13.1.1-Apul-sRNAseq-ShortStack-R1-reads-cnidarian_miRBase

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 seen in 13.1-Apul-sRNAseq-ShortStack-R1-reads.Rmd, but this analysis uses a customized miRBase database, created by Jill Ashley, which includes published cnidarian miRNAs:

• cnidarian_miRNAs.fasta

The A.millepora genome will be used as the reference genome for A.pulchra, as A.pulchra does not currently have a sequenced genome and A.millepora had highest alignment rates for standard RNAseq data compared to other published genomes tested.

---

Inputs:

• Requires trimmed sRNAseq files generated by 08.1-Dapul-sRNAseq-trimming-R1-only.Rmd

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

• A.millepora genome FastA. See 12-Apul-sRNAseq-MirMachine.Rmd for download info if needed.

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-4.0.3_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-R1-31bp-auto_adapters-polyG.fq.gz'"

echo "# Data directories"
echo 'export deep_dive_dir=/home/shared/8TB_HDD_01/sam/gitrepos/deep-dive'
echo 'export deep_dive_data_dir="${deep_dive_dir}/DEF-cross-species/data"'
echo 'export output_dir_top=${deep_dive_dir}/D-Apul/output/13.1.1-Apul-sRNAseq-ShortStack-R1-reads-cnidarian_miRBase'
echo 'export trimmed_fastqs_dir="${deep_dive_dir}/D-Apul/output/08.1-Dapul-sRNAseq-trimming-R1-only/trimmed-reads"'
echo ""

echo "# Input/Output files"
echo 'export genome_fasta_dir=${deep_dive_dir}/D-Apul/data/Amil/ncbi_dataset/data/GCF_013753865.1'
echo 'export genome_fasta_name="GCF_013753865.1_Amil_v2.1_genomic.fna"'
echo 'export shortstack_genome_fasta_name="GCF_013753865.1_Amil_v2.1_genomic.fa"'

echo 'export mirbase_mature_fasta_version=cnidarian_miRNAs.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-R1-31bp-auto_adapters-polyG.fq.gz'
# Data directories
export deep_dive_dir=/home/shared/8TB_HDD_01/sam/gitrepos/deep-dive
export deep_dive_data_dir="${deep_dive_dir}/DEF-cross-species/data"
export output_dir_top=${deep_dive_dir}/D-Apul/output/13.1.1-Apul-sRNAseq-ShortStack-R1-reads-cnidarian_miRBase
export trimmed_fastqs_dir="${deep_dive_dir}/D-Apul/output/08.1-Dapul-sRNAseq-trimming-R1-only/trimmed-reads"

# Input/Output files
export genome_fasta_dir=${deep_dive_dir}/D-Apul/data/Amil/ncbi_dataset/data/GCF_013753865.1
export genome_fasta_name="GCF_013753865.1_Amil_v2.1_genomic.fna"
export shortstack_genome_fasta_name="GCF_013753865.1_Amil_v2.1_genomic.fa"
export mirbase_mature_fasta_version=cnidarian_miRNAs.fasta
export genome_fasta="${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)

# Check successful env loading
py_config()

python:         /home/sam/programs/mambaforge/envs/ShortStack-4.0.3_env/bin/python
libpython:      /home/sam/programs/mambaforge/envs/ShortStack-4.0.3_env/lib/libpython3.10.so
pythonhome:     /home/sam/programs/mambaforge/envs/ShortStack-4.0.3_env:/home/sam/programs/mambaforge/envs/ShortStack-4.0.3_env
version:        3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0]
numpy:          /home/sam/programs/mambaforge/envs/ShortStack-4.0.3_env/lib/python3.10/site-packages/numpy
numpy_version:  1.26.4

NOTE: Python version was forced by use_python() function

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_01/sam/gitrepos/deep-dive/D-Apul/data/Amil/ncbi_dataset/data/GCF_013753865.1/GCF_013753865.1_Amil_v2.1_genomic.fa

Already exists. Nothing to do.

-rw-r--r-- 1 sam sam 460M Nov  6 12:40 /home/shared/8TB_HDD_01/sam/gitrepos/deep-dive/D-Apul/data/Amil/ncbi_dataset/data/GCF_013753865.1/GCF_013753865.1_Amil_v2.1_genomic.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 ${deep_dive_data_dir}/${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: 53m45.903s

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 38 MIRNA loci


Non-MIRNA loci by DicerCall:
N 19182
22 38
23 31
21 10
24 5

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

Thu 15 Feb 2024 13:23:12 -0800 PST
Run Completed!

real    53m45.903s
user    954m40.397s
sys 305m46.174s

ShortStack identified 38 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
NC_058066.1:152483-152906   Cluster_1   NC_058066.1 152483  152906  424 142 34  0.035211267605633804    -   UAAGUACUUUAUCAACUAACUCUAGGCA    71  2   127 0   3   0   10  N   N   NA
NC_058066.1:161064-161674   Cluster_2   NC_058066.1 161064  161674  611 500 218 0.246   .   UUUUAGCCUAGUGCGGGUUUCCAGACGU    42  26  439 13  1   3   18  N   N   NA
NC_058066.1:203241-203651   Cluster_3   NC_058066.1 203241  203651  411 109 48  0.6055045871559633  .   UUCUGACUCUAUUAGCAACGAAGACUUU    26  1   106 0   0   2   0   N   N   NA
NC_058066.1:204533-205150   Cluster_4   NC_058066.1 204533  205150  618 316 160 0.7911392405063291  .   UCCCAACACGUCUAGACUGUACAAUUUCU   30  1   304 1   1   3   6   N   N   NA
NC_058066.1:205739-206966   Cluster_5   NC_058066.1 205739  206966  1228    2006    415 0.3369890329012961  .   CAAAAGAGCGGACAAAAUAGUCGACAGAUU  787 7   1953    11  6   15  14  N   N   NA
NC_058066.1:210855-211344   Cluster_6   NC_058066.1 210855  211344  490 1207    332 0.7514498757249378  .   UAAUACUUGUAGUGAAGGUUCAAUCUCGA   99  12  1088    6   6   19  76  N   N   NA
NC_058066.1:349655-351297   Cluster_7   NC_058066.1 349655  351297  1643    3370    1222    0.8124629080118695  +   UCAGCUUGGAAAUGACAGCUUUUGACGU    281 50  3210    13  20  21  56  N   N   NA
NC_058066.1:351491-353439   Cluster_8   NC_058066.1 351491  353439  1949    9099    1743    0.41938674579624136 .   UUUCAAAUCAAAGAUCUUCGCAACGAUGA   786 111 8640    25  45  132 146 N   N   NA
NC_058066.1:368012-368427   Cluster_9   NC_058066.1 368012  368427  416 209 9   0.0 -   AAAGGAUUCUAAUAAU    132 209 0   0   0   0   0   N   N   NA

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

Nummber of potential loci:
19304

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:
38

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

Number of loci _not_ characterized as miRNA:
19266

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:
39

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

Number of loci _not_ matching miRBase miRNAs:
19266

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_01/sam/gitrepos/deep-dive/D-Apul/output/13.1.1-Apul-sRNAseq-ShortStack-R1-reads-cnidarian_miRBase/
├── [ 22K]  shortstack.log
└── [ 36K]  ShortStack_out
    ├── [ 31K]  alignment_details.tsv
    ├── [1.1M]  Counts.txt
    ├── [ 20K]  known_miRNAs.gff3
    ├── [ 35K]  known_miRNAs_unaligned.fasta
    ├── [5.3M]  merged_alignments_21_m.bw
    ├── [5.7M]  merged_alignments_21_p.bw
    ├── [5.0M]  merged_alignments_22_m.bw
    ├── [5.4M]  merged_alignments_22_p.bw
    ├── [ 10M]  merged_alignments_23-24_m.bw
    ├── [ 11M]  merged_alignments_23-24_p.bw
    ├── [1.4G]  merged_alignments.bam
    ├── [222K]  merged_alignments.bam.csi
    ├── [ 72M]  merged_alignments_other_m.bw
    ├── [ 75M]  merged_alignments_other_p.bw
    ├── [ 48M]  merged_alignments_sRNA-ACR-140-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bw
    ├── [ 54M]  merged_alignments_sRNA-ACR-145-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bw
    ├── [ 51M]  merged_alignments_sRNA-ACR-150-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bw
    ├── [ 44M]  merged_alignments_sRNA-ACR-173-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bw
    ├── [ 45M]  merged_alignments_sRNA-ACR-178-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bw
    ├── [ 11K]  mir.fasta
    ├── [1.9M]  Results.gff3
    ├── [2.9M]  Results.txt
    ├── [261M]  sRNA-ACR-140-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam
    ├── [227K]  sRNA-ACR-140-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam.csi
    ├── [299M]  sRNA-ACR-145-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam
    ├── [227K]  sRNA-ACR-145-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam.csi
    ├── [311M]  sRNA-ACR-150-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam
    ├── [230K]  sRNA-ACR-150-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam.csi
    ├── [275M]  sRNA-ACR-173-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam
    ├── [228K]  sRNA-ACR-173-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam.csi
    ├── [248M]  sRNA-ACR-178-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam
    ├── [230K]  sRNA-ACR-178-S1-TP2_R1_001.fastp-R1-31bp-auto_adapters-polyG.bam.csi
    └── [4.0K]  strucVis
        ├── [ 12K]  Cluster_10352.ps
        ├── [ 34K]  Cluster_10352.txt
        ├── [ 11K]  Cluster_10499.ps
        ├── [5.9K]  Cluster_10499.txt
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        ├── [ 11K]  Cluster_1560.ps
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        ├── [ 12K]  Cluster_1950.ps
        ├── [9.8K]  Cluster_1950.txt
        ├── [ 12K]  Cluster_2059.ps
        ├── [ 36K]  Cluster_2059.txt
        ├── [ 12K]  Cluster_2119.ps
        ├── [8.9K]  Cluster_2119.txt
        ├── [ 12K]  Cluster_2137.ps
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        ├── [ 12K]  Cluster_2613.ps
        ├── [ 45K]  Cluster_2613.txt
        ├── [ 11K]  Cluster_2614.ps
        ├── [ 31K]  Cluster_2614.txt
        ├── [ 12K]  Cluster_2645.ps
        ├── [ 25K]  Cluster_2645.txt
        ├── [ 12K]  Cluster_3133.ps
        ├── [5.2K]  Cluster_3133.txt
        ├── [ 12K]  Cluster_3207.ps
        ├── [7.5K]  Cluster_3207.txt
        ├── [ 12K]  Cluster_3210.ps
        ├── [3.8K]  Cluster_3210.txt
        ├── [ 11K]  Cluster_326.ps
        ├── [ 20K]  Cluster_326.txt
        ├── [ 12K]  Cluster_3814.ps
        ├── [ 18K]  Cluster_3814.txt
        ├── [ 12K]  Cluster_4132.ps
        ├── [ 21K]  Cluster_4132.txt
        ├── [ 12K]  Cluster_4411.ps
        ├── [ 31K]  Cluster_4411.txt
        ├── [ 11K]  Cluster_5044.ps
        ├── [ 16K]  Cluster_5044.txt
        ├── [ 11K]  Cluster_5188.ps
        ├── [1.1K]  Cluster_5188.txt
        ├── [ 11K]  Cluster_535.ps
        ├── [3.8K]  Cluster_535.txt
        ├── [ 12K]  Cluster_5436.ps
        ├── [ 65K]  Cluster_5436.txt
        ├── [ 12K]  Cluster_571.ps
        ├── [ 56K]  Cluster_571.txt
        ├── [ 12K]  Cluster_6590.ps
        ├── [ 22K]  Cluster_6590.txt
        ├── [ 11K]  Cluster_6661.ps
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        ├── [ 12K]  Cluster_7188.ps
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        ├── [ 11K]  Cluster_8739.ps
        ├── [4.7K]  Cluster_8739.txt
        ├── [ 12K]  Cluster_8810.ps
        ├── [ 30K]  Cluster_8810.txt
        ├── [ 12K]  Cluster_9747.ps
        └── [ 31K]  Cluster_9747.txt

2 directories, 109 files

---

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.