2. SnakeChunks library

The library contains a variety of files, including scripts and configuration files.

You can find a description of these hereafter.

2.1. Snakemake files (snakefiles)

Snakefiles are based on the scripting language Python 3, and use a specific syntax.

For organization purpose, we have distinguished two types of Snakefiles:

  • Rules are typically “bricks” to build workflows with. Each rule corresponds to a specific operation.
  • Workflows are combinations of rules that serve a specific purpose: quality check of sequencing data, ChIP-seq peaks analysis…

2.1.1. Workflows (.wf)

File extension: *.wf

2.1.1.1. import_from_sra

2.1.1.2. quality_control

2.1.1.3. ChIP-seq

2.1.1.4. RNA-seq_DEG

2.1.1.5. integration_ChIP_RNA

2.1.2. Rules (.rules)

This section describes briefly each rule and its parameters. For details on the tools and how to install them, please check this section.

2.1.2.1. annotate_peaks

This rule runs a program that is part of the HOMER tools suite. It outputs a list of gene identifiers using a bed file, a fasta file and a gtf file. The bed file can be a peak file produced by any peak-calling rule.

More: http://homer.salk.edu/homer/ngs/annotation.html

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“fasta_file”]
  • config[“genome”][“gtf_file”]

2.1.2.2. bam_by_name

Sort aligned reads (in bam format) by name using ‘samtools sort’.

Required parameters:

  • config[“qsub”]

2.1.2.3. bam_by_pos

Sort aligned reads (in bam format) by positions, using ‘samtools sort’.

Required parameters:

  • config[“qsub”]

2.1.2.4. bam_stats

Computes mapping statistics using the ‘samtools flagstat’ tool.

Requires samtools 1.3+ version (not in apt-get repository as of 2016-03).

Required parameters:

  • config[“qsub”]

2.1.2.5. bam_to_bed

Converts bam files into bed files using ‘bedtools bamtobed’.

Required parameters:

  • config[“qsub”]

2.1.2.6. bbduk

Performs trimming of raw reads using bbduk of the bbmap suite. Currently only handling single-end data.

Required parameters:

  • config[“qsub”]
  • config[“metadata”][“seq_type”]

Optional parameters:

  • config[“bbduk”][“length_threshold”]
  • config[“bbduk”][“qual_threshold”]
  • config[“metadata”][“strands”]

2.1.2.7. bed_to_fasta

Get a fasta file from a bedfile using the ‘fetch-sequences’ tool from the RSAT suite.

Fetch sequences from UCSC to obtain a fasta file from a set of genomic coordinates described in a bed file. Requires RSAT installation.

Alternative is to use getfasta.rules .

Example:

mkdir -p test/fetch_seq; cd test/fetch_seq;
wget http://pedagogix-tagc.univ-mrs.fr/rsat/demo_files/fetch-sequences_Schmidt_2011_mm9_CEBPA_SWEMBL_R0.12_702peaks.bed;
cd -
snakemake --snakefile ${RSAT}/snakemake_files/chip-seq_motifs.py test/fetch_seq/fetch-sequences_Schmidt_2011_mm9_CEBPA_SWEMBL_R0.12_702peaks.fasta

Required parameters:

  • config[“qsub”]

2.1.2.8. bedgraph_to_bigwig

Convert bedgraph to bigWig format using Deeptools.

Required parameters:

  • config[“qsub”]

2.1.2.9. bedgraph_to_tdf

Convert bedgraph to TDF format, which is recommended to load coverage data in IGV.

The conversion relies on igvtools.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“fasta_file”]

2.1.2.10. bedtools_closest

Bedtools closest searches for overlapping features in two coordinate files. In the event that no feature in B overlaps the current feature in A, closest will report the nearest (that is, least genomic distance from the start or end of A) feature in B.

Usage: bedtools closest [OPTIONS] -a <FILE> -b <FILE1, FILE2, …, FILEN>

More: http://bedtools.readthedocs.io/en/latest/content/tools/closest.html

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“gff3_file”]

2.1.2.11. bedtools_intersect

Bedtools intersect allows one to screen for overlaps between two sets of genomic features. Moreover, it allows one to have fine control as to how the intersections are reported. bedtools intersect works with both BED/GFF/VCF and BAM files as input.

More: http://bedtools.readthedocs.io/en/latest/content/tools/intersect.html

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“gff3_file”]

2.1.2.12. bedtools_window

Similar to bedtools intersect, window searches for overlapping features in A and B. However, window adds a specified number (1000, by default) of base pairs upstream and downstream of each feature in A. In effect, this allows features in B that are near features in A to be detected.

More: http://bedtools.readthedocs.io/en/latest/content/tools/window.html

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“gff3_file”]

Opional parameters:

  • config[“bedtools”][“window”]

2.1.2.13. blast_formatdb

Run the formatdb program of the BLAST suite in order to index all k-mers of the reference database. This has to be done only once, then the DB can be used for multiple searches with blastall.

Required parameters:

  • config[“qsub”]

2.1.2.14. blastall

Where {blast_program} should be replaced by one of the supported program options in blastall: blastp, blastn, blastx, tblastn.

Output file name: {query}_{blast_program}_hits.txt

Required parameters:

  • config[“qsub”]
  • config[“blastall”][“db”]

Optional parameters:

  • config[“blastall”][“matrix”]
  • config[“blastall”][“expect”]
  • config[“blastall”][“view”]

2.1.2.15. bowtie_index

Rule for the creation of Bowtie 1 index. Has to be done only once. The output file is used to test whether the index already exists when aligning.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]

2.1.2.16. bowtie

Read mapping using bowtie. Requires the indexing to have previously been done (using the rule bowtie_index).

Required parameters:

  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]
  • config[“qsub”]
  • config[“dir”][“fastq”]
  • config[“dir”][“samples”]

Optional parameters:

  • config[“bowtie”][“max_mismatches”]
  • config[“bowtie”][“threads”]

2.1.2.17. bowtie2_index

Rule for the creation of Bowtie 2 index. Has to be done only once. The output file is used to test whether the index already exists when aligning.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“fasta_file”]

2.1.2.18. bowtie2

Read mapping using Bowtie2. Requires the indexing to have previously been done (using the rule bowtie2_index).

Required parameters:

  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]
  • config[“qsub”]
  • config[“dir”][“fastq”]
  • config[“dir”][“samples”]

Optional parameters:

  • config[“bowtie2”][“threads”]
  • config[“bowtie2”][“max_mismatches”]

2.1.2.19. bPeaks

bPeaks is a peak-calling tool running in R. It was specifically designed for small eucaryotic organisms, such as the yeast. It is thus not recommanded for bigger genomes, as it could be very slow. You should choose parameters carefully. Input in bam, output in bed.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“samples”]
  • config[“dir”][“peaks”]

Optional parameters:

  • config[“bPeaks”][“IPcoeff”]
  • config[“bPeaks”][“controlCoeff”]
  • config[“bPeaks”][“log2FC”]
  • config[“bPeaks”][“averageQuantiles”]
  • config[“bPeaks”][“windowSize”]
  • config[“bPeaks”][“windowOverlap”]

2.1.2.20. bwa_index

Rule for the creation of BWA index. Has to be done only once. The output file is used to test whether the index already exists when aligning.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]

2.1.2.21. bwa

Requires the indexing to have previously been done (using the rule bwa_index).

Required parameters:

  • config[“dir”][“fastq”]
  • config[“dir”][“samples”]
  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]
  • config[“qsub”]

Optional parameters:

  • config[“bwa”][“dir”]
  • config[“bwa”][“threads”]

2.1.2.22. count_reads

A set of rules to count the number of reads in NGS files with different formats.

Includes:

  • rule count_reads_fastq

Count number of reads in a fastq-formatted file (unaligned reads).

  • rule count_reads_fastq_gz

Count number of reads in a gzipped fastq-formatted file (unaligned reads).

  • rule count_reads_bam

Count number of reads in a bam-formatted file (binary alignment map, compressed sam).

-rule count_reads_sam

Count number of reads in a bam-formatted file (binary alignment map, compressed sam).

-rule count_features_bed

Count number of features in a bed-formatted file.

2.1.2.24. cutadapt

Trimming of raw read files using the tool cutadapt and the wrapper Trim Galore.

Currently only working with single end data.

Required parameters:

  • config[“qsub”]
  • config[“metadata”][“seq_type”]

Optional parameters:

  • config[“cutadapt”][“length_threshold”]
  • config[“cutadapt”][“qual_threshold”]
  • config[“metadata”][“strands”]

2.1.2.25. dot_graph

This rule generates dot files for snakemake’s DAG and rulegraph.

Required parameter:

  • config[“metadata”][“configfile”]

2.1.2.26. dot_to_image

Following rule dot_graph, this rule creates png, pdf and svg outputs for dot graphs from snakemake.

2.1.2.27. fastqc

Check the quality of the reads in a fastq or a bam file using the program fastQC (quality control). Results are stored in folder named ‘{reads}_fastqc’.

Custom parameters specified in the configuration file with the option config[“fastqc”][“other options”] will be passed to fastQC.

Required parameters:

  • config[‘qsub’]

Optional parameters:

  • config[‘fastqc’][‘other_options’]

2.1.2.28. featnb_from_bed

Count number of features in a bed-formatted file.

Required parameters:

  • config[“qsub”]

2.1.2.29. genome_coverage_bedgraph_strands

Compute two strand-specific genome coverage files from a bam-formatted file with aligned reads. The coverage files are in bedgraph format, which can be loaded in the genome viewer IGV.

Required parameters:

  • config[“qsub”]

2.1.2.30. genome_coverage_bedgraph

Compute genome coverage from a bam-formatted file with aligned reads. The coverage file is in bedgraph format (with extension .bedgraph), which can be loaded in the genome viewer IGV.

Note however that IGV issues a warning when bedgraph files are given in input, and recommends to use the tdf format instead. We implemented hereafter rules to convert bedgraph to tdf.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“fasta_file”]

2.1.2.31. genome_coverage_bigwig_normalized

Uses bamCompare tool from the deepTools suite.

Required parameters:

  • config[“qsub”]

2.1.2.32. genome_coverage_bigwig

Compute genome coverage from a bam-formatted file with aligned reads and produce a bigWig file. Uses bamCoverage tool from the deepTools suite.

Required parameters:

  • config[“qsub”]

2.1.2.33. genome_coverage_dz

Compute coverage (reads per position) for each position of a genome, from a bam-formatted file with aligned reads.

BEWARE: this rule is useful for small genomes (Bacteria, Fungi) but would produce a very big file for Metazoan or Plant genomes.

Required parameters:

  • config[“qsub”]

2.1.2.34. genome_coverage_wig

Compute genome coverage from a bam-formatted file with aligned reads and produce a wig file, the recommended format to upload coverage-type data as UCSC tracks.

Required parameters:

  • config[“qsub”]

2.1.2.35. get_chrom_sizes

This rule generates a file containg the chromosome sizes, with file extension *.genome. This file is required by a number of bedtools utilities.

Required parameters:

  • config[“qsub”]

2.1.2.36. getfasta

Get fasta from bed file using the bedtools suite.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“fasta_file”]

2.1.2.37. gunzip

Uncompress a file with the gunzip program. The rule is very simple, but is convenient to use in a workflow: it can be used to fix some dependencies on.gz extensions, and/or to send compression jobs to a queue.

Required parameters:

  • config[“qsub”]

2.1.2.38. gzip

Uncompress a file with the gunzip program. The rule is very simple, but is convenient to use in a workflow: it can be used to fix some dependencies on.gz extensions, and/or to send compression jobs to a queue.

Required parameters:

  • config[“qsub”]

2.1.2.39. homer

Peak-calling with HOMER software, findPeaks algorithm. Input formats: .sam, .bam, .bed (bam input requires samtools to be installed)

The genome parameter can be either the code of a genome installed in Homer (eg HG18, dm3…) or a fasta file (see http://homer.salk.edu/homer-fdr{fdr}_peaks/introduction/update.html)

Required parameters:

  • config[“qsub”]
  • config[“dir”][“samples”]
  • config[“dir”][“peaks”]
  • config[“genome”][“fasta_file”]
  • config[“genome”][“size”]

Optional parameters:

  • config[“homer”][“style”]
  • config[“homer”][“L”]
  • config[“homer”][“F”]
  • config[“homer”][“P”]
  • config[“homer”][“fdr”]

2.1.2.40. index_bam

Index a bam file by creating a .bai file with Samtools The input bam MUST be sorted by position (rule bam_by_pos).

Required parameters:

  • config[“qsub”]

2.1.2.41. index_fasta

Index a fasta file by creating an .fai file with Samtools.

Required parameters:

  • config[“qsub”]

2.1.2.42. macs14

Peak-calling with macs14. Input: bed (others supported) Output: bed

Required parameters:

  • config[“genome”][“size”]
  • config[“qsub”]
  • config[“dir”][“samples”]
  • config[“dir”][“peaks”]

Optional parameters:

  • config[“macs14”][“pval”]
  • config[“macs14”][“keep_dup”]
  • config[“macs14”][“bandwidth”]
  • config[“macs14”][“mfold”]
  • config[“macs14”][“other_options”]

2.1.2.43. macs2

Peak-calling with MACS2. Input: bed Output: bed

Required parameters:

  • config[“dir”][“samples”]
  • config[“dir”][“peaks”]
  • config[“genome”][“size”]
  • config[“qsub”]

Optional parameters:

  • config[“macs2”][“qval”]
  • config[“macs2”][“keep_dup”]
  • config[“macs2”][“band_width”]
  • config[“macs2”][“mfold_min”]
  • config[“macs2”][“mfold_max”]
  • config[“macs2”][“other_options”]
  • config[“macs2”][“type”]

2.1.2.44. matrix_clustering

This rule is currently incomplete.

Motif discovery using the peak-motifs pipeline.

Documentation of tool here.

Required parameters:

  • config[“qsub”]

Optional parameters:

2.1.2.45. matrix_quality

Measuring peak enrichment for motifs. Requires at least 2 sets of peaks and 1 motif database.

Documentation of tool here.

Required parameters:

  • config[“qsub”]
  • config[“matrix-quality”][“background”]

Optional parameters:

2.1.2.46. md5sum

Compute the md5sum signature for a given file, which enables to check the consistency of its content after transfer.

Note: md5sum is recommended for submitting NGS data to GEO.

Usage: integrate in the targets of a workflow. Alternatively, can be called directly on the command line with find.

Example: find all the fastq files in a directory (named fastq) and compute one md5sum file for each, and assign the takss to 20 jobs in the scheduler.

find fastq/ -name '*.fastq'  \
   | awk '{print $1".md5sum"}' \
   | xargs snakemake  -j 20 -p \
       -s gene-regulation/scripts/snakefiles/rules/md5sum.rules \
       --configfile metadata/Glossina_palpalis.yml

Required parameters:

  • config[“qsub”]

2.1.2.47. merge_lanes

Concatenate multiple fastq files to produce a merged fastq file.

This rule typically serves to merge raw reads (fastq) corresponding to multiple sequencing lanes for the same sample into a single fastq file per sample.

Since the file naming conventions are highly dependent on the sequencing platform, the file grouping is read from a user-provided text file with tab-separated values (extension .tsv). This file must have been specified in the config file, as config[“metadata”][“lane_merging”].

This file must contain at least two columns with this precise header:

  • source_file
  • merged_file

Additional columns can be included but will be ignored.

There should be a N to 1 correspondence from source file to merge file
(each source file should in principle be assigned to a single merged file).

Source files are supposed to be compressed fastq sequence files (.fastq.gz).

The output file is an uncompressed fastq file, because bowtie version 1 does not support gzipped files as input.

Required parameters:

  • config[“metadata”][“lane_merging”] file indicating the source/merged file names
  • config[“dir”][“fastq”] base of the directory containing the fastq files

2.1.2.48. mosaics

Peak-calling with mosaics R package. Input: bed Output: bed

Required parameters:

  • config[“dir”][“samples”]
  • config[“qsub”]

Optional parameters:

  • config[“mosaics”][“FDR”]
  • config[“mosaics”][“frag_len”]
  • config[“mosaics”][“bin_size”]
  • config[“mosaics”][“type”]

2.1.2.49. peak_motifs

Motif discovery using the peak-motifs pipeline from RSAT.

Documentation.

Required parameters:

  • config[“qsub”]
  • config[“peak-motifs”][“motif_db”]

Optional parameters:

  • config[“peak-motifs”][“tasks”]
  • config[“peak-motifs”][“disco”]

2.1.2.50. readnb_from_bam

Count number of reads in a bam-formatted file (binary alignment map, compressed sam).

Required parameters:

  • config[“qsub”]

2.1.2.51. readnb_from_fastq

Count number of reads in a fastq-formatted file (unaligned reads).

Required parameters:

  • config[“qsub”]

2.1.2.52. readnb_from_sam

Count number of reads in a bam-formatted file (binary alignment map, compressed sam).

Required parameters:

  • config[“qsub”]

2.1.2.53. sam_to_bam

Convert reads from SAM (sequence alignment map) to BAM (binary alignment map) format.

Required parameters:

  • config[“qsub”]

2.1.2.54. sartools_DESeq2

This rule is designed to perform differential expression analysis of RNA-seq data with DESeq2, using the R package SARTools.

It requires replicated data to run properly.

Required parameters:

  • config[“qsub”]
  • config[“author”]
  • config[“dir”][“samples”]
  • config[“dir”][“diffexpr”]

Optional parameters:

  • config[“DESeq2”][“featuresToRemove”]
  • config[“DESeq2”][“varInt”]
  • config[“DESeq2”][“condRef”]
  • config[“DESeq2”][“batch”]
  • config[“DESeq2”][“alpha”]
  • config[“DESeq2”][“pAdjustMethod”]
  • config[“DESeq2”][“fitType”]
  • config[“DESeq2”][“cooksCutoff”]
  • config[“DESeq2”][“independentFiltering”]
  • config[“DESeq2”][“typeTrans”]
  • config[“DESeq2”][“locfunc”]

2.1.2.55. sartools_edgeR

This rule is designed to perform differential expression analysis of RNA-seq data with edgeR, using the R package SARTools.

It requires replicated data to run properly.

Required parameters:

  • config[“qsub”]
  • config[“author”]
  • config[“dir”][“samples”]
  • config[“dir”][“diffexpr”]

Optional parameters:

  • config[“edgeR”][“featuresToRemove”]
  • config[“edgeR”][“varInt”]
  • config[“edgeR”][“condRef”]
  • config[“edgeR”][“batch”]
  • config[“edgeR”][“alpha”]
  • config[“edgeR”][“pAdjustMethod”]
  • config[“edgeR”][“fitType”]
  • config[“edgeR”][“cpmCutoff”]
  • config[“edgeR”][“gene_selection”]
  • config[“edgeR”][“normalizationMethod”]

2.1.2.56. sartools_targetfile

This rule creates a so-called “targetfile”, which is required by SARTools to run differential expression analyses with rules sartools_DESeq2 and sartools_edgeR.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“samples”]
  • config[“dir”][“diffexpr”]

2.1.2.57. sickle

Trimming raw reads with sickle.

Required parameters:

  • config[“qsub”]
  • config[“metadata”][“seq_type”]

Optional parameters:

  • config[“sickle”][“qual_threshold”]
  • config[“sickle”][“length_threshold”]
  • config[“sickle”][“qual_type”]
  • config[“metadata”][“strands”]

2.1.2.58. split_bam_by_strands

Split a bam file in two separate bam files containing respectively the reads on the plus and minus strand.

Required parameters:

  • config[“qsub”]

2.1.2.59. spp

Peak-calling with SPP (R package). Input: bam Output: narrowPeak + bed format

Required parameters:

  • config[“qsub”]
  • config[“dir”][“samples”]
  • config[“dir”][“peaks”]

Optional parameters:

  • config[“spp”][“fdr”]

2.1.2.60. sra_to_fastq_split

Converts SRA files in separate fastq files for paired-end data with SRA toolkit.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“reads_source”]
  • config[“dir”][“fastq”]

Optional parameters:

  • config[“fastq_dump”][“options”]

Usage example :

IMPORT = expand(FASTQ_DIR + “/{samples}/{samples}.fastq”, samples=SAMPLE_IDS)

2.1.2.61. sra_to_fastq

Converts SRA files in fastq format with SRA toolkit (songle-end data only).

Required parameters:

  • config[“qsub”]
  • config[“dir”][“reads_source”]
  • config[“dir”][“fastq”]

2.1.2.62. subread_align

Align each sample with the R-package subread.

To align each sample on the reference genome the R-package subread first needs to build a index with the function builindex(). The alignment is then executed with the function align(), which calls the tool read mapping tool Subread.

Reference: Liao Y, Smyth GK and Shi W (2013). The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Research, 41(10):e108

Required parameters:

  • config[“dir”][“fastq”]
  • config[“dir”][“samples”]
  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]
  • config[“qsub”]
  • config[“metadata”][“seq_type”]

Optional parameters:

  • config[“subread-align”][“dir”]
  • config[“subread-align”][“threads”]
  • config[“subread-align”][“max_mismatches”]
  • config[“subread-align”][“align_options”]
  • config[“subread-align”][“seq_data”]

2.1.2.63. subread_featureCounts_all

This rule computes bam files and produces a tab file containing feature counts for all samples, using featureCounts from the subread toolkit.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“gtf_file”]

Optional parameters:

  • config[“subread-featureCounts”][“attr_type”]
  • config[“subread-featureCounts”][“feature_type”]
  • config[“subread-featureCounts”][“multi_mapping”]
  • config[“subread-featureCounts”][“strand_specificity”]

Usage:

featureCounts [options] -a <annotation_file> -o <output_file> input_file1 [input_file2] ...

2.1.2.64. subread_featureCounts

This rule computes bam files and produces tab files containing feature counts for every sample separately, using featureCounts from the subread toolkit.

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“gtf_file”]

Optional parameters:

  • config[“subread-featureCounts”][“attr_type”]
  • config[“subread-featureCounts”][“feature_type”]
  • config[“subread-featureCounts”][“multi_mapping”]
  • config[“subread-featureCounts”][“strand_specificity”]

Usage:

featureCounts [options] -a <annotation_file> -o <output_file> input_file1 [input_file2] ...

2.1.2.65. subread_index

Rule for the creation of subread index. Has to be done only once. The output file is used to test whether the index already exists when aligning.

Reference: Liao Y, Smyth GK and Shi W (2013). The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Research, 41(10):e108

Required parameters:

  • config[“qsub”]
  • config[“dir”][“genome”]
  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]

2.1.2.66. swembl

Peak-calling with SWEMBL.

Beware: for SWEMBL the peaks MUST be sorted by position, other wise SWEMBL runs indefinitely. Usually by default we sort all bam files by position after alignment (with rule bam_by_pos).

Required parameters:

  • config[“qsub”]
  • config[“dir”][“samples”]
  • config[“dir”][“peaks”]

Optional parameters:

  • config[“swembl”][“x”]
  • config[“swembl”][“R”]
  • config[“swembl”][“N”]

2.1.2.67. tophat

Read mapping for single or paired end data using Tophat. Requires the indexing to have previously been done (using the rule bowtie2_index).

Required parameters:

  • config[“dir”][“fastq”]
  • config[“dir”][“samples”]
  • config[“metadata”][“seq_type”]
  • config[“dir”][“genome”]
  • config[“genome”][“version”]
  • config[“genome”][“fasta_file”]
  • config[“qsub”]

Optional parameters:

  • config[“tophat”][“max_mismatches”]
  • config[“tophat”][“threads”]

2.2. Python scripts (.py)

todo

2.3. R scripts

todo

2.4. Configuration files (yaml)

todo

2.5. R markdown files (.Rmd)

todo

2.6. Tabulated files (.tab)

We use tabulated files in order to define and describe the samples to be processed in the workflows.

Examples of these files are available in the examples folder of the library.

2.6.1. Sample description files (samples.tab)

todo

2.6.2. Experimental design files (design.tab)

todo