = "Endoribonuclease ZC3H12D (Regnase-4)", # Other "DCP2" = "mRNA-decapping enzyme subunit DCP2", "YBX1" = "Nuclease-sensitive element-binding protein 1 (YBX1/YB-1)", "RAG1" = "V(D)J recombination-activating protein 1 (RAG1)", "ALG13" = "UDP-N-acetylglucosamine transferase subunit ALG13", "HSPBAP1"= "HSPA binding protein 1 (HSPBAP1)" ) strip_isoform <- function(label) { # Remove trailing isoform number, e.g. Dnmt1-201 -> Dnmt1 sub("-\\d+$", "", label) } species_from_id <- function(eid) { dplyr::case_when( startsWith(eid, "ENSMUSP") ~ "Mus musculus", startsWith(eid, "ENSDARP") ~ "Danio rerio", TRUE ~ "Homo sapiens" ) } lookup_protein_name <- function(gene_name) { key <- toupper(gene_name) if (!is.na(protein_names[key])) return(unname(protein_names[key])) # Strip alias suffixes and try progressively shorter hyphen-joined prefixes simplified <- gsub("[\u2014\u2013/].*", "", key) parts <- strsplit(simplified, "-")[[1]] for (i in seq(length(parts), 1)) { candidate <- paste(parts[seq_len(i)], collapse = "-") if (!is.na(protein_names[candidate])) return(unname(protein_names[candidate])) } "" } # Parse fasta and build table lines <- readLines("../../data/Machinery.fasta") ``` ## Warning in readLines("../../data/Machinery.fasta"): incomplete final line found ## on '../../data/Machinery.fasta' ``` r headers <- lines[startsWith(lines, ">")] parsed <- regmatches(headers, regexec("^>(\\S+)\\s+peptide:\\s+(\\S+)", headers)) label <- sapply(parsed, `[`, 2) # e.g. "Dnmt1-201" eid <- sapply(parsed, `[`, 3) # e.g. "ENSMUSP00000004202" gene_name <- strip_isoform(label) protein_name <- sapply(gene_name, lookup_protein_name, USE.NAMES = FALSE) machinery_ref <- data.frame( accession = eid, protein_name = protein_name, gene_name = gene_name, species = species_from_id(eid), stringsAsFactors = FALSE ) write.csv(machinery_ref, "../output/20-supplementary-files/Machinery_reference_table.csv", row.names = FALSE) ``` # 2 Epimachinery category labels for Machinery.fasta table ``` r machinery <- read_csv("../output/20-supplementary-files/Machinery_reference_table.csv", show_col_types = FALSE) classify_gene <- function(gene) { # Uppercase to handle mouse mixed-case gene names gene <- toupper(gene) cats <- list( "ADP-ribosylation" = list( prefix = c("PARP", "TNKS", "TARG", "MACROD", "OARD", "ADPRH", "ARH", "TIPARP"), exact = c("PARG", "ARTD1") ), "DNA methylation & reading" = list( prefix = c("DNMT", "TET", "MBD", "UHRF", "MECP", "ZBTB"), exact = c("PRDM14", "ZFP57") ), "RNA modification" = list( prefix = c("METTL", "YTHDF", "YTHDC", "ALKBH", "WTAP", "VIRMA", "ZC3H13", "IGF2BP", "NSUN", "DKC", "TRMT", "PUS", "NAT10", "ADAR", "TRUB", "RPUSD"), exact = c("FTO", "RBM15", "RBM15B", "PCIF1", "BUD23", "WDR4", "RBMX", "TRDMT1", "NOP2", "HNRNPA2B1", "DUS1L", # tRNA dihydrouridine synthase "TYW5", # tRNA wybutosine-synthesizing protein (Supp. folder: # "Histone demethylation" — incorrect; TYW5 is a # tRNA modification enzyme, not a histone demethylase) "CBLL1") # Hakai, E3 ubiquitin ligase component of the m6A # writer complex ), "ncRNA biogenesis & silencing" = list( prefix = c("AGO", "TNRC6", "DICER", "DROSHA", "DGCR8", "PIWI", "EXPORTIN", "TARBP", "PRKRA", "LIN28", "KHSRP", "ZCCHC", "DIS3", "INTS", "EXOSC", "PAN2", "PAN3", "DDX5", "DDX17", "DDX6", "EDC", "DCP", "XRN", "CNOT", "UPF", "SMG", "TUT", "MOV10", "FMR", "PUM", "STAU", "NCBP", "PHAX", "RBM7", "ZC3H12"), # ZC3H12A-D are endoribonucleases (Regnase-1-4) # that degrade target mRNAs in the cytoplasm — # mRNA turnover/silencing machinery exact = c("RDRP", "MTREX", "SKIV2L2", "MTR4", "ZFC3H1", "SRRT", "ARS2", "XPO5", "XPO1", "CRM1", "RAN", "EWSR1", "FUS", "TARDBP", "HNRNPK", "PARN", "PABPC1", "PAPD5", "DZIP3", "NONO", "SFPQ", "PSPC1", "YBX1", "HSPA8", "HSP90AA1", "HSP90AB1", "PQBP1", "MCM3AP", "PRPF8", # pre-mRNA splicing factor 8 — core spliceosome # component involved in snRNA processing "EIF3H", # eIF3 subunit H — translation initiation factor # involved in mRNA silencing/regulation "EIF3F") # eIF3 subunit F — translation initiation factor; # has MPN deubiquitinase fold but functions in # translation regulation and miRNA-mediated silencing ), "Histone modification and variants" = list( prefix = c("KMT", "KDM", "SETD", "EZH", "SUV", "EHMT", "MLL", "ASH1", "DOT1", "JMJD", "HDAC", "SIRT", "KAT", "PRMT", "CARM", "NSD", "WHSC", "SMYD", "CDYL", "RIOX", "MINA", "NO66", "PRDM", "SUMO", "SENP", "H1-", "H2A", "H2B", "H3", "H4", # histone variants "NAT"), # NAT8, NAT8B, NAT8L — N-acetyltransferases (camello # family); Supp. folder groups all NATs under # "Histone acetylation" exact = c("CREBBP", "EP300", "HAT1", "ELP3", "PHF8", "PHF2", "JARID2", "PKN1", "HSPBAP1", "WDR5", "ASH2L", "SETMAR", "OGT", "OGA", "MACROH2A1", # histone variant macro-H2A.1 "ATAT1", # alpha-tubulin acetyltransferase; Supp. folder: # "Histone acetylation" (grouped with KATs) "GTF3C1", "GTF3C2", # TFIIIC subunits; Supp. folder: # "Histone acetylation" (have HAT activity) "NAA10", "NAA50", "NAA60", # N-alpha-acetyltransferases; # Supp. folder: "Histone acetylation" "BRD4", # bromodomain-containing protein 4; reads # acetylated histones; Supp. folder: # "Histone acetylation" "ESCO1", "ESCO2", # N-acetyltransferases (cohesin establishment); # Supp. folder: "Histone acetylation" "DLAT", # dihydrolipoyllysine-residue acetyltransferase; # Supp. folder: "Histone acetylation" "ATF2", # cyclic AMP-dependent TF; histone acetyltransferase # activity; Supp. folder: "Histone acetylation" "BLOC1S1", # biogenesis of lysosome-related organelles; # GCN5L1; Supp. folder: "Histone acetylation" "ACAT1", "ACAT2", # acetyl-CoA acetyltransferases; Supp. folder: # "Histone acetylation" "UTY", # inactive histone demethylase (KDM6 family homolog) "HR", # lysine demethylase and nuclear receptor corepressor "MAPT", # microtubule-associated protein tau; has histone # H3 binding / chromatin roles "BAZ2B") # bromodomain adjacent to zinc finger; chromatin # remodeling reader (Supp. folder: not present, # but BAZ1B is in "Histone phosphorylation") ), "Ubiquitin signaling" = list( prefix = c("USP", "UBE", "UBR", "UBA", "UCHL", "UCH", "RNF", "TRIM", "HUWE", "HERC", "MARCH", "RBX", "CUL", "FBX", "SKP", "SOCS", "OTU", "UBQLN", "PSMD", "JOSD", "DDB", "MIB"), exact = c("VCPIP1", "BAP1", "ATXN3", "ATXN3L", # ataxin-3-like protein; Josephin domain DUB "COPS6", "COPS5", "PARK2", "ZRANB1", "BRCA1", "RAG1", "TOM1", "DTX3L", "BRCC3", # Lys-63-specific deubiquitinase; Supp. folder: # "Histone Ubiquitination" (Deubiquitination) "MYSM1", # histone H2A deubiquitinase; Supp. folder: # "Histone Ubiquitination" (Deubiquitination) "STAMBPL1", # AMSH-LP; deubiquitinase; Supp. folder: # "Histone Ubiquitination" (Deubiquitination) "MPND", # MPN domain-containing protein; DUB fold; # associated with ubiquitin signaling "ALG13") # UDP-N-acetylglucosamine transferase subunit; # has deubiquitinase-like fold; Supp. folder: # not present, but classified here per its # role in ubiquitin-related pathways ), "Chromatin signaling" = list( prefix = c("MAP3K", "MAP2K", "MAPK", "EYA", "PPP4", "PPP6"), exact = c("CHUK", "IKBKB", "IKBKG", "PPP1CB", "PPP1CA", "PPP1CC", "PPP1R7", "PPP1R10", "PPP1R8", "PPP2CA", "AURKB", "MSK1", "MSK2", "RPS6KA4", "RPS6KA5", "PARK7", "BUB1", "DLK1", "DAPK3", "PKM", "JAK2", "DBF4", "DYRK1A", "YWHAQ", "PRKCB", "PRKCD", "AURKA", "AURKC", "STK4", "MST1", "MDC1", "MCPH1", "PAK2", "HIRA", "ATM", "ATR", "CSNK2B", "PRKAA1", "PRKAA2", "PRKDC", "PIM1", "CDK8", "CDK1", "CHEK1", "PPM1D", "PPP6C", "PPP4C", "CDCA2", "APBB1", "BAZ1B", "JKAMP", "ARID1A", "ARID1B", "SLK", # STE20-like kinase; Supp. folder: # "Histone phosphorylation" (slkb) "WEE1", # Wee1-like kinase; Supp. folder: # "Histone phosphorylation" "HASPIN", # histone H3T3 kinase (GSG2); chromatin signaling "SGO1", "SGO2") # Shugoshin proteins; protect centromeric # cohesion by recruiting PP2A; chromatin- # associated signaling at kinetochores ) ) for (cat_name in names(cats)) { cat_def <- cats[[cat_name]] if (gene %in% cat_def$exact) return(cat_name) for (p in cat_def$prefix) { if (str_starts(gene, fixed(p))) return(cat_name) } } return("Unclassified") } # Apply classification and add category column machinery_categorized <- machinery %>% mutate(epimachinery_category = vapply(gene_name, classify_gene, character(1))) machinery_categorized ``` ## # A tibble: 499 × 5 ## accession protein_name gene_name species epimachinery_category ## ## 1 ENSMUSP00000004202 DNA (cytosine-5-)… Dnmt1 Mus mu… DNA methylation & re… ## 2 ENSMUSP00000020991 DNA (cytosine-5-)… Dnmt3a Mus mu… DNA methylation & re… ## 3 ENSMUSP00000051830 DNA (cytosine-5-)… Dnmt3b Mus mu… DNA methylation & re… ## 4 ENSMUSP00000000746 DNA (cytosine-5-)… Dnmt3l Mus mu… DNA methylation & re… ## 5 ENSP00000342812 Ubiquitin carboxy… USP9Y Homo s… Ubiquitin signaling ## 6 ENSP00000219473 Ubiquitin carboxy… USP10 Homo s… Ubiquitin signaling ## 7 ENSP00000218348 Ubiquitin carboxy… USP11 Homo s… Ubiquitin signaling ## 8 ENSP00000282344 Ubiquitin carboxy… USP12 Homo s… Ubiquitin signaling ## 9 ENSP00000261601 Ubiquitin carboxy… USP14 Homo s… Ubiquitin signaling ## 10 ENSP00000280377 Ubiquitin carboxy… USP15 Homo s… Ubiquitin signaling ## # ℹ 489 more rows ``` r write_csv(machinery_categorized, "../output/20-supplementary-files/Machinery_categorized.csv") ``` # 3 Alignment summary tables Compiling per-sample alignment and processing statistics for all three species ## 3.1 RNA-seq Set up the metadata ``` r species_meta <- tibble( species = c("D-Apul", "E-Peve", "F-Ptuh"), species_full = c("Acropora pulchra", "Porites evermanni", "Pocillopora tuahiniensis"), genome = c("Apulcra-genome.fa", "Porites_evermanni_v1.fa", "Pocillopora_meandrina_HIv1.assembly.fasta"), rna_prefix = c("../../D-Apul", "../../E-Peve", "../../F-Ptuh") ) species_meta ``` ## # A tibble: 3 × 4 ## species species_full genome rna_prefix ## ## 1 D-Apul Acropora pulchra Apulcra-genome.fa ../../D-A… ## 2 E-Peve Porites evermanni Porites_evermanni_v1.fa ../../E-P… ## 3 F-Ptuh Pocillopora tuahiniensis Pocillopora_meandrina_HIv1.assemb… ../../F-P… ### 3.1.1 1.1 Raw reads from MultiQC FastQC on raw reads ``` r raw_reads <- rbind( read_tsv("../../D-Apul/output/01-Apul-RNA-trimming-FastQC/raw-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences'), read_tsv("../../E-Peve/output/01-Peve-RNA-trimming-FastQC/raw-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences'), read_tsv("../../F-Ptuh/output/01-Ptuh-RNA-trimming-FastQC/raw-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences') ) ``` ## Rows: 18 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (16): Sample, Filename, File type, Encoding, Total Bases, basic_statisti... ## dbl (7): Total Sequences, Sequences flagged as poor quality, Sequence lengt... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 20 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (16): Sample, Filename, File type, Encoding, Total Bases, basic_statisti... ## dbl (7): Total Sequences, Sequences flagged as poor quality, Sequence lengt... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 20 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (16): Sample, Filename, File type, Encoding, Total Bases, basic_statisti... ## dbl (7): Total Sequences, Sequences flagged as poor quality, Sequence lengt... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` r raw_reads ``` ## # A tibble: 15 × 2 ## Sample `Total Sequences` ## ## 1 RNA-ACR-140-S1-TP2_R1_001 48199794 ## 2 RNA-ACR-145-S1-TP2_R1_001 43288869 ## 3 RNA-ACR-150-S1-TP2_R1_001 44216948 ## 4 RNA-ACR-173-S1-TP2_R1_001 47976843 ## 5 RNA-ACR-178-S1-TP2_R1_001 43185440 ## 6 RNA-POR-71-S1-TP2_R1_001 51248209 ## 7 RNA-POR-73-S1-TP2_R1_001 51750431 ## 8 RNA-POR-76-S1-TP2_R1_001 50199897 ## 9 RNA-POR-79-S1-TP2_R1_001 50299323 ## 10 RNA-POR-82-S1-TP2_R1_001 49279402 ## 11 RNA-POC-47-S1-TP2_R1_001 54705696 ## 12 RNA-POC-48-S1-TP2_R1_001 52036501 ## 13 RNA-POC-50-S1-TP2_R1_001 55792440 ## 14 RNA-POC-53-S1-TP2_R1_001 53888583 ## 15 RNA-POC-57-S1-TP2_R1_001 42934866 ### 3.1.2 1.2 Trimmed reads from MultiQC FastQC on trimmed reads ``` r trimmed_reads <- rbind( read_tsv("../../D-Apul/output/01-Apul-RNA-trimming-FastQC/trimmed-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences'), read_tsv("../../E-Peve/output/01-Peve-RNA-trimming-FastQC/trimmed-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences'), read_tsv("../../F-Ptuh/output/01-Ptuh-RNA-trimming-FastQC/trimmed-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences') ) ``` ## Rows: 10 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (17): Sample, Filename, File type, Encoding, Total Bases, Sequence lengt... ## dbl (6): Total Sequences, Sequences flagged as poor quality, %GC, total_ded... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 10 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (17): Sample, Filename, File type, Encoding, Total Bases, Sequence lengt... ## dbl (6): Total Sequences, Sequences flagged as poor quality, %GC, total_ded... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 10 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (17): Sample, Filename, File type, Encoding, Total Bases, Sequence lengt... ## dbl (6): Total Sequences, Sequences flagged as poor quality, %GC, total_ded... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` r trimmed_reads ``` ## # A tibble: 15 × 2 ## Sample `Total Sequences` ## ## 1 RNA-ACR-140-S1-TP2_R1_001 47710408 ## 2 RNA-ACR-145-S1-TP2_R1_001 42864294 ## 3 RNA-ACR-150-S1-TP2_R1_001 43712298 ## 4 RNA-ACR-173-S1-TP2_R1_001 47501524 ## 5 RNA-ACR-178-S1-TP2_R1_001 42677752 ## 6 RNA-POR-71-S1-TP2_R1_001 50831351 ## 7 RNA-POR-73-S1-TP2_R1_001 51385213 ## 8 RNA-POR-76-S1-TP2_R1_001 49828147 ## 9 RNA-POR-79-S1-TP2_R1_001 49976568 ## 10 RNA-POR-82-S1-TP2_R1_001 48908730 ## 11 RNA-POC-47-S1-TP2_R1_001 54219233 ## 12 RNA-POC-48-S1-TP2_R1_001 51579450 ## 13 RNA-POC-50-S1-TP2_R1_001 55302459 ## 14 RNA-POC-53-S1-TP2_R1_001 53353608 ## 15 RNA-POC-57-S1-TP2_R1_001 42513488 ### 3.1.3 1.3 Retained after trimming (%) ``` r read_counts <- raw_reads %>% left_join(trimmed_reads, by = c("Sample")) %>% mutate(retained_pct = round(trimmed_reads$'Total Sequences' / raw_reads$'Total Sequences' * 100, 2)) read_counts ``` ## # A tibble: 15 × 4 ## Sample `Total Sequences.x` `Total Sequences.y` retained_pct ## ## 1 RNA-ACR-140-S1-TP2_R1_0… 48199794 47710408 99.0 ## 2 RNA-ACR-145-S1-TP2_R1_0… 43288869 42864294 99.0 ## 3 RNA-ACR-150-S1-TP2_R1_0… 44216948 43712298 98.9 ## 4 RNA-ACR-173-S1-TP2_R1_0… 47976843 47501524 99.0 ## 5 RNA-ACR-178-S1-TP2_R1_0… 43185440 42677752 98.8 ## 6 RNA-POR-71-S1-TP2_R1_001 51248209 50831351 99.2 ## 7 RNA-POR-73-S1-TP2_R1_001 51750431 51385213 99.3 ## 8 RNA-POR-76-S1-TP2_R1_001 50199897 49828147 99.3 ## 9 RNA-POR-79-S1-TP2_R1_001 50299323 49976568 99.4 ## 10 RNA-POR-82-S1-TP2_R1_001 49279402 48908730 99.2 ## 11 RNA-POC-47-S1-TP2_R1_001 54705696 54219233 99.1 ## 12 RNA-POC-48-S1-TP2_R1_001 52036501 51579450 99.1 ## 13 RNA-POC-50-S1-TP2_R1_001 55792440 55302459 99.1 ## 14 RNA-POC-53-S1-TP2_R1_001 53888583 53353608 99.0 ## 15 RNA-POC-57-S1-TP2_R1_001 42934866 42513488 99.0 ### 3.1.4 1.4 Alignment rate, unique %, multi % ``` r parse_hisat2_log <- function(lines) { text <- paste(lines, collapse = "\n") total_pairs <- as.numeric(str_match(text, "^(\\d+) reads")[2]) total_reads <- total_pairs * 2 # Overall alignment rate overall_rate <- as.numeric(str_match(text, "(\\S+)% overall alignment rate")[2]) # Concordant pairs conc_0 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned concordantly 0 times")[2]) conc_1 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned concordantly exactly 1 time")[2]) conc_gt1 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned concordantly >1 times")[2]) # Discordant pairs (from concordant-0-times) disc_1 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned discordantly 1 time")[2]) disc_1 <- if (is.na(disc_1)) 0 else disc_1 # Individual mates (from pairs that aligned 0 times concordantly AND discordantly) mates_total <- as.numeric(str_match(text, "(\\d+) mates make up the pairs")[2]) mates_total <- if (is.na(mates_total)) 0 else mates_total mates_0 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned 0 times\n")[2]) mates_0 <- if (is.na(mates_0)) 0 else mates_0 mates_1 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned exactly 1 time\n")[2]) mates_1 <- if (is.na(mates_1)) 0 else mates_1 mates_gt1 <- as.numeric(str_match(text, "(\\d+) \\(\\S+%\\) aligned >1 times\n")[2]) mates_gt1 <- if (is.na(mates_gt1)) 0 else mates_gt1 # Compute READ-level unique/multi (combining all alignment levels) # Each concordant pair = 2 reads; each discordant pair = 2 reads unique_reads <- (conc_1 * 2) + (disc_1 * 2) + mates_1 multi_reads <- (conc_gt1 * 2) + mates_gt1 unmapped_reads <- mates_0 mapped_reads <- unique_reads + multi_reads mapped_read_pairs <- mapped_reads/2 # Sanity check: unique + multi + unmapped should ≈ total_reads # (may not be exact due to mates from discordant pairs that also aligned individually) tibble( total_pairs = total_pairs, total_reads = total_reads, overall_align_rate = overall_rate, unique_pct = round(unique_reads / total_reads * 100, 2), multi_pct = round(multi_reads / total_reads * 100, 2), mapped_read_pairs = mapped_read_pairs ) } # D-Apul: consolidated log (all 5 samples in one file) parse_hisat2_consolidated <- function(path, samples) { lines <- readLines(path) block_starts <- which(str_detect(lines, "^\\d+ reads; of these:")) results <- list() for (i in seq_along(block_starts)) { end <- if (i < length(block_starts)) block_starts[i + 1] - 1 else length(lines) block <- lines[block_starts[i]:end] results[[i]] <- parse_hisat2_log(block) %>% mutate(sample = samples[i]) } bind_rows(results) } # E-Peve / F-Ptuh: one stderr file per sample parse_hisat2_per_file <- function(dir_path) { files <- list.files(dir_path, pattern = "_hisat\\.stderr$", full.names = TRUE) results <- list() for (f in files) { sname <- basename(f) %>% str_remove("_hisat.stderr") %>% str_remove("^RNA-") results[[sname]] <- parse_hisat2_log(readLines(f)) %>% mutate(sample = sname) } bind_rows(results) } hisat2_stats <- bind_rows( parse_hisat2_consolidated( "../../D-Apul/output/07-Apul-Hisat/hisat.out", samples = c("ACR-140", "ACR-145", "ACR-150", "ACR-173", "ACR-178") ), parse_hisat2_per_file("../../E-Peve/output/06-Peve-Hisat"), parse_hisat2_per_file("../../F-Ptuh/output/06-Ptuh-Hisat") ) %>% mutate(species = case_when( str_detect(sample, "^ACR") ~ "D-Apul", str_detect(sample, "^POR") ~ "E-Peve", str_detect(sample, "^POC") ~ "F-Ptuh" )) %>% select(species, sample, everything()) hisat2_stats ``` ## # A tibble: 15 × 8 ## species sample total_pairs total_reads overall_align_rate unique_pct ## ## 1 D-Apul ACR-140 47710408 95420816 69.3 65.6 ## 2 D-Apul ACR-145 42864294 85728588 71.7 66.0 ## 3 D-Apul ACR-150 43712298 87424596 54.3 48.4 ## 4 D-Apul ACR-173 47501524 95003048 67.4 62.9 ## 5 D-Apul ACR-178 42677752 85355504 71.1 64.7 ## 6 E-Peve POR-71 50831351 101662702 86.4 67.0 ## 7 E-Peve POR-73 51385213 102770426 87.1 63.6 ## 8 E-Peve POR-76 49828147 99656294 86.0 65.9 ## 9 E-Peve POR-79 49976568 99953136 89.1 63.6 ## 10 E-Peve POR-82 48908730 97817460 84.2 66.2 ## 11 F-Ptuh POC-47 54219233 108438466 64.2 54.7 ## 12 F-Ptuh POC-48 51579450 103158900 60.9 51.0 ## 13 F-Ptuh POC-50 55302459 110604918 62.0 56.6 ## 14 F-Ptuh POC-53 53353608 106707216 60.6 56.1 ## 15 F-Ptuh POC-57 42513488 85026976 50.6 47.6 ## # ℹ 2 more variables: multi_pct , mapped_read_pairs ### 3.1.5 1.5 Mismatch % This requires collecting info from the full \*.sorted.bam files, which are very large and live on Gannet (). ``` bash # The files are backed up to Steven's backup directory: # /volume/web/seashell/bu-github/deep-dive-expression # The BAM paths are: # D-Apul: D-Apul/output/07-Apul-Hisat/RNA-ACR-{140,145,150,173,178}.sorted.bam # E-Peve: E-Peve/output/06-Peve-Hisat/RNA-POR-{71,73,76,79,82}.sorted.bam # F-Ptuh: F-Ptuh/output/06-Ptuh-Hisat/RNA-POC-{47,48,50,53,57}.sorted.bam mkdir -p ../output/20-supplementary-files/RNAseq_HISAT_samtools_stats # D-Apul BAMs wget -r -l1 -nd -nc -A "*.sorted.bam" \ -P ../output/20-supplementary-files/RNAseq_HISAT_samtools_stats \ https://gannet.fish.washington.edu/seashell/bu-github/deep-dive-expression/D-Apul/output/07-Apul-Hisat/ # E-Peve BAMs wget -r -l1 -nd -nc -A "*.sorted.bam" \ -P ../output/20-supplementary-files/RNAseq_HISAT_samtools_stats \ https://gannet.fish.washington.edu/seashell/bu-github/deep-dive-expression/E-Peve/output/06-Peve-Hisat/ # F-Ptuh BAMs wget -r -l1 -nd -nc -A "*.sorted.bam" \ -P ../output/20-supplementary-files/RNAseq_HISAT_samtools_stats \ https://gannet.fish.washington.edu/seashell/bu-github/deep-dive-expression/F-Ptuh/output/06-Ptuh-Hisat/ # Then run samtools on each BAM for bam in ../output/20-supplementary-files/RNAseq_HISAT_samtools_stats/*.sorted.bam; do base=$(basename "$bam" .sorted.bam) /home/shared/samtools-1.12/samtools stats "$bam" > "../output/20-supplementary-files/RNAseq_HISAT_samtools_stats/${base}.stats.txt" /home/shared/samtools-1.12/samtools flagstat "$bam" > "../output/20-supplementary-files/RNAseq_HISAT_samtools_stats/${base}.flagstat.txt" done ``` ``` bash # Remove BAMs once done (they're quite large) rm ../output/20-supplementary-files/RNAseq_HISAT_samtools_stats/*.sorted.bam ``` Parse samtools stats output for mismatch rate ``` r samtools_dir <- "../output/20-supplementary-files/RNAseq_HISAT_samtools_stats" # Sample configuration samples_cfg <- list( D_Apul = c("ACR-140", "ACR-145", "ACR-150", "ACR-173", "ACR-178"), E_Peve = c("POR-71", "POR-73", "POR-76", "POR-79", "POR-82"), F_Ptuh = c("POC-47", "POC-48", "POC-50", "POC-53", "POC-57") ) species_map <- c(D_Apul = "D-Apul", E_Peve = "E-Peve", F_Ptuh = "F-Ptuh") # Parse a single samtools stats file parse_samtools_stats <- function(path, sample_name, species_code) { if (!file.exists(path)) { return(tibble(species = species_code, sample = sample_name, mapped_reads = NA, mismatch_rate_pct = NA)) } lines <- readLines(path) # Mapped reads: SN\treads mapped:\t28412345 mapped <- str_match(lines[grepl("^SN\\treads mapped:", lines)], "^SN\\treads mapped:\\t(\\d+)")[2] mapped <- if (!is.na(mapped)) as.numeric(mapped) else NA # Mismatch rate (reported as "error rate"): # SN\terror rate:\t7.151265e-03\t# mismatches / bases mapped (cigar) error_line <- lines[grepl("^SN\\terror rate:", lines)] error_rate <- str_match(error_line, "^SN\\terror rate:\\t([\\d.eE+-]+)")[2] mismatch_rate_pct <- if (!is.na(error_rate)) as.numeric(error_rate) * 100 else NA tibble(species = species_code, sample = sample_name, mapped_reads = mapped, mismatch_rate_pct = round(mismatch_rate_pct, 4)) } # Build results from single directory samtools_results <- bind_rows(lapply(names(samples_cfg), function(sp) { species_code <- species_map[sp] bind_rows(lapply(samples_cfg[[sp]], function(s) { parse_samtools_stats( file.path(samtools_dir, paste0("RNA-", s, ".stats.txt")), s, species_code ) })) })) # Rename for final output samtools_summary <- samtools_results %>% rename( Species = species, Sample = sample, `Mapped reads` = mapped_reads, `Mismatch rate (%)` = mismatch_rate_pct ) samtools_summary ``` ## # A tibble: 15 × 4 ## Species Sample `Mapped reads` `Mismatch rate (%)` ## ## 1 D-Apul ACR-140 66166297 0.715 ## 2 D-Apul ACR-145 61488934 0.695 ## 3 D-Apul ACR-150 47452375 0.668 ## 4 D-Apul ACR-173 64013149 0.745 ## 5 D-Apul ACR-178 60684881 0.760 ## 6 E-Peve POR-71 87835896 0.338 ## 7 E-Peve POR-73 89515483 0.268 ## 8 E-Peve POR-76 85668912 0.293 ## 9 E-Peve POR-79 89024198 0.226 ## 10 E-Peve POR-82 82338762 0.351 ## 11 F-Ptuh POC-47 69558951 0.954 ## 12 F-Ptuh POC-48 62823383 0.928 ## 13 F-Ptuh POC-50 68623296 1.08 ## 14 F-Ptuh POC-53 64638718 1.10 ## 15 F-Ptuh POC-57 43011432 1.16 ### 3.1.6 1.6 Number of lncRNAs identified ``` r count_lncRNAs <- function(filepath) { df <- read_tsv(filepath, col_names = TRUE) sample_cols <- grep(".sorted.bam$", colnames(df), value = TRUE) sample_names <- str_remove(sample_cols, ".*pipeline\\.RNA\\.") %>% str_remove("\\.sorted\\.bam$") # Count non-zero entries per sample column nonzero_counts <- sapply(sample_cols, function(col) { sum(df[[col]] > 0, na.rm = TRUE) }) # Build a summary tibble summary <- tibble( sample = sample_names, n_unique_lncRNA = nonzero_counts ) print(summary) } lncRNA_counts <- rbind( count_lncRNAs("../output/01.6-lncRNA-pipeline/Apul-lncRNA-counts-filtered.txt"), count_lncRNAs("../output/01.6-lncRNA-pipeline/Peve-lncRNA-counts-filtered.txt"), count_lncRNAs("../output/01.6-lncRNA-pipeline/Ptuh-lncRNA-counts-filtered.txt") ) ``` ## Rows: 31490 Columns: 11 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (3): Geneid, Chr, Strand ## dbl (8): Start, End, Length, X.home.shared.8TB_HDD_02.zbengt.github.deep.div... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## # A tibble: 5 × 2 ## sample n_unique_lncRNA ## ## 1 ACR.140 27515 ## 2 ACR.145 21807 ## 3 ACR.150 28248 ## 4 ACR.173 24585 ## 5 ACR.178 29713 ## Rows: 10085 Columns: 11 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (3): Geneid, Chr, Strand ## dbl (8): Start, End, Length, X.home.shared.8TB_HDD_02.zbengt.github.deep.div... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## # A tibble: 5 × 2 ## sample n_unique_lncRNA ## ## 1 POR.71 8577 ## 2 POR.73 7763 ## 3 POR.76 9498 ## 4 POR.79 8007 ## 5 POR.82 8681 ## Rows: 16152 Columns: 11 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (3): Geneid, Chr, Strand ## dbl (8): Start, End, Length, ...output.01.6.Ptuh.lncRNA.pipeline.RNA.POC.47.... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## # A tibble: 5 × 2 ## sample n_unique_lncRNA ## ## 1 POC.47.S1.TP2 12017 ## 2 POC.48.S1.TP2 13866 ## 3 POC.50.S1.TP2 14294 ## 4 POC.53.S1.TP2 14409 ## 5 POC.57.S1.TP2 12197 ``` r lncRNA_counts ``` ## # A tibble: 15 × 2 ## sample n_unique_lncRNA ## ## 1 ACR.140 27515 ## 2 ACR.145 21807 ## 3 ACR.150 28248 ## 4 ACR.173 24585 ## 5 ACR.178 29713 ## 6 POR.71 8577 ## 7 POR.73 7763 ## 8 POR.76 9498 ## 9 POR.79 8007 ## 10 POR.82 8681 ## 11 POC.47.S1.TP2 12017 ## 12 POC.48.S1.TP2 13866 ## 13 POC.50.S1.TP2 14294 ## 14 POC.53.S1.TP2 14409 ## 15 POC.57.S1.TP2 12197 ## 3.2 sRNA-seq alignment statistics ### 3.2.1 2.1 Species metadata (genome) ``` r # Genome files are referenced in the ShortStack Rmd scripts: srna_genomes <- tibble( species = c("D-Apul", "E-Peve", "F-Ptuh"), genome = c("Apulcra-genome.fa", "Porites_evermanni_v1.fa", "Pocillopora_meandrina_HIv1.assembly.fasta") ) srna_genomes ``` ## # A tibble: 3 × 2 ## species genome ## ## 1 D-Apul Apulcra-genome.fa ## 2 E-Peve Porites_evermanni_v1.fa ## 3 F-Ptuh Pocillopora_meandrina_HIv1.assembly.fasta ### 3.2.2 2.1 Raw reads ``` r s_raw_reads <- rbind( read_tsv("~/deep-dive/D-Apul/output/08-Apul-sRNAseq-trimming/raw-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences'), read_tsv("~/deep-dive/E-Peve/output/06-Peve-sRNAseq-trimming/raw-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences'), read_tsv("~/deep-dive/F-Pmea/output/08-Pmea-sRNAseq-trimming/raw-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "RNA-")) %>% filter(str_detect(Sample, "_R1_001$")) %>% select(Sample, 'Total Sequences') ) ``` ## Rows: 10 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (16): Sample, Filename, File type, Encoding, Total Bases, basic_statisti... ## dbl (7): Total Sequences, Sequences flagged as poor quality, Sequence lengt... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 6 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (16): Sample, Filename, File type, Encoding, Total Bases, basic_statisti... ## dbl (7): Total Sequences, Sequences flagged as poor quality, Sequence lengt... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 10 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (16): Sample, Filename, File type, Encoding, Total Bases, basic_statisti... ## dbl (7): Total Sequences, Sequences flagged as poor quality, Sequence lengt... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` r s_raw_reads$Sample <- str_remove(s_raw_reads$Sample, "_R1_001") s_raw_reads ``` ## # A tibble: 13 × 2 ## Sample `Total Sequences` ## ## 1 sRNA-ACR-140-S1-TP2 18506972 ## 2 sRNA-ACR-145-S1-TP2 21295979 ## 3 sRNA-ACR-150-S1-TP2 22586680 ## 4 sRNA-ACR-173-S1-TP2 19686271 ## 5 sRNA-ACR-178-S1-TP2 17687362 ## 6 sRNA-POR-73-S1-TP2 22960890 ## 7 sRNA-POR-79-S1-TP2 19762559 ## 8 sRNA-POR-82-S1-TP2 20675792 ## 9 sRNA-POC-47-S1-TP2 27579504 ## 10 sRNA-POC-48-S1-TP2 24884997 ## 11 sRNA-POC-50-S1-TP2 26073937 ## 12 sRNA-POC-53-S1-TP2 25726329 ## 13 sRNA-POC-57-S1-TP2 26459749 ### 3.2.3 2.2 Trimmed reads ``` r s_trimmed_reads <- rbind( read_tsv("~/deep-dive/D-Apul/output/08.2-Apul-sRNAseq-trimming-31bp-fastp-merged/trimmed-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "sRNA-")) %>% select(Sample, 'Total Sequences'), read_tsv("~/deep-dive/E-Peve/output/06.2-Peve-sRNAseq-trimming-31bp-fastp-merged/trimmed-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% select(Sample, 'Total Sequences') %>% mutate(Sample = paste0("sRNA-", Sample)), read_tsv("~/deep-dive/F-Pmea/output/08.2-Pmea-sRNAseq-trimming-31bp-fastp-merged/trimmed-fastqc/multiqc_data/multiqc_fastqc.txt", col_names = TRUE) %>% filter(str_detect(Sample, "sRNA-")) %>% select(Sample, 'Total Sequences') ) ``` ## Rows: 5 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (17): Sample, Filename, File type, Encoding, Total Bases, Sequence lengt... ## dbl (6): Total Sequences, Sequences flagged as poor quality, %GC, total_ded... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 3 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (17): Sample, Filename, File type, Encoding, Total Bases, Sequence lengt... ## dbl (6): Total Sequences, Sequences flagged as poor quality, %GC, total_ded... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ## Rows: 5 Columns: 23 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "\t" ## chr (17): Sample, Filename, File type, Encoding, Total Bases, Sequence lengt... ## dbl (6): Total Sequences, Sequences flagged as poor quality, %GC, total_ded... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` r s_trimmed_reads$Sample <- str_remove(s_trimmed_reads$Sample, "-fastp-adapters-polyG-31bp-merged") s_trimmed_reads ``` ## # A tibble: 13 × 2 ## Sample `Total Sequences` ## ## 1 sRNA-ACR-140-S1-TP2 15076001 ## 2 sRNA-ACR-145-S1-TP2 17265123 ## 3 sRNA-ACR-150-S1-TP2 18395038 ## 4 sRNA-ACR-173-S1-TP2 16332044 ## 5 sRNA-ACR-178-S1-TP2 14295261 ## 6 sRNA-POR-73-S1-TP2 12238952 ## 7 sRNA-POR-79-S1-TP2 12259576 ## 8 sRNA-POR-82-S1-TP2 13933812 ## 9 sRNA-POC-47-S1-TP2 12066776 ## 10 sRNA-POC-48-S1-TP2 14045042 ## 11 sRNA-POC-50-S1-TP2 12141955 ## 12 sRNA-POC-53-S1-TP2 15732665 ## 13 sRNA-POC-57-S1-TP2 14136486 ### 3.2.4 2.3 Retained after trimming (%) ``` r s_read_counts <- s_raw_reads %>% left_join(s_trimmed_reads, by = c("Sample")) %>% mutate(retained_pct = round(s_trimmed_reads$'Total Sequences' / s_raw_reads$'Total Sequences' * 100, 2)) s_read_counts ``` ## # A tibble: 13 × 4 ## Sample `Total Sequences.x` `Total Sequences.y` retained_pct ## ## 1 sRNA-ACR-140-S1-TP2 18506972 15076001 81.5 ## 2 sRNA-ACR-145-S1-TP2 21295979 17265123 81.1 ## 3 sRNA-ACR-150-S1-TP2 22586680 18395038 81.4 ## 4 sRNA-ACR-173-S1-TP2 19686271 16332044 83.0 ## 5 sRNA-ACR-178-S1-TP2 17687362 14295261 80.8 ## 6 sRNA-POR-73-S1-TP2 22960890 12238952 53.3 ## 7 sRNA-POR-79-S1-TP2 19762559 12259576 62.0 ## 8 sRNA-POR-82-S1-TP2 20675792 13933812 67.4 ## 9 sRNA-POC-47-S1-TP2 27579504 12066776 43.8 ## 10 sRNA-POC-48-S1-TP2 24884997 14045042 56.4 ## 11 sRNA-POC-50-S1-TP2 26073937 12141955 46.6 ## 12 sRNA-POC-53-S1-TP2 25726329 15732665 61.2 ## 13 sRNA-POC-57-S1-TP2 26459749 14136486 53.4 ### 3.2.5 2.4 ShortStack alignment stats from alignment_details.tsv NOTE: the Github repo README.md states that the H tag means “H: Very highly multi-mapped read (\>=50 hits)”. However,the log from running ShortStack 4.1.0 (e.g., the file ~/deep-dive-expression/D-Apul/output/11-Apul-sRNA-ShortStack_4.1.0-pulchra_genome/shortstack.log) states that the tag represents “Very highly multi-mapped (\>=20 hits)(H)”. It looks like the \>=50 threshold was used in previous versions of ShortStack (confirmed by looking at deep-dive ShortStack logs), but the updated 4.1.0 version switched to a \>=20 threshold. The Github repo’s README is out of date. ``` r # alignment_details.tsv columns: # readfile mapping_type sequence_length sequence_count read_count # mapping_type (from https://github.com/MikeAxtell/ShortStack) # U: Uniquely mapped (not a multimapper). # P: Multimapper placed using the method set by option --mmap. # R: Multimapper placed at random. # H: Very highly multi-mapped read (>=20 hits). # N: Unmapped reads. read_alignment_details <- function(path, species_code) { df <- read_tsv(path, show_col_types = FALSE) # Extract sample name from readfile path df <- df %>% mutate( sample = str_extract(readfile, "(ACR|POR|POC)-\\d+"), species = species_code ) # Aggregate by sample × mapping_type, sum sequence_count agg <- df %>% group_by(species, sample, mapping_type) %>% summarise(seq_count = sum(read_count), .groups = "drop") # Pivot to wide format agg_wide <- agg %>% pivot_wider(names_from = mapping_type, values_from = seq_count, values_fill = 0) # totals and percentages # U=unique, P=guided, R=random, H=very-high, N=not mapped agg_wide %>% mutate( total_reads = U + P + R + H + N, mapped_reads = U + P + R + H, overall_align_pct = round((mapped_reads) / total_reads * 100, 2), unique_pct = round(U / total_reads * 100, 2), guided_pct = round(P / total_reads * 100, 2), random_pct = round(R / total_reads * 100, 2), very_high_pct = round(H / total_reads * 100, 2), ) %>% select( species, sample, total_reads, overall_align_pct, unique_pct, guided_pct, random_pct, very_high_pct, mapped_reads ) %>% rename( `Overall alignment (%)` = overall_align_pct, `Uniquely mapped reads (%)` = unique_pct, `Multi-mapped reads placed with guidance (%)` = guided_pct, `Multi-mapped reads placed randomly (%)` = random_pct, `Very high multi-mapping reads (>=50 hits; %)` = very_high_pct, `Mapped sRNA reads` = mapped_reads ) } srna_align <- bind_rows( read_alignment_details("../../D-Apul/output/11-Apul-sRNA-ShortStack_4.1.0-pulchra_genome/ShortStack_out/alignment_details.tsv", "D-Apul"), read_alignment_details("../../E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0/ShortStack_out/alignment_details.tsv", "E-Peve"), read_alignment_details("../../F-Ptuh/output/05-Ptuh-sRNA-ShortStack_4.1.0/ShortStack_out/alignment_details.tsv", "F-Ptuh") ) srna_align ``` ## # A tibble: 13 × 9 ## species sample total_reads `Overall alignment (%)` Uniquely mapped reads (…¹ ## ## 1 D-Apul ACR-140 15076180 80.0 33.2 ## 2 D-Apul ACR-145 17265150 78.8 28.9 ## 3 D-Apul ACR-150 18395068 77.8 35.0 ## 4 D-Apul ACR-173 16332641 78.1 31.1 ## 5 D-Apul ACR-178 14295278 80.1 31.9 ## 6 E-Peve POR-73 12238972 73.6 30.7 ## 7 E-Peve POR-79 12259629 79.3 40.3 ## 8 E-Peve POR-82 13933829 81.4 43.7 ## 9 F-Ptuh POC-47 12068141 62.4 17.8 ## 10 F-Ptuh POC-48 14046678 62.4 12.9 ## 11 F-Ptuh POC-50 12142097 55.9 16.2 ## 12 F-Ptuh POC-53 15732765 75.8 18.3 ## 13 F-Ptuh POC-57 14137264 83.2 16.8 ## # ℹ abbreviated name: ¹​`Uniquely mapped reads (%)` ## # ℹ 4 more variables: `Multi-mapped reads placed with guidance (%)` , ## # `Multi-mapped reads placed randomly (%)` , ## # `Very high multi-mapping reads (>=50 hits; %)` , ## # `Mapped sRNA reads` ### 3.2.6 2.5 Mapped miRNA reads from ShortStack Counts.txt ``` r # Counts.txt is a TSV: Coords, Name, MIRNA, _condensed, ... # Filter rows where MIRNA == "Y", then sum each sample column. read_mirna_counts <- function(path, species_code) { df <- read_tsv(path, show_col_types = FALSE) sample_cols <- colnames(df)[-(1:3)] # skip Coords, Name, MIRNA # Extract clean sample names from column names # e.g. "sRNA-ACR-140-S1-TP2-..._condensed" -> "ACR-140" samples <- str_extract(sample_cols, "(ACR|POR|POC)-\\d+") mirna_only <- df %>% filter(MIRNA == "Y") # Sum each sample column across all miRNA rows mapped_mirna <- colSums(mirna_only[, sample_cols]) tibble( species = species_code, sample = samples, mapped_mirna_reads = mapped_mirna ) } srna_mirna_reads <- bind_rows( read_mirna_counts("../../D-Apul/output/11-Apul-sRNA-ShortStack_4.1.0-pulchra_genome/ShortStack_out/Counts.txt", "D-Apul"), read_mirna_counts("../../E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0/ShortStack_out/Counts.txt", "E-Peve"), read_mirna_counts("../../F-Ptuh/output/05-Ptuh-sRNA-ShortStack_4.1.0/ShortStack_out/Counts.txt", "F-Ptuh") ) srna_mirna_reads ``` ## # A tibble: 13 × 3 ## species sample mapped_mirna_reads ## ## 1 D-Apul ACR-140 226397 ## 2 D-Apul ACR-145 444883 ## 3 D-Apul ACR-150 668500 ## 4 D-Apul ACR-173 373547 ## 5 D-Apul ACR-178 455227 ## 6 E-Peve POR-73 373247 ## 7 E-Peve POR-79 279129 ## 8 E-Peve POR-82 399594 ## 9 F-Ptuh POC-47 623726 ## 10 F-Ptuh POC-48 368009 ## 11 F-Ptuh POC-50 512651 ## 12 F-Ptuh POC-53 430756 ## 13 F-Ptuh POC-57 178801 ### 3.2.7 2.6 Number of miRNAs identified ``` r count_expressed_mirnas <- function(path) { df <- read_tsv(path, show_col_types = FALSE) sample_cols <- grep("merged_condensed", colnames(df), value = TRUE) as.data.frame(t(df %>% filter(MIRNA == "Y") %>% summarise(across(all_of(sample_cols), ~ sum(.x > 0, na.rm = TRUE))))) } miRNA_counts <- rbind( count_expressed_mirnas("../../D-Apul/output/11-Apul-sRNA-ShortStack_4.1.0-pulchra_genome/ShortStack_out/Counts.txt"), count_expressed_mirnas("../../E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0/ShortStack_out/Counts.txt"), count_expressed_mirnas("../../F-Ptuh/output/05-Ptuh-sRNA-ShortStack_4.1.0/ShortStack_out/Counts.txt") ) colnames(miRNA_counts) <- c("miRNA_count") miRNA_counts$Sample <- rownames(miRNA_counts) miRNA_counts <- miRNA_counts %>% select(Sample, miRNA_count) %>% remove_rownames() miRNA_counts$Sample <- miRNA_counts$Sample %>% str_remove("-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed") %>% str_remove("sRNA-") miRNA_counts ``` ## Sample miRNA_count ## 1 ACR-140 38 ## 2 ACR-145 39 ## 3 ACR-150 39 ## 4 ACR-173 39 ## 5 ACR-178 38 ## 6 POR-73 44 ## 7 POR-79 42 ## 8 POR-82 43 ## 9 POC-47 37 ## 10 POC-48 37 ## 11 POC-50 37 ## 12 POC-53 37 ## 13 POC-57 37 # 4 Mature miRNA names and sequences for all species Already have per-species, just need to combine into single table ``` r miRNA_details <- rbind( read.csv("../../D-Apul/output/11-Apul-sRNA-ShortStack_4.1.0-pulchra_genome/ShortStack_out/Apul_Results_mature_named_miRNAs.csv", header = TRUE) %>% dplyr::select(-X), # Including an NA filter here, because it looks like the Peve file has an extra row indicating miRNAs identified in # Ashey et al 2026 which were not also identified here using the new version of ShortStack (4.1.0) read.csv("../../E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0/ShortStack_out/Peve_Results_mature_named_miRNAs.csv", header = TRUE) %>% dplyr::select(-X) %>% drop_na(Name), read.csv("../../F-Ptuh/output/05-Ptuh-sRNA-ShortStack_4.1.0/ShortStack_out/Ptuh_Results_mature_named_miRNAs.csv", header = TRUE) %>% dplyr::select(-X) ) miRNA_details ``` ## Locus Name ## 1 ntLink_6:13351746-13351842 Cluster_1951 ## 2 ntLink_6:4847443-4847535 Cluster_1826 ## 3 ntLink_6:5157537-5157626 Cluster_1832 ## 4 ntLink_6:7263486-7263580 Cluster_1862 ## 5 ptg000001l:20063072-20063168 Cluster_2859 ## 6 ptg000001l:5548841-5548934 Cluster_2463 ## 7 ptg000002l:14046235-14046328 Cluster_3366 ## 8 ptg000002l:14046541-14046634 Cluster_3367 ## 9 ptg000002l:7337508-7337601 Cluster_3250 ## 10 ptg000004l:1859862-1859953 Cluster_3437 ## 11 ptg000007l:3377283-3377376 Cluster_4254 ## 12 ptg000007l:915905-916002 Cluster_4220 ## 13 ptg000008l:10754767-10754862 Cluster_5012 ## 14 ptg000009l:4940515-4940609 Cluster_5981 ## 15 ptg000009l:616285-616377 Cluster_5899 ## 16 ptg000009l:616579-616673 Cluster_5900 ## 17 ptg000016l:11751358-11751448 Cluster_10093 ## 18 ptg000016l:7795508-7795602 Cluster_10051 ## 19 ptg000016l:8599833-8599925 Cluster_10057 ## 20 ptg000017l:6374963-6375054 Cluster_10207 ## 21 ptg000017l:7471118-7471210 Cluster_10228 ## 22 ptg000018l:2286780-2286870 Cluster_10419 ## 23 ptg000023l:18708903-18708996 Cluster_14402 ## 24 ptg000023l:37965243-37965338 Cluster_14768 ## 25 ptg000024l:4086202-4086295 Cluster_15316 ## 26 ptg000024l:5256454-5256549 Cluster_15340 ## 27 ptg000025l:10501030-10501125 Cluster_15851 ## 28 ptg000025l:10668901-10668998 Cluster_15854 ## 29 ptg000025l:15156415-15156507 Cluster_15928 ## 30 ptg000025l:1981692-1981783 Cluster_15671 ## 31 ptg000025l:7472531-7472623 Cluster_15775 ## 32 ptg000026l:8745540-8745635 Cluster_16409 ## 33 ptg000031l:5461305-5461397 Cluster_17776 ## 34 ptg000031l:6751935-6752029 Cluster_17791 ## 35 ptg000035l:4339550-4339641 Cluster_18711 ## 36 ptg000035l:4808338-4808432 Cluster_18723 ## 37 ptg000035l:5346032-5346127 Cluster_18728 ## 38 ptg000035l:8367726-8367821 Cluster_18772 ## 39 ptg000039l:35764-35856 Cluster_19193 ## 40 Porites_evermani_scaffold_1128:125526-125618 Cluster_9983 ## 41 Porites_evermani_scaffold_1159:6653-6743 Cluster_10060 ## 42 Porites_evermani_scaffold_1159:7406-7500 Cluster_10061 ## 43 Porites_evermani_scaffold_138:127944-128038 Cluster_2787 ## 44 Porites_evermani_scaffold_1415:72049-72142 Cluster_10934 ## 45 Porites_evermani_scaffold_1429:47235-47326 Cluster_10965 ## 46 Porites_evermani_scaffold_145:5384-5473 Cluster_2854 ## 47 Porites_evermani_scaffold_148:94531-94624 Cluster_2882 ## 48 Porites_evermani_scaffold_1503:47558-47651 Cluster_11134 ## 49 Porites_evermani_scaffold_1503:47749-47842 Cluster_11135 ## 50 Porites_evermani_scaffold_16:383386-383478 Cluster_589 ## 51 Porites_evermani_scaffold_1732:76482-76574 Cluster_11997 ## 52 Porites_evermani_scaffold_1:1404250-1404342 Cluster_29 ## 53 Porites_evermani_scaffold_2259:40195-40285 Cluster_13502 ## 54 Porites_evermani_scaffold_253:200943-201039 Cluster_4079 ## 55 Porites_evermani_scaffold_253:202235-202327 Cluster_4080 ## 56 Porites_evermani_scaffold_257:110308-110402 Cluster_4115 ## 57 Porites_evermani_scaffold_26:382550-382645 Cluster_796 ## 58 Porites_evermani_scaffold_2738:56836-56926 Cluster_14500 ## 59 Porites_evermani_scaffold_3072:29428-29521 Cluster_14999 ## 60 Porites_evermani_scaffold_316:88415-88506 Cluster_4629 ## 61 Porites_evermani_scaffold_334:153554-153646 Cluster_4735 ## 62 Porites_evermani_scaffold_3707:36102-36199 Cluster_15726 ## 63 Porites_evermani_scaffold_3893:3797-3890 Cluster_15890 ## 64 Porites_evermani_scaffold_430:205865-205960 Cluster_5563 ## 65 Porites_evermani_scaffold_461:215454-215549 Cluster_5882 ## 66 Porites_evermani_scaffold_47:475972-476066 Cluster_1140 ## 67 Porites_evermani_scaffold_49:151587-151681 Cluster_1167 ## 68 Porites_evermani_scaffold_5010:12339-12433 Cluster_16498 ## 69 Porites_evermani_scaffold_502:58948-59038 Cluster_6255 ## 70 Porites_evermani_scaffold_590:199106-199199 Cluster_6904 ## 71 Porites_evermani_scaffold_590:199370-199465 Cluster_6905 ## 72 Porites_evermani_scaffold_590:199803-199896 Cluster_6906 ## 73 Porites_evermani_scaffold_594:158176-158270 Cluster_6914 ## 74 Porites_evermani_scaffold_613:156453-156546 Cluster_7053 ## 75 Porites_evermani_scaffold_6219:6499-6590 Cluster_16738 ## 76 Porites_evermani_scaffold_730:81363-81456 Cluster_7657 ## 77 Porites_evermani_scaffold_730:82401-82494 Cluster_7658 ## 78 Porites_evermani_scaffold_768:137973-138066 Cluster_7855 ## 79 Porites_evermani_scaffold_866:22803-22895 Cluster_8634 ## 80 Porites_evermani_scaffold_910:118720-118809 Cluster_8887 ## 81 Porites_evermani_scaffold_910:139331-139420 Cluster_8888 ## 82 Porites_evermani_scaffold_910:99233-99322 Cluster_8884 ## 83 Porites_evermani_scaffold_942:133648-133739 Cluster_8988 ## 84 Porites_evermani_scaffold_984:51832-51924 Cluster_9149 ## 85 Pocillopora_meandrina_HIv1___Sc0000000:20372416-20372510 Cluster_360 ## 86 Pocillopora_meandrina_HIv1___Sc0000000:2872019-2872110 Cluster_36 ## 87 Pocillopora_meandrina_HIv1___Sc0000000:818027-818120 Cluster_21 ## 88 Pocillopora_meandrina_HIv1___Sc0000001:1459402-1459496 Cluster_390 ## 89 Pocillopora_meandrina_HIv1___Sc0000001:19145766-19145858 Cluster_757 ## 90 Pocillopora_meandrina_HIv1___Sc0000002:10845722-10845816 Cluster_1015 ## 91 Pocillopora_meandrina_HIv1___Sc0000002:15749261-15749351 Cluster_1068 ## 92 Pocillopora_meandrina_HIv1___Sc0000002:16106559-16106650 Cluster_1080 ## 93 Pocillopora_meandrina_HIv1___Sc0000002:3841944-3842042 Cluster_925 ## 94 Pocillopora_meandrina_HIv1___Sc0000003:10129512-10129606 Cluster_1289 ## 95 Pocillopora_meandrina_HIv1___Sc0000003:10366033-10366130 Cluster_1296 ## 96 Pocillopora_meandrina_HIv1___Sc0000003:495066-495158 Cluster_1116 ## 97 Pocillopora_meandrina_HIv1___Sc0000005:10385497-10385597 Cluster_1938 ## 98 Pocillopora_meandrina_HIv1___Sc0000005:11260734-11260823 Cluster_1952 ## 99 Pocillopora_meandrina_HIv1___Sc0000005:11261356-11261449 Cluster_1953 ## 100 Pocillopora_meandrina_HIv1___Sc0000005:601572-601666 Cluster_1793 ## 101 Pocillopora_meandrina_HIv1___Sc0000008:1783802-1783891 Cluster_2793 ## 102 Pocillopora_meandrina_HIv1___Sc0000008:3576420-3576514 Cluster_2837 ## 103 Pocillopora_meandrina_HIv1___Sc0000008:3619341-3619435 Cluster_2839 ## 104 Pocillopora_meandrina_HIv1___Sc0000008:5387738-5387830 Cluster_2859 ## 105 Pocillopora_meandrina_HIv1___Sc0000009:3894900-3894992 Cluster_2973 ## 106 Pocillopora_meandrina_HIv1___Sc0000010:8012916-8013008 Cluster_3392 ## 107 Pocillopora_meandrina_HIv1___Sc0000012:6455183-6455276 Cluster_3661 ## 108 Pocillopora_meandrina_HIv1___Sc0000014:2366038-2366132 Cluster_4039 ## 109 Pocillopora_meandrina_HIv1___Sc0000014:2594743-2594833 Cluster_4040 ## 110 Pocillopora_meandrina_HIv1___Sc0000014:9365459-9365552 Cluster_4118 ## 111 Pocillopora_meandrina_HIv1___Sc0000016:7549557-7549654 Cluster_4435 ## 112 Pocillopora_meandrina_HIv1___Sc0000016:7550593-7550685 Cluster_4437 ## 113 Pocillopora_meandrina_HIv1___Sc0000016:7553282-7553379 Cluster_4438 ## 114 Pocillopora_meandrina_HIv1___Sc0000016:7555639-7555733 Cluster_4439 ## 115 Pocillopora_meandrina_HIv1___Sc0000017:5050908-5051000 Cluster_4572 ## 116 Pocillopora_meandrina_HIv1___Sc0000018:4564884-4564976 Cluster_4754 ## 117 Pocillopora_meandrina_HIv1___Sc0000018:6855499-6855592 Cluster_4823 ## 118 Pocillopora_meandrina_HIv1___Sc0000021:4351817-4351909 Cluster_5253 ## 119 Pocillopora_meandrina_HIv1___Sc0000024:4808666-4808760 Cluster_5612 ## 120 Pocillopora_meandrina_HIv1___Sc0000026:1154719-1154813 Cluster_5740 ## 121 Pocillopora_meandrina_HIv1___Sc0000035:1989820-1989912 Cluster_6382 ## Chrom Start End Length Reads ## 1 ntLink_6 13351746 13351842 97 14107 ## 2 ntLink_6 4847443 4847535 93 12648 ## 3 ntLink_6 5157537 5157626 90 11203 ## 4 ntLink_6 7263486 7263580 95 7470 ## 5 ptg000001l 20063072 20063168 97 4553 ## 6 ptg000001l 5548841 5548934 94 80076 ## 7 ptg000002l 14046235 14046328 94 4003 ## 8 ptg000002l 14046541 14046634 94 5645 ## 9 ptg000002l 7337508 7337601 94 346 ## 10 ptg000004l 1859862 1859953 92 10448 ## 11 ptg000007l 3377283 3377376 94 332 ## 12 ptg000007l 915905 916002 98 414 ## 13 ptg000008l 10754767 10754862 96 75971 ## 14 ptg000009l 4940515 4940609 95 11540 ## 15 ptg000009l 616285 616377 93 2056 ## 16 ptg000009l 616579 616673 95 80457 ## 17 ptg000016l 11751358 11751448 91 25321 ## 18 ptg000016l 7795508 7795602 95 24796 ## 19 ptg000016l 8599833 8599925 93 24703 ## 20 ptg000017l 6374963 6375054 92 29473 ## 21 ptg000017l 7471118 7471210 93 1843 ## 22 ptg000018l 2286780 2286870 91 2790 ## 23 ptg000023l 18708903 18708996 94 926 ## 24 ptg000023l 37965243 37965338 96 11 ## 25 ptg000024l 4086202 4086295 94 15328 ## 26 ptg000024l 5256454 5256549 96 7544 ## 27 ptg000025l 10501030 10501125 96 397 ## 28 ptg000025l 10668901 10668998 98 232 ## 29 ptg000025l 15156415 15156507 93 1415 ## 30 ptg000025l 1981692 1981783 92 837 ## 31 ptg000025l 7472531 7472623 93 45209 ## 32 ptg000026l 8745540 8745635 96 2854 ## 33 ptg000031l 5461305 5461397 93 149 ## 34 ptg000031l 6751935 6752029 95 249212 ## 35 ptg000035l 4339550 4339641 92 50361 ## 36 ptg000035l 4808338 4808432 95 1270 ## 37 ptg000035l 5346032 5346127 96 1360205 ## 38 ptg000035l 8367726 8367821 96 1830 ## 39 ptg000039l 35764 35856 93 579 ## 40 Porites_evermani_scaffold_1128 125526 125618 93 2705 ## 41 Porites_evermani_scaffold_1159 6653 6743 91 12683 ## 42 Porites_evermani_scaffold_1159 7406 7500 95 6558 ## 43 Porites_evermani_scaffold_138 127944 128038 95 3344 ## 44 Porites_evermani_scaffold_1415 72049 72142 94 717 ## 45 Porites_evermani_scaffold_1429 47235 47326 92 13224 ## 46 Porites_evermani_scaffold_145 5384 5473 90 314 ## 47 Porites_evermani_scaffold_148 94531 94624 94 7981 ## 48 Porites_evermani_scaffold_1503 47558 47651 94 54746 ## 49 Porites_evermani_scaffold_1503 47749 47842 94 208179 ## 50 Porites_evermani_scaffold_16 383386 383478 93 4060 ## 51 Porites_evermani_scaffold_1732 76482 76574 93 1108 ## 52 Porites_evermani_scaffold_1 1404250 1404342 93 9574 ## 53 Porites_evermani_scaffold_2259 40195 40285 91 458 ## 54 Porites_evermani_scaffold_253 200943 201039 97 44919 ## 55 Porites_evermani_scaffold_253 202235 202327 93 62077 ## 56 Porites_evermani_scaffold_257 110308 110402 95 20997 ## 57 Porites_evermani_scaffold_26 382550 382645 96 93348 ## 58 Porites_evermani_scaffold_2738 56836 56926 91 190 ## 59 Porites_evermani_scaffold_3072 29428 29521 94 609 ## 60 Porites_evermani_scaffold_316 88415 88506 92 1390 ## 61 Porites_evermani_scaffold_334 153554 153646 93 1061 ## 62 Porites_evermani_scaffold_3707 36102 36199 98 1764 ## 63 Porites_evermani_scaffold_3893 3797 3890 94 1787 ## 64 Porites_evermani_scaffold_430 205865 205960 96 2539 ## 65 Porites_evermani_scaffold_461 215454 215549 96 1353 ## 66 Porites_evermani_scaffold_47 475972 476066 95 3061 ## 67 Porites_evermani_scaffold_49 151587 151681 95 267836 ## 68 Porites_evermani_scaffold_5010 12339 12433 95 2367 ## 69 Porites_evermani_scaffold_502 58948 59038 91 554 ## 70 Porites_evermani_scaffold_590 199106 199199 94 2976 ## 71 Porites_evermani_scaffold_590 199370 199465 96 1333 ## 72 Porites_evermani_scaffold_590 199803 199896 94 2870 ## 73 Porites_evermani_scaffold_594 158176 158270 95 44619 ## 74 Porites_evermani_scaffold_613 156453 156546 94 5980 ## 75 Porites_evermani_scaffold_6219 6499 6590 92 785 ## 76 Porites_evermani_scaffold_730 81363 81456 94 2935 ## 77 Porites_evermani_scaffold_730 82401 82494 94 1351 ## 78 Porites_evermani_scaffold_768 137973 138066 94 353 ## 79 Porites_evermani_scaffold_866 22803 22895 93 405 ## 80 Porites_evermani_scaffold_910 118720 118809 90 67563 ## 81 Porites_evermani_scaffold_910 139331 139420 90 21132 ## 82 Porites_evermani_scaffold_910 99233 99322 90 35797 ## 83 Porites_evermani_scaffold_942 133648 133739 92 32177 ## 84 Porites_evermani_scaffold_984 51832 51924 93 191 ## 85 Pocillopora_meandrina_HIv1___Sc0000000 20372416 20372510 95 522228 ## 86 Pocillopora_meandrina_HIv1___Sc0000000 2872019 2872110 92 177 ## 87 Pocillopora_meandrina_HIv1___Sc0000000 818027 818120 94 12096 ## 88 Pocillopora_meandrina_HIv1___Sc0000001 1459402 1459496 95 3332 ## 89 Pocillopora_meandrina_HIv1___Sc0000001 19145766 19145858 93 6322 ## 90 Pocillopora_meandrina_HIv1___Sc0000002 10845722 10845816 95 12003 ## 91 Pocillopora_meandrina_HIv1___Sc0000002 15749261 15749351 91 267 ## 92 Pocillopora_meandrina_HIv1___Sc0000002 16106559 16106650 92 2969 ## 93 Pocillopora_meandrina_HIv1___Sc0000002 3841944 3842042 99 106356 ## 94 Pocillopora_meandrina_HIv1___Sc0000003 10129512 10129606 95 11292 ## 95 Pocillopora_meandrina_HIv1___Sc0000003 10366033 10366130 98 858857 ## 96 Pocillopora_meandrina_HIv1___Sc0000003 495066 495158 93 990 ## 97 Pocillopora_meandrina_HIv1___Sc0000005 10385497 10385597 101 58190 ## 98 Pocillopora_meandrina_HIv1___Sc0000005 11260734 11260823 90 30986 ## 99 Pocillopora_meandrina_HIv1___Sc0000005 11261356 11261449 94 11335 ## 100 Pocillopora_meandrina_HIv1___Sc0000005 601572 601666 95 129423 ## 101 Pocillopora_meandrina_HIv1___Sc0000008 1783802 1783891 90 1051 ## 102 Pocillopora_meandrina_HIv1___Sc0000008 3576420 3576514 95 301 ## 103 Pocillopora_meandrina_HIv1___Sc0000008 3619341 3619435 95 366 ## 104 Pocillopora_meandrina_HIv1___Sc0000008 5387738 5387830 93 1517 ## 105 Pocillopora_meandrina_HIv1___Sc0000009 3894900 3894992 93 578 ## 106 Pocillopora_meandrina_HIv1___Sc0000010 8012916 8013008 93 26666 ## 107 Pocillopora_meandrina_HIv1___Sc0000012 6455183 6455276 94 2417 ## 108 Pocillopora_meandrina_HIv1___Sc0000014 2366038 2366132 95 1393 ## 109 Pocillopora_meandrina_HIv1___Sc0000014 2594743 2594833 91 329 ## 110 Pocillopora_meandrina_HIv1___Sc0000014 9365459 9365552 94 11095 ## 111 Pocillopora_meandrina_HIv1___Sc0000016 7549557 7549654 98 19764 ## 112 Pocillopora_meandrina_HIv1___Sc0000016 7550593 7550685 93 88654 ## 113 Pocillopora_meandrina_HIv1___Sc0000016 7553282 7553379 98 4991 ## 114 Pocillopora_meandrina_HIv1___Sc0000016 7555639 7555733 95 15915 ## 115 Pocillopora_meandrina_HIv1___Sc0000017 5050908 5051000 93 132750 ## 116 Pocillopora_meandrina_HIv1___Sc0000018 4564884 4564976 93 530 ## 117 Pocillopora_meandrina_HIv1___Sc0000018 6855499 6855592 94 6913 ## 118 Pocillopora_meandrina_HIv1___Sc0000021 4351817 4351909 93 163 ## 119 Pocillopora_meandrina_HIv1___Sc0000024 4808666 4808760 95 3154 ## 120 Pocillopora_meandrina_HIv1___Sc0000026 1154719 1154813 95 23971 ## 121 Pocillopora_meandrina_HIv1___Sc0000035 1989820 1989912 93 4602 ## DistinctSequences FracTop Strand MajorRNA MajorRNAReads ## 1 186 0.000 - UAAAAUGUCGGUUGCUUAAGCU 7452 ## 2 343 0.001 - AUGAUCAUAGCACUUUCUUUGU 3018 ## 3 216 0.998 + CAAUGUUUCGGCUUGUUCCCG 5366 ## 4 224 0.000 - UUUCAAAUUAGGAAGGGAGGUGUU 3004 ## 5 186 1.000 + AAGAACACCCAAAAUAGCUGAGGA 982 ## 6 322 0.003 - UCUCAGAUUACAGUAGUUAAGU 56403 ## 7 184 1.000 + UCUGGCAGUAUGUUAUUUUUCCAAU 802 ## 8 220 1.000 + UCUGGCAGUAUGUUAUUUUUCCC 1012 ## 9 45 1.000 + UCAUAACAGUGAGGACCAUUCU 127 ## 10 227 0.000 - UAGCAUAACAUUGUAAGAGAUCU 5104 ## 11 37 1.000 + UUUGAUUGCUGUGAUCUGGUUA 143 ## 12 39 0.000 - AAAGGAAGAAAGGAGACCCUGG 139 ## 13 546 0.000 - AAAGAAGUACAAGUGGUAGGG 18322 ## 14 210 0.000 - CAAGUGAGAGAAGGUUAGUGUGG 5129 ## 15 84 0.000 - AUUUCACUAGAUGAGCGCUAAC 1178 ## 16 407 0.000 - UCUGCGUUAUCGGUGAAAUUGU 52366 ## 17 287 0.000 - AGUGCACUUUUCUCAGGAUGAA 9236 ## 18 199 1.000 + UGGGUGUCAUCUAUUAUGUUUU 18199 ## 19 398 0.000 - UAAUGUUCGCAACUGCCUUGUU 5902 ## 20 383 0.000 - UAGGCGUAUUUCCGAUUGUCC 9407 ## 21 89 1.000 + UUUGCUAGUUGCUUUUGUCCCGU 912 ## 22 143 1.000 + UUCUUUGCACUGUUCAAACCCU 895 ## 23 90 1.000 + UUCGUCUCUUGUAUUUUCCUGG 256 ## 24 5 1.000 + UCGGACACCUGUAAUUGGAUA 5 ## 25 136 1.000 + AUUUUUAGCCCGCGGAAGUUGCU 7236 ## 26 271 0.000 - UACAUGUGCUGCACUGGAACUCU 2505 ## 27 53 0.997 + AUUGAUGAGCACAACAAAAGGU 143 ## 28 25 0.000 - UAAAGCUUUUGUGAAGAAACACG 94 ## 29 87 1.000 + UUACCACAUUCAGGAUAGACAU 616 ## 30 88 0.999 + UAUUGAUUAGUACUUGAUAGACA 315 ## 31 444 0.000 - UACAAAAACAAGAUGAGUGCAGG 21945 ## 32 64 0.000 - UGUGAUUGGAGACUUUUAUCGU 1690 ## 33 28 0.000 - ACGCUAGGAAGGGAUGCCGGGA 84 ## 34 491 0.000 - UUAACGAGUAGAUAAAUGAAGAGU 149551 ## 35 407 0.000 - AUUGAUUGUAGACAAGCCUCUGA 12975 ## 36 101 1.000 + CUCACUGCUUAAAAGAGUCACU 502 ## 37 589 1.000 + UCCCGUAGAUCCGAACUUGU 571297 ## 38 122 0.000 - UAUAUUGUACGACUCUCAUCGUGU 533 ## 39 64 0.000 - CAAGGAGGAAGCAUGAUACGUA 130 ## 40 97 0.001 - CAUGUGUUAAUUUGGACGAUU 1459 ## 41 256 0.992 + UGAGGUAAUUGUUGUUUAAAACU 2958 ## 42 252 1.000 + UAAGGUUGUUGUUGCUUAAAACU 1471 ## 43 133 1.000 + UCACUGAAGCCGUCUAUCUUGAU 924 ## 44 73 1.000 + ACAGGUGAAUGUUGUGUGAUCGCU 218 ## 45 205 0.000 - UGCAUCACUAAAAUUUCUCAAU 6555 ## 46 24 0.984 + UUGGGAAAUCCGGAUUUAGAUU 107 ## 47 155 0.000 - UAAUUGUCCUUUAGUGUUUGU 5087 ## 48 235 1.000 + UCAAGUCUAGGCUGGUUAGUUU 43003 ## 49 408 1.000 + UCAGGUCUAGGCUGGUUAGUUU 151922 ## 50 194 0.000 - UUAAGGGCCUGGGAGAGAGAAG 1239 ## 51 120 0.997 + UUAUGCAGAAAGUAAUAGUUCA 171 ## 52 313 0.002 - UUAUGCUCUGUAUACUGGUAGU 2849 ## 53 30 0.856 + UCUAAAUCCGGAUUUCCCAAUC 324 ## 54 606 1.000 + UAGAAAACUCGUGUACGUGACCCU 6501 ## 55 554 1.000 + UAAAAAACUCGUCGACGUGA 17855 ## 56 517 0.000 - AAGCACAACAUUAAUAGGAGU 8549 ## 57 537 0.000 - UCUCAGCUCACCAAUCUCUGCU 55070 ## 58 33 1.000 + UUUCUCUAAUCUCUAUGG 56 ## 59 40 1.000 + AUUUUUAGCCCGCGGAAGUUGC 325 ## 60 52 0.000 - UCCCCGCCAGGAAUUCUUGUAUU 458 ## 61 119 0.051 - UGCAGGUACAGUUAUAAGGU 75 ## 62 89 0.000 - UUGAACACUGAAACCCUGU 583 ## 63 140 0.108 - UUAAGAAUAUCAGUGAUAGGGA 429 ## 64 137 0.000 - UAUAUUGUACGACUCUCAUC 600 ## 65 74 1.000 + CGGUUCUUGUAUGCUGUUCUAAU 605 ## 66 140 0.000 - UAAUUAUAUCAGAAGGAAGAAGA 1119 ## 67 483 0.000 - UCCCGUAGAUCCGAACUUGU 173986 ## 68 136 0.995 + UUAUAUUAUAGACUUUCCUUGU 733 ## 69 39 0.000 - UAGCAUAACAUUGUAAG 345 ## 70 140 1.000 + UCACGAAUGGCUAGCGCACGAUU 968 ## 71 70 1.000 + UCACGAAUGGCUAGCGCACGAUU 450 ## 72 135 1.000 + UCACGAAUGGCUAGUGCACGAUU 1102 ## 73 365 1.000 + AAAGAAGUACAAGUGGUAGGG 12472 ## 74 96 1.000 + UUCCUAAGUUGUUGUAGCUGUU 3276 ## 75 94 0.181 - AUGUGUACAGCUAAGAGCAGUU 232 ## 76 185 0.000 - UGUUAGUUUACAGUUAGUGUUU 649 ## 77 115 0.001 - UGUUAGUUUACAGUUAGUGUUU 299 ## 78 48 1.000 + UUUUUCGUGUGCUUAUGCUUAU 115 ## 79 47 0.000 - UAUUUCAAACGUCGCGUAACCU 199 ## 80 167 0.944 + CAAUGUUUCGGCUUGUUCCCG 59917 ## 81 222 0.977 + AAAUGUUUCGGCUUGUUCCC 9474 ## 82 362 0.976 + CAAUGUUUCGGCUUGUUCCCA 13239 ## 83 505 1.000 + UAGGUAACUCUGUUAGUUUGUUU 8153 ## 84 24 0.000 - UCUGAGCCUGUAGCCCAUUU 45 ## 85 378 0.000 - UCAAGUCUAGGCUGGUUAGUUU 361684 ## 86 29 1.000 + AUAGUAUUGUCGACAGGUACU 63 ## 87 283 0.999 + UGUUCUCUCUGCAGUAAGCAUGU 3258 ## 88 118 1.000 + AAAGAAGUGCAAGUGGUAGAGA 1704 ## 89 255 1.000 + CGCUCUUAAAAUCUCCUAGCUAU 1605 ## 90 340 0.009 - UUGCCGUUAACCCAAAACCUUCA 4844 ## 91 31 1.000 + ACGCUAGGAAGGGAUGCCGGGA 162 ## 92 94 1.000 + UGUUAUUUUUAUCGUCCAUUAGU 1172 ## 93 263 0.000 - UUGUGUAACUCCCUAAGGAAGG 66971 ## 94 233 0.000 - CGAUGUACUUCAGAGCAAUUUUU 2869 ## 95 595 1.000 + ACCCGUAGAUCCGAACUUGU 302900 ## 96 64 1.000 + UAUAUUGUACGACUCUCAUCGU 438 ## 97 312 1.000 + UCAGGGAUUGUGGUGAGUUAGUU 21926 ## 98 390 1.000 + UCAGGUUAGGAGCAUGGUAUAGA 11353 ## 99 284 1.000 + UAUGCCAGGUCCUAACUUGAAA 3074 ## 100 404 1.000 + AAAGAAGUACAAGUGGUAGGG 43823 ## 101 80 1.000 + UAUGUUUUGGCUUGUUCCCA 330 ## 102 45 1.000 + CGAUGGAACACACAAGUAGAGA 89 ## 103 65 0.005 - UGUGCUUUGGUUCAAAAAUGUC 148 ## 104 131 0.002 - AACUUUUCUACAAUUCCAGUGGA 274 ## 105 48 0.002 - UAGCAUAACAUUGUAAG 330 ## 106 313 0.991 + UACUUCAAUGUUAUCGGUUUGU 7987 ## 107 125 1.000 + UGAAAUACUCUGACGGAGUCAGC 1099 ## 108 67 0.000 - AUUUUUAGCCCGCGGAAGUUGC 641 ## 109 44 0.997 + CGUGGUGCGUUAUUGUUUCGGU 54 ## 110 130 0.000 - UUCCAGAUUUGUAUCAUUGUUAU 7555 ## 111 352 1.000 + UCAGUUCCACCAGCUCACCAAU 5579 ## 112 512 1.000 + UCAGUCCCACCAUCUCACCAAU 40428 ## 113 191 1.000 + AGGUGAGCUGUGGGAACUCUUA 2482 ## 114 280 1.000 + UCAGUCCCACCAGCUCACCAAU 3822 ## 115 610 0.000 - AACUGCUGAGAUUCUAUGGAUUU 47680 ## 116 60 1.000 + UAACCUUAUUUGUCUUCACAUGU 143 ## 117 289 1.000 + GGAUUAAAAUUUGUUGGGUGUGC 1244 ## 118 39 1.000 + ACUGAUAUUCACCAAGUG 34 ## 119 144 1.000 + AGAACCCAAGAAUCUCGA 894 ## 120 150 0.000 - UAAGAUCAAGUUCAUGGUAUUCU 14088 ## 121 200 1.000 + UAUUUACAACUCUCAAAACAAC 1726 ## Short Long X21 X22 X23 X24 DicerCall MIRNA ## 1 653 7 468 12598 318 63 22 Y ## 2 1048 9 758 5669 4675 489 22 Y ## 3 3224 34 7253 334 344 14 N Y ## 4 124 2136 131 115 826 4138 N Y ## 5 329 144 1035 476 1435 1134 23 Y ## 6 1600 61 2455 70964 4578 418 22 Y ## 7 229 1294 834 736 795 115 N Y ## 8 291 1076 1032 939 1507 800 N Y ## 9 37 0 20 231 49 9 22 Y ## 10 475 296 99 1109 7865 604 23 Y ## 11 22 0 80 167 59 4 22 Y ## 12 71 0 20 293 30 0 22 Y ## 13 24349 10 33761 15714 1522 615 N Y ## 14 209 73 2546 285 7999 428 23 Y ## 15 187 5 33 1452 377 2 22 Y ## 16 2468 1367 2466 71781 2281 94 22 Y ## 17 4881 7 5372 10326 4494 241 22 Y ## 18 462 7 1053 21591 1646 37 22 Y ## 19 607 211 1680 11437 8547 2221 22 Y ## 20 3824 189 22149 2297 717 297 21 Y ## 21 24 28 33 58 1485 215 23 Y ## 22 147 63 251 1457 279 593 22 Y ## 23 69 2 105 549 185 16 22 Y ## 24 3 0 7 0 1 0 N Y ## 25 176 86 447 3022 11118 479 23 Y ## 26 289 381 1410 796 4070 598 23 Y ## 27 57 0 54 243 42 1 22 Y ## 28 10 1 5 7 177 32 23 Y ## 29 185 36 436 668 89 1 22 Y ## 30 98 10 31 189 482 27 23 Y ## 31 5021 267 4534 2938 31330 1119 23 Y ## 32 133 3 43 2337 336 2 22 Y ## 33 7 0 13 110 18 1 22 Y ## 34 3846 1813 14249 11217 37270 180817 24 Y ## 35 3270 3059 4025 3782 27285 8940 23 Y ## 36 134 7 46 733 321 29 22 Y ## 37 758793 417 31482 556861 12194 458 N Y ## 38 215 104 25 160 571 755 24 Y ## 39 55 0 179 205 136 4 22 Y ## 40 798 0 1727 157 20 3 N Y ## 41 2218 44 1357 1788 7247 29 23 Y ## 42 492 82 1349 346 4053 236 23 Y ## 43 878 108 125 887 1116 230 N Y ## 44 144 24 70 48 172 259 N Y ## 45 4484 5 227 7951 495 62 N Y ## 46 61 9 71 155 9 9 N Y ## 47 481 8 6125 241 968 158 21 Y ## 48 1714 37 2952 48770 1092 181 22 Y ## 49 8215 251 19527 172650 7006 530 22 Y ## 50 1297 46 158 1819 699 41 N Y ## 51 262 101 279 335 96 35 N Y ## 52 3870 80 468 4427 685 44 N Y ## 53 53 7 61 333 4 0 22 Y ## 54 15384 2459 1381 8809 6453 10433 N Y ## 55 48007 477 3649 7018 1002 1924 N Y ## 56 2899 133 11315 2395 3478 777 21 Y ## 57 3610 1891 2578 71538 12756 975 22 Y ## 58 87 1 27 51 11 13 N Y ## 59 22 0 29 514 44 0 22 Y ## 60 155 12 102 531 578 12 23 Y ## 61 500 3 196 266 90 6 N Y ## 62 974 9 102 590 38 51 N Y ## 63 375 0 404 574 358 76 N Y ## 64 1259 46 46 107 480 601 N Y ## 65 397 44 24 174 681 33 N Y ## 66 334 114 218 268 1384 743 23 Y ## 67 216104 92 14714 35566 1169 191 N Y ## 68 599 1 589 995 138 45 N Y ## 69 391 2 16 42 103 0 N Y ## 70 715 18 131 963 1133 16 N Y ## 71 305 0 68 272 629 59 N Y ## 72 491 1 215 823 1309 31 23 Y ## 73 24239 0 16335 3821 216 8 N Y ## 74 662 19 447 4484 368 0 22 Y ## 75 225 0 132 373 40 15 N Y ## 76 414 160 691 756 577 337 22 Y ## 77 180 32 353 352 303 131 21 Y ## 78 80 0 14 214 37 8 N Y ## 79 79 6 27 251 37 5 N Y ## 80 3615 66 62951 413 503 15 21 Y ## 81 19234 4 1717 108 57 12 N Y ## 82 18134 120 16648 693 173 29 N Y ## 83 7669 302 3095 5335 10466 5310 N Y ## 84 59 28 7 56 35 6 N Y ## 85 1294 19 20216 498308 2255 136 22 Y ## 86 16 0 116 42 3 0 21 Y ## 87 393 78 202 3329 5487 2607 23 Y ## 88 629 3 340 2293 56 11 22 Y ## 89 1316 82 585 1212 2501 626 N Y ## 90 286 129 442 5105 5860 181 23 Y ## 91 9 4 32 201 19 2 22 Y ## 92 1055 9 61 209 1607 28 N Y ## 93 4181 3 2786 98832 535 19 22 Y ## 94 397 119 3756 240 5015 1765 23 Y ## 95 453621 35 21036 380606 3365 194 N Y ## 96 71 6 12 681 173 47 22 Y ## 97 10279 126 1422 10174 32234 3955 23 Y ## 98 11069 138 1185 4477 13443 674 N Y ## 99 2510 37 1887 4105 2298 498 N Y ## 100 39962 4 68302 20915 235 5 N Y ## 101 914 11 105 14 5 2 N Y ## 102 31 9 14 185 60 2 22 Y ## 103 65 6 15 212 56 12 22 Y ## 104 246 37 133 391 660 50 23 Y ## 105 351 7 3 170 45 2 N Y ## 106 397 129 142 12953 11855 1190 22 Y ## 107 309 151 42 196 1432 287 23 Y ## 108 54 1 97 930 301 10 22 Y ## 109 60 0 17 167 49 36 22 Y ## 110 84 19 144 240 10206 402 23 Y ## 111 2156 16 1034 9200 7318 40 22 Y ## 112 12735 185 7610 60346 7494 284 22 Y ## 113 362 35 782 3195 529 88 22 Y ## 114 3453 42 3693 5606 3059 62 N Y ## 115 4764 285 1096 22198 101160 3247 23 Y ## 116 35 0 42 119 278 56 23 Y ## 117 738 316 522 428 3428 1481 23 Y ## 118 72 4 45 26 13 3 N Y ## 119 2822 1 301 26 3 1 N Y ## 120 61 53 21 570 22819 447 23 Y ## 121 614 49 387 2009 1506 37 22 Y ## known_miRNAs ## 1 ## 2 ## 3 ami-spi-miR-L-temp-14-5p_Acropora_millepora_Praher_et_al._2021_NA ## 4 Adi-Mir-Novel-2_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2026-5p ## 5 nve-miR-9425;Adi-Mir-9425_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-9425;_nve-miR-9425;ami-nve-F-miR-9425-5p_Acropora_millepora_Praher_et_al._2021_NA;miR-9425_Nematostella_vectensis_Moran_et_al._2014_NA ## 6 ## 7 adi-miR-P-novel-2-3p_Acropora_digitifera__Praher_et_al._2021_NA ## 8 adi-miR-P-novel-3-3p_Acropora_digitifera__Praher_et_al._2021_NA ## 9 adi-miR-P-novel-7-3p_Acropora_digitifera__Praher_et_al._2021_NA ## 10 Adi-Mir-2030_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2030-5p;_nve-miR-2030-5p;_spi-miR-temp-40;ami-nve-F-miR-2030-5p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-2030_Acropora_digitifera__Praher_et_al._2021_NA;spi-mir-temp-40_Stylophora_pistillata_Liew_et_al._2014_Close_match_of_nve-miR-2030;eca-nve-F-miR-2030_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;miR-2030_Nematostella_vectensis_Moran_et_al._2014_NA ## 11 apa-mir-2050_Exaiptasia_pallida_Baumgarten_et_al._2017_miR-2050;_Nve;_Spis;_Adi;Adi-Mir-2050_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2050-3p;_nve-miR-2050-3p;ami-nve-F-miR-2050-3p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-2050_Acropora_digitifera__Praher_et_al._2021_NA ## 12 ## 13 nve-miR-2023-3p;spi-mir-temp-4_Stylophora_pistillata_Liew_et_al._2014_Exact_match_of_nve-miR-2023.;apa-mir-2023_Exaiptasia_pallida_Baumgarten_et_al._2017_miR-2023;_Nve;_Spis;_Adi;mse-nve-F-miR-2023_Metridium_senile_Praher_et_al._2021_Transcriptome-level;sca-nve-miR-2023-3p_Scolanthus_callimorphus_Praher_et_al._2021_Transcriptome-level;Adi-Mir-2023_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2023-3p;_nve-miR-2023-3p;_spi-miR-temp-4;ami-nve-F-miR-2023-3p_Acropora_millepora_Praher_et_al._2021_NA;epa-nve-F-miR-2023_Exaiptasia_pallida_Praher_et_al._2021_NA;spi-nve-F-miR-2023_Stylophora_pistillata_Praher_et_al._2021_NA;miR-2023_Nematostella_vectensis_Moran_et_al._2014_NA;avi-miR-temp-2023_Anemonia_viridis_Urbarova_et_al._2018_NA;eca-nve-F-miR-2023_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level ## 14 Adi-Mir-Novel-5_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_NA;ami-Adi-MiR-G-Novel-5-3p_Acropora_millepora_Praher_et_al._2021_NA;Adi-MiR-G-Novel-5_3p_Acropora_digitifera__Praher_et_al._2021_NA ## 15 ## 16 ## 17 ## 18 ## 19 Adi-Mir-Novel-3_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2028-5p ## 20 ## 21 spi-mir-temp-25_Stylophora_pistillata_Liew_et_al._2014_Considered_bona_fideas_close_match_to_nve-_and_hma-miR-2022.;Adi-Mir-2022_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2022-3p;_nve-miR-2022-3p;_spi-miR-temp-25;ami-nve-F-miR-2022-3p_Acropora_millepora_Praher_et_al._2021_NA;eca-nve-F-miR-2022_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level ## 22 ## 23 ## 24 ami-miR-P-novel-10-3p_Acropora_millepora_Praher_et_al._2021_NA;ami-miR-P-novel-11-3p_Acropora_millepora_Praher_et_al._2021_NA ## 25 Adi-Mir-2025_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2025-3p;_nve-miR-2025-3p;ami-nve-F-miR-2025-3p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-2025_Acropora_digitifera__Praher_et_al._2021_NA ## 26 ## 27 adi-miR-P-novel-4-3p_Acropora_digitifera__Praher_et_al._2021_NA ## 28 ami-miR-P-novel-17-3p_Acropora_millepora_Praher_et_al._2021_NA ## 29 ## 30 ## 31 ami-miR-P-novel-3-5p_Acropora_millepora_Praher_et_al._2021_NA ## 32 spi-mir-temp-20_Stylophora_pistillata_Liew_et_al._2014_NA;apa-mir-2037_Exaiptasia_pallida_Baumgarten_et_al._2017_miR-2037;_Nve;_Spis;mse-nve-F-miR-2037-3p_Metridium_senile_Praher_et_al._2021_Transcriptome-level;sca-nve-F-miR-2037-3p_Scolanthus_callimorphus_Praher_et_al._2021_Transcriptome-level;ami-nve-F-miR-2037-3p_Acropora_millepora_Praher_et_al._2021_NA;spi-nve-F-miR-2037_Stylophora_pistillata_Praher_et_al._2021_NA;avi-miR-temp-2037_Anemonia_viridis_Urbarova_et_al._2018_NA;eca-nve-F-miR-2037_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level ## 33 ## 34 Adi-Mir-Novel-1_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-9437;ami-miR-P-novel-1-3p_Acropora_millepora_Praher_et_al._2021_NA;adi-miR-P-novel-1-3p_Acropora_digitifera__Praher_et_al._2021_NA ## 35 ## 36 ## 37 Adi-Mir-100_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_mir-100;_spi-miR-temp-1;ami-nve-F-miR-100-5p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-100_Acropora_digitifera__Praher_et_al._2021_NA ## 38 spi-mir-temp-30_Stylophora_pistillata_Liew_et_al._2014_Close_match_of_nve-miR-2036.;spi-nve-F-miR-2036_Stylophora_pistillata_Praher_et_al._2021_NA;Adi-Mir-2036_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2036-3p;_nve-miR-2036-3p;_spi-miR-temp-30;ami-nve-F-miR-2036-3p_Acropora_millepora_Praher_et_al._2021_NA;eca-nve-F-miR-2036_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level ## 39 ami-miR-P-novel-13-3p_Acropora_millepora_Praher_et_al._2021_NA ## 40 ## 41 ## 42 ## 43 ## 44 ## 45 ## 46 ## 47 ## 48 spi-mir-temp-15_Stylophora_pistillata_Liew_et_al._2014_NA;spi-miR-L-temp-15_Stylophora_pistillata_Praher_et_al._2021_NA ## 49 ## 50 ## 51 ## 52 ## 53 ## 54 ## 55 ## 56 ## 57 ## 58 ## 59 ami-nve-F-miR-2025-3p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-2025_Acropora_digitifera__Praher_et_al._2021_NA;Adi-Mir-2025_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2025-3p;_nve-miR-2025-3p ## 60 ## 61 ## 62 ## 63 ## 64 spi-mir-temp-30_Stylophora_pistillata_Liew_et_al._2014_Close_match_of_nve-miR-2036.;spi-nve-F-miR-2036_Stylophora_pistillata_Praher_et_al._2021_NA;ami-nve-F-miR-2036-3p_Acropora_millepora_Praher_et_al._2021_NA;Adi-Mir-2036_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2036-3p;_nve-miR-2036-3p;_spi-miR-temp-30;eca-nve-F-miR-2036_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level ## 65 ## 66 ## 67 ami-nve-F-miR-100-5p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-100_Acropora_digitifera__Praher_et_al._2021_NA;Adi-Mir-100_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_mir-100;_spi-miR-temp-1 ## 68 ## 69 ami-nve-F-miR-2030-5p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-2030_Acropora_digitifera__Praher_et_al._2021_NA;Adi-Mir-2030_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2030-5p;_nve-miR-2030-5p;_spi-miR-temp-40;spi-mir-temp-40_Stylophora_pistillata_Liew_et_al._2014_Close_match_of_nve-miR-2030;eca-nve-F-miR-2030_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;miR-2030_Nematostella_vectensis_Moran_et_al._2014_NA ## 70 ## 71 ## 72 ## 73 nve-miR-2023-3p;spi-mir-temp-4_Stylophora_pistillata_Liew_et_al._2014_Exact_match_of_nve-miR-2023.;apa-mir-2023_Exaiptasia_pallida_Baumgarten_et_al._2017_miR-2023;_Nve;_Spis;_Adi;eca-nve-F-miR-2023_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;mse-nve-F-miR-2023_Metridium_senile_Praher_et_al._2021_Transcriptome-level;sca-nve-miR-2023-3p_Scolanthus_callimorphus_Praher_et_al._2021_Transcriptome-level;ami-nve-F-miR-2023-3p_Acropora_millepora_Praher_et_al._2021_NA;epa-nve-F-miR-2023_Exaiptasia_pallida_Praher_et_al._2021_NA;spi-nve-F-miR-2023_Stylophora_pistillata_Praher_et_al._2021_NA;miR-2023_Nematostella_vectensis_Moran_et_al._2014_NA;avi-miR-temp-2023_Anemonia_viridis_Urbarova_et_al._2018_NA;Adi-Mir-2023_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2023-3p;_nve-miR-2023-3p;_spi-miR-temp-4 ## 74 ## 75 ## 76 ## 77 ## 78 ## 79 ## 80 adi-miR-P-novel-6-3p_Acropora_digitifera__Praher_et_al._2021_NA;spi-mir-temp-14_Stylophora_pistillata_Liew_et_al._2014_NA;ami-spi-miR-L-temp-14-5p_Acropora_millepora_Praher_et_al._2021_NA ## 81 ami-spi-miR-L-temp-14-5p_Acropora_millepora_Praher_et_al._2021_NA ## 82 adi-miR-P-novel-6-3p_Acropora_digitifera__Praher_et_al._2021_NA ## 83 ## 84 ## 85 spi-mir-temp-15_Stylophora_pistillata_Liew_et_al._2014_NA;spi-miR-L-temp-15_Stylophora_pistillata_Praher_et_al._2021_NA ## 86 ## 87 ## 88 ## 89 ## 90 ## 91 ## 92 ## 93 ## 94 spi-mir-temp-23_Stylophora_pistillata_Liew_et_al._2014_NA ## 95 hsa-miR-100-5p;mmu-miR-100-5p;rno-miR-100-5p;gga-miR-100-5p;aga-miR-100;dre-miR-100-5p;lla-miR-100;ggo-miR-100;age-miR-100;ppa-miR-100;ppy-miR-100;ptr-miR-100;mml-miR-100-5p;sla-miR-100;tni-miR-100;fru-miR-100;xtr-miR-100;mdo-miR-100-5p;ame-miR-100-5p;oan-miR-100-5p;tca-miR-100-5p;bta-miR-100;lgi-miR-100;sko-miR-100;bmo-miR-100;eca-miR-100;ssc-miR-100;bma-miR-100b;aae-miR-100;cqu-miR-100-5p;tgu-miR-100-5p;nvi-miR-100;pma-miR-100a-5p;aca-miR-100;sha-miR-100;ola-miR-100;cgr-miR-100-5p;mse-miR-100;ccr-miR-100;ipu-miR-100;pmi-miR-100-5p;bbe-miR-100-5p;ssa-miR-100a-5p;cpi-miR-100-5p;ami-miR-100-5p;cli-miR-100-5p;pbv-miR-100-5p;chi-miR-100-5p;tch-miR-100-5p;pal-miR-100-5p;tcf-miR-100;abu-miR-100;mze-miR-100;nbr-miR-100;oni-miR-100;pny-miR-100;gmo-miR-100a-5p;pte-miR-100b-5p;xla-miR-100-5p;cpo-miR-100-5p;dno-miR-100-5p;ocu-miR-100-5p;mmr-miR-100;dma-miR-100;cja-miR-100;nle-miR-100;sbo-miR-100;pha-miR-100;oga-miR-100;dqu-miR-100-5p;pca-miR-100-5p;spi-mir-temp-1_Stylophora_pistillata_Liew_et_al._2014_Matches_miR-100_family.;eca-nve-F-miR-100_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;mse-nve-F-miR-100_Metridium_senile_Praher_et_al._2021_Transcriptome-level;sca-nve-F-miR-100_Scolanthus_callimorphus_Praher_et_al._2021_Transcriptome-level;apa-mir-100_Exaiptasia_pallida_Baumgarten_et_al._2017_miR-100;_Nve;_Spis;_Adi;epa-nve-F-miR-100_Exaiptasia_pallida_Praher_et_al._2021_NA;spi-nve-F-miR-100_Stylophora_pistillata_Praher_et_al._2021_NA;avi-miR-temp-100_Anemonia_viridis_Urbarova_et_al._2018_NA;miR-100_Nematostella_vectensis_Moran_et_al._2014_NA ## 96 spi-mir-temp-30_Stylophora_pistillata_Liew_et_al._2014_Close_match_of_nve-miR-2036.;eca-nve-F-miR-2036_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;ami-nve-F-miR-2036-3p_Acropora_millepora_Praher_et_al._2021_NA;spi-nve-F-miR-2036_Stylophora_pistillata_Praher_et_al._2021_NA;Adi-Mir-2036_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2036-3p;_nve-miR-2036-3p;_spi-miR-temp-30 ## 97 spi-mir-temp-3_Stylophora_pistillata_Liew_et_al._2014_NA ## 98 ## 99 ## 100 nve-miR-2023-3p;spi-mir-temp-4_Stylophora_pistillata_Liew_et_al._2014_Exact_match_of_nve-miR-2023.;eca-nve-F-miR-2023_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;mse-nve-F-miR-2023_Metridium_senile_Praher_et_al._2021_Transcriptome-level;sca-nve-miR-2023-3p_Scolanthus_callimorphus_Praher_et_al._2021_Transcriptome-level;ami-nve-F-miR-2023-3p_Acropora_millepora_Praher_et_al._2021_NA;apa-mir-2023_Exaiptasia_pallida_Baumgarten_et_al._2017_miR-2023;_Nve;_Spis;_Adi;epa-nve-F-miR-2023_Exaiptasia_pallida_Praher_et_al._2021_NA;spi-nve-F-miR-2023_Stylophora_pistillata_Praher_et_al._2021_NA;miR-2023_Nematostella_vectensis_Moran_et_al._2014_NA;Adi-Mir-2023_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2023-3p;_nve-miR-2023-3p;_spi-miR-temp-4;avi-miR-temp-2023_Anemonia_viridis_Urbarova_et_al._2018_NA ## 101 ## 102 ## 103 ## 104 ## 105 adi-nve-F-miR-2030_Acropora_digitifera__Praher_et_al._2021_NA;ami-nve-F-miR-2030-5p_Acropora_millepora_Praher_et_al._2021_NA;Adi-Mir-2030_5p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2030-5p;_nve-miR-2030-5p;_spi-miR-temp-40;eca-nve-F-miR-2030_Edwardsiella_carnea_Praher_et_al._2021_Transcriptome-level;spi-mir-temp-40_Stylophora_pistillata_Liew_et_al._2014_Close_match_of_nve-miR-2030;miR-2030_Nematostella_vectensis_Moran_et_al._2014_NA ## 106 ## 107 ## 108 ami-nve-F-miR-2025-3p_Acropora_millepora_Praher_et_al._2021_NA;adi-nve-F-miR-2025_Acropora_digitifera__Praher_et_al._2021_NA;Adi-Mir-2025_3p_Acropora_digitifera_Gajigan_&_Conaco_2017_nve-miR-2025-3p;_nve-miR-2025-3p ## 109 ## 110 ## 111 ## 112 ## 113 ## 114 ## 115 ## 116 ## 117 ## 118 spi-mir-temp-34_Stylophora_pistillata_Liew_et_al._2014_NA ## 119 ## 120 ## 121 ## given_miRNA_name ## 1 apul-mir-novel-3 ## 2 apul-mir-novel-6 ## 3 apul-mir-novel-27 ## 4 apul-mir-novel-5 ## 5 apul-mir-9425 ## 6 apul-mir-novel-13 ## 7 apul-mir-novel-28 ## 8 apul-mir-novel-29 ## 9 apul-mir-novel-21 ## 10 apul-mir-2030 ## 11 apul-mir-2050 ## 12 apul-mir-novel-10 ## 13 apul-mir-2023 ## 14 apul-mir-novel-9 ## 15 apul-mir-novel-30 ## 16 apul-mir-novel-8a; apul-mir-novel-8b ## 17 apul-mir-novel-20 ## 18 apul-mir-novel-15 ## 19 apul-mir-2028 ## 20 apul-mir-novel-4 ## 21 apul-mir-2022 ## 22 apul-mir-novel-14 ## 23 apul-mir-novel-22 ## 24 apul-mir-novel-26 ## 25 apul-mir-2025 ## 26 apul-mir-novel-19 ## 27 apul-mir-novel-11 ## 28 apul-mir-novel-12 ## 29 apul-mir-novel-31 ## 30 apul-mir-novel-32 ## 31 apul-mir-novel-33 ## 32 apul-mir-novel-34 ## 33 apul-mir-novel-7 ## 34 apul-mir-novel-2 ## 35 apul-mir-novel-16 ## 36 apul-mir-novel-17 ## 37 apul-mir-100 ## 38 apul-mir-2036 ## 39 apul-mir-novel-23 ## 40 peve-mir-novel-25 ## 41 peve-mir-novel-26 ## 42 peve-mir-novel-27 ## 43 peve-mir-novel-39 ## 44 peve-mir-novel-28 ## 45 peve-mir-novel-29 ## 46 peve-mir-novel-40 ## 47 peve-mir-novel-6 ## 48 peve-mir-novel-30 ## 49 peve-mir-novel-31 ## 50 peve-mir-novel-2 ## 51 peve-mir-novel-32 ## 52 peve-mir-novel-1 ## 53 peve-mir-novel-41 ## 54 peve-mir-novel-7 ## 55 peve-mir-novel-8 ## 56 peve-mir-novel-9 ## 57 peve-mir-novel-3 ## 58 peve-mir-novel-34 ## 59 peve-mir-2025 ## 60 peve-mir-novel-10 ## 61 peve-mir-novel-11 ## 62 peve-mir-novel-35 ## 63 peve-mir-novel-36 ## 64 peve-mir-2036 ## 65 peve-mir-novel-13 ## 66 peve-mir-novel-4 ## 67 peve-mir-100 ## 68 peve-mir-novel-38 ## 69 peve-mir-2030 ## 70 peve-mir-novel-14a; peve-mir-novel-14b ## 71 peve-mir-novel-14a; peve-mir-novel-14b ## 72 peve-mir-novel-15 ## 73 peve-mir-2023 ## 74 peve-mir-novel-16 ## 75 peve-mir-novel-42 ## 76 peve-mir-novel-17a; peve-mir-novel-17b ## 77 peve-mir-novel-17a; peve-mir-novel-17b ## 78 peve-mir-novel-18 ## 79 peve-mir-novel-19 ## 80 peve-mir-novel-21 ## 81 peve-mir-novel-22 ## 82 peve-mir-novel-20 ## 83 peve-mir-novel-23 ## 84 peve-mir-novel-24 ## 85 ptuh-mir-novel-32 ## 86 ptuh-mir-novel-27 ## 87 ptuh-mir-novel-31 ## 88 ptuh-mir-novel-33 ## 89 ptuh-mir-novel-1 ## 90 ptuh-mir-novel-5 ## 91 ptuh-mir-novel-7 ## 92 ptuh-mir-novel-2 ## 93 ptuh-mir-novel-24 ## 94 ptuh-mir-novel-8 ## 95 ptuh-mir-100 ## 96 ptuh-mir-2036 ## 97 ptuh-mir-novel-6 ## 98 ptuh-mir-novel-4 ## 99 ptuh-mir-novel-3 ## 100 ptuh-mir-2023 ## 101 ptuh-mir-novel-20 ## 102 ptuh-mir-novel-26 ## 103 ptuh-mir-novel-25 ## 104 ptuh-mir-novel-18 ## 105 ptuh-mir-2030 ## 106 ptuh-mir-novel-10 ## 107 ptuh-mir-novel-17 ## 108 ptuh-mir-2025 ## 109 ptuh-mir-novel-28 ## 110 ptuh-mir-novel-9 ## 111 ptuh-mir-novel-11; ptuh-mir-novel-15 ## 112 ptuh-mir-novel-14 ## 113 ptuh-mir-novel-13 ## 114 ptuh-mir-novel-12 ## 115 ptuh-mir-novel-19 ## 116 ptuh-mir-novel-22 ## 117 ptuh-mir-novel-16 ## 118 ptuh-mir-novel-23 ## 119 ptuh-mir-novel-21 ## 120 ptuh-mir-novel-30 ## 121 ptuh-mir-novel-29 ``` r write.csv(miRNA_details, "../output/20-supplementary-files/AllSpecies_Results_mature_named_miRNAs.csv", col.names = TRUE, row.names = FALSE) ``` ## Warning in write.csv(miRNA_details, ## "../output/20-supplementary-files/AllSpecies_Results_mature_named_miRNAs.csv", ## : attempt to set 'col.names' ignored # 5 miRanda binding information + PCC coexpression (miRNA-mRNA and miRNA-lncRNA) ## 5.1 miRNA-mRNA ``` r consolidate_regions <- function(path_3UTR, path_CDS, path_5UTR){ # Load for each region data_3UTR <- read.csv(path_3UTR) %>% dplyr::select(-X, -X.1) data_CDS <- read.csv(path_CDS) %>% dplyr::select(-X) data_5UTR <- read.csv(path_5UTR) %>% dplyr::select(-X) # Format and combine into pooled targets data_3UTR$region <- "3UTR" colnames(data_CDS) <- c("miRNA", "mRNA_coord", "score", "energy", "query_start", "query_end", "subject_start", "subject_end", "total_bp_shared", "query_similar", "subject_similar", "mRNA", "PCC.cor", "p_value", "adjusted_p_value") data_CDS$query_start_end <- paste0(data_CDS$query_start, " ", data_CDS$query_end) data_CDS$subject_start_end <- paste0(data_CDS$subject_start, " ", data_CDS$subject_end) data_CDS$region <- "CDS" data_CDS <- data_CDS %>% dplyr::select(colnames(data_3UTR)) colnames(data_5UTR) <- c("miRNA", "mRNA_coord", "score", "energy", "query_start", "query_end", "subject_start", "subject_end", "total_bp_shared", "query_similar", "subject_similar", "mRNA", "PCC.cor", "p_value", "adjusted_p_value") data_5UTR$query_start_end <- paste0(data_5UTR$query_start, " ", data_5UTR$query_end) data_5UTR$subject_start_end <- paste0(data_5UTR$subject_start, " ", data_5UTR$subject_end) data_5UTR$region <- "5UTR" data_5UTR <- data_5UTR %>% dplyr::select(colnames(data_3UTR)) # Confirm all 3 region tables have same column names and orders ifelse(identical(colnames(data_3UTR), colnames(data_CDS)) == TRUE, ifelse(identical(colnames(data_3UTR), colnames(data_5UTR)) == TRUE, data <- rbind(data_3UTR, data_CDS, data_5UTR), data <- NULL) ) data } apul_miRanda_PCC_mRNA <- consolidate_regions( "../../D-Apul/output/09-Apul-mRNA-miRNA-interactions/miranda_PCC_miRNA_mRNA.csv", "../../D-Apul/output/09.01-Apul-mRNA-miRNA-interactions-CDS_5UTR/miRanda_PCC_miRNA_CDS.csv", "../../D-Apul/output/09.01-Apul-mRNA-miRNA-interactions-CDS_5UTR/miRanda_PCC_miRNA_5UTR.csv") peve_miRanda_PCC_mRNA <- consolidate_regions( "../../E-Peve/output/10-Peve-mRNA-miRNA-interactions/Peve-miranda_PCC_miRNA_mRNA.csv", "../../E-Peve/output/10.01-Peve-mRNA-miRNA-interactions-CDS_5UTR/miRanda_PCC_miRNA_CDS.csv", "../../E-Peve/output/10.01-Peve-mRNA-miRNA-interactions-CDS_5UTR/miRanda_PCC_miRNA_5UTR.csv" ) ptuh_miRanda_PCC_mRNA <- consolidate_regions( "../../F-Ptuh/output/11-Ptuh-mRNA-miRNA-interactions/three_prime_interaction/Ptuh-miranda_PCC_miRNA_mRNA.csv", "../../F-Ptuh/output/11.01-Ptuh-mRNA-miRNA-interactions-CDS_5UTR/miRanda_PCC_miRNA_CDS.csv", "../../F-Ptuh/output/11.01-Ptuh-mRNA-miRNA-interactions-CDS_5UTR/miRanda_PCC_miRNA_5UTR.csv" ) # Combine miRanda_PCC_mRNA <- rbind(apul_miRanda_PCC_mRNA, peve_miRanda_PCC_mRNA, ptuh_miRanda_PCC_mRNA) # Filter to only significant PCC (this consitutes our putative interaction set, which is both # predicted to bind via miRanda and significantly coexpressed) miRanda_PCC_mRNA_sig <- miRanda_PCC_mRNA %>% filter(p_value < 0.05) # Save write.csv(miRanda_PCC_mRNA_sig, "../output/20-supplementary-files/miRanda_PCC_mRNA_sig.csv", row.names = FALSE) write.csv(miRanda_PCC_mRNA, "../output/20-supplementary-files/miRanda_PCC_mRNA.csv", row.names = FALSE) ``` ## 5.2 miRNA-lncRNA ``` r apul_miRanda_PCC_lncRNA <- read.csv("../../D-Apul/output/28-Apul-miRNA-lncRNA-interactions/miranda_PCC_miRNA_lncRNA.csv") %>% dplyr::select(-X, -X.1) peve_miRanda_PCC_lncRNA <- read.csv("../../E-Peve/output/15-Peve-miRNA-lncRNA-PCC/miranda_PCC_miRNA_lncRNA.csv") %>% dplyr::select(-X, -X.1) ptuh_miRanda_PCC_lncRNA <- read.csv("../../F-Ptuh/output/15-Ptuh-miRNA-lncRNA-PCC/miranda_PCC_miRNA_lncRNA.csv") %>% dplyr::select(-X, -X.1) # Ensure all three tables have matching column names and order identical(colnames(apul_miRanda_PCC_lncRNA), colnames(peve_miRanda_PCC_lncRNA)) ``` ## [1] TRUE ``` r identical(colnames(apul_miRanda_PCC_lncRNA), colnames(ptuh_miRanda_PCC_lncRNA)) ``` ## [1] TRUE ``` r # Combine miRanda_PCC_lncRNA <- rbind(apul_miRanda_PCC_lncRNA, peve_miRanda_PCC_lncRNA, ptuh_miRanda_PCC_lncRNA) # Reorder columns to have same order as miRNA-mRNA table (above) miRanda_PCC_lncRNA <- miRanda_PCC_lncRNA %>% dplyr::select(miRNA, lncRNA, PCC.cor, p_value, adjusted_p_value, score, energy, query_start_end, subject_start_end, total_bp_shared, query_similar, subject_similar) # Filter to only significant PCC (this constitutes our putative interaction set, which is both # predicted to bind via miRanda and significantly coexpressed) miRanda_PCC_lncRNA_sig <- miRanda_PCC_lncRNA %>% filter(p_value < 0.05) # Save write.csv(miRanda_PCC_lncRNA_sig, "../output/20-supplementary-files/miRanda_PCC_lncRNA_sig.csv", row.names = FALSE) write.csv(miRanda_PCC_lncRNA, "../output/20-supplementary-files/miRanda_PCC_lncRNA.csv", row.names = FALSE) ```