Using RNA-Seq data to improve microRNA target prediction accuracy in animals

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FilTar is a tool to integrate RNA-Seq data to pre-existing miRNA target prediction workflows in order to increase prediction accuracy.

It achieves this by:

  1. Removing transcripts which are not expressed or poorly expressed for a given cell type or tissue

  2. Generating 3'UTR annotations specific to a given cell type or tissue

It also operates as a fully functional wrapper around the pre-existing TargetScan7 and miRanda target prediction workflows.

Installation

Instructions on how to install FilTar can be found at the following location: https://tbradley27.github.io/FilTar/

Basic Usage

FilTar can be used by following 2 steps:

  1. Specify the options you would like to use to run FilTar by editing config/basic.yaml .

  2. Run the following command: 

snakemake --use-conda --cores $N target_predictions.txt

After running the command, all target predictions are contained inside target_predictions.txt .

The following video presents a concise demonstration of basic FilTar usage:

https://www.youtube.com/watch?v=Xhl-nsg7_xo

More detailed instructions can be found inside the full documentation: https://tbradley27.github.io/FilTar/

Publication

The article describing FilTar can be found in Volume 36, Issue 8 (pages 2410-2416) of the Bioinformatics journal published by Oxford University Press. An online, open access version of the article is available here .

DOI: 10.1093/bioinformatics/btaa007

PMCID: PMC7178423

PMID: 31930382

The default method of citing the article is to use the following:

Thomas Bradley, Simon Moxon, FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals, Bioinformatics, Volume 36, Issue 8, 15 April 2020, Pages 2410–2416, https://doi.org/10.1093/bioinformatics/btaa007

Getting Help

In order to ensure your enquiries are seen by the most people possible who may be sharing your problem, it is best to share the problems that you are having publicly. The first port of call is to post questions on the biostars bioinformatics online forum (https://www.biostars.org/). If using biostars, please make sure to use the 'filtar' tag when asking questions to notify me, so I can answer promptly.

If this option doesn't work for whatever reason, I happen to accept correspondence via my academic email address: [email protected]

Reporting bugs, suggested enhancements or any other issues

The issues page of this repository is the best place to post this.

Contributions and Acknowledgements

Simox Moxon came up with the original idea and project proposal for FilTar. The FilTar concept was extended and developed further between Simon Moxon and Thomas Bradley through the course of the latter's BBSRC (Biotechnology and Biological Sciences Research Council) PhD Studentship on the Norwich Research Park (NRP) Bioscience Doctoral Training Partnership (DTP) programme, when Thomas Bradley worked under the primary supervision of Simon Moxon initially predominantly at the Earlham Institute, and then later predominantly at the School of Biological Sciences, University of East Anglia.

Full acknowledgements can be found within the preprinted article associated with FilTar.

Code Snippets

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__author__ = "Johannes Köster"
__copyright__ = "Copyright 2016, Johannes Köster"
__email__ = "[email protected]"
__license__ = "MIT"


import os
from snakemake.shell import shell


prefix = os.path.splitext(snakemake.output[0])[0]

shell(
    "samtools sort {snakemake.params} -@ {snakemake.threads} -o {snakemake.output[0]} "
    "-T {prefix} {snakemake.input[0]}")
Python Snakemake SAMtools From line 1 of sort/wrapper.py 
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import sys
import os

if 'single_end' in snakemake.output[0]:
	os.system("wget -nv --directory-prefix=data/single_end/ ftp://ftp.sra.ebi.ac.uk/vol1/fastq/{}/00{}/{}/{}.fastq.gz || wget -nv --directory-prefix=data/single_end/ ftp://ftp.sra.ebi.ac.uk/vol1/fastq/{}/{}/{}.fastq.gz".format(snakemake.wildcards['accession'][0:6],snakemake.wildcards['accession'][-1],snakemake.wildcards['accession'],snakemake.wildcards['accession'],snakemake.wildcards['accession'][0:6],snakemake.wildcards['accession'],snakemake.wildcards['accession']))
elif 'paired_end' in snakemake.output[0]:
	os.system("wget -nv --directory-prefix=data/paired_end/ ftp://ftp.sra.ebi.ac.uk/vol1/fastq/{}/00{}/{}/{}_{}.fastq.gz || wget -nv --directory-prefix=data/paired_end/ ftp://ftp.sra.ebi.ac.uk/vol1/fastq/{}/{}/{}_{}.fastq.gz".format(snakemake.wildcards['accession'][0:6],snakemake.wildcards['accession'][-1],snakemake.wildcards['accession'],snakemake.wildcards['accession'],snakemake.wildcards['mate_number'],snakemake.wildcards['accession'][0:6],snakemake.wildcards['accession'],snakemake.wildcards['accession'],snakemake.wildcards['mate_number']))
Python From line 17 of ENA/download_fastq.py 
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script: "download_fastq.py"
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script: "download_fastq.py"
SnakeMake From line 30 of ENA/Snakefile 
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shell: "rsync -av rsync://ftp.ensembl.org/ensembl/pub/release-{config[ensembl_release]}/gtf/{params}/{wildcards.genus_species}.{wildcards.build}.{config[ensembl_release]}.chr.gtf.gz data/ && gunzip data/{wildcards.genus_species}.{wildcards.build}.{config[ensembl_release]}.chr.gtf.gz && sed 's/^chr//g' data/{wildcards.genus_species}.{wildcards.build}.{config[ensembl_release]}.chr.gtf > tmp && mv tmp data/{wildcards.genus_species}.{wildcards.build}.{config[ensembl_release]}.chr.gtf"
SnakeMake From line 33 of data_download/Snakefile 
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        shell: "sed -r 's/^MT//g' {input} | sed '/^\t/d'  > {output}" # delete mitochondiral records and records not assigned to a chromosome

rule get_transcript_ids:
        input:
                gtf=get_gtf_file
        output: "results/bed/{species}_chr{chrom}_all_transcripts.txt"
SnakeMake From line 38 of data_download/Snakefile 
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shell: "scripts/get_all_transcripts.sh {input} {wildcards.chrom} > {output}"
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shell: "rsync -av rsync://ftp.ensembl.org/ensembl/pub/release-{config[ensembl_release]}/fasta/{params}/dna/{wildcards.genus_species}.{wildcards.build}.dna.toplevel.fa.gz data/"
SnakeMake From line 51 of data_download/Snakefile 
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shell: "rsync -av rsync://ftp.ensembl.org/ensembl/pub/release-{config[ensembl_release]}/fasta/{params}/dna/{wildcards.genus_species}.{wildcards.build}.dna.primary_assembly.fa.gz data/"
SnakeMake From line 56 of data_download/Snakefile 
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shell: "rsync -av rsync://ftp.ensembl.org/ensembl/pub/release-{config[ensembl_release]}/fasta/{params}/dna/{wildcards.genus_species}.{wildcards.build}.dna.chromosome.{wildcards.chrom}.fa.gz data/"
SnakeMake From line 62 of data_download/Snakefile 
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shell: "pigz -p {threads} -d {input}"
SnakeMake From line 69 of data_download/Snakefile 
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shell:
   "wget --directory-prefix=data/maf_hsa/ -nv http://hgdownload.soe.ucsc.edu/goldenPath/hg38/multiz100way/maf/chr{wildcards.chrom}.maf.gz && gunzip {output}.gz"
SnakeMake From line 75 of data_download/Snakefile 
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shell:
   "wget --directory-prefix=data/maf_mmu/ -nv http://hgdownload.cse.ucsc.edu/goldenPath/mm10/multiz60way/maf/chr{wildcards.chrom}.maf.gz && gunzip {output}.gz"
SnakeMake From line 82 of data_download/Snakefile 
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shell: "rsync -av rsync://ftp.ensembl.org/ensembl/pub/release-{config[ensembl_release]}/fasta/{params}/cdna/{wildcards.genus_species}.{wildcards.build}.cdna.all.fa.gz data"
SnakeMake From line 88 of data_download/Snakefile 
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shell: "gunzip {input}"
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shell: 'wget https://sourceforge.net/projects/apatrap/files/APAtrap_Linux.zip/download && mv download scripts && unzip -d scripts/ scripts/download'
SnakeMake From line 97 of data_download/Snakefile 
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shell: "fasterq-dump -O data/single_end/ --threads {threads} {wildcards.accession}"
SnakeMake From line 24 of SRAtoolkit/Snakefile 
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shell: "fasterq-dump -O data/paired_end/ --threads {threads} {wildcards.accession}"
SnakeMake From line 31 of SRAtoolkit/Snakefile 
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shell: "gzip {input}"
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shell: "gzip {input.file1} {input.file2}"
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library(tidyverse)

# load in target predictions

target_predictions = readr::read_tsv(snakemake@input[['targets']])

utr_genomic_positions = readr::read_tsv(snakemake@input[['bed_file']], col_names=c('chromosome','start','stop','strand','transcript'), 
	col_types='ciicc')

utr_genomic_positions = utr_genomic_positions[!duplicated(utr_genomic_positions$transcript),]

print(target_predictions)
print(utr_genomic_positions)

target_predictions = dplyr::inner_join(target_predictions, utr_genomic_positions, by=c(`Gene ID`='transcript'))
target_predictions$genomic_start = target_predictions$start + target_predictions$`UTR start`
target_predictions$genomic_end = target_predictions$start + target_predictions$`UTR end`

print(target_predictions %>% select(`Gene ID`,`Mirbase ID`,`UTR start`,`UTR end`,start,stop,genomic_start,genomic_end), width=Inf)

write.table(x=target_predictions, file=snakemake@output[[1]], sep='\t', quote=FALSE, col.names=TRUE, row.names=FALSE)
R tidyverse From line 3 of get_target_coordinates/get_target_genomic_coordinates.R 
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script: 'get_target_genomic_coordinates.R'
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import sys
import functools
from functools import reduce
import re
import os
from Bio import AlignIO
from Bio.AlignIO import MafIO
from subprocess import call

def add_alignment():
           global start_pos
           global end_pos
           if strand == -1:
               start_pos = start_pos[::-1] # Reverse Order
               end_pos = end_pos[::-1]
           else:
               pass

           new_multiple_alignment = idx.get_spliced(start_pos, end_pos, strand) # splice through the index
           for i in range(len(new_multiple_alignment)):
              new_multiple_alignment[i].id = re.sub('\..*','', new_multiple_alignment[i].id) # strip chomosome information from id
              new_multiple_alignment[i].id = accession + '\t' + new_multiple_alignment[i].id # label all alignments with relevant transcript accession

           global big_alignment   
           big_alignment += new_multiple_alignment.format('fasta')
           return()

if os.stat(snakemake.input['bed']).st_size == 0:
     target = open(snakemake.output[0], 'w')
else:
    if snakemake.wildcards['species'] == "hsa":    #Identify the species identifier passed through the command line
       build = "hg38"
    elif snakemake.wildcards['species'] == "mmu":
       build = "mm10"
    else:
       build = ''

    idx = AlignIO.MafIO.MafIndex(snakemake.input['maf_index'], snakemake.input['maf'], "{}.chr{}".format(build, snakemake.wildcards['chrom'])  )

    start_pos = []
    end_pos = []
    accession = 'empty'
    with open(snakemake.input['bed'] ) as f:
       big_alignment = ''
       for line in f:    #Open and loop through line-by-line the relevant BED file
          parts = line.split()  

          if accession == 'empty':
             accession = parts[4]   
             start_pos.append(int(parts[1]))
             end_pos.append(int(parts[2]))
             strand = (int(parts[3]))

          elif parts[4] == accession:       # If accession has multiple entries in bed, add additional genome co-ordinates to rel. lists
             start_pos.append(int(parts[1]))
             end_pos.append(int(parts[2]))
             strand = (int(parts[3]))

          else:
             add_alignment()

             start_pos = [] # initialise a new transcript record
             end_pos = []
             start_pos.append(int(parts[1]))
             end_pos.append(int(parts[2]))
             strand = (int(parts[3]))
             accession = parts[4]
       else:

         add_alignment()     

       result = reduce(lambda x, y: x.replace(y, snakemake.config["TaxID"][y]), snakemake.config["TaxID"], big_alignment) # get NCBI taxonomic IDs
       result = result.replace('\n','') # convert from fasta to tsv
       result = result.replace('>','\n') # convert from fasta to tsv
       result = re.sub('(\s[0-9]{4,7})',r'\1\t',result) # convert from fasta to tsv
       result = re.sub('\n','',result, count=1) # remove leading empty line

       target = open(snakemake.output[0], 'w')

       for line in iter(result.splitlines()):
          pattern = re.compile('\.[0-9][0-9]?\sN+')
          pattern2 = re.compile('T[0-9]+\sN+')
          pattern3 = re.compile('^N+$') # when ref transcript is unknown - it leaves a trailing lines of Ns which need to be removed
          if 'delete' in line:
              pass
          elif 'unknown' in line:
              pass
          elif pattern.search(line):
              pass
          elif pattern2.search(line):
              pass
          elif pattern3.search(line):
              pass
          else:
              line2 = line + '\n'
              target.write(line2)
Python Biopython From line 19 of with_conservation/biopython_maf_processing2.py 
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import sys
import re
from Bio import AlignIO
from Bio.AlignIO import MafIO

if snakemake.wildcards['species'] == "hsa":
       build = "hg38" 
elif snakemake.wildcards['species'] == "mmu":
	build = "mm10"
else:
	build = ''

idx = AlignIO.MafIO.MafIndex(snakemake.output[0],  snakemake.input[0], "{}.chr{}".format(build, snakemake.wildcards['chrom'])  )
Python Biopython From line 3 of with_conservation/biopython_maf_processing.py 
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script:
   "biopython_maf_processing.py"
SnakeMake From line 23 of with_conservation/Snakefile 
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script: 
   "biopython_maf_processing2.py"
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if ( file.info(snakemake@input[[1]])$size == 0 ) {
        file.copy(from=snakemake@input[[1]], to=snakemake@output[[1]])
} else {
	output = filtar::MergeFasta(snakemake@input[[1]])
	write.table(output, snakemake@output[[1]], quote=FALSE, sep="\t", col.names=FALSE, row.names=FALSE)
}
R From line 3 of without_conservation/merge_fasta.R 
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shell: "scripts/get_bedtools_bed.sh {input} {output}"
SnakeMake From line 29 of without_conservation/Snakefile 
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shell: "bedtools getfasta -name -s -fi {input.dna} -bed {input.bed} -fo {output}"
SnakeMake BEDTools From line 37 of without_conservation/Snakefile 
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script: "merge_fasta.R"
SnakeMake From line 42 of without_conservation/Snakefile 
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shell: "scripts/convert_fa_to_tsv2.sh {input} {params} {output}"
SnakeMake From line 48 of without_conservation/Snakefile 
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from Bio import SeqIO

records = list(SeqIO.parse(snakemake.input[0],'fasta'))
filtered_records = []

for record in records:
	if record.id in snakemake.config['mirnas']:
		filtered_records.append(record)
	elif len(snakemake.config['mirnas']) == 0: # use all records if mirna config entry is empty
		filtered_records.append(record)
	else:
		pass

SeqIO.write(filtered_records,snakemake.output[0],'fasta')
Python Biopython From line 17 of mirna/filter_mirna_fasta.py 
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shell: "grep -A 1 {wildcards.species} {input} | awk '{{ print $1 }}' | sed 's/--//g' | sed '/^$/d' > {output}"
SnakeMake From line 26 of mirna/Snakefile 
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script: "filter_mirna_fasta.py"
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united_quant = readr::read_tsv(
        file=snakemake@input[[1]],
        col_types=readr::cols(.default = 'd', Name = 'c')
)

united_quant2 = filtar::AvgSalmonQuant(united_quant)

avg_quant = united_quant2[,c('Name','avg')]
colnames(avg_quant) = c('Name','TPM')

write.table(
        x=avg_quant,
        file=snakemake@output[[1]],
        row.names=FALSE,
        col.names=TRUE,
        quote=FALSE,
        sep="\t"
)
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united_quant = readr::read_tsv(
        file=snakemake@input[[1]],
        col_types=readr::cols(.default = 'd', Name = 'c')
)

united_quant2 = filtar::AvgSalmonQuant(united_quant)

avg_quant = tibble::tibble(Name=united_quant2$Name, Length=20, EffectiveLength=20.00, TPM=united_quant2$avg, NumReads=20.00)

real_output = paste(snakemake@output[[1]],'quant.sf',sep="/")
dir.create(snakemake@output[[1]])

write.table(
        x=avg_quant,
        file=real_output,
        row.names=FALSE,
        col.names=TRUE,
        quote=FALSE,
        sep="\t"
)
R From line 17 of salmon/get_average_quant.R 
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import sys
import os

if len(snakemake.input) == 2:
        os.system('salmon quant --validateMappings --rangeFactorizationBins 4 --seqBias --posBias -p {} -i {} -l A -r {} -o {}'.format(snakemake.threads,snakemake.input['index'],snakemake.input['reads'][0],snakemake.output))
else:
        os.system('salmon quant --validateMappings --rangeFactorizationBins 4 --seqBias --posBias -p {} -i {} -l A -1 {} -2 {} -o {}'.format(snakemake.threads,snakemake.input['index'],snakemake.input['reads'][0],snakemake.input['reads'][1],snakemake.output))
Python Quant Salmon From line 17 of salmon/quant_salmon.py 
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shell:
    "salmon index --threads {threads} -t {input} -i {output} --type quasi -k 31"
SnakeMake Salmon From line 30 of salmon/Snakefile 
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shell:
    "salmon index --threads {threads} -t {input} -i {output} --type quasi -k 31"
SnakeMake Salmon From line 40 of salmon/Snakefile 
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script:
	"quant_salmon.py"
SnakeMake From line 66 of salmon/Snakefile 
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script:
	"quant_salmon.py"
SnakeMake From line 79 of salmon/Snakefile 
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shell: "grep 'expected' {input}/lib_format_counts.json | awk '{{print $2}}' | sed 's/\"//g' | sed 's/,//g' > {output}"
SnakeMake From line 87 of salmon/Snakefile 
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shell: "salmon quantmerge --quants {input} --names {input} -o {output}"
SnakeMake Salmon From line 101 of salmon/Snakefile 
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script: "get_average_quant.R"
SnakeMake From line 108 of salmon/Snakefile 
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shell: "salmon quantmerge --quants {input} --names {input} -o {output}"
SnakeMake Salmon From line 122 of salmon/Snakefile 
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script: "get_average_quant2.R"
SnakeMake From line 127 of salmon/Snakefile 
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data = readr::read_tsv(snakemake@input[[1]], col_names=FALSE)
data = tidyr::separate(data, X5, into=c('X5.1','X5.2'), remove=TRUE)
data = tidyr::separate(data, X6, into=c('X6.1','X6.2'), remove=TRUE)

colnames(data) = c('miRNA_ID','transcript_ID','score','energy(kCal/Mol)','miRNA_start','miRNA_end','3UTR_start','3UTR_end','alignment_length','percent_matches', 'percent_matches_and_wobbles')

write.table(
	x=data,
	file=snakemake@output[[1]],
	quote=FALSE,
	row.names=FALSE,
	col.names=TRUE,
	sep="\t"
)
R From line 1 of miRanda/add_miRanda_header.R 
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shell: "sed 's/(+)//g' {input} | sed 's/(-)//g' > {output}"
SnakeMake From line 34 of miRanda/Snakefile 
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shell: "miranda {input.mirna} {input.utr} {params} -sc {config[miRanda.minimum_alignment_score]} -en {config[miRanda.minimum_energy_score]} -scale {config[miRanda.5_prime_3_prime_scaling_factor]} -go {config[miRanda.alignment_gap_open_penalty]} -ge {config[miRanda.alignment_gap_extension_penalty]} > {output}"
SnakeMake miranda From line 43 of miRanda/Snakefile 
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shell: "scripts/convert_miRanda_to_tsv.sh {input} {output}"
SnakeMake From line 48 of miRanda/Snakefile 
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shell: "cat {input} > {output}"
SnakeMake From line 55 of miRanda/Snakefile 
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script: "add_miRanda_header.R"
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if ( file.info(snakemake@input[['miRanda_scores']])$size == 0  ) {
	file.create(snakemake@output[[1]])	
} else {

	filtered_miRanda_scores = filtar::filter_miRanda_scores(snakemake@input[['miRanda_scores']], snakemake@input[['expression_values']],snakemake@params[['tpm_expression_threshold']])
	write.table(filtered_miRanda_scores, snakemake@output[[1]], row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
}
R From line 1 of without_reannotation/filter_miRanda_scores.R 
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script: 'filter_miRanda_scores.R'
SnakeMake From line 8 of without_reannotation/Snakefile 
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script: 'filter_miRanda_scores.R'
SnakeMake From line 18 of without_reannotation/Snakefile 
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shell: 'cp {input} {output}'
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if ( file.info(snakemake@input[['miRanda_scores']])$size == 0  ) {
	file.create(snakemake@output[[1]])	
} else {

	filtered_miRanda_scores = filtar::filter_miRanda_scores(snakemake@input[['miRanda_scores']], snakemake@input[['expression_values']],snakemake@params[['tpm_expression_threshold']])
	write.table(filtered_miRanda_scores, snakemake@output[[1]], row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
}
R From line 1 of with_reannotation/filter_miRanda_scores.R 
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script: 'filter_miRanda_scores.R'
SnakeMake From line 8 of with_reannotation/Snakefile 
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script: 'filter_miRanda_scores.R'
SnakeMake From line 19 of with_reannotation/Snakefile 
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filtar::filter_mir_families(snakemake@input[['mature_mirnas']],snakemake@input[['mirna_families']], snakemake@config[["mirnas"]], snakemake@output[[1]])
R From line 1 of targetscan/filter_mir_families.R 
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filtar::filter_mature_mirs(snakemake@input[[1]], snakemake@config[["mirnas"]], snakemake@output[[1]])
R From line 1 of targetscan/filter_mir_for_context_scores.R 
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species = snakemake@wildcards$species

test = filtar::get_mirna_context(snakemake@input$mirna_seed, snakemake@input$mature_mirnas, snakemake@config$tax_ids[[species]])

readr::write_tsv(test, snakemake@output[[1]], col_names=FALSE)
R From line 1 of targetscan/get_mirna_context.R 
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species = snakemake@wildcards$species

mirna_seeds = filtar::get_mirna_family(snakemake@input[[1]], snakemake@wildcards$species, snakemake@config$tax_ids[[species]])

write.table(mirna_seeds, snakemake@output[[1]], sep="\t", quote=FALSE, col.names=FALSE, row.names=FALSE)
R From line 1 of targetscan/get_mirna_family.R 
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shell:
   "{input.script} {input.data} > {output}"
SnakeMake From line 28 of targetscan/Snakefile 
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script:
   "get_mirna_family.R"
SnakeMake From line 36 of targetscan/Snakefile 
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shell:
   "{input.script} {input.data} {wildcards.species} {params} > {output}"
SnakeMake From line 45 of targetscan/Snakefile 
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script:
   "get_mirna_context.R"
SnakeMake From line 54 of targetscan/Snakefile 
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script: "filter_mir_for_context_scores.R"
SnakeMake From line 60 of targetscan/Snakefile 
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script: "filter_mir_families.R"
SnakeMake From line 67 of targetscan/Snakefile 
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	shell:
           "{input.script} {input.mirna_families} {input.msa} {output}"
SnakeMake From line 78 of targetscan/Snakefile 
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shell:
    "{input.script} {input.msa_3UTR} {params.tax_id}  > {output}"
SnakeMake From line 89 of targetscan/Snakefile 
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shell:
    "{input.script} {input.mirna_family} {input.mirna_sites} {input.branch_lengths} > {output}"
SnakeMake From line 102 of targetscan/Snakefile 
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shell:
    "{input.script} {input.mirna_seeds} {input.CDS} > {output.eightmer_counts}"
SnakeMake From line 113 of targetscan/Snakefile 
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shell:
    "{input.script} {input.mirnas} {input.msa} {input.PCTs} {input.CDS_lengths} {input.eightmer_counts} {output} {params.tax_id} {input.AIRs} {params.RNAplfold_dir}"
SnakeMake From line 134 of targetscan/Snakefile 
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shell: "cat {input} | sed '1b;/Gene/d' > {output}"
SnakeMake From line 140 of targetscan/Snakefile 
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shell: "cat {input} | sed '1b;/Gene/d' > {output}"
SnakeMake From line 145 of targetscan/Snakefile 
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shell: "awk '{{ print $5}}' {input} | sort | uniq > {output}"
SnakeMake From line 152 of targetscan/Snakefile 
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       shell:
           "cd scripts/targetscan7 && wget http://www.targetscan.org/vert_72/vert_72_data_download/targetscan_70.zip && unzip targetscan_70.zip && rm UTR_Sequences_sample.txt miR_Family_info_sample.txt\
targetscan_70_output.txt README_70.txt targetscan_70.zip"
SnakeMake From line 158 of targetscan/Snakefile 
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      shell:
          "wget http://www.targetscan.org/vert_72/vert_72_data_download/targetscan_70_BL_PCT.zip && unzip targetscan_70_BL_PCT.zip && mv\
TargetScan7_BL_PCT/PCT_parameters data/ && mv TargetScan7_BL_PCT/targetscan_70_BL_bins.pl TargetScan7_BL_PCT/targetscan_70_BL_PCT.pl scripts/targetscan7 && rm -rf targetscan_70_BL_PCT.zip TargetScan7_BL_PCT/"
SnakeMake From line 168 of targetscan/Snakefile 
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shell:
  "wget http://www.targetscan.org/vert_72/vert_72_data_download/TargetScan7_context_scores.zip && unzip TargetScan7_context_scores.zip && mv TargetScan7_context_scores/Agarwal_2015_parameters.txt TargetScan7_context_scores/TA_SPS_by_seed_region.txt TargetScan7_context_scores/targetscan_count_8mers.pl TargetScan7_context_scores/targetscan_70_context_scores.pl scripts/targetscan7/ && rm -rf TargetScan7_context_scores/ TargetScan7_context_scores.zip"
SnakeMake From line 179 of targetscan/Snakefile 
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shell: "sed -e '40 s/9606/$ARGV[1]/g' {input} | sed -e '38 s;PCT_parameters;data\/PCT_parameters;g' > {output} && chmod +x {output}"
SnakeMake oschmod From line 185 of targetscan/Snakefile 
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shell: "sed -e '55 s;PCT_parameters;data\/PCT_parameters;g' {input} > {output} && chmod +x {output}"
SnakeMake oschmod From line 190 of targetscan/Snakefile 
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filtered_contextpp_scores = filtar::filter_contextpp_scores(snakemake@input[['contextpp_scores']], snakemake@input[['expression_values']], snakemake@params[['tpm_expression_threshold']])

write.table(filtered_contextpp_scores, snakemake@output[[1]], row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
R From line 1 of without_reannotation/filter_contextpp_scores.R 
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script: 'filter_contextpp_scores.R'
SnakeMake From line 8 of without_reannotation/Snakefile 
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script: 'filter_contextpp_scores.R'
SnakeMake From line 18 of without_reannotation/Snakefile 
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shell: "cp {input} {output}"
SnakeMake From line 24 of without_reannotation/Snakefile 
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filtered_contextpp_scores = filtar::filter_contextpp_scores(snakemake@input[['contextpp_scores']], snakemake@input[['expression_values']], snakemake@params[['tpm_expression_threshold']])

write.table(filtered_contextpp_scores, snakemake@output[[1]], row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
R From line 1 of with_reannotation/filter_contextpp_scores.R 
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script: 'filter_contextpp_scores.R'
SnakeMake From line 8 of with_reannotation/Snakefile 
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script: 'filter_contextpp_scores.R'
SnakeMake From line 17 of with_reannotation/Snakefile 
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shell:
   "trim_galore --output_dir results/trimmed_fastq/  --length {config[trim_galore.length]} --stringency {config[trim_galore.stringency]} {input}"
SnakeMake Trim_Galore From line 35 of trim_reads/Snakefile 
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shell:
   "trim_galore --output_dir results/trimmed_fastq/ --length  {config[trim_galore.length]} --stringency {config[trim_galore.stringency]}  --paired {input[0]} {input[1]}"
SnakeMake Trim_Galore From line 46 of trim_reads/Snakefile 
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input = readr::read_tsv(snakemake@input[[1]], col_names=FALSE, col_types='ciiic')

if (length(snakemake@config[['transcripts']]) == 0) {

	file.copy(from=snakemake@input[[1]],to=snakemake@output[[1]])

} else {

	for (transcript in snakemake@config[['transcripts']]) {
		if (!transcript %in% input$X5) {
			write(stringr::str_interp("The transcript identifer '${transcript}' is not a valid identifier for the selected species for ensembl release ${snakemake@config[['ensembl_release']]}"), stderr())
		}
	}

	filtered_input = input[input$X5 %in% snakemake@config[['transcripts']],]

	if (dim(filtered_input)[1] == 0) {
		write("No valid transcript identifiers in input. Halting execution.", stderr())
		quit(save="no", status=1)
	}

	write.table(filtered_input,snakemake@output[[1]], col.names=FALSE, row.names=FALSE, quote=FALSE, sep="\t")
}
R From line 17 of without_reannotation/filter_bed6.R 
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AIRs = filtar::get_canonical_AIRs(snakemake@input[[1]])

write.table(AIRs, snakemake@output[[1]], quote=FALSE, row.names=FALSE, col.names=FALSE, sep="\t")
R From line 1 of without_reannotation/get_canonical_AIR.R 
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utr_lengths = filtar::get_utr_lengths(snakemake@input[[1]]) 

write.table(
	x=utr_lengths,
	file=snakemake@output[[1]],
	sep="\t",
	quote=FALSE,
	row.names=FALSE,
	col.names=TRUE
)
R From line 1 of without_reannotation/get_utr_lengths.R 
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shell:
    "{input.script} {input.gtf} {wildcards.feature} {output}"
SnakeMake From line 34 of without_reannotation/Snakefile 
40
script: 'get_utr_lengths.R'
SnakeMake From line 40 of without_reannotation/Snakefile 
45
script: 'get_canonical_AIR.R'
SnakeMake From line 45 of without_reannotation/Snakefile 
50
script: 'get_canonical_AIR.R'
SnakeMake From line 50 of without_reannotation/Snakefile 
57
shell: 'grep -E "^{wildcards.chrom}\s" {input} > {output}'
SnakeMake From line 57 of without_reannotation/Snakefile 
62
script: 'filter_bed6.R'
SnakeMake From line 62 of without_reannotation/Snakefile 
67
shell: 'grep -E "^{wildcards.chrom}\s" {input} > {output} || true'
SnakeMake From line 67 of without_reannotation/Snakefile 
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if ( file.info(snakemake@input$normal_bed)$size == 0 | file.info(snakemake@input$extended_bed)$size == 0 ) {
	file.copy(from=snakemake@input$normal_bed, to=snakemake@output[[1]])	
} else {
	full_set_sorted = filtar::get_full_bed(snakemake@input$normal_bed, snakemake@input$extended_bed, snakemake@input$all_transcripts, snakemake@input$tx_quant)
        write.table(full_set_sorted, file=snakemake@output[[1]], quote=FALSE, row.names=FALSE, col.names=FALSE, sep="\t")
}
R From line 3 of with_reannotation/extend_bed2.R 
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input = readr::read_tsv(snakemake@input[[1]], col_names=FALSE, col_types='ciiciciiiicc')

if (length(snakemake@config[['transcripts']]) == 0) {
        file.copy(from=snakemake@input[[1]],to=snakemake@output[[1]])
} else {
	transcripts = gsub('\\..*','', snakemake@config[['transcripts']])

	filtered_input = input[input$X4 %in% transcripts,]

	write.table(filtered_input,snakemake@output[[1]], col.names=FALSE, row.names=FALSE, quote=FALSE, sep="\t")
}
R From line 17 of with_reannotation/filter_bed12.R 
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input = readr::read_tsv(snakemake@input[[1]], col_names=FALSE, col_types='ciiic')

if (length(snakemake@config[['transcripts']]) == 0) {

	file.copy(from=snakemake@input[[1]],to=snakemake@output[[1]])

} else {

	for (transcript in snakemake@config[['transcripts']]) {
		if (!transcript %in% input$X5) {
			write(stringr::str_interp("The transcript identifer '${transcript}' is not a valid identifier for the selected species for ensembl release ${snakemake@config[['ensembl_release']]}"), stderr())
		}
	}

	filtered_input = input[input$X5 %in% snakemake@config[['transcripts']],]

	if (dim(filtered_input)[1] == 0) {
		write("No valid transcript identifiers in input. Halting execution.", stderr())
		quit(save="no", status=1)
	}

	write.table(filtered_input,snakemake@output[[1]], col.names=FALSE, row.names=FALSE, quote=FALSE, sep="\t")
}
R From line 17 of with_reannotation/filter_bed6.R 
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united_bedgraph = readr::read_tsv(
	file=snakemake@input[[1]],
	col_names=FALSE,
	col_types=readr::cols(.default = 'd', X1 = 'c', X2 = 'i', X3 = 'i')
)

united_bedgraph = filtar::AvgBedgraph(united_bedgraph)

united_bedgraph = united_bedgraph[,c('X1','X2','X3','avg')]

write.table(
	x=united_bedgraph,
	file=snakemake@output[[1]],
	row.names=FALSE,
	col.names=FALSE,
	quote=FALSE,
	sep="\t"
)
R From line 3 of with_reannotation/get_average_bedgraph.R 
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if ( file.info(snakemake@input[[2]])$size == 1 ) {
        file.copy(from=snakemake@input[[1]], to=snakemake@output[[1]])
} else {
	AIR_file = filtar::get_AIR_file(snakemake@input[[1]], snakemake@input[[2]])
	write.table(AIR_file, snakemake@output[[1]], sep="\t", col.names=FALSE, row.names=FALSE, quote=FALSE)
}
R From line 1 of with_reannotation/get_tissue_specific_APA_file.R 
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utr_lengths = filtar::get_utr_lengths(snakemake@input[[1]]) 

write.table(
	x=utr_lengths,
	file=snakemake@output[[1]],
	sep="\t",
	quote=FALSE,
	row.names=FALSE,
	col.names=TRUE
)
R From line 1 of with_reannotation/get_utr_lengths.R 
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import sys
import os

index_base_name = snakemake.input['index'].replace('.1.ht2','')

if len(snakemake.input['reads']) == 1:
	print('single_end_processing')
	os.system('hisat2 -x {} -p {} -U {} -S {} {}'.format(index_base_name,snakemake.threads,snakemake.input['reads'],snakemake.output,snakemake.params[0]))
else:
	print('paired_end_processing')
	os.system('hisat2 -x {} -p {} -1 {} -2 {} -S {} {}'.format(index_base_name,snakemake.threads,snakemake.input['reads'][0], snakemake.input['reads'][1], snakemake.output, snakemake.params[0]))
Python HISAT2 From line 1 of hisat2/map_reads.py 
73
shell: "python {input.py_script} {input.gtf} > {output}"
SnakeMake From line 73 of hisat2/Snakefile 
80
shell: "python {input.py_script} {input.gtf} > {output}"
SnakeMake From line 80 of hisat2/Snakefile 
90
shell: "hisat2-build -f -p {threads} --ss {input.splice_sites} --exon {input.exons} {input.assembly} data/{wildcards.species}"
SnakeMake HISAT2 From line 90 of hisat2/Snakefile 
99
shell: "hisat2-build -f -p {threads} {input.assembly} data/{wildcards.species}"
SnakeMake HISAT2 From line 99 of hisat2/Snakefile 
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script:
	"map_reads.py"
SnakeMake From line 115 of hisat2/Snakefile 
124
shell: "samtools view -@ {threads} -Sb  {input}  >  {output}"
SnakeMake SAMtools From line 124 of hisat2/Snakefile 
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wrapper:
    "0.27.0/bio/samtools/sort"
SnakeMake From line 134 of hisat2/Snakefile 
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import os

if len(snakemake.input) > 1:
	os.system("bedtools unionbedg -i {} > {}".format(snakemake.input, snakemake.output))
else:
	os.system("cp {} {}".format(snakemake.input, snakemake.output))
Python BEDTools From line 1 of with_reannotation/merge_bedgraphs.py 
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shell:
    "{input.script} {input.gtf} {output}"
SnakeMake From line 49 of with_reannotation/Snakefile 
55
script: 'filter_bed6.R'
SnakeMake From line 55 of with_reannotation/Snakefile 
60
shell: 'grep -E "^{wildcards.chrom}\s" {input} > {output} || true'
SnakeMake From line 60 of with_reannotation/Snakefile 
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shell:
    "gtfToGenePred {input.gtf} {output}"
SnakeMake gtftogenepred From line 70 of with_reannotation/Snakefile 
81
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shell:
    "genePredToBed {input.genepred} {output}"
SnakeMake From line 81 of with_reannotation/Snakefile 
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shell:
    "genomeCoverageBed -bg -ibam {input.sorted_bam} -split > {output}"
SnakeMake From line 94 of with_reannotation/Snakefile 
100
shell: 'grep -E "^{wildcards.chrom}\s" {input} > {output}'
SnakeMake From line 100 of with_reannotation/Snakefile 
118
script: "merge_bedgraphs.py"
SnakeMake From line 118 of with_reannotation/Snakefile 
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script:
        "get_average_bedgraph.R"
SnakeMake From line 127 of with_reannotation/Snakefile 
138
script: "merge_bedgraphs.py"
SnakeMake From line 138 of with_reannotation/Snakefile 
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script:
        "get_average_bedgraph.R"
SnakeMake From line 147 of with_reannotation/Snakefile 
153
shell: 'grep -E "^{wildcards.chrom}\s" {input} > {output}'
SnakeMake From line 153 of with_reannotation/Snakefile 
158
script: "filter_bed12.R"
SnakeMake From line 158 of with_reannotation/Snakefile 
167
shell: "./{input.script} -i {input.bedgraphs} -p {config[APAtrap.min_proportion_of_valid_nucleotides_in_window]} -c {config[APAtrap.min_window_coverage]} -w {config[APAtrap.window_size]} -e {config[APAtrap.utr_extension_size]} -m {input.bed} -o {output}"
SnakeMake From line 167 of with_reannotation/Snakefile 
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script:
    "extend_bed2.R"
SnakeMake From line 177 of with_reannotation/Snakefile 
183
shell: "cat {input} > {output}"
SnakeMake From line 183 of with_reannotation/Snakefile 
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shell:
   "./{input.script} -i {input.bedgraphs} -g 1 -n 1 -d {config[APAtrap.min_cov_variation_between_APA_sites]} -c {config[APAtrap.min_average_cov]} -a {config[APAtrap.min_distance_between_APA_sites]} -w {config[APAtrap.predictAPA_window_size]} -u {input.bed}  -o {output}"
SnakeMake From line 194 of with_reannotation/Snakefile 
200
shell: "cat {input} | sed '1b;/Gene/d' > {output}"
SnakeMake From line 200 of with_reannotation/Snakefile 
205
script: 'get_utr_lengths.R'
SnakeMake From line 205 of with_reannotation/Snakefile 
210
script: 'get_utr_lengths.R'
SnakeMake From line 210 of with_reannotation/Snakefile 
215
script: "get_tissue_specific_APA_file.R"
SnakeMake From line 215 of with_reannotation/Snakefile 
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shell:
    "{input.script} {input.gtf} CDS {output}"
SnakeMake From line 225 of with_reannotation/Snakefile 
231
script: "filter_bed6.R"
SnakeMake From line 231 of with_reannotation/Snakefile 
236
shell: 'grep -E "^{wildcards.chrom}\s" {input} > {output} || true'
SnakeMake From line 236 of with_reannotation/Snakefile 
1
sed 's/(+)//g' $1 | sed 's/(-)//g' | sed 's/(-)//g' | tr '\n' '\t' | sed 's/>/\n/g' | sed '/^$/d' | awk -v species=$2 '{OFS="\t"}{ print $1,species,$2}' > $3
Shell From line 1 of scripts/convert_fa_to_tsv2.sh 
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grep 'hit' $1
command=$? # get exit code

if [[ $command -eq 0  ]]
then
	grep -A 1 'hit' $1 | sed '/--/d' | grep '>' | sed 's/>//' > $2
else
	touch $2
fi
Shell From line 1 of scripts/convert_miRanda_to_tsv.sh 
1
cat $1 | awk '$3 == "transcript"' | awk '{OFS="\t"}{print $1,$14,$16}' | sed 's/"//g' | sed 's/;//g' | sed 's/\t/\./2' | grep -E "^$2	" | awk '{print $2}'
Shell From line 1 of scripts/get_all_transcripts.sh 
1
awk '{OFS="\t"}{ print $1,$2,$3,$5,$5,$4}' $1 | sed 's/\t1$/\t+/g' | sed 's/\t-1$/\t-/g' > $2
Shell From line 1 of scripts/get_bedtools_bed.sh
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Free

Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://tbradley27.github.io/FilTar/
Name: filtar
Version: v1.3.0
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Downloaded: 0
Copyright: Public Domain
License: GNU General Public License v3.0
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