17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid ================ Kathleen Durkin 2024-12-11 - 1 Prep for BLASTs - 1.1 Isolate the pre-mirna and mature mirna sequences - 1.2 Check miRNA lengths - 1.3 check lncRNAs - 2 BLASTs - 2.1 Make databases - 2.2 Run BLASTn - 3 Examine BLAST tables - 3.1 LncRNAs as miRNA precursors - 3.2 LncRNAs as miRNA sponges - 4 miRanda - 4.1 Run miRanda - 5 Summarize results - 6 RNAhybrid - 6.1 Get lncRNA gtf - 6.2 Break up >100bp sequences - 6.3 Get fasta of broken-up lncRNA gtf - 6.4 Run RNAhybrid - 6.5 Summarize RNAhybrid results - 7 Summarize final results - 8 References ``` r library(dplyr) ``` ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ``` r library(ggplot2) knitr::opts_chunk$set( echo = TRUE, # Display code chunks eval = FALSE # Evaluate code chunks ) ``` Two possible interactions between miRNA and lncRNA are: 1) lncRNA acting as a precursor molecule for miRNA(s), so that the lncRNA contains one or many pre-miRNA sequences and will be broken down into pre-miRNAs molecules, which will then be processed into mature miRNAs. 2) lncRNA acting as a “sponge” for miRNAs, so that an miRNA will bind to the lncRNA instead of being incorporated into an RISC complex to alter gene expression. In situation 1 we would expect one or several **pre-miRNA sequences to appear inside of a lncRNA**. This should be identifiable via BLASTn. In situation 2 we would expect the **mature miRNA sequence to appear inside a lncRNA**. Note that situation 2 is a bit more complicated, because we can’t say for certain what sequence similarity is required for binding. In cnidarians, miRNAs seem to act, like plants, through complementarity of the full mature miRNA (this is in contrast to e.g. mammals, where only binding of a short seed region is required) (Moran et al. ([2014](#ref-moran_cnidarian_2014)), Admoni et al. ([2023](#ref-admoni_target_2023))). However, for lncRNA acting as sponges, I don’t know whether to expect complementarity of the full mature miRNA or only a section, and I don’t know what degree of complementarity is required. **Work to identify lncRNA sponges could use BLASTn, but will likely need to include additional methods like miranda or RNAhybrid to identify potential binding.** # 1 Prep for BLASTs ## 1.1 Isolate the pre-mirna and mature mirna sequences ``` bash full_mirna_fasta="../output/11-Apul-sRNA-ShortStack_4.1.0-pulchra_genome/ShortStack_out/mir.fasta" premirna_fasta="../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_precursor.fasta" mature_mirna_fasta="../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature.fasta" star_mirna_fasta="../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_star.fasta" # Pull out all sequences that DON'T contain "mature" or "star" in sequence name # Note the pre-miRNAs have sequences for both strands awk ' # If the line starts with ">", check the header /^>/ { if ($0 ~ /mature/ || $0 ~ /star/) { print_seq = 0 # Skip sequences with "mature" or "star" in the header } else { print_seq = 1 # Mark sequences for printing } } # Print the header and the next two lines if marked for printing print_seq { print if (!/^>/) { getline; print } # Capture second sequence line } ' "$full_mirna_fasta" > "$premirna_fasta" # Pull out all sequences that contain "mature" in sequence name grep -A 1 "mature" $full_mirna_fasta | grep -v "^--$" > $mature_mirna_fasta # Pull out all sequences that contain "star" in sequence name grep -A 1 "star" $full_mirna_fasta | grep -v "^--$" > $star_mirna_fasta ``` ``` bash premirna_fasta="../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_precursor.fasta" mature_mirna_fasta="../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature.fasta" star_mirna_fasta="../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_star.fasta" # Check we have appropriate headers, same number of sequences in each grep "^>" $premirna_fasta | head -2 echo "" grep "^>" $mature_mirna_fasta | head -2 echo "" grep "^>" $star_mirna_fasta | head -2 echo "" grep "^>" $premirna_fasta | wc -l echo "" grep "^>" $mature_mirna_fasta | wc -l echo "" grep "^>" $star_mirna_fasta | wc -l echo "" ``` ## >Cluster_1826::ntLink_6:4847443-4847535(-) ## >Cluster_1832::ntLink_6:5157537-5157626(+) ## ## >Cluster_1826.mature::ntLink_6:4847465-4847486(-) ## >Cluster_1832.mature::ntLink_6:5157559-5157579(+) ## ## >Cluster_1826.star::ntLink_6:4847494-4847515(-) ## >Cluster_1832.star::ntLink_6:5157586-5157606(+) ## ## 39 ## ## 39 ## ## 39 ## 1.2 Check miRNA lengths ``` bash # Extract sequence lengths for precursors awk '/^>/ {if (seqlen){print seqlen}; printf $0" " ;seqlen=0;next; } { seqlen += length($0)}END{print seqlen}' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_precursor.fasta > ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_precursor_lengths.txt # Sequence lengths for matures awk '/^>/ {if (seqlen){print seqlen}; printf $0" " ;seqlen=0;next; } { seqlen += length($0)}END{print seqlen}' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature.fasta > ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature_lengths.txt ``` ``` r # Summary stats of precursor and mature lengths precursor_lengths <- read.table("../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_precursor_lengths.txt", sep = " ", header = FALSE, col.names = c("seqID", "length")) mature_lengths <- read.table("../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature_lengths.txt", sep = " ", header = FALSE, col.names = c("seqID", "length")) cat("Average pre-miRNA length: ", mean(precursor_lengths$length)) ``` ## Average pre-miRNA length: 94.20513 ``` r cat("\n") ``` ``` r cat("Range of pre-miRNA lengths: ", range(precursor_lengths$length)) ``` ## Range of pre-miRNA lengths: 90 98 ``` r cat("\n") ``` ``` r cat("Average mature miRNA length: ", mean(mature_lengths$length)) ``` ## Average mature miRNA length: 22.25641 ``` r cat("\n") ``` ``` r cat("Range of mature miRNA lengths: ", range(mature_lengths$length)) ``` ## Range of mature miRNA lengths: 21 24 ## 1.3 check lncRNAs LncRNAs were identified from Apul RNA-seq data in `deep-dive-expression/D-Apul/code/10-Apul-lncRNA` – see details there. Fasta of Apul lncRNAs stored at `deep-dive-expression/D-Apul/output/10-Apul-lncRNA/Apul_lncRNA.fasta` ``` bash curl -L https://gannet.fish.washington.edu/acropora/E5-deep-dive-expression/output/01.6-lncRNA-pipline/Apul_lncRNA.fasta -o ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.fasta echo "Number of lncRNAs:" grep "^>" ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.fasta | wc -l ``` ## % Total % Received % Xferd Average Speed Time Time Time Current ## Dload Upload Total Spent Left Speed ## 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 12 392M 12 47.8M 0 0 85.0M 0 0:00:04 --:--:-- 0:00:04 85.0M 40 392M 40 157M 0 0 101M 0 0:00:03 0:00:01 0:00:02 101M 68 392M 68 268M 0 0 104M 0 0:00:03 0:00:02 0:00:01 104M 90 392M 90 353M 0 0 99.2M 0 0:00:03 0:00:03 --:--:-- 99.2M100 392M 100 392M 0 0 97.6M 0 0:00:04 0:00:04 --:--:-- 97.6M ## Number of lncRNAs: ## 77200 ``` bash # Extract sequence lengths for precursors awk '/^>/ {if (seqlen){print seqlen}; printf $0" " ;seqlen=0;next; } { seqlen += length($0)}END{print seqlen}' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.fasta > ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_lengths.txt ``` ``` r # Summary stats of lncRNA lengths lncRNA_lengths <- read.table("../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_lengths.txt", sep = " ", header = FALSE, col.names = c("seqID", "length")) cat("Average lncRNA length: ", mean(lncRNA_lengths$length)) ``` ## Average lncRNA length: 5201.144 ``` r cat("\n") ``` ``` r cat("Range of lncRNA lengths: ", range(lncRNA_lengths$length)) ``` ## Range of lncRNA lengths: 201 181838 ``` r ggplot(lncRNA_lengths, aes(x = length)) + geom_histogram(binwidth = 500) + labs(title = "A. pulchra lncRNA sequence lengths", x = "Sequence Length [nucleotides]", y = "Frequency") + xlim(200, 200000) + ylim(0, 1000) + theme_minimal() ``` ## Warning: Removed 18 rows containing missing values or values outside the scale range ## (`geom_bar()`). ![](17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid_files/figure-gfm/unnamed-chunk-5-1.png) # 2 BLASTs ## 2.1 Make databases Database of pre-miRNAs: ``` bash /home/shared/ncbi-blast-2.11.0+/bin/makeblastdb \ -in ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_precursor.fasta \ -dbtype nucl \ -out ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/Apul-db/Apul_ShortStack_4.1.0_precursor ``` Database of mature miRNAs: ``` bash /home/shared/ncbi-blast-2.11.0+/bin/makeblastdb \ -in ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature.fasta \ -dbtype nucl \ -out ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/Apul-db/Apul_ShortStack_4.1.0_mature ``` ## 2.2 Run BLASTn Generate a list of blast results. It seems plausible that a single lncRNA, which would be hundreds or thousands of nucleotides long, could interact with multiple miRNAs, so I will allow up to 10 hits (\~25% of Apul miRNAs) for each lncRNA. I want to see the top hits no matter how poor the match is, so I will not filter by e-value at this stage. I’ll also include the “-word_size 4” option, which reduces the required length of the initial match. Full pre-miRNAs: ``` bash /home/shared/ncbi-blast-2.11.0+/bin/blastn \ -task blastn \ -query ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.fasta \ -db ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/Apul-db/Apul_ShortStack_4.1.0_precursor \ -out ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_precursor_blastn.tab \ -num_threads 40 \ -word_size 4 \ -max_target_seqs 10 \ -max_hsps 1 \ -outfmt 6 ``` ``` bash wc -l ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_precursor_blastn.tab ``` ## 738169 ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_precursor_blastn.tab Note we have less than (10 \* $$# of lncRNAs$$) output alignments because, while I did not set an evalue threshold, the default evalue threshold of evalue=10 is still in place. That means extremely poor matches were still excluded by default. Mature miRNAs: Note that I’m using the blastn-short option here because all of our mature miRNAs are less than 30 nucleotides long (recommended by [BLAST user manual](https://www.ncbi.nlm.nih.gov/books/NBK279684/table/appendices.T.blastn_application_options/)) ``` bash /home/shared/ncbi-blast-2.11.0+/bin/blastn \ -task blastn \ -query ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.fasta \ -db ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/Apul-db/Apul_ShortStack_4.1.0_mature \ -out ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_mature_blastn.tab \ -num_threads 40 \ -word_size 4 \ -max_target_seqs 10 \ -max_hsps 1 \ -outfmt 6 ``` ``` bash wc -l ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_mature_blastn.tab ``` ## 751007 ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_mature_blastn.tab # 3 Examine BLAST tables Read into R and assign informative column labels ``` r precursor_lncRNA_BLASTn <- read.table("../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_precursor_blastn.tab", sep="\t", header=FALSE) mature_lncRNA_BLASTn <- read.table("../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/blasts/lncRNA_to_mature_blastn.tab", sep="\t", header=FALSE) colnames(precursor_lncRNA_BLASTn) <- c("qseqid", "sseqid", "pident", "length", "mismatch", "gapopen", "qstart", "qend", "sstart", "send", "evalue", "bitscore") colnames(mature_lncRNA_BLASTn) <- c("qseqid", "sseqid", "pident", "length", "mismatch", "gapopen", "qstart", "qend", "sstart", "send", "evalue", "bitscore") ``` ## 3.1 LncRNAs as miRNA precursors Are there any alignments of the full precursor miRNA to a lncRNA? Our precursor sequences are 90-98 nucleotides long, so let’s look for any alignments of at least 90 nucleotides with 0 mismatches. ``` r precursor_lncRNA_BLASTn %>% filter(length >= 90) %>% filter(mismatch == 0) %>% unique() %>% nrow() ``` ## [1] 34 ``` r precursor_lncRNA_BLASTn %>% filter(length >= 90) %>% filter(mismatch == 0) %>% select(qseqid) %>% unique() %>% nrow() ``` ## [1] 31 ``` r precursor_lncRNA_BLASTn %>% filter(length >= 90) %>% filter(mismatch == 0) %>% select(sseqid) %>% unique() %>% nrow() ``` ## [1] 23 ``` r precursor_lncRNA_BLASTn %>% filter(length >= 90) %>% filter(mismatch == 0) %>% unique() ``` ## qseqid ## 1 transcript::ntLink_6:5155137-5159371 ## 3 transcript::ntLink_6:5156340-5159371 ## 4 transcript::ntLink_6:4834719-4876081 ## 5 transcript::ntLink_6:13329954-13369257 ## 6 transcript::ptg000001l:5544306-5580019 ## 7 transcript::ptg000002l:14041039-14052409 ## 8 transcript::ptg000002l:14041039-14052409 ## 9 transcript::ptg000007l:908509-921806 ## 10 transcript::ptg000009l:611774-618362 ## 11 transcript::ptg000009l:611774-618362 ## 12 transcript::ptg000009l:613601-618362 ## 13 transcript::ptg000009l:613601-618362 ## 14 transcript::ptg000009l:4929027-4948436 ## 15 transcript::ptg000016l:7787959-7823075 ## 16 transcript::ptg000016l:8593882-8603986 ## 17 transcript::ptg000016l:11748348-11760789 ## 18 transcript::ptg000016l:11748351-11760789 ## 19 transcript::ptg000016l:11748671-11760568 ## 20 transcript::ptg000016l:11748715-11760789 ## 21 transcript::ptg000018l:2280996-2290234 ## 22 transcript::ptg000023l:18698522-18723762 ## 23 transcript::ptg000023l:37964178-37968910 ## 24 transcript::ptg000023l:38035263-38040884 ## 25 transcript::ptg000024l:4085668-4087287 ## 26 transcript::ptg000025l:1976289-1990446 ## 27 transcript::ptg000025l:10491944-10502595 ## 29 transcript::ptg000025l:10668087-10677231 ## 30 transcript::ptg000035l:5335161-5347816 ## 31 transcript::ptg000035l:5335634-5347816 ## 32 transcript::ptg000035l:5336170-5347816 ## 33 transcript::ptg000035l:5336225-5347816 ## 34 transcript::ptg000035l:5336231-5347816 ## 35 transcript::ptg000035l:4337555-4340587 ## 36 transcript::ptg000039l:33382-41881 ## sseqid pident length mismatch ## 1 Cluster_1832::ntLink_6:5157537-5157626(+) 100 90 0 ## 3 Cluster_1832::ntLink_6:5157537-5157626(+) 100 90 0 ## 4 Cluster_1826::ntLink_6:4847443-4847535(-) 100 93 0 ## 5 Cluster_1951::ntLink_6:13351746-13351842(-) 100 97 0 ## 6 Cluster_2463::ptg000001l:5548841-5548934(-) 100 94 0 ## 7 Cluster_3367::ptg000002l:14046541-14046634(+) 100 94 0 ## 8 Cluster_3366::ptg000002l:14046235-14046328(+) 100 94 0 ## 9 Cluster_4220::ptg000007l:915905-916002(-) 100 98 0 ## 10 Cluster_5900::ptg000009l:616579-616673(-) 100 95 0 ## 11 Cluster_5899::ptg000009l:616285-616377(-) 100 93 0 ## 12 Cluster_5900::ptg000009l:616579-616673(-) 100 95 0 ## 13 Cluster_5899::ptg000009l:616285-616377(-) 100 93 0 ## 14 Cluster_5981::ptg000009l:4940515-4940609(-) 100 95 0 ## 15 Cluster_10051::ptg000016l:7795508-7795602(+) 100 95 0 ## 16 Cluster_10057::ptg000016l:8599833-8599925(-) 100 93 0 ## 17 Cluster_10093::ptg000016l:11751358-11751448(-) 100 91 0 ## 18 Cluster_10093::ptg000016l:11751358-11751448(-) 100 91 0 ## 19 Cluster_10093::ptg000016l:11751358-11751448(-) 100 91 0 ## 20 Cluster_10093::ptg000016l:11751358-11751448(-) 100 91 0 ## 21 Cluster_10419::ptg000018l:2286780-2286870(+) 100 91 0 ## 22 Cluster_14402::ptg000023l:18708903-18708996(+) 100 94 0 ## 23 Cluster_14768::ptg000023l:37965243-37965338(+) 100 96 0 ## 24 Cluster_14768::ptg000023l:37965243-37965338(+) 100 96 0 ## 25 Cluster_15316::ptg000024l:4086202-4086295(+) 100 94 0 ## 26 Cluster_15671::ptg000025l:1981692-1981783(+) 100 92 0 ## 27 Cluster_15851::ptg000025l:10501030-10501125(+) 100 96 0 ## 29 Cluster_15854::ptg000025l:10668901-10668998(-) 100 98 0 ## 30 Cluster_18728::ptg000035l:5346032-5346127(+) 100 96 0 ## 31 Cluster_18728::ptg000035l:5346032-5346127(+) 100 96 0 ## 32 Cluster_18728::ptg000035l:5346032-5346127(+) 100 96 0 ## 33 Cluster_18728::ptg000035l:5346032-5346127(+) 100 96 0 ## 34 Cluster_18728::ptg000035l:5346032-5346127(+) 100 96 0 ## 35 Cluster_18711::ptg000035l:4339550-4339641(-) 100 92 0 ## 36 Cluster_19193::ptg000039l:35764-35856(-) 100 93 0 ## gapopen qstart qend sstart send evalue bitscore ## 1 0 2400 2489 1 90 7.22e-43 163 ## 3 0 1197 1286 1 90 5.16e-43 163 ## 4 0 12724 12816 93 1 1.60e-43 168 ## 5 0 21792 21888 97 1 1.02e-45 176 ## 6 0 4535 4628 94 1 3.96e-44 170 ## 7 0 5502 5595 1 94 1.28e-44 170 ## 8 0 5196 5289 1 94 1.28e-44 170 ## 9 0 7396 7493 98 1 1.00e-46 178 ## 10 0 4805 4899 95 1 2.14e-45 172 ## 11 0 4511 4603 93 1 2.61e-44 168 ## 12 0 2978 3072 95 1 1.57e-45 172 ## 13 0 2684 2776 93 1 1.91e-44 168 ## 14 0 11488 11582 95 1 6.24e-45 172 ## 15 0 7549 7643 1 95 1.11e-44 172 ## 16 0 5951 6043 93 1 4.01e-44 168 ## 17 0 3010 3100 91 1 5.93e-43 165 ## 18 0 3007 3097 91 1 5.93e-43 165 ## 19 0 2687 2777 91 1 5.67e-43 165 ## 20 0 2643 2733 91 1 5.76e-43 165 ## 21 0 5784 5874 1 91 4.46e-43 165 ## 22 0 10381 10474 1 94 2.80e-44 170 ## 23 0 1065 1160 1 96 4.46e-46 174 ## 24 0 1070 1165 1 96 5.23e-46 174 ## 25 0 534 627 1 94 1.87e-45 170 ## 26 0 5403 5494 1 92 1.93e-43 167 ## 27 0 9086 9181 1 96 9.93e-46 174 ## 29 0 814 911 98 1 7.00e-47 178 ## 30 0 10871 10966 1 96 1.17e-45 174 ## 31 0 10398 10493 1 96 1.12e-45 174 ## 32 0 9862 9957 1 96 1.07e-45 174 ## 33 0 9807 9902 1 96 1.07e-45 174 ## 34 0 9801 9896 1 96 1.07e-45 174 ## 35 0 1995 2086 92 1 4.24e-44 167 ## 36 0 2382 2474 93 1 3.37e-44 168 We have 34 alignments of a full pre-miRNA to a lncRNA with no mismatches. 31 lncRNA and 23 miRNA are represented. Two things are notable. First, the lncRNA which contain full pre-miRNA sequences also sit in the same genomic region as the pre-miRNA. This isn’t particularly surprising, but it’s important to note that these lncRNA precursors are not *in addition to* the pre-miRNA regions. Second, there are several instances where multiple lncRNA not only contain the same pre-miRNA, but are also overlapping (in terms of genomic position). For example, the Cluster_10093 precursor is present in the following lncRNA: transcript::ptg000016l:11748348-11760789, transcript::ptg000016l:11748351-11760789, transcript::ptg000016l:11748671-11760568, and transcript::ptg000016l:11748715-11760789. These four lncRNA sit on the same scaffold and share the same end position. This suggests to me that there are instances of lncRNA isoforms – multiple variants of the same molecule. For the purposes of summary, these will likely be treated as distinct lncRNA, since we don’t have a god idea of what degree of difference is necessary to alter lncRNA function. It is also possible that a lncRNA may be directly processed into a mature miRNA, without first being processed into pre-miRNA. Let’s look for those by searching for alignments of mature miRNAs to lncRNAs. Our mature miRNAs range 21-24 nucleotides in length. Let’s look for alignments of at least 21 nucleotides in length with 0 mismatches and 0 gaps. Save these results ``` r precursor_lncRNAs <- precursor_lncRNA_BLASTn %>% filter(length >= 90) %>% filter(mismatch == 0) write.table(precursor_lncRNAs, "../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/lncRNAs_as_miRNA_precursors.txt") ``` ## 3.2 LncRNAs as miRNA sponges I’m not sure whether to expect lncRNAs to bind miRNAs in the same way cnidarian miRNA-mRNA binding occurs (nearly perfect complementarity of mature sequence), or whether the mechanism could differ (e.g., requires only a complementary seed region, as in vertebrate miRNA-mRNA binding). that means I don’t know what alignment parameters to require for our BLAST results. For now let’s say the aligned region must be at least 8 nucleotides (the expected length of an miRNA seed region), and let’s require a low evalue of 1e-3, to generally restrict results to those with high complementarity. ``` r mature_lncRNA_BLASTn %>% filter(length >= 8) %>% filter(evalue <= 0.001) ``` ## qseqid ## 1 transcript::ntLink_6:5155137-5159371 ## 2 transcript::ntLink_6:5155137-5159371 ## 3 transcript::ntLink_6:5156340-5159371 ## 4 transcript::ntLink_6:4834719-4876081 ## 5 transcript::ntLink_6:4863260-4876081 ## 6 transcript::ntLink_6:13329954-13369257 ## 7 transcript::ntLink_6:16464141-16469269 ## 8 transcript::ntLink_6:6543820-6544705 ## 9 transcript::ntLink_8:486960-488023 ## 10 transcript::ntLink_8:3186326-3196541 ## 11 transcript::ntLink_8:3305138-3308767 ## 12 transcript::ntLink_8:22685890-22707953 ## 13 transcript::ntLink_8:22685890-22707953 ## 14 transcript::ntLink_8:668902-669217 ## 15 transcript::ntLink_8:4437374-4477709 ## 16 transcript::ntLink_8:4437376-4456957 ## 17 transcript::ntLink_8:4439933-4477709 ## 18 transcript::ntLink_8:10028596-10038034 ## 19 transcript::ntLink_8:10028632-10044386 ## 20 transcript::ntLink_8:10028669-10038034 ## 21 transcript::ntLink_8:10029178-10037663 ## 22 transcript::ntLink_8:19660513-19664606 ## 23 transcript::ntLink_8:26538658-26550794 ## 24 transcript::ntLink_8:26538658-26550794 ## 25 transcript::ntLink_8:26539821-26550794 ## 26 transcript::ntLink_8:2019087-2019406 ## 27 transcript::ntLink_8:5011621-5011845 ## 28 transcript::ntLink_8:5882049-5882621 ## 29 transcript::ntLink_8:7264347-7265303 ## 30 transcript::ntLink_8:7710817-7712117 ## 31 transcript::ntLink_8:35371419-35371867 ## 32 transcript::ptg000001l:12164041-12187052 ## 33 transcript::ptg000001l:14329678-14339113 ## 34 transcript::ptg000001l:20300959-20305253 ## 35 transcript::ptg000001l:4724869-4729321 ## 36 transcript::ptg000001l:5544306-5580019 ## 37 transcript::ptg000001l:5998212-6000651 ## 38 transcript::ptg000001l:10644454-10649597 ## 39 transcript::ptg000001l:16246677-16264746 ## 40 transcript::ptg000001l:17411559-17412717 ## 41 transcript::ptg000002l:14041039-14052409 ## 42 transcript::ptg000002l:14041039-14052409 ## 43 transcript::ptg000002l:4720943-4723122 ## 44 transcript::ptg000002l:5121476-5123331 ## 45 transcript::ptg000002l:5122370-5123331 ## 46 transcript::ptg000002l:8695162-8699329 ## 47 transcript::ptg000002l:8695162-8699329 ## 48 transcript::ptg000002l:8709640-8710034 ## 49 transcript::ptg000002l:8946097-8947707 ## 50 transcript::ptg000002l:9549210-9564066 ## 51 transcript::ptg000002l:9549247-9564066 ## 52 transcript::ptg000002l:9549354-9564066 ## 53 transcript::ptg000002l:9549733-9562715 ## 54 transcript::ptg000002l:12978526-12982461 ## 55 transcript::ptg000002l:1383381-1383717 ## 56 transcript::ptg000002l:12980969-12981195 ## 57 transcript::ptg000004l:567157-571808 ## 58 transcript::ptg000004l:13570684-13574992 ## 59 transcript::ptg000004l:6702800-6706683 ## 60 transcript::ptg000004l:1098398-1099254 ## 61 transcript::ptg000004l:13769536-13770031 ## 62 transcript::ptg000007l:908509-921806 ## 63 transcript::ptg000007l:2791406-2791715 ## 64 transcript::ptg000007l:6664571-6674626 ## 65 transcript::ptg000007l:7178407-7179102 ## 66 transcript::ptg000007l:7299212-7307849 ## 67 transcript::ptg000007l:9586839-9587490 ## 68 transcript::ptg000008l:22187313-22188105 ## 69 transcript::ptg000008l:24309893-24315279 ## 70 transcript::ptg000008l:43441-43948 ## 71 transcript::ptg000008l:1574209-1575066 ## 72 transcript::ptg000008l:22810033-22817561 ## 73 transcript::ptg000008l:22810033-22817561 ## 74 transcript::ptg000008l:23154675-23160297 ## 75 transcript::ptg000008l:23156767-23159540 ## 76 transcript::ptg000008l:24476657-24479596 ## 77 transcript::ptg000008l:11694626-11695619 ## 78 transcript::ptg000008l:25572824-25573194 ## 79 transcript::ptg000008l:29764952-29765232 ## 80 transcript::ptg000009l:3067182-3070131 ## 81 transcript::ptg000009l:3067366-3069923 ## 82 transcript::ptg000009l:3067618-3070131 ## 83 transcript::ptg000009l:611774-618362 ## 84 transcript::ptg000009l:611774-618362 ## 85 transcript::ptg000009l:613601-618362 ## 86 transcript::ptg000009l:613601-618362 ## 87 transcript::ptg000009l:4929027-4948436 ## 88 transcript::ptg000009l:9757122-9763155 ## 89 transcript::ptg000009l:6485786-6486105 ## 90 transcript::ptg000010l:496924-544737 ## 91 transcript::ptg000010l:511224-544737 ## 92 transcript::ptg000010l:511755-544737 ## 93 transcript::ptg000010l:511755-544737 ## 94 transcript::ptg000010l:543528-544737 ## 95 transcript::ptg000010l:644795-661260 ## 96 transcript::ptg000010l:646771-661704 ## 97 transcript::ptg000010l:646832-660585 ## 98 transcript::ptg000011l:6306487-6306773 ## 99 transcript::ptg000011l:1427911-1431394 ## 100 transcript::ptg000011l:4169626-4173486 ## 101 transcript::ptg000011l:5552041-5556134 ## 102 transcript::ptg000011l:11748489-11751325 ## 103 transcript::ptg000011l:13267609-13268257 ## 104 transcript::ptg000012l:8690502-8694128 ## 105 transcript::ptg000012l:209105-221118 ## 106 transcript::ptg000012l:209105-230917 ## 107 transcript::ptg000012l:209113-224136 ## 108 transcript::ptg000012l:210636-230917 ## 109 transcript::ptg000012l:8696749-8701622 ## 110 transcript::ptg000012l:8696749-8719892 ## 111 transcript::ptg000012l:8699097-8711423 ## 112 transcript::ptg000012l:12816099-12821132 ## 113 transcript::ptg000015l:6682048-6682420 ## 114 transcript::ptg000015l:10500419-10505388 ## 115 transcript::ptg000015l:11083780-11084101 ## 116 transcript::ptg000015l:2489793-2490942 ## 117 transcript::ptg000015l:8949731-8952903 ## 118 transcript::ptg000015l:8951319-8952903 ## 119 transcript::ptg000015l:10716991-10719083 ## 120 transcript::ptg000015l:1837134-1837430 ## 121 transcript::ptg000015l:5493755-5494425 ## 122 transcript::ptg000015l:6877278-6878452 ## 123 transcript::ptg000016l:7787959-7823075 ## 124 transcript::ptg000016l:8593882-8603986 ## 125 transcript::ptg000016l:11748348-11760789 ## 126 transcript::ptg000016l:11748351-11760789 ## 127 transcript::ptg000016l:11748671-11760568 ## 128 transcript::ptg000016l:11748715-11760789 ## 129 transcript::ptg000016l:7924625-7927489 ## 130 transcript::ptg000017l:6610157-6614214 ## 131 transcript::ptg000017l:6663205-6666987 ## 132 transcript::ptg000017l:6663205-6666987 ## 133 transcript::ptg000017l:7791158-7794702 ## 134 transcript::ptg000017l:7791466-7794702 ## 135 transcript::ptg000017l:8069280-8069537 ## 136 transcript::ptg000018l:2280996-2290234 ## 137 transcript::ptg000018l:1178196-1178577 ## 138 transcript::ptg000018l:3750411-3754055 ## 139 transcript::ptg000018l:3767996-3771672 ## 140 transcript::ptg000018l:4041685-4045953 ## 141 transcript::ptg000018l:4269165-4272768 ## 142 transcript::ptg000018l:4472290-4475949 ## 143 transcript::ptg000018l:4536497-4540765 ## 144 transcript::ptg000018l:4763977-4767580 ## 145 transcript::ptg000018l:9541756-9542015 ## 146 transcript::ptg000018l:5706103-5706575 ## 147 transcript::ptg000018l:7640158-7640363 ## 148 transcript::ptg000019l:454985-456434 ## 149 transcript::ptg000019l:4600832-4609460 ## 150 transcript::ptg000019l:2123324-2123600 ## 151 transcript::ptg000020l:1314938-1315340 ## 152 transcript::ptg000020l:1768626-1770203 ## 153 transcript::ptg000020l:4217558-4218084 ## 154 transcript::ptg000020l:615954-621880 ## 155 transcript::ptg000020l:617421-621014 ## 156 transcript::ptg000020l:1313648-1318092 ## 157 transcript::ptg000020l:1314814-1316327 ## 158 transcript::ptg000020l:1337771-1342704 ## 159 transcript::ptg000020l:1423894-1427670 ## 160 transcript::ptg000020l:1746901-1747592 ## 161 transcript::ptg000020l:1746922-1747966 ## 162 transcript::ptg000020l:4216792-4221114 ## 163 transcript::ptg000020l:4248313-4252564 ## 164 transcript::ptg000020l:5844736-5860466 ## 165 transcript::ptg000020l:5845880-5860466 ## 166 transcript::ptg000020l:5850706-5857299 ## 167 transcript::ptg000020l:6512247-6517004 ## 168 transcript::ptg000020l:4098333-4098592 ## 169 transcript::ptg000020l:12568237-12568621 ## 170 transcript::ptg000021l:13502731-13514774 ## 171 transcript::ptg000021l:13503260-13514774 ## 172 transcript::ptg000021l:13505533-13524949 ## 173 transcript::ptg000021l:13505815-13514774 ## 174 transcript::ptg000021l:13822331-13873009 ## 175 transcript::ptg000021l:13842888-13873009 ## 176 transcript::ptg000021l:13843080-13866689 ## 177 transcript::ptg000021l:13843252-13877113 ## 178 transcript::ptg000021l:16927850-16951190 ## 179 transcript::ptg000021l:19195511-19200021 ## 180 transcript::ptg000021l:19207344-19212121 ## 181 transcript::ptg000021l:12156895-12157167 ## 182 transcript::ptg000021l:13553132-13555030 ## 183 transcript::ptg000023l:2635562-2636412 ## 184 transcript::ptg000023l:2690489-2693741 ## 185 transcript::ptg000023l:18698522-18723762 ## 186 transcript::ptg000023l:22895865-22909910 ## 187 transcript::ptg000023l:22896062-22909910 ## 188 transcript::ptg000023l:22897078-22909910 ## 189 transcript::ptg000023l:24874248-24874516 ## 190 transcript::ptg000023l:25121113-25135457 ## 191 transcript::ptg000023l:25121316-25136081 ## 192 transcript::ptg000023l:26751020-26756152 ## 193 transcript::ptg000023l:37964178-37968910 ## 194 transcript::ptg000023l:38035263-38040884 ## 195 transcript::ptg000023l:3633190-3636532 ## 196 transcript::ptg000023l:3633462-3636532 ## 197 transcript::ptg000023l:14476955-14492814 ## 198 transcript::ptg000023l:15900851-15906610 ## 199 transcript::ptg000023l:29261981-29262762 ## 200 transcript::ptg000023l:7881897-7882272 ## 201 transcript::ptg000023l:22827886-22828248 ## 202 transcript::ptg000023l:27548133-27548827 ## 203 transcript::ptg000024l:3071153-3077218 ## 204 transcript::ptg000024l:4085668-4087287 ## 205 transcript::ptg000025l:1976289-1990446 ## 206 transcript::ptg000025l:10491944-10502595 ## 207 transcript::ptg000025l:10491944-10502595 ## 208 transcript::ptg000025l:11114996-11126477 ## 209 transcript::ptg000025l:11115026-11124835 ## 210 transcript::ptg000025l:1794902-1798840 ## 211 transcript::ptg000025l:3300700-3324958 ## 212 transcript::ptg000025l:10668087-10677231 ## 213 transcript::ptg000025l:10687372-10693526 ## 214 transcript::ptg000025l:15677553-15683336 ## 215 transcript::ptg000025l:17390422-17391830 ## 216 transcript::ptg000025l:7437716-7438377 ## 217 transcript::ptg000025l:15784341-15784905 ## 218 transcript::ptg000025l:15805797-15806833 ## 219 transcript::ptg000025l:18567820-18568066 ## 220 transcript::ptg000026l:1848361-1849652 ## 221 transcript::ptg000026l:1848748-1850032 ## 222 transcript::ptg000026l:6257379-6258705 ## 223 transcript::ptg000026l:10976950-10994993 ## 224 transcript::ptg000026l:10977767-10998419 ## 225 transcript::ptg000026l:13542914-13544592 ## 226 transcript::ptg000027l:12369353-12370324 ## 227 transcript::ptg000027l:14957936-14959033 ## 228 transcript::ptg000027l:15254638-15255702 ## 229 transcript::ptg000027l:15899910-15900948 ## 230 transcript::ptg000028l:607498-608599 ## 231 transcript::ptg000029c:30378-30679 ## 232 transcript::ptg000030l:1796595-1796930 ## 233 transcript::ptg000030l:2571981-2573130 ## 234 transcript::ptg000030l:2983299-2984288 ## 235 transcript::ptg000030l:3114544-3114851 ## 236 transcript::ptg000031l:6673205-6674498 ## 237 transcript::ptg000033l:812213-813203 ## 238 transcript::ptg000035l:5335161-5347816 ## 239 transcript::ptg000035l:5335634-5347816 ## 240 transcript::ptg000035l:5336170-5347816 ## 241 transcript::ptg000035l:5336225-5347816 ## 242 transcript::ptg000035l:5336231-5347816 ## 243 transcript::ptg000035l:3568395-3588450 ## 244 transcript::ptg000035l:4179611-4183830 ## 245 transcript::ptg000035l:4337555-4340587 ## 246 transcript::ptg000035l:4735323-4735894 ## 247 transcript::ptg000035l:4745735-4746320 ## 248 transcript::ptg000035l:4748344-4749044 ## 249 transcript::ptg000035l:4758339-4759387 ## 250 transcript::ptg000035l:4761389-4761971 ## 251 transcript::ptg000035l:4763998-4764581 ## 252 transcript::ptg000036l:1647657-1649308 ## 253 transcript::ptg000036l:1647657-1649308 ## 254 transcript::ptg000036l:3107888-3108320 ## 255 transcript::ptg000036l:3107888-3108320 ## 256 transcript::ptg000039l:33382-41881 ## 257 transcript::ptg000047l:944557-966962 ## 258 transcript::ptg000047l:944557-967239 ## 259 transcript::ptg000047l:944577-966962 ## 260 transcript::ptg000047l:947376-966854 ## 261 transcript::ptg000047l:8057376-8058484 ## 262 transcript::ptg000047l:8253896-8258722 ## 263 transcript::ptg000047l:164714-165595 ## 264 transcript::ptg000047l:2314993-2323437 ## 265 transcript::ptg000047l:2314993-2323437 ## 266 transcript::ptg000047l:2314993-2323437 ## 267 transcript::ptg000047l:2315100-2323437 ## 268 transcript::ptg000047l:2315108-2323437 ## 269 transcript::ptg000047l:2315768-2322229 ## 270 transcript::ptg000047l:6228838-6229808 ## 271 transcript::ptg000047l:1248153-1248450 ## 272 transcript::ptg000047l:9202194-9203168 ## 273 transcript::ptg000056l:8228-8735 ## 274 transcript::ptg000057l:12147-12654 ## 275 transcript::ptg000060c:17547-22922 ## 276 transcript::ptg000065l:44680-45187 ## 277 transcript::ptg000099l:1121-2503 ## 278 transcript::ptg000099l:43788-44669 ## 279 transcript::ptg000116l:32415-32922 ## 280 transcript::ptg000121l:37294-37801 ## 281 transcript::ptg000159l:17542-18049 ## sseqid pident length ## 1 Cluster_1832.mature::ntLink_6:5157559-5157579(+) 100.000 21 ## 2 Cluster_1832.mature::ntLink_6:5157559-5157579(+) 100.000 21 ## 3 Cluster_1832.mature::ntLink_6:5157559-5157579(+) 100.000 21 ## 4 Cluster_1826.mature::ntLink_6:4847465-4847486(-) 100.000 22 ## 5 Cluster_1826.mature::ntLink_6:4847465-4847486(-) 100.000 22 ## 6 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 100.000 22 ## 7 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.476 21 ## 8 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 9 Cluster_10228.mature::ptg000017l:7471168-7471190(+) 90.909 22 ## 10 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.238 21 ## 11 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 90.476 21 ## 12 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 90.909 22 ## 13 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 90.909 22 ## 14 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.737 19 ## 15 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.238 21 ## 16 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.238 21 ## 17 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.238 21 ## 18 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 90.909 22 ## 19 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 90.909 22 ## 20 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 90.909 22 ## 21 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 90.909 22 ## 22 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 16 ## 23 Cluster_4220.mature::ptg000007l:915927-915948(-) 95.455 22 ## 24 Cluster_4220.mature::ptg000007l:915927-915948(-) 95.455 22 ## 25 Cluster_4220.mature::ptg000007l:915927-915948(-) 95.455 22 ## 26 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.444 18 ## 27 Cluster_4220.mature::ptg000007l:915927-915948(-) 91.304 23 ## 28 Cluster_5899.mature::ptg000009l:616336-616357(-) 94.444 18 ## 29 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 90.909 22 ## 30 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 90.909 22 ## 31 Cluster_3437.mature::ptg000004l:1859911-1859933(-) 100.000 15 ## 32 Cluster_4220.mature::ptg000007l:915927-915948(-) 95.000 20 ## 33 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.909 22 ## 34 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 35 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 95.000 20 ## 36 Cluster_2463.mature::ptg000001l:5548893-5548914(-) 100.000 22 ## 37 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 90.476 21 ## 38 Cluster_19193.mature::ptg000039l:35786-35807(-) 94.737 19 ## 39 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 40 Cluster_2463.mature::ptg000001l:5548893-5548914(-) 90.000 20 ## 41 Cluster_3367.mature::ptg000002l:14046591-14046614(+) 100.000 24 ## 42 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 100.000 24 ## 43 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 44 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 45 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 46 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 19 ## 47 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 19 ## 48 Cluster_2463.mature::ptg000001l:5548893-5548914(-) 94.737 19 ## 49 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 50 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 17 ## 51 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 17 ## 52 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 17 ## 53 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 17 ## 54 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 55 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 86.364 22 ## 56 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 57 Cluster_10228.mature::ptg000017l:7471168-7471190(+) 94.737 19 ## 58 Cluster_1862.mature::ntLink_6:7263537-7263560(-) 100.000 16 ## 59 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.909 22 ## 60 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 61 Cluster_17791.mature::ptg000031l:6751957-6751979(-) 100.000 15 ## 62 Cluster_4220.mature::ptg000007l:915927-915948(-) 100.000 22 ## 63 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 89.474 19 ## 64 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 100.000 17 ## 65 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.444 18 ## 66 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 100.000 17 ## 67 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 68 Cluster_16409.mature::ptg000026l:8745562-8745583(-) 95.238 21 ## 69 Cluster_10093.mature::ptg000016l:11751407-11751428(-) 100.000 16 ## 70 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 71 Cluster_15854.mature::ptg000025l:10668923-10668945(-) 100.000 15 ## 72 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 91.667 24 ## 73 Cluster_3367.mature::ptg000002l:14046591-14046614(+) 90.909 22 ## 74 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 87.500 24 ## 75 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 87.500 24 ## 76 Cluster_4220.mature::ptg000007l:915927-915948(-) 100.000 16 ## 77 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 78 Cluster_17791.mature::ptg000031l:6751957-6751979(-) 100.000 14 ## 79 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 100.000 14 ## 80 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 81 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 82 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 83 Cluster_5900.mature::ptg000009l:616601-616622(-) 100.000 22 ## 84 Cluster_5899.mature::ptg000009l:616336-616357(-) 100.000 22 ## 85 Cluster_5900.mature::ptg000009l:616601-616622(-) 100.000 22 ## 86 Cluster_5899.mature::ptg000009l:616336-616357(-) 100.000 22 ## 87 Cluster_5981.mature::ptg000009l:4940537-4940559(-) 100.000 23 ## 88 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 89 Cluster_3437.mature::ptg000004l:1859911-1859933(-) 100.000 14 ## 90 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 91 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 92 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 93 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 94 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 95 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 96 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 97 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 98 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 99 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 90.476 21 ## 100 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 95.000 20 ## 101 Cluster_15851.mature::ptg000025l:10501052-10501073(+) 94.737 19 ## 102 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 103 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 18 ## 104 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 105 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.455 22 ## 106 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.455 22 ## 107 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.455 22 ## 108 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 95.455 22 ## 109 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 17 ## 110 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 17 ## 111 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 17 ## 112 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 113 Cluster_10228.mature::ptg000017l:7471168-7471190(+) 100.000 15 ## 114 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 19 ## 115 Cluster_15854.mature::ptg000025l:10668923-10668945(-) 94.118 17 ## 116 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 20 ## 117 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 18 ## 118 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 18 ## 119 Cluster_19193.mature::ptg000039l:35786-35807(-) 100.000 16 ## 120 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 121 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 122 Cluster_4220.mature::ptg000007l:915927-915948(-) 94.444 18 ## 123 Cluster_10051.mature::ptg000016l:7795530-7795551(+) 100.000 22 ## 124 Cluster_10057.mature::ptg000016l:8599884-8599905(-) 100.000 22 ## 125 Cluster_10093.mature::ptg000016l:11751407-11751428(-) 100.000 22 ## 126 Cluster_10093.mature::ptg000016l:11751407-11751428(-) 100.000 22 ## 127 Cluster_10093.mature::ptg000016l:11751407-11751428(-) 100.000 22 ## 128 Cluster_10093.mature::ptg000016l:11751407-11751428(-) 100.000 22 ## 129 Cluster_2463.mature::ptg000001l:5548893-5548914(-) 100.000 16 ## 130 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 16 ## 131 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 94.737 19 ## 132 Cluster_3367.mature::ptg000002l:14046591-14046614(+) 100.000 16 ## 133 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 90.476 21 ## 134 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 90.476 21 ## 135 Cluster_10057.mature::ptg000016l:8599884-8599905(-) 89.474 19 ## 136 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 22 ## 137 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.476 21 ## 138 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 139 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 140 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 141 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 142 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 143 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 144 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 145 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 14 ## 146 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 147 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 86.364 22 ## 148 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 91.304 23 ## 149 Cluster_17776.mature::ptg000031l:5461327-5461348(-) 100.000 17 ## 150 Cluster_1826.mature::ntLink_6:4847465-4847486(-) 94.118 17 ## 151 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 152 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 153 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 154 Cluster_10228.mature::ptg000017l:7471168-7471190(+) 90.476 21 ## 155 Cluster_10228.mature::ptg000017l:7471168-7471190(+) 90.476 21 ## 156 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 157 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 158 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 159 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 160 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 161 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 162 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 163 Cluster_5900.mature::ptg000009l:616601-616622(-) 90.476 21 ## 164 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 17 ## 165 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 17 ## 166 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 17 ## 167 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 168 Cluster_15316.mature::ptg000024l:4086254-4086275(+) 100.000 14 ## 169 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 170 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 171 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 172 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 173 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 174 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 95.238 21 ## 175 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 95.238 21 ## 176 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 95.238 21 ## 177 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 95.238 21 ## 178 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 179 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 180 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 181 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 182 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 183 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 184 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 185 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 100.000 22 ## 186 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 95.000 20 ## 187 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 95.000 20 ## 188 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 95.000 20 ## 189 Cluster_10057.mature::ptg000016l:8599884-8599905(-) 94.118 17 ## 190 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 19 ## 191 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 19 ## 192 Cluster_3437.mature::ptg000004l:1859911-1859933(-) 90.476 21 ## 193 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 21 ## 194 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 100.000 21 ## 195 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 95.000 20 ## 196 Cluster_3250.mature::ptg000002l:7337560-7337581(+) 95.000 20 ## 197 Cluster_14402.mature::ptg000023l:18708925-18708946(+) 95.000 20 ## 198 Cluster_3437.mature::ptg000004l:1859911-1859933(-) 91.304 23 ## 199 Cluster_1832.mature::ntLink_6:5157559-5157579(+) 100.000 15 ## 200 Cluster_15775.mature::ptg000025l:7472581-7472603(-) 94.444 18 ## 201 Cluster_3437.mature::ptg000004l:1859911-1859933(-) 94.118 17 ## 202 Cluster_4220.mature::ptg000007l:915927-915948(-) 100.000 15 ## 203 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 204 Cluster_15316.mature::ptg000024l:4086254-4086275(+) 100.000 22 ## 205 Cluster_15671.mature::ptg000025l:1981742-1981763(+) 100.000 22 ## 206 Cluster_15851.mature::ptg000025l:10501052-10501073(+) 100.000 22 ## 207 Cluster_15851.mature::ptg000025l:10501052-10501073(+) 100.000 22 ## 208 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 100.000 17 ## 209 Cluster_18723.mature::ptg000035l:4808391-4808412(+) 100.000 17 ## 210 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 90.476 21 ## 211 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 212 Cluster_15854.mature::ptg000025l:10668923-10668945(-) 100.000 23 ## 213 Cluster_18711.mature::ptg000035l:4339600-4339621(-) 94.737 19 ## 214 Cluster_1826.mature::ntLink_6:4847465-4847486(-) 94.737 19 ## 215 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 100.000 17 ## 216 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 217 Cluster_10051.mature::ptg000016l:7795530-7795551(+) 94.444 18 ## 218 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 90.000 20 ## 219 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.118 17 ## 220 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.444 18 ## 221 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.444 18 ## 222 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.444 18 ## 223 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 224 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 225 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 94.737 19 ## 226 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 227 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 228 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 229 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 230 Cluster_15854.mature::ptg000025l:10668923-10668945(-) 90.000 20 ## 231 Cluster_3437.mature::ptg000004l:1859911-1859933(-) 94.118 17 ## 232 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 233 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 234 Cluster_10057.mature::ptg000016l:8599884-8599905(-) 100.000 15 ## 235 Cluster_5981.mature::ptg000009l:4940537-4940559(-) 100.000 14 ## 236 Cluster_15854.mature::ptg000025l:10668923-10668945(-) 100.000 15 ## 237 Cluster_17791.mature::ptg000031l:6751957-6751979(-) 90.000 20 ## 238 Cluster_18728.mature::ptg000035l:5346054-5346075(+) 100.000 22 ## 239 Cluster_18728.mature::ptg000035l:5346054-5346075(+) 100.000 22 ## 240 Cluster_18728.mature::ptg000035l:5346054-5346075(+) 100.000 22 ## 241 Cluster_18728.mature::ptg000035l:5346054-5346075(+) 100.000 22 ## 242 Cluster_18728.mature::ptg000035l:5346054-5346075(+) 100.000 22 ## 243 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 100.000 18 ## 244 Cluster_4220.mature::ptg000007l:915927-915948(-) 90.909 22 ## 245 Cluster_18711.mature::ptg000035l:4339600-4339621(-) 100.000 22 ## 246 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 247 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 248 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 249 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 250 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 251 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 252 Cluster_3367.mature::ptg000002l:14046591-14046614(+) 100.000 16 ## 253 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 100.000 16 ## 254 Cluster_3367.mature::ptg000002l:14046591-14046614(+) 100.000 16 ## 255 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 100.000 16 ## 256 Cluster_19193.mature::ptg000039l:35786-35807(-) 100.000 22 ## 257 Cluster_5981.mature::ptg000009l:4940537-4940559(-) 100.000 17 ## 258 Cluster_5981.mature::ptg000009l:4940537-4940559(-) 100.000 17 ## 259 Cluster_5981.mature::ptg000009l:4940537-4940559(-) 100.000 17 ## 260 Cluster_5981.mature::ptg000009l:4940537-4940559(-) 100.000 17 ## 261 Cluster_2859.mature::ptg000001l:20063094-20063116(+) 100.000 15 ## 262 Cluster_1951.mature::ntLink_6:13351801-13351822(-) 90.476 21 ## 263 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 264 Cluster_15928.mature::ptg000025l:15156437-15156458(+) 100.000 21 ## 265 Cluster_15928.mature::ptg000025l:15156437-15156458(+) 100.000 21 ## 266 Cluster_15928.mature::ptg000025l:15156437-15156458(+) 100.000 21 ## 267 Cluster_15928.mature::ptg000025l:15156437-15156458(+) 100.000 21 ## 268 Cluster_15928.mature::ptg000025l:15156437-15156458(+) 100.000 21 ## 269 Cluster_15928.mature::ptg000025l:15156437-15156458(+) 100.000 21 ## 270 Cluster_3366.mature::ptg000002l:14046285-14046308(+) 100.000 16 ## 271 Cluster_14768.mature::ptg000023l:37965298-37965318(+) 94.737 19 ## 272 Cluster_10419.mature::ptg000018l:2286829-2286850(+) 94.444 18 ## 273 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 274 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 275 Cluster_5012.mature::ptg000008l:10754789-10754809(-) 100.000 17 ## 276 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 277 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 278 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 279 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 280 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## 281 Cluster_4254.mature::ptg000007l:3377335-3377356(+) 100.000 15 ## mismatch gapopen qstart qend sstart send evalue bitscore ## 1 0 0 2422 2442 1 21 2.22e-06 39.2 ## 2 0 0 2422 2442 1 21 2.22e-06 39.2 ## 3 0 0 1219 1239 1 21 1.58e-06 39.2 ## 4 0 0 36848 36869 22 1 3.96e-06 41.0 ## 5 0 0 8307 8328 22 1 1.46e-06 41.0 ## 6 0 0 21847 21868 22 1 3.76e-06 41.0 ## 7 2 0 4291 4311 21 1 1.00e-03 30.1 ## 8 1 0 272 289 1 18 1.00e-03 29.2 ## 9 1 1 853 873 22 1 1.00e-03 28.3 ## 10 1 0 9861 9881 22 2 2.00e-04 34.6 ## 11 2 0 3310 3330 22 2 1.00e-03 30.1 ## 12 2 0 696 717 23 2 1.00e-03 31.9 ## 13 2 0 696 717 23 2 1.00e-03 31.9 ## 14 1 0 112 130 3 21 1.13e-04 31.0 ## 15 1 0 12465 12485 2 22 5.73e-04 34.6 ## 16 1 0 12463 12483 2 22 3.31e-04 34.6 ## 17 1 0 9906 9926 2 22 5.37e-04 34.6 ## 18 2 0 6293 6314 22 1 6.46e-04 31.9 ## 19 2 0 6257 6278 22 1 1.00e-03 31.9 ## 20 2 0 6220 6241 22 1 6.41e-04 31.9 ## 21 2 0 5711 5732 22 1 5.80e-04 31.9 ## 22 0 0 2902 2917 6 21 1.00e-03 30.1 ## 23 1 0 4468 4489 1 22 6.82e-05 36.5 ## 24 1 0 4468 4489 1 22 6.82e-05 36.5 ## 25 1 0 3305 3326 1 22 6.16e-05 36.5 ## 26 1 0 78 95 2 19 3.99e-04 29.2 ## 27 1 1 48 70 22 1 7.90e-05 30.1 ## 28 1 0 116 133 3 20 6.61e-04 29.2 ## 29 1 1 32 53 2 22 1.00e-03 28.3 ## 30 1 1 240 261 2 22 1.00e-03 28.3 ## 31 0 0 236 250 4 18 5.14e-04 28.3 ## 32 1 0 12132 12151 2 21 1.00e-03 32.8 ## 33 2 0 6545 6566 22 1 6.45e-04 31.9 ## 34 2 0 1831 1851 22 2 1.00e-03 30.1 ## 35 1 0 873 892 1 20 3.46e-04 32.8 ## 36 0 0 4587 4608 22 1 3.42e-06 41.0 ## 37 2 0 203 223 21 1 6.60e-04 30.1 ## 38 1 0 2028 2046 21 3 1.00e-03 31.0 ## 39 2 0 15249 15270 1 22 1.00e-03 31.9 ## 40 2 0 1087 1106 22 3 1.00e-03 28.3 ## 41 0 0 5552 5575 1 24 1.23e-07 44.6 ## 42 0 0 5246 5269 1 24 1.23e-07 44.6 ## 43 2 0 1103 1123 22 2 5.89e-04 30.1 ## 44 2 0 1764 1784 22 2 5.62e-04 30.1 ## 45 2 0 870 890 22 2 2.89e-04 30.1 ## 46 0 0 3056 3074 19 1 2.66e-05 35.6 ## 47 0 0 3056 3074 19 1 2.66e-05 35.6 ## 48 1 0 287 305 21 3 1.29e-04 31.0 ## 49 2 0 1082 1102 2 22 4.87e-04 30.1 ## 50 0 0 11889 11905 20 4 8.77e-04 31.9 ## 51 0 0 11852 11868 20 4 8.75e-04 31.9 ## 52 0 0 11745 11761 20 4 8.68e-04 31.9 ## 53 0 0 11366 11382 20 4 7.66e-04 31.9 ## 54 1 0 2546 2564 19 1 1.00e-03 31.0 ## 55 3 0 250 271 23 2 1.00e-03 27.4 ## 56 1 0 103 121 19 1 7.98e-05 31.0 ## 57 1 0 3199 3217 1 19 1.00e-03 31.0 ## 58 0 0 351 366 1 16 1.00e-03 30.1 ## 59 2 0 3383 3404 22 1 3.02e-04 31.9 ## 60 1 0 188 205 18 1 8.98e-04 29.2 ## 61 0 0 156 170 3 17 5.70e-04 28.3 ## 62 0 0 7418 7439 22 1 1.51e-06 41.0 ## 63 2 0 274 292 21 3 1.00e-03 26.5 ## 64 0 0 644 660 5 21 6.88e-04 31.9 ## 65 1 0 131 148 1 18 8.06e-04 29.2 ## 66 0 0 644 660 5 21 5.91e-04 31.9 ## 67 1 0 33 50 18 1 7.54e-04 29.2 ## 68 1 0 282 302 1 21 2.16e-05 34.6 ## 69 0 0 3360 3375 7 22 1.00e-03 30.1 ## 70 0 0 68 82 8 22 5.84e-04 28.3 ## 71 0 0 585 599 16 2 8.99e-04 28.3 ## 72 2 0 2517 2540 1 24 4.23e-05 35.6 ## 73 2 0 2517 2538 1 22 5.15e-04 31.9 ## 74 3 0 2361 2384 1 24 1.00e-03 31.0 ## 75 3 0 269 292 1 24 7.51e-04 31.0 ## 76 0 0 1793 1808 1 16 7.96e-04 30.1 ## 77 0 0 903 917 15 1 1.00e-03 28.3 ## 78 0 0 308 321 14 1 1.00e-03 26.5 ## 79 0 0 19 32 17 4 1.00e-03 26.5 ## 80 2 0 911 931 22 2 7.99e-04 30.1 ## 81 2 0 727 747 22 2 6.92e-04 30.1 ## 82 2 0 475 495 22 2 6.80e-04 30.1 ## 83 0 0 4827 4848 22 1 8.69e-07 41.0 ## 84 0 0 4562 4583 22 1 8.69e-07 41.0 ## 85 0 0 3000 3021 22 1 7.14e-07 41.0 ## 86 0 0 2735 2756 22 1 7.14e-07 41.0 ## 87 0 0 11510 11532 23 1 6.34e-07 42.8 ## 88 1 0 2798 2816 1 19 1.00e-03 31.0 ## 89 0 0 85 98 1 14 1.00e-03 26.5 ## 90 0 0 47655 47672 1 18 6.80e-04 33.7 ## 91 0 0 33355 33372 1 18 4.76e-04 33.7 ## 92 0 0 32824 32841 1 18 5.58e-04 33.7 ## 93 0 0 32824 32841 1 18 5.58e-04 33.7 ## 94 0 0 1051 1068 1 18 2.99e-05 33.7 ## 95 0 0 15524 15541 1 18 2.78e-04 33.7 ## 96 0 0 13548 13565 1 18 2.52e-04 33.7 ## 97 0 0 13487 13504 1 18 2.33e-04 33.7 ## 98 0 0 235 249 15 1 3.56e-04 28.3 ## 99 2 0 998 1018 22 2 1.00e-03 30.1 ## 100 1 0 1322 1341 21 2 3.00e-04 32.8 ## 101 1 0 3477 3495 2 20 1.00e-03 31.0 ## 102 1 0 2230 2248 19 1 7.68e-04 31.0 ## 103 0 0 153 170 2 19 1.76e-05 33.7 ## 104 1 0 424 442 1 19 1.00e-03 31.0 ## 105 0 1 5421 5441 1 22 8.22e-04 32.8 ## 106 0 1 5421 5441 1 22 1.00e-03 32.8 ## 107 0 1 5413 5433 1 22 8.87e-04 32.8 ## 108 0 1 3890 3910 1 22 1.00e-03 32.8 ## 109 0 0 3790 3806 21 5 3.79e-04 31.9 ## 110 0 0 3790 3806 21 5 1.00e-03 31.9 ## 111 0 0 1442 1458 21 5 8.44e-04 31.9 ## 112 1 0 2678 2696 1 19 1.00e-03 31.0 ## 113 0 0 57 71 20 6 4.25e-04 28.3 ## 114 0 0 824 842 1 19 3.17e-05 35.6 ## 115 1 0 110 126 5 21 1.00e-03 27.4 ## 116 0 0 729 748 2 21 2.34e-06 37.4 ## 117 0 0 2677 2694 2 19 7.05e-05 33.7 ## 118 0 0 1089 1106 2 19 3.93e-05 33.7 ## 119 0 0 599 614 21 6 5.65e-04 30.1 ## 120 1 0 217 234 18 1 3.69e-04 29.2 ## 121 2 0 601 621 2 22 2.22e-04 30.1 ## 122 1 0 115 132 3 20 1.00e-03 29.2 ## 123 0 0 7571 7592 1 22 3.36e-06 41.0 ## 124 0 0 6002 6023 22 1 1.33e-06 41.0 ## 125 0 0 3059 3080 22 1 1.64e-06 41.0 ## 126 0 0 3056 3077 22 1 1.64e-06 41.0 ## 127 0 0 2736 2757 22 1 1.57e-06 41.0 ## 128 0 0 2692 2713 22 1 1.60e-06 41.0 ## 129 0 0 2650 2665 7 22 7.76e-04 30.1 ## 130 0 0 1638 1653 2 17 1.00e-03 30.1 ## 131 1 0 1617 1635 6 24 1.00e-03 31.0 ## 132 0 0 1617 1632 6 21 1.00e-03 30.1 ## 133 2 0 2279 2299 1 21 1.00e-03 30.1 ## 134 2 0 1971 1991 1 21 8.77e-04 30.1 ## 135 2 0 214 232 4 22 1.00e-03 26.5 ## 136 0 0 5833 5854 1 22 1.22e-06 41.0 ## 137 2 0 138 158 21 1 1.25e-04 30.1 ## 138 2 0 347 367 2 22 1.00e-03 30.1 ## 139 2 0 818 838 2 22 1.00e-03 30.1 ## 140 2 0 1391 1411 2 22 1.00e-03 30.1 ## 141 2 0 660 680 2 22 1.00e-03 30.1 ## 142 2 0 231 251 2 22 1.00e-03 30.1 ## 143 2 0 1391 1411 2 22 1.00e-03 30.1 ## 144 2 0 660 680 2 22 1.00e-03 30.1 ## 145 0 0 22 35 5 18 1.00e-03 26.5 ## 146 0 0 131 145 1 15 5.43e-04 28.3 ## 147 3 0 19 40 23 2 8.77e-04 27.4 ## 148 1 1 248 270 2 23 4.38e-04 30.1 ## 149 0 0 6150 6166 2 18 5.90e-04 31.9 ## 150 1 0 123 139 6 22 1.00e-03 27.4 ## 151 2 0 7 27 2 22 1.32e-04 30.1 ## 152 2 0 114 134 22 2 4.77e-04 30.1 ## 153 2 0 373 393 2 22 1.74e-04 30.1 ## 154 2 0 3392 3412 21 1 1.00e-03 30.1 ## 155 2 0 1925 1945 21 1 1.00e-03 30.1 ## 156 2 0 1123 1143 2 22 1.00e-03 30.1 ## 157 2 0 131 151 2 22 4.58e-04 30.1 ## 158 2 0 663 683 2 22 1.00e-03 30.1 ## 159 2 0 46 66 2 22 1.00e-03 30.1 ## 160 2 0 636 656 22 2 2.30e-04 30.1 ## 161 2 0 963 983 22 2 3.15e-04 30.1 ## 162 2 0 1139 1159 2 22 1.00e-03 30.1 ## 163 2 0 141 161 2 22 1.00e-03 30.1 ## 164 0 0 9218 9234 6 22 1.00e-03 31.9 ## 165 0 0 8074 8090 6 22 8.61e-04 31.9 ## 166 0 0 3248 3264 6 22 4.51e-04 31.9 ## 167 1 0 3967 3985 19 1 1.00e-03 31.0 ## 168 0 0 56 69 16 3 1.00e-03 26.5 ## 169 1 0 349 366 1 18 4.39e-04 29.2 ## 170 2 0 8594 8615 1 22 8.24e-04 31.9 ## 171 2 0 8065 8086 1 22 7.88e-04 31.9 ## 172 2 0 5792 5813 1 22 1.00e-03 31.9 ## 173 2 0 5510 5531 1 22 6.13e-04 31.9 ## 174 1 0 30903 30923 22 2 7.20e-04 34.6 ## 175 1 0 10346 10366 22 2 5.10e-04 34.6 ## 176 1 0 10154 10174 22 2 3.99e-04 34.6 ## 177 1 0 9982 10002 22 2 4.81e-04 34.6 ## 178 2 0 214 235 1 22 1.00e-03 31.9 ## 179 2 0 3828 3848 2 22 1.00e-03 30.1 ## 180 2 0 3829 3849 2 22 1.00e-03 30.1 ## 181 2 0 58 78 2 22 9.68e-05 30.1 ## 182 1 0 358 376 19 1 5.75e-04 31.0 ## 183 1 0 612 630 1 19 2.55e-04 31.0 ## 184 1 0 868 886 19 1 8.81e-04 31.0 ## 185 0 0 10403 10424 1 22 2.88e-06 41.0 ## 186 1 0 8344 8363 1 20 8.29e-04 32.8 ## 187 1 0 8147 8166 1 20 8.17e-04 32.8 ## 188 1 0 7131 7150 1 20 7.57e-04 32.8 ## 189 1 0 112 128 17 1 1.00e-03 27.4 ## 190 0 0 10626 10644 1 19 6.95e-05 35.6 ## 191 0 0 10423 10441 1 19 7.15e-05 35.6 ## 192 2 0 3588 3608 1 21 1.00e-03 30.1 ## 193 0 0 1120 1140 1 21 2.48e-06 39.2 ## 194 0 0 1125 1145 1 21 2.59e-06 39.2 ## 195 1 0 2913 2932 1 20 2.59e-04 32.8 ## 196 1 0 2641 2660 1 20 2.38e-04 32.8 ## 197 1 0 12881 12900 20 1 1.00e-03 32.8 ## 198 1 1 5447 5469 1 22 1.00e-03 30.1 ## 199 0 0 572 586 15 1 1.00e-03 28.3 ## 200 1 0 95 112 21 4 4.28e-04 29.2 ## 201 1 0 155 171 17 1 1.00e-03 27.4 ## 202 0 0 265 279 16 2 8.05e-04 28.3 ## 203 2 0 1408 1428 2 22 1.00e-03 30.1 ## 204 0 0 586 607 1 22 2.71e-07 41.0 ## 205 0 0 5453 5474 1 22 1.61e-06 41.0 ## 206 0 0 9108 9129 1 22 1.41e-06 41.0 ## 207 0 0 9108 9129 1 22 1.41e-06 41.0 ## 208 0 0 2733 2749 22 6 7.86e-04 31.9 ## 209 0 0 2703 2719 22 6 6.71e-04 31.9 ## 210 2 0 3441 3461 21 1 1.00e-03 30.1 ## 211 2 0 15984 16005 22 1 1.00e-03 31.9 ## 212 0 0 836 858 23 1 3.46e-07 42.8 ## 213 1 0 32 50 4 22 1.00e-03 31.0 ## 214 1 0 2110 2128 19 1 1.00e-03 31.0 ## 215 0 0 279 295 3 19 1.22e-04 31.9 ## 216 1 0 418 435 1 18 7.66e-04 29.2 ## 217 1 0 258 275 4 21 6.51e-04 29.2 ## 218 2 0 794 813 1 20 1.00e-03 28.3 ## 219 1 0 83 99 17 1 1.00e-03 27.4 ## 220 1 0 1082 1099 4 21 1.00e-03 29.2 ## 221 1 0 695 712 4 21 1.00e-03 29.2 ## 222 1 0 736 753 4 21 1.00e-03 29.2 ## 223 2 0 13088 13109 1 22 1.00e-03 31.9 ## 224 2 0 12271 12292 1 22 1.00e-03 31.9 ## 225 1 0 252 270 2 20 5.08e-04 31.0 ## 226 1 0 793 810 18 1 1.00e-03 29.2 ## 227 1 0 609 626 1 18 1.00e-03 29.2 ## 228 1 0 609 626 1 18 1.00e-03 29.2 ## 229 1 0 572 589 1 18 1.00e-03 29.2 ## 230 2 0 610 629 21 2 1.00e-03 28.3 ## 231 1 0 124 140 1 17 1.00e-03 27.4 ## 232 1 0 192 209 18 1 4.20e-04 29.2 ## 233 1 0 629 646 1 18 1.00e-03 29.2 ## 234 0 0 566 580 3 17 1.00e-03 28.3 ## 235 0 0 271 284 21 8 1.00e-03 26.5 ## 236 0 0 822 836 21 7 1.00e-03 28.3 ## 237 2 0 849 868 20 1 1.00e-03 28.3 ## 238 0 0 10893 10914 1 22 1.67e-06 41.0 ## 239 0 0 10420 10441 1 22 1.61e-06 41.0 ## 240 0 0 9884 9905 1 22 1.54e-06 41.0 ## 241 0 0 9829 9850 1 22 1.53e-06 41.0 ## 242 0 0 9823 9844 1 22 1.53e-06 41.0 ## 243 0 0 16964 16981 18 1 3.39e-04 33.7 ## 244 2 0 3798 3819 22 1 3.28e-04 31.9 ## 245 0 0 2045 2066 22 1 4.54e-07 41.0 ## 246 0 0 144 158 15 1 6.60e-04 28.3 ## 247 0 0 144 158 15 1 6.76e-04 28.3 ## 248 0 0 144 158 15 1 8.12e-04 28.3 ## 249 0 0 585 599 15 1 1.00e-03 28.3 ## 250 0 0 144 158 15 1 6.73e-04 28.3 ## 251 0 0 144 158 15 1 6.74e-04 28.3 ## 252 0 0 1111 1126 21 6 5.00e-04 30.1 ## 253 0 0 1111 1126 21 6 5.00e-04 30.1 ## 254 0 0 130 145 21 6 1.42e-04 30.1 ## 255 0 0 130 145 21 6 1.42e-04 30.1 ## 256 0 0 2404 2425 22 1 1.12e-06 41.0 ## 257 0 0 9948 9964 22 6 1.00e-03 31.9 ## 258 0 0 9948 9964 22 6 1.00e-03 31.9 ## 259 0 0 9928 9944 22 6 1.00e-03 31.9 ## 260 0 0 7129 7145 22 6 1.00e-03 31.9 ## 261 0 0 1028 1042 15 1 1.00e-03 28.3 ## 262 2 0 1495 1515 2 22 1.00e-03 30.1 ## 263 0 0 68 82 8 22 1.00e-03 28.3 ## 264 0 0 3611 3631 1 21 3.89e-06 39.2 ## 265 0 0 3611 3631 1 21 3.89e-06 39.2 ## 266 0 0 3611 3631 1 21 3.89e-06 39.2 ## 267 0 0 3504 3524 1 21 3.84e-06 39.2 ## 268 0 0 3496 3516 1 21 3.84e-06 39.2 ## 269 0 0 2836 2856 1 21 2.98e-06 39.2 ## 270 0 0 366 381 24 9 2.92e-04 30.1 ## 271 1 0 114 132 1 19 1.06e-04 31.0 ## 272 1 0 357 374 1 18 1.00e-03 29.2 ## 273 0 0 68 82 8 22 5.84e-04 28.3 ## 274 0 0 68 82 8 22 5.84e-04 28.3 ## 275 0 0 2836 2852 20 4 3.67e-04 31.9 ## 276 0 0 68 82 8 22 5.84e-04 28.3 ## 277 0 0 68 82 8 22 1.00e-03 28.3 ## 278 0 0 68 82 8 22 1.00e-03 28.3 ## 279 0 0 68 82 8 22 5.84e-04 28.3 ## 280 0 0 68 82 8 22 5.84e-04 28.3 ## 281 0 0 68 82 8 22 5.84e-04 28.3 281 putative lncRNA sponges with these parameters. Ultimately though these results are insufficient to determine lncRNA sponging. We need to evaluate miRNA-lncRNA binding. # 4 miRanda miRanda is a target prediction software, used to identify likely miRNA-mRNA interactions. Inputs: - FASTA of A.pulchra lncRNAs - FASTA of A.pulchra mature miRNAs ## 4.1 Run miRanda ``` bash # score cutoff >100 # energy cutoff <-20 # strict binding /home/shared/miRanda-3.3a/src/miranda \ ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_ShortStack_4.1.0_mature.fasta \ ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.fasta \ -sc 100 \ -en -20 \ -strict \ -out ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab ``` # 5 Summarize results Let’s look at the output ``` bash echo "miranda run finished!" echo "Counting number of interacting miRNA-lncRNA pairs" zgrep -c "Performing Scan" ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab echo "Parsing output" grep -A 1 "Scores for this hit:" ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab | sort | grep '>' > ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt echo "counting number of putative interactions predicted (can include multiple interactions between single miRNA-lncRNA pair)" wc -l ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab ``` ## miranda run finished! ## Counting number of interacting miRNA-lncRNA pairs ## 3010800 ## Parsing output ## counting number of putative interactions predicted (can include multiple interactions between single miRNA-lncRNA pair) ## 24962563 ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab This is a lot of putative interactions! We can probably narrow it down though. In vertebrates, miRNA-mRNA binding only requires complementarity of an miRNA seed region of \~8 nucleotides. This requirement is built in to miRanda target prediction. In cnidarians, however, miRNA-mRNA binding is believed to require near-complete complementarity of the full mature miRNA, similarly to plants ( Admoni et al. ([2023](#ref-admoni_target_2023)) , Admoni et al. ([2025](#ref-admoni_mirna-target_2025)) ). While I couldn’t find any information on expected requirements for miRNA-lncRNA sponges, its possible the binding will function similarly to miRNA-mRNA binding. Let’s look at how many putative interactions are predicted for a binding length of at least 21 nucleotides (the length of our smallest mature miRNA). ``` bash echo "number of putative interactions of at least 21 nucleotides" awk -F'\t' '$7 >= 21' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt | wc -l echo "" echo "check some:" awk -F'\t' '$7 >= 21' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt | head -5 ``` ## number of putative interactions of at least 21 nucleotides ## 19024 ## ## check some: ## >Cluster_10051.mature::ptg000016l:7795530-7795551(+) transcript::ntLink_6:10103156-10120487 155.00 -21.12 2 21 9484 9507 21 66.67% 76.19% ## >Cluster_10051.mature::ptg000016l:7795530-7795551(+) transcript::ntLink_6:10103183-10120487 155.00 -21.12 2 21 9457 9480 21 66.67% 76.19% ## >Cluster_10051.mature::ptg000016l:7795530-7795551(+) transcript::ntLink_6:10103183-10120487 155.00 -21.12 2 21 9457 9480 21 66.67% 76.19% ## >Cluster_10051.mature::ptg000016l:7795530-7795551(+) transcript::ntLink_6:10103183-10120487 155.00 -21.12 2 21 9457 9480 21 66.67% 76.19% ## >Cluster_10051.mature::ptg000016l:7795530-7795551(+) transcript::ntLink_6:10104556-10114046 155.00 -21.12 2 21 8084 8107 21 66.67% 76.19% The header for this output is formatted as: mirna Target Score Energy-Kcal/Mol Query-Aln(start-end) Subjetct-Al(Start-End) Al-Len Subject-Identity Query-Identity We can see from the percent identities (last 2 entries) that this number includes alignments with multiple mismatches. Let’s filter again to reduce the number of permissible mismatches. Let’s say we want no more than 3 mismatches (a gap is counted as a mismatch). For an alignment of 21 nucleotides, this would be an percent identity of (21-3)/21 = 85.7%. The miRNA is our “subject”, so we will filter by column 8. ``` bash echo "number of putative interactions of at least 21 nucleotides, with at most 3 mismatches" awk -F'\t' '$7 >= 21' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt | awk -F'\t' '$8 >= 85' | wc -l echo "" echo "check some:" awk -F'\t' '$7 >= 21' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt | awk -F'\t' '$8 >= 85' | head -5 ``` ## number of putative interactions of at least 21 nucleotides, with at most 3 mismatches ## 163 ## ## check some: ## >Cluster_10228.mature::ptg000017l:7471168-7471190(+) transcript::ptg000019l:1503591-1507652 188.00 -31.70 2 22 3554 3577 21 85.71% 95.24% ## >Cluster_10228.mature::ptg000017l:7471168-7471190(+) transcript::ptg000019l:1505407-1507652 188.00 -31.70 2 22 1738 1761 21 85.71% 95.24% ## >Cluster_10228.mature::ptg000017l:7471168-7471190(+) transcript::ptg000019l:1505407-1507652 188.00 -31.70 2 22 1738 1761 21 85.71% 95.24% ## >Cluster_10228.mature::ptg000017l:7471168-7471190(+) transcript::ptg000039l:790534-797321 188.00 -25.92 2 22 5787 5810 21 90.48% 90.48% ## >Cluster_10228.mature::ptg000017l:7471168-7471190(+) transcript::ptg000039l:790555-797321 188.00 -25.92 2 22 5766 5789 21 90.48% 90.48% This is a dramatically smaller number – only 163 interactions are at least 21 nucleotides with \<=3 mismatches # 6 RNAhybrid **NOTE: the below code for RNAhybrid has NOT been rerun using updated lncRNA files, since we have decided not to use the tool in final downstream analyses (in favor of miRanda)** RNAhybrid is another miRNA-mRNA target prediction tool, which bases its predictions primarily on thermodynamic binding stability (unlike miRanda, which considers sequence features expected of miRNA targets). While the tool is normally used to predict miRNA-mRNA binding, it should also work for miRNA-lncRNA binding First we need to format our lncRNA and mature miRNA data. RNAhybrid requires a query fasta file of mature miRNAs, and a target fasta file (in this case, of lncRNAs). The problem is that RNAhybrid can only handle fastas that contain sequences of 1000 nucleotides or fewer. Some of our lncRNAs are thousands of nucleotides long, so we’ll need to reformat this file. I need to: 1. Get a gff/gtf/bed file of our lncRNAs 2. Use a bash script to modify the gff so that any sequences of \>1000 nucleotides are broken up into multiple sub-sequences (and appropriately annotated as such) 3. Convert this modified gff back into a fasta file. ## 6.1 Get lncRNA gtf We have a *candidate* lncRNA gtf that then underwent some filtering and was converted to our final Apul_lncRNA.fasta. Let’s filter the gtf to retain only the lncRNAs that made it into our final Apul_lncRNA.fasta. ``` bash lncRNAfasta=../output/10-Apul-lncRNA/Apul_lncRNA.fasta lncRNAcoordinates=../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_coordinates.txt candidategtf=../output/10-Apul-lncRNA/Apul_lncRNA_candidates.gtf lncRNAgtf=../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_unformatted.gtf # Step 1: Extract coordinates from FASTA headers grep "^>" $lncRNAfasta | \ sed 's/>//' | \ awk -F'[:\\-]' '{print $3, $4+1, $5}' OFS="\t" \ > $lncRNAcoordinates # Step 2: Keep only the candidate gtf entries whose coordinates # exactly match those included in the lncRNAfasta coordinates awk 'NR==FNR {ref[$1,$2,$3]; next} ($1,$4,$5) in ref' \ $lncRNAcoordinates \ $candidategtf \ > $lncRNAgtf ``` ``` bash lncRNAfasta=../output/10-Apul-lncRNA/Apul_lncRNA.fasta lncRNAgtf=../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_unformatted.gtf # Check echo "some lncRNA fasta sequences: " grep "^>" $lncRNAfasta | head -100 |tail -3 echo "" echo "same index of filtered lncRNA gtf sequences: " head -100 $lncRNAgtf | tail -3 echo "" echo "number of lncRNA fasta sequences: " grep "^>" $lncRNAfasta | wc -l echo "number of filtered lncRNA gtf sequences: " wc -l $lncRNAgtf ``` ## some lncRNA fasta sequences: ## >transcript::ntLink_6:12654168-12654858 ## >transcript::ntLink_6:13210102-13212000 ## >transcript::ntLink_6:13210122-13212000 ## ## same index of filtered lncRNA gtf sequences: ## ntLink_6 StringTie transcript 12654169 12654858 . + . transcript_id "MSTRG.1611.1"; gene_id "MSTRG.1611"; xloc "XLOC_000892"; class_code "u"; tss_id "TSS1327"; ## ntLink_6 StringTie transcript 13210103 13212000 . + . transcript_id "MSTRG.1688.1"; gene_id "MSTRG.1688"; xloc "XLOC_000920"; class_code "u"; tss_id "TSS1373"; ## ntLink_6 StringTie transcript 13210123 13212000 . + . transcript_id "MSTRG.1688.2"; gene_id "MSTRG.1688"; xloc "XLOC_000920"; class_code "u"; tss_id "TSS1373"; ## ## number of lncRNA fasta sequences: ## 24183 ## number of filtered lncRNA gtf sequences: ## 24183 ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_unformatted.gtf Looks like we’re good! Before we proceed I also just want to fix the gtf formatting. Right now it looks like, instead of being contained as single column 9, all the extra info (transcript ID, gene ID, etc.) is in separate tab-delimited columns. Let’s get it all correctly formatted inside of the 9th column. ``` bash unformatted=../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_unformatted.gtf formatted=../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.gtf awk -F'\t' '{ combined = $9 for (i = 10; i <= 18; i++) { combined = combined $i } gsub(/ /, "", combined) # Remove spaces from the combined column $9 = combined for (i = 10; i <= 18; i++) { $i = "" } $0 = $1 OFS $2 OFS $3 OFS $4 OFS $5 OFS $6 OFS $7 OFS $8 OFS $9 print $0 }' OFS='\t' $unformatted > $formatted # Check head -3 $formatted | awk -F'\t' '{print $9}' ``` ## 6.2 Break up \>100bp sequences ``` bash # mRNA-only genome gff # Count total sequences in lncRNA gtf wc -l ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.gtf # Count the number of sequences that contain >1000 bp awk '{if ($5 - $4 > 1000) count++} END {print count}' ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.gtf # Check how the sequence names are formatted head -2 ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.gtf ``` ## 24183 ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.gtf ## 9601 ## ntLink_0 StringTie transcript 84516 93551 . + . transcript_id"MSTRG.10.1";gene_id"MSTRG.10";xloc"XLOC_000009";class_code"u";tss_id"TSS16"; ## ntLink_0 StringTie transcript 15629 19151 . - . transcript_id"MSTRG.3.1";gene_id"MSTRG.3";xloc"XLOC_000010";class_code"u";tss_id"TSS17"; about 40% of our lncRNAs are too long, so we’ll need to break them up I want to break up any sequence \>1000bp into 1000bp chunks, adding a line to the gff for each chunk. (I also want there to be overlap among the chunks, in case the break between two chunks falls in the middle of an miRNA binding site. Let’s say a 25bp overlap, since that is just over the maximum expected miRNA length.) for now though let’s not worry about the overlap. The below code checks every sequence in the gtf and, for sequences over 1000 nucleotides long, breaks them up iteratively into 1000bp chunks. When it breaks up a sequence, it also appends to the final column of the line a “parent ID” showing the original lncRNA ID. ``` bash awk -v chunk_size=1000 ' BEGIN {OFS="\t"} { seq_length = $5 - $4 parent_id = $1 ":" $4 "-" $5 if (seq_length > chunk_size) { start = $4 ogend = $5 while (start < ogend) { end = start + chunk_size if (end > ogend) end = ogend $4 = start $5 = end temp_col9 = $9 "parent_id\"" parent_id "\"" # Preserve the existing content and append parent_id print $1, $2, $3, $4, $5, $6, $7, $8, temp_col9 start = end } } else { $9 = $9 "parent_id\"" parent_id "\"" # Append parent_id to the existing content in $9 print } }' "../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA.gtf" > "../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.gtf" ``` ``` bash MAX1000gtf=../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.gtf # mRNA-only genome gff # Count total sequences in genome gff wc -l $MAX1000gtf # Count the number of sequences that contain >1000 bp awk '{if ($5 - $4 > 1000) count++} END {print count}' $MAX1000gtf # Check how the sequence names are formatted head -5 $MAX1000gtf ``` ## 65494 ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.gtf ## ## ntLink_0 StringTie transcript 84516 85516 . + . transcript_id"MSTRG.10.1";gene_id"MSTRG.10";xloc"XLOC_000009";class_code"u";tss_id"TSS16";parent_id"ntLink_0:84516-93551" ## ntLink_0 StringTie transcript 85516 86516 . + . transcript_id"MSTRG.10.1";gene_id"MSTRG.10";xloc"XLOC_000009";class_code"u";tss_id"TSS16";parent_id"ntLink_0:84516-93551" ## ntLink_0 StringTie transcript 86516 87516 . + . transcript_id"MSTRG.10.1";gene_id"MSTRG.10";xloc"XLOC_000009";class_code"u";tss_id"TSS16";parent_id"ntLink_0:84516-93551" ## ntLink_0 StringTie transcript 87516 88516 . + . transcript_id"MSTRG.10.1";gene_id"MSTRG.10";xloc"XLOC_000009";class_code"u";tss_id"TSS16";parent_id"ntLink_0:84516-93551" ## ntLink_0 StringTie transcript 88516 89516 . + . transcript_id"MSTRG.10.1";gene_id"MSTRG.10";xloc"XLOC_000009";class_code"u";tss_id"TSS16";parent_id"ntLink_0:84516-93551" Looks good! ## 6.3 Get fasta of broken-up lncRNA gtf ``` bash # Use lncRNA gtf and genome fasta to extract lncRNA fastas /home/shared/bedtools2/bin/bedtools getfasta \ -fi "../data/Apulchra-genome.fa" \ -bed "../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.gtf" \ -fo "../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.fa" ``` ## 6.4 Run RNAhybrid Now we can run RNAhybrid! I was getting a weird issue with all-zero pvalues when I used RNAcalibrate-generated shape distribution parameters in `16-Apul-RNAhybrid`, so I’ll just use the built-in 3utr_worm parameter again. I have RNAhybrid installed on a miniconda environment # Check path to the conda environment I'm using which conda # Install RNAhybrid if neccessary conda install -y -c genomedk rnahybrid # Check installation conda list rnahybrid ``` bash #Start time: 12/12/2024 15:36 # RNAhybrid \ -s 3utr_worm \ -e -20 \ -p 0.05 \ -c \ -t ../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.fa \ -q ../data/16-Apul-RNAhybrid/miRNA_mature-Apul-ShortStack_4.1.0-pulchra_genome.fasta \ > ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-RNAhybrid-lncRNA-compact_3utrworm.txt ``` ## 6.5 Summarize RNAhybrid results ``` bash # How many significant hybridizations predicted for each input? wc -l ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-RNAhybrid-lncRNA-compact_3utrworm.txt echo "" head -3 ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-RNAhybrid-lncRNA-compact_3utrworm.txt ``` ## 1566 ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-RNAhybrid-lncRNA-compact_3utrworm.txt ## ## ntLink_6:15566281-15566670:389:Cluster_1826.mature::ntLink_6:4847465-4847486(-):22:-26.2:0.020079:150: U C : GGAAAGUGCUAUGA : UCUUUCACGAUACU :UGUU AGUA ## ntLink_8:10040388-10040616:228:Cluster_1826.mature::ntLink_6:4847465-4847486(-):22:-27.2:0.004032:123:G A : ACAAAGAGAGUGCUA : UGUUUCUUUCACGAU : ACUAGUA ## ptg000001l:25192-26193:1000:Cluster_1826.mature::ntLink_6:4847465-4847486(-):22:-29.5:0.014814:926: G A : GAAGAAAGUGCUAUGA : UUUCUUUCACGAUACU :UG AGUA Now let’s read our RNAhybrid results into R for visualization. Note this is going to be slightly more complicated than it sounds because the RNAhybrid compact output is colon-delimited and our target- and query-IDs contain intentional colons than could get confused with column delimiters. ``` r RNAhybrid_lncRNA <- read.table("../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-RNAhybrid-lncRNA-compact_3utrworm.txt", sep=":") # Recombine Columns 1 and 2 (fix incorrect separation of target ID components) RNAhybrid_lncRNA$V1 <- paste(RNAhybrid_lncRNA$V1, RNAhybrid_lncRNA$V2, sep = ":") RNAhybrid_lncRNA$V2 <- NULL # Do the same for Columns 4-7 (query ID components) RNAhybrid_lncRNA$V4 <- paste(RNAhybrid_lncRNA$V4, RNAhybrid_lncRNA$V5, RNAhybrid_lncRNA$V6, RNAhybrid_lncRNA$V7 , sep = ":") RNAhybrid_lncRNA$V4 <- gsub(":NA:", "::", RNAhybrid_lncRNA$V4) RNAhybrid_lncRNA$V5 <- NULL RNAhybrid_lncRNA$V6 <- NULL RNAhybrid_lncRNA$V7 <- NULL # Rename all columns for readability/accessibility colnames(RNAhybrid_lncRNA) <- c("target_name", "target_length", "query_name", "query_length", "mfe", "pval", "position", "noncomp_target_seq", "comp_target_seq", "comp_query_seq", "noncomp_query_seq") ``` Right now the “target” names are, for many lncRNAs, the broken-up “chunks” of 1000bp. Let’s associate all of these chunks back to their original “parent” lncRNAs. ``` r # Read in current gtf MAX1000gtf <- read.table("../data/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000.gtf", sep="\t", head=FALSE) # make new column for the full sequence ID (scaffold:beginning location-end location) MAX1000gtf$target_name <- paste(MAX1000gtf$V1, paste(MAX1000gtf$V4-1, MAX1000gtf$V5, sep = "-"), sep = ":") # separate out all of the extra info contained in column 9 # Define the ID types id_types <- c("transcript_id", "gene_id", "xloc", "class_code", "cmp_ref_gene", "tss_id", "parent_id") # Function to extract values for all ID types extract_ids <- function(row, id_types) { # Split the row by ';' entries <- strsplit(row, ";")[[1]] # Initialize a named list with NA for all fields result <- setNames(rep(NA, length(id_types)), id_types) # Populate the list with actual values from the row for (entry in entries) { for (id_type in id_types) { if (startsWith(entry, id_type)) { result[[id_type]] <- sub(paste0("^", id_type), "", entry) } } } # Return the result as a named vector return(result) } # Apply the function to each row in column V9 parsed_data <- t(sapply(MAX1000gtf$V9, extract_ids, id_types = id_types)) # Convert the result into a data frame parsed_df <- as.data.frame(parsed_data, stringsAsFactors = FALSE) rownames(parsed_df) <- NULL # Reset row names # Combine the parsed data back into the original data frame MAX1000gtf <- cbind(MAX1000gtf, parsed_df) # Keep only the columns we may want to use MAX1000gtf_reduced <- MAX1000gtf %>% select(target_name, parent_id, gene_id, cmp_ref_gene) # remove duplicate rows (I believe stemming from isoforms) MAX1000gtf_reduced <- MAX1000gtf_reduced[!duplicated(MAX1000gtf_reduced$target_name), ] # Save these for later use write.table(MAX1000gtf, "../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000_expandedgtf_large.txt", sep="\t") write.table(MAX1000gtf_reduced, "../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul_lncRNA_MAX1000_expandedgtf.txt", sep="\t") ``` Now we can merge this gtf-based association table with the RNAhybrid outputs ``` r # merge and keep only columns of interest RNAhybrid_lncRNA_annot <- left_join(RNAhybrid_lncRNA, MAX1000gtf_reduced, by = c("target_name" = "target_name")) %>% select(target_name, query_name, mfe, pval, parent_id, gene_id, cmp_ref_gene) # Also, grab just the miRNA cluster name, for simplicity RNAhybrid_lncRNA_annot$miRNA_cluster <- sub("\\..*", "", RNAhybrid_lncRNA_annot$query_name) # move lncRNA parent ID and miRNA cluster name to first two columns RNAhybrid_lncRNA_annot <- RNAhybrid_lncRNA_annot %>% select(parent_id, miRNA_cluster, everything()) # take a look head(RNAhybrid_lncRNA_annot) ``` ## parent_id miRNA_cluster target_name ## 1 ntLink_6:15566282-15566670 Cluster_1826 ntLink_6:15566281-15566670 ## 2 ntLink_8:10040389-10040616 Cluster_1826 ntLink_8:10040388-10040616 ## 3 ptg000001l:25193-27088 Cluster_1826 ptg000001l:25192-26193 ## 4 ptg000001l:10926731-10928207 Cluster_1826 ptg000001l:10927730-10928207 ## 5 ptg000002l:5112686-5113594 Cluster_1826 ptg000002l:5112685-5113594 ## 6 ptg000007l:7556635-7557971 Cluster_1826 ptg000007l:7557634-7557971 ## query_name mfe pval gene_id ## 1 Cluster_1826.mature::ntLink_6:4847465-4847486(-) -26.2 0.020079 MSTRG.1961 ## 2 Cluster_1826.mature::ntLink_6:4847465-4847486(-) -27.2 0.004032 MSTRG.4113 ## 3 Cluster_1826.mature::ntLink_6:4847465-4847486(-) -29.5 0.014814 MSTRG.7321 ## 4 Cluster_1826.mature::ntLink_6:4847465-4847486(-) -29.1 0.005259 MSTRG.8409 ## 5 Cluster_1826.mature::ntLink_6:4847465-4847486(-) -27.5 0.037381 MSTRG.10224 ## 6 Cluster_1826.mature::ntLink_6:4847465-4847486(-) -25.8 0.019897 MSTRG.14688 ## cmp_ref_gene ## 1 ## 2 ## 3 ## 4 ## 5 ## 6 ``` r # save write.table(RNAhybrid_lncRNA_annot, "../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-RNAhybrid-lncRNA-compact_3utrworm-annot.txt", sep = "\t") ``` # 7 Summarize final results LncRNA as miRNA precursors: ``` r cat("Number of putative lncRNA precursors: ", length(filter(filter(precursor_lncRNA_BLASTn, length >= 90), mismatch == 0)$qseqid), "\n", "Number of miRNA whose precursors are lncRNA: ", length(unique(filter(filter(precursor_lncRNA_BLASTn, length >= 90), mismatch == 0)$sseqid))) ``` ## Number of putative lncRNA precursors: 36 ## Number of miRNA whose precursors are lncRNA: 23 Note: So there are 3 instances of a *unique* lncRNA containing a full pre-miRNA sequence (and one of those instances occurs in 5 lncRNA isoforms) LncRNA as miRNA sponges: ``` bash echo "miRanda Results:" echo "" echo "Number of putative interactions:" wc -l ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab echo "" echo "Number of putative interactions of at least 21 nucleotides" awk -F'\t' '$7 >= 21' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt | wc -l echo "" echo "Number of putative interactions of at least 21 nucleotides, with at most 3 mismatches" awk -F'\t' '$7 >= 21' ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict-parsed.txt | awk -F'\t' '$8 >= 85' | wc -l ``` ## miRanda Results: ## ## Number of putative interactions: ## 24962563 ../output/17-Apul-miRNA-lncRNA-BLASTs-RNAhybrid/Apul-miRanda-lncRNA-strict_all.tab ## ## Number of putative interactions of at least 21 nucleotides ## 19024 ## ## Number of putative interactions of at least 21 nucleotides, with at most 3 mismatches ## 163 **NOTE: below RNAhybrid results used lncRNA files that are now outdated** ``` r cat("RNAhybrid Results: ", "\n", "\n", "(as a reminder)", "\n", "Number of A.pulchra lncRNAs: ", length(lncRNA_lengths$seqID), "\n", "Number of A.pulchra miRNAs: ", length(mature_lengths$seqID), "\n", "~~~~~~~~~~~~~~~~~~~~~~", "\n", "for p < 0.05: ", "\n", "Number of significant lncRNA-miRNA hybridizations: ", length(RNAhybrid_lncRNA_annot$parent_id), "\n", "Number of putative lncRNA sponges: ", length(unique(RNAhybrid_lncRNA_annot$parent_id)), "\n", "Number of miRNA putatively sequestered by lncRNA: ", length(unique(RNAhybrid_lncRNA_annot$miRNA_cluster)), "\n", "~~~~~~~~~~~~~~~~~~~~~~", "\n", "for p < 0.01: ", "\n", "Number of lncRNA-miRNA hybridizations: ", length(filter(RNAhybrid_lncRNA_annot, pval < 0.01)$parent_id), "\n", "Number of putative lncRNA sponges: ", length(unique(filter(RNAhybrid_lncRNA_annot, pval < 0.01)$parent_id)), "\n", "Number of miRNA putatively sequestered by lncRNA sponges: ", length(unique(filter(RNAhybrid_lncRNA_annot, pval < 0.01)$miRNA_cluster))) ``` ## RNAhybrid Results: ## ## (as a reminder) ## Number of A.pulchra lncRNAs: 77200 ## Number of A.pulchra miRNAs: 39 ## ~~~~~~~~~~~~~~~~~~~~~~ ## for p < 0.05: ## Number of significant lncRNA-miRNA hybridizations: 1566 ## Number of putative lncRNA sponges: 1265 ## Number of miRNA putatively sequestered by lncRNA: 39 ## ~~~~~~~~~~~~~~~~~~~~~~ ## for p < 0.01: ## Number of lncRNA-miRNA hybridizations: 313 ## Number of putative lncRNA sponges: 259 ## Number of miRNA putatively sequestered by lncRNA sponges: 35 # 8 References

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