05-Peve-sRNA-ShortStack_4.1.0

Use ShortStack (Axtell 2013; Shahid and Axtell 2014; Johnson et al. 2016)to perform alignment of sRNAseq data and annotation of sRNA-producing genes.

This is the same ShortStack analysis as performed in the deeep-dive project. However, ShortStack has an updated version, ShortStack 4.1.0, which provides much faster analysis times and additional functionality for visualizing miRNA hairpin structures and generating genome-browser-ready quantitative coverage tracks of aligned small RNAs.

As in deep-dive, we will also include a customized miRBase database, utilizing cnidarian miRNAs curated by Jill Ashley, which includes published cnidarian miRNAs:

• cnidarian-mirbase-mature-v22.1.fasta

---

Inputs:

• Requires trimmed sRNAseq files generated by 06.2-Peve-sRNAseq-trimming-31bp-fastp-merged.Rmd

  • Filenames formatted: *fastp-R1-31bp-auto_adapters-polyG.fq.gz

• Genome FastA. Stored on deep-dive wiki

• Modified MiRBase v22.1 FastA. Includes cnidarian miRNAs provided by Jill Ashley.

Outputs:

• See ShortStack outputs documentation for full list and detailed descriptions.

Software requirements:

• Utilizes a ShortStack Conda/Mamba environment, per the installation instructions.

Replace with name of your ShortStack environment and the path to the corresponding conda installation (find this after you’ve activated the environment).

E.g.

# Activate environment
conda activate ShortStack4_env

# Find conda path
which conda

---

1 Set R variables

shortstack_conda_env_name <- c("ShortStack-4.1.0_env")
shortstack_cond_path <- c("/home/sam/programs/mambaforge/condabin/conda")

2 Create a Bash variables file

This allows usage of Bash variables across R Markdown chunks.

{
echo "#### Assign Variables ####"
echo ""

echo "# Trimmed FastQ naming pattern"
echo "export trimmed_fastqs_pattern='*fastp-adapters-polyG-31bp-merged.fq.gz'"

echo "# Data directories"
echo 'export expression_dir=/home/shared/8TB_HDD_02/shedurkin/deep-dive-expression'
echo 'export expression_data_dir="${expression_dir}/data"'
echo 'export output_dir_top=${expression_dir}/E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0'
echo ""

echo "# Input/Output files"
echo 'export genome_fasta_dir=${expression_dir}/E-Peve/data'
echo 'export genome_fasta_name="Porites_evermanni_v1.fa"'
echo 'export shortstack_genome_fasta_name="Porites_evermanni_v1.fa"'
echo 'export trimmed_fastqs_dir="${genome_fasta_dir}/sRNA-trimmed-reads"'

echo 'export mirbase_mature_fasta_version=cnidarian-mirbase-mature-v22.1.fasta'
echo 'export genome_fasta="${genome_fasta_dir}/${shortstack_genome_fasta_name}"'
echo ""

echo "# Set number of CPUs to use"
echo 'export threads=40'
echo ""

echo "# Initialize arrays"
echo 'export trimmed_fastqs_array=()'


} > .bashvars

cat .bashvars

#### Assign Variables ####

# Trimmed FastQ naming pattern
export trimmed_fastqs_pattern='*fastp-adapters-polyG-31bp-merged.fq.gz'
# Data directories
export expression_dir=/home/shared/8TB_HDD_02/shedurkin/deep-dive-expression
export expression_data_dir="${expression_dir}/data"
export output_dir_top=${expression_dir}/E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0

# Input/Output files
export genome_fasta_dir=${expression_dir}/E-Peve/data
export genome_fasta_name="Porites_evermanni_v1.fa"
export shortstack_genome_fasta_name="Porites_evermanni_v1.fa"
export trimmed_fastqs_dir="${genome_fasta_dir}/sRNA-trimmed-reads"
export mirbase_mature_fasta_version=cnidarian-mirbase-mature-v22.1.fasta
export genome_fasta="${genome_fasta_dir}/${shortstack_genome_fasta_name}"

# Set number of CPUs to use
export threads=40

# Initialize arrays
export trimmed_fastqs_array=()

3 Load ShortStack conda environment

If this is successful, the first line of output should show that the Python being used is the one in your [ShortStack](https://github.com/MikeAxtell/ShortStack conda environment path.

E.g.

python: /home/sam/programs/mambaforge/envs/mirmachine_env/bin/python

use_condaenv(condaenv = shortstack_conda_env_name, conda = shortstack_cond_path)
py_config()

python:         /home/sam/programs/mambaforge/envs/ShortStack-4.1.0_env/bin/python
libpython:      /home/sam/programs/mambaforge/envs/ShortStack-4.1.0_env/lib/libpython3.12.so
pythonhome:     /home/sam/programs/mambaforge/envs/ShortStack-4.1.0_env:/home/sam/programs/mambaforge/envs/ShortStack-4.1.0_env
version:        3.12.7 | packaged by conda-forge | (main, Oct  4 2024, 16:05:46) [GCC 13.3.0]
numpy:          /home/sam/programs/mambaforge/envs/ShortStack-4.1.0_env/lib/python3.12/site-packages/numpy
numpy_version:  2.1.1

NOTE: Python version was forced by use_python() function

Note: I sometimes get an error “failed to initialize requested version of Python,” which seems to stem from the reticulate package default loading a python environment. I’ve been able to fix this by manually uninstalling the reticulate package, then restarting R and reinstalling reticulate before rerunning this code document. # Download reference files

3.1 P.evermanni genome

# Load bash variables into memory
source .bashvars

wget -O ${genome_fasta_dir}/${shortstack_genome_fasta_name} "https://gannet.fish.washington.edu/seashell/snaps/Porites_evermanni_v1.fa"

# Load bash vairables into memory
source .bashvars

head ${genome_fasta_dir}/${shortstack_genome_fasta_name}

>Porites_evermani_scaffold_1
GGCGGGGGGGGGGGGGGGGGGGGTACTCCCATACATTACCTATACGGGTATGTGCCGCCC
AAAAGGGGCCGTGATTTTGAAGCTCCTGATTTAGAACGGGGTATCCATTTCAGAGGCGTT
TTCTAGAACGGGGTGTAATATTTCGAACGCACGAAAGCTCCACTTTTGTGTAAGCAGCCA
TTTGAAATTATTCAAGGACAGATTGCTTTTAAAAATACGGTTCAGCGCGTTAACAAGCAA
ACCGTTGTACTCTTGTTGCACCCTAGAACGGTGTATAAAAAATTGGCCCATTTCTAGAAC
GGGGTATCAGTTTTAGGGAGAATTCTAGAACGGGGTATAAAAAATTGGCCCTTTTCTGAA
CGGGGCATCAATGTTAGGGGAAATTTTTTCCAGAACGGGGTGCCAATTTGGAGTCCCGGG
CGGCACATACCCACCCAAAAAATACCCAAGTGCCCCCCCGGGGTCTAAACCCACATATTC
TTCACACTGTTCACAATTTACCTCTTTTGGCTCTTCTAAGGAGAGCTCATCTAAATATTG

3.2 Cnidarian+miRBase database

Available n deep-dive repo, here

# Load bash variables into memory
source .bashvars

wget -O ${expression_data_dir}/"${mirbase_mature_fasta_version}" "https://raw.githubusercontent.com/urol-e5/deep-dive/refs/heads/main/data/cnidarian-mirbase-mature-v22.1.fasta"

# Load bash variables into memory
source .bashvars

head -5 ${expression_data_dir}/"${mirbase_mature_fasta_version}"

>cel-let-7-5p MIMAT0000001 Caenorhabditis elegans let-7-5p
UGAGGUAGUAGGUUGUAUAGUU
>cel-let-7-3p MIMAT0015091 Caenorhabditis elegans let-7-3p
CUAUGCAAUUUUCUACCUUACC
>cel-lin-4-5p MIMAT0000002 Caenorhabditis elegans lin-4-5p

3.3 Trimmed sRNA-seq reads

Trimmed in deep-dive, 06.2-Peve-sRNAseq-trimming-31bp-fastp-merged

# Load bash variables into memory
source .bashvars

wget \
--directory-prefix ${trimmed_fastqs_dir} \
--recursive \
--no-check-certificate \
--continue \
--cut-dirs 7 \
--no-host-directories \
--no-parent \
--quiet \
--accept ${trimmed_fastqs_pattern} \
"https://gannet.fish.washington.edu/Atumefaciens/gitrepos/deep-dive/E-Peve/output/06.2-Peve-sRNAseq-trimming-31bp-fastp-merged/trimmed-reads/"

4 Run ShortStack

4.1 Modify genome filename for ShortStack compatability

# Load bash variables into memory
source .bashvars

# Check for FastA file first
# Then create rename file if doesn't exist
if [ -f "${genome_fasta_dir}/${shortstack_genome_fasta_name}" ]; then
  echo "${genome_fasta_dir}/${shortstack_genome_fasta_name}"
  echo ""
  echo "Already exists. Nothing to do."
  echo ""
else

  # Copy genome FastA to ShortStack-compatible filename (ending with .fa)
  cp ${genome_fasta_dir}/${genome_fasta_name} ${genome_fasta_dir}/${shortstack_genome_fasta_name}
fi

# Confirm
ls -lh ${genome_fasta_dir}/${shortstack_genome_fasta_name}

/home/shared/8TB_HDD_02/shedurkin/deep-dive-expression/E-Peve/data/Porites_evermanni_v1.fa

Already exists. Nothing to do.

-rw-r--r-- 1 shedurkin labmembers 586M Jun 30  2023 /home/shared/8TB_HDD_02/shedurkin/deep-dive-expression/E-Peve/data/Porites_evermanni_v1.fa

4.2 Excecute ShortStack command

Uses the --dn_mirna option to identify miRNAs in the genome, without relying on the --known_miRNAs.

This part of the code redirects the output of time to the end of shortstack.log file.

• ; } \ 2>> ${output_dir_top}/shortstack.log

# Load bash variables into memory
source .bashvars

# Make output directory, if it doesn't exist
mkdir --parents "${output_dir_top}"

# Create array of trimmed FastQs
trimmed_fastqs_array=(${trimmed_fastqs_dir}/${trimmed_fastqs_pattern})


# Pass array contents to new variable as space-delimited list
trimmed_fastqs_list=$(echo "${trimmed_fastqs_array[*]}")


###### Run ShortStack ######
{ time \
ShortStack \
--genomefile "${genome_fasta}" \
--readfile ${trimmed_fastqs_list} \
--known_miRNAs ${expression_data_dir}/${mirbase_mature_fasta_version} \
--dn_mirna \
--threads ${threads} \
--outdir ${output_dir_top}/ShortStack_out \
&> ${output_dir_top}/shortstack.log ; } \
2>> ${output_dir_top}/shortstack.log

4.3 Check runtime

# Load bash variables into memory
source .bashvars

tail -n 3 ${output_dir_top}/shortstack.log \
| grep "real" \
| awk '{print "ShortStack runtime:" "\t" $2}'

ShortStack runtime: 11m17.143s

5 Results

5.1 ShortStack synopsis

# Load bash variables into memory
source .bashvars

tail -n 25 ${output_dir_top}/shortstack.log

Writing final files

Found a total of 45 MIRNA loci


Non-MIRNA loci by DicerCall:
N 16673
22 60
23 53
21 36
24 26

Creating visualizations of microRNA loci with strucVis
<<< WARNING >>>
Do not rely on these results alone to annotate new MIRNA loci!
The false positive rate for de novo MIRNA identification is low, but NOT ZERO
Insepct each mirna locus, especially the strucVis output, and see
https://doi.org/10.1105/tpc.17.00851 , https://doi.org/10.1093/nar/gky1141

Tue 22 Oct 2024 16:33:44 -0700 PDT
Run Completed!

real    11m17.143s
user    241m43.184s
sys 18m24.229s

ShortStack identified 45 miRNAs

5.2 Inspect Results.txt

# Load bash variables into memory
source .bashvars

head ${output_dir_top}/ShortStack_out/Results.txt

echo ""
echo "----------------------------------------------------------"
echo ""

echo "Nummber of potential loci:"
awk '(NR>1)' ${output_dir_top}/ShortStack_out/Results.txt | wc -l

Locus   Name    Chrom   Start   End Length  Reads   DistinctSequences   FracTop Strand  MajorRNA    MajorRNAReads   Short   Long    21  22  23  24  DicerCall   MIRNA   known_miRNAs
Porites_evermani_scaffold_1:1671-2096   Cluster_1   Porites_evermani_scaffold_1 1671    2096    426 61  8   0.984   +   AGGACAACAACAAUUAACUGCAGAGU  52  3   56  0   0   1   1   N   N   NA
Porites_evermani_scaffold_1:45711-46131 Cluster_2   Porites_evermani_scaffold_1 45711   46131   421 88  38  1.0 +   CAGUAGAGGUGGCCAAGAAUCAGU    8   24  27  9   8   9   11  N   N   NA
Porites_evermani_scaffold_1:313446-313846   Cluster_3   Porites_evermani_scaffold_1 313446  313846  401 50  27  0.0 -   CUGACGUUUUAAGCUCAAUAGU  13  10  15  1   17  3   4   N   N   NA
Porites_evermani_scaffold_1:406133-406734   Cluster_4   Porites_evermani_scaffold_1 406133  406734  602 251 128 0.036   -   UGAGUGUAUUCUUGAACUGUUUUCCAAC    37  2   225 3   3   8   10  N   N   NA
Porites_evermani_scaffold_1:409836-410269   Cluster_5   Porites_evermani_scaffold_1 409836  410269  434 190 63  0.0 -   UGGAACUCCGAUUUAGAACUUGCAAACUUU  54  0   184 2   0   1   3   N   N   NA
Porites_evermani_scaffold_1:465244-465668   Cluster_6   Porites_evermani_scaffold_1 465244  465668  425 169 49  0.0 -   AAGUUGCUCUGAAGAUUAUGU   39  34  52  48  8   20  7   N   N   NA
Porites_evermani_scaffold_1:468473-468950   Cluster_7   Porites_evermani_scaffold_1 468473  468950  478 91900   807 0.0 -   AGCACUGAUGACUGUUCAGUUUUUCUGAAUU 68534   2227    88188   115 138 153 1079    N   N   NA
Porites_evermani_scaffold_1:476827-477250   Cluster_8   Porites_evermani_scaffold_1 476827  477250  424 116 37  0.0 -   CGUGUCUUCGUAAUCGUCUCGUAC    14  33  38  0   12  15  18  N   N   NA
Porites_evermani_scaffold_1:486441-486868   Cluster_9   Porites_evermani_scaffold_1 486441  486868  428 57  11  0.07    -   AUAUUGACGAAUCCUGGCCUAGUGAACC    26  0   53  0   0   4   0   N   N   NA

----------------------------------------------------------

Nummber of potential loci:
16893

Column 20 of the Results.txt file identifies if a cluster is a miRNA or not (Y or N).

# Load bash variables into memory
source .bashvars

echo "Number of loci characterized as miRNA:"
awk '$20=="Y" {print $0}' ${output_dir_top}/ShortStack_out/Results.txt \
| wc -l
echo ""

echo "----------------------------------------------------------"

echo ""
echo "Number of loci _not_ characterized as miRNA:"
awk '$20=="N" {print $0}' ${output_dir_top}/ShortStack_out/Results.txt \
| wc -l

Number of loci characterized as miRNA:
45

----------------------------------------------------------

Number of loci _not_ characterized as miRNA:
16848

Column 21 of the Results.txt file identifies if a cluster aligned to a known miRNA (miRBase) or not (Y or NA).

Since there are no miRNAs, the following code will not print any output.

The echo command after the awk command is simply there to prove that the chunk executed.

# Load bash variables into memory
source .bashvars

echo "Number of loci matching miRBase miRNAs:"
awk '$21!="NA" {print $0}' ${output_dir_top}/ShortStack_out/Results.txt \
| wc -l
echo ""

echo "----------------------------------------------------------"

echo ""
echo "Number of loci _not_ matching miRBase miRNAs:"
awk '$21=="NA" {print $0}' ${output_dir_top}/ShortStack_out/Results.txt \
| wc -l

Number of loci matching miRBase miRNAs:
38

----------------------------------------------------------

Number of loci _not_ matching miRBase miRNAs:
16856

Although there are loci with matches to miRBase miRNAs, ShortStack did not annotate these clusters as miRNAs likely because they do not also match secondary structure criteria.

5.2.1 Directory tree of all ShortStack outputs

Many of these are large (by GitHub standards) BAM files, so will not be added to the repo.

Additionally, it’s unlikely we’ll utilize most of the other files (bigwig) generated by ShortStack.

# Load bash variables into memory
source .bashvars

tree -h ${output_dir_top}/

/home/shared/8TB_HDD_02/shedurkin/deep-dive-expression/E-Peve/output/05-Peve-sRNA-ShortStack_4.1.0/
├── [4.0K]  figures
│   ├── [117K]  Peve_ShortStack_dbmatch_histogram.png
│   ├── [120K]  Peve_ShortStack_dbmatch_histogram_reduced.png
│   ├── [207K]  Peve_ShortStack_miRNA_histogram.png
│   ├── [199K]  Peve_ShortStack_miRNA_histogram_reduced.png
│   └── [201K]  Peve_ShortStack_venn.png
├── [6.6K]  shortstack.log
└── [244K]  ShortStack_out
    ├── [ 17K]  alignment_details.tsv
    ├── [1.1M]  Counts.txt
    ├── [212K]  known_miRNAs.gff3
    ├── [1.8M]  known_miRNAs_unaligned.fasta
    ├── [133M]  merged_alignments.bam
    ├── [315K]  merged_alignments.bam.csi
    ├── [ 15K]  mir.fasta
    ├── [ 32M]  POR-73-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.bam
    ├── [278K]  POR-73-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.bam.csi
    ├── [ 98M]  POR-73-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.fa
    ├── [ 45M]  POR-79-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.bam
    ├── [287K]  POR-79-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.bam.csi
    ├── [153M]  POR-79-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.fa
    ├── [ 54M]  POR-82-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.bam
    ├── [293K]  POR-82-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.bam.csi
    ├── [183M]  POR-82-S1-TP2-fastp-adapters-polyG-31bp-merged_condensed.fa
    ├── [1.9M]  Results.gff3
    ├── [2.8M]  Results.txt
    └── [4.0K]  strucVis
        ├── [9.4K]  Cluster_10060.ps.pdf
        ├── [ 25K]  Cluster_10060.txt
        ├── [9.3K]  Cluster_10061.ps.pdf
        ├── [ 27K]  Cluster_10061.txt
        ├── [10.0K]  Cluster_10934.ps.pdf
        ├── [8.3K]  Cluster_10934.txt
        ├── [ 11K]  Cluster_10965.ps.pdf
        ├── [ 22K]  Cluster_10965.txt
        ├── [ 10K]  Cluster_11134.ps.pdf
        ├── [ 25K]  Cluster_11134.txt
        ├── [ 10K]  Cluster_11135.ps.pdf
        ├── [ 43K]  Cluster_11135.txt
        ├── [10.0K]  Cluster_1140.ps.pdf
        ├── [ 15K]  Cluster_1140.txt
        ├── [ 12K]  Cluster_1167.ps.pdf
        ├── [ 51K]  Cluster_1167.txt
        ├── [9.8K]  Cluster_11997.ps.pdf
        ├── [ 13K]  Cluster_11997.txt
        ├── [8.0K]  Cluster_13502.ps.pdf
        ├── [2.4K]  Cluster_13502.txt
        ├── [8.8K]  Cluster_14500.ps.pdf
        ├── [4.0K]  Cluster_14500.txt
        ├── [9.3K]  Cluster_14999.ps.pdf
        ├── [4.9K]  Cluster_14999.txt
        ├── [9.8K]  Cluster_15726.ps.pdf
        ├── [ 10K]  Cluster_15726.txt
        ├── [9.6K]  Cluster_15890.ps.pdf
        ├── [ 13K]  Cluster_15890.txt
        ├── [8.2K]  Cluster_16498.ps.pdf
        ├── [ 14K]  Cluster_16498.txt
        ├── [8.3K]  Cluster_16738.ps.pdf
        ├── [8.9K]  Cluster_16738.txt
        ├── [9.7K]  Cluster_2787.ps.pdf
        ├── [ 14K]  Cluster_2787.txt
        ├── [7.6K]  Cluster_2854.ps.pdf
        ├── [3.0K]  Cluster_2854.txt
        ├── [8.8K]  Cluster_2882.ps.pdf
        ├── [ 17K]  Cluster_2882.txt
        ├── [8.8K]  Cluster_29.ps.pdf
        ├── [ 32K]  Cluster_29.txt
        ├── [9.9K]  Cluster_4079.ps.pdf
        ├── [ 66K]  Cluster_4079.txt
        ├── [ 10K]  Cluster_4080.ps.pdf
        ├── [ 57K]  Cluster_4080.txt
        ├── [ 10K]  Cluster_4115.ps.pdf
        ├── [ 55K]  Cluster_4115.txt
        ├── [ 10K]  Cluster_4629.ps.pdf
        ├── [5.6K]  Cluster_4629.txt
        ├── [8.7K]  Cluster_4735.ps.pdf
        ├── [ 12K]  Cluster_4735.txt
        ├── [8.8K]  Cluster_5563.ps.pdf
        ├── [ 15K]  Cluster_5563.txt
        ├── [ 10K]  Cluster_5882.ps.pdf
        ├── [8.6K]  Cluster_5882.txt
        ├── [ 11K]  Cluster_589.ps.pdf
        ├── [ 20K]  Cluster_589.txt
        ├── [9.7K]  Cluster_6255.ps.pdf
        ├── [4.1K]  Cluster_6255.txt
        ├── [ 11K]  Cluster_6904.ps.pdf
        ├── [ 13K]  Cluster_6904.txt
        ├── [9.0K]  Cluster_6905.ps.pdf
        ├── [8.1K]  Cluster_6905.txt
        ├── [9.7K]  Cluster_6906.ps.pdf
        ├── [ 14K]  Cluster_6906.txt
        ├── [8.3K]  Cluster_6914.ps.pdf
        ├── [ 39K]  Cluster_6914.txt
        ├── [ 11K]  Cluster_7053.ps.pdf
        ├── [9.7K]  Cluster_7053.txt
        ├── [9.9K]  Cluster_7657.ps.pdf
        ├── [ 20K]  Cluster_7657.txt
        ├── [10.0K]  Cluster_7658.ps.pdf
        ├── [ 13K]  Cluster_7658.txt
        ├── [8.3K]  Cluster_7855.ps.pdf
        ├── [5.7K]  Cluster_7855.txt
        ├── [ 10K]  Cluster_796.ps.pdf
        ├── [ 58K]  Cluster_796.txt
        ├── [9.2K]  Cluster_8634.ps.pdf
        ├── [5.4K]  Cluster_8634.txt
        ├── [10.0K]  Cluster_8884.ps.pdf
        ├── [ 30K]  Cluster_8884.txt
        ├── [8.7K]  Cluster_8887.ps.pdf
        ├── [ 13K]  Cluster_8887.txt
        ├── [8.9K]  Cluster_8888.ps.pdf
        ├── [ 21K]  Cluster_8888.txt
        ├── [8.8K]  Cluster_8988.ps.pdf
        ├── [ 52K]  Cluster_8988.txt
        ├── [ 10K]  Cluster_9149.ps.pdf
        ├── [3.2K]  Cluster_9149.txt
        ├── [9.4K]  Cluster_9983.ps.pdf
        └── [ 11K]  Cluster_9983.txt

3 directories, 114 files

5.3 Visualize

We noticed that a) not all of the identified miRNAs have database matches, and b) some reads have a match in the database but are not classified as miRNAs. Let’s look at this in more depth.

Peve_shortstack_results <- read.csv("../output/05-Peve-sRNA-ShortStack_4.1.0/ShortStack_out/Results.txt", sep="\t")

# Reads identified as miRNAs (but not necessarily known)
Peve_shortstack_results %>% 
  filter(MIRNA == "Y") %>%
  mutate(known_miRNAs = str_sub(known_miRNAs, 1, 40)) %>%
  mutate(Locus = str_sub(Locus, 20, 40)) %>%
  ggplot(aes(x = reorder(Locus, Reads), y = Reads, fill = known_miRNAs)) +
  geom_bar(stat = "identity") +
   geom_text(aes(label = Reads), vjust = 0.5, hjust = 0, color = "black", size = 2.5, angle = 90) +
  labs(x = "miRNA", y = "Read count", 
       title = "Reads identified by ShortStack as miRNAs",
       fill = "Annotation") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank())

ggsave("../output/05-Peve-sRNA-ShortStack_4.1.0/figures/Peve_ShortStack_miRNA_histogram.png", width = 12, height = 7, units = "in")


# Reads matched in the reference db (but not necessarily identified as miRNA)
Peve_shortstack_results %>% 
  filter(!is.na(known_miRNAs)) %>%
  mutate(known_miRNAs = str_sub(known_miRNAs, 1, 40)) %>%
  mutate(Locus = str_sub(Locus, 20, 40)) %>%
  ggplot(aes(x = reorder(Locus, Reads), y = Reads, fill = MIRNA)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = Reads), vjust = 0.5, hjust = 0, color = "black", size = 2.5, angle = 90) +
  labs(x = "miRNA", y = "Read count", 
       title = "Reads with miRBase+cnidarian database matches",
       fill = "Identified as miRNA?") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank())

ggsave("../output/05-Peve-sRNA-ShortStack_4.1.0/figures/Peve_ShortStack_dbmatch_histogram.png", width = 12, height = 7, units = "in")

There’s two miRNAs with very high read counts, and it’s making visualization of the rest difficult. Let’s remove them and retry visualizing the rest.

# Reads identified as miRNAs (but not necessarily known)
Peve_shortstack_results %>% 
  filter(MIRNA == "Y") %>%
  filter(Reads < 200000) %>%
  mutate(known_miRNAs = str_sub(known_miRNAs, 1, 40)) %>%
  mutate(Locus = str_sub(Locus, 20, 40)) %>%
  ggplot(aes(x = reorder(Locus, Reads), y = Reads, fill = known_miRNAs)) +
  geom_bar(stat = "identity") +
   geom_text(aes(label = Reads), vjust = 0.5, hjust = 0, color = "black", size = 2.5, angle = 90) +
  labs(x = "miRNA", y = "Read count", 
       title = "Reads identified by ShortStack as miRNAs",
       fill = "Annotation") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank())

ggsave("../output/05-Peve-sRNA-ShortStack_4.1.0/figures/Peve_ShortStack_miRNA_histogram_reduced.png", width = 12, height = 7, units = "in")


# Reads matched in the reference db (but not necessarily identified as miRNA)
Peve_shortstack_results %>% 
  filter(!is.na(known_miRNAs)) %>%
  filter(Reads < 200000) %>%
  mutate(known_miRNAs = str_sub(known_miRNAs, 1, 40)) %>%
  mutate(Locus = str_sub(Locus, 20, 40)) %>%
  ggplot(aes(x = reorder(Locus, Reads), y = Reads, fill = MIRNA)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = Reads), vjust = 0.5, hjust = 0, color = "black", size = 2.5, angle = 90) +
  labs(x = "miRNA", y = "Read count", 
       title = "Reads with miRBase+cnidarian database matches",
       fill = "Identified as miRNA?") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank())

ggsave("../output/05-Peve-sRNA-ShortStack_4.1.0/figures/Peve_ShortStack_dbmatch_histogram_reduced.png", width = 12, height = 7, units = "in")

# Make list
mirnas <- Peve_shortstack_results %>% filter(MIRNA == "Y") %>% pull(Locus)
matches <- Peve_shortstack_results %>% filter(!is.na(known_miRNAs)) %>% pull(Locus)

Peve_shortstack_vennlist <- list(
  "Identified as miRNA" = mirnas,
  "Database match" = matches
)

# Make venn diagrams
ggvenn(Peve_shortstack_vennlist)

ggsave("../output/05-Peve-sRNA-ShortStack_4.1.0/figures/Peve_ShortStack_venn.png", width = 12, height = 7, units = "in")

---

Citations

Axtell, Michael J. 2013. “ShortStack: Comprehensive Annotation and Quantification of Small RNA Genes.” RNA 19 (6): 740–51. https://doi.org/10.1261/rna.035279.112.

Johnson, Nathan R, Jonathan M Yeoh, Ceyda Coruh, and Michael J Axtell. 2016. “Improved Placement of Multi-Mapping Small RNAs.” G3 Genes|Genomes|Genetics 6 (7): 2103–11. https://doi.org/10.1534/g3.116.030452.

Shahid, Saima, and Michael J. Axtell. 2014. “Identification and Annotation of Small RNA Genes Using ShortStack.” Methods 67 (1): 20–27. https://doi.org/10.1016/j.ymeth.2013.10.004.