What it does

The snakePipes noncoding-RNA-seq workflow allows users to process their single or paired-end ribosomal-depleted RNA-seq fastq files upto the point of gene/transcript/repeat-element counts and differential expression. Repeat elements are quantified and tested for differential expression at the name, family and class level. Since changes in repeat element expression tend to be unidirectional, size factors from gene expression are used when normalizing repeat element expression.

Note that in addition to the normal GTF file, this pipeline requires a repeat masker output file, which can be downloaded from UCSC or other sites. The chromosome names here must match that used in the other files.


Input requirements

The only requirement is a directory of gzipped fastq files. Files could be single or paired end, and the read extensions could be modified using the keys in the defaults.yaml file below.

Configuration file

There is a configuration file in snakePipes/workflows/noncoding-RNA-seq/defaults.yaml:

## General/Snakemake parameters, only used/set by wrapper or in Snakemake cmdl, but not in Snakefile
pipeline: noncoding-rna-seq
local: False
maxJobs: 5
## directory with fastq files
## preconfigured target genomes (mm9,mm10,dm3,...) , see /path/to/snakemake_workflows/shared/organisms/
## Value can be also path to your own genome config file!
## FASTQ file extension (default: ".fastq.gz")
ext: '.fastq.gz'
## paired-end read name extension (default: ["_R1", "_R2"])
reads: ["_R1","_R2"]
## assume paired end reads
pairedEnd: True
## Number of reads to downsample from each FASTQ file
## Options for trimming
trim: False
trimmer: fastp
## further options
mode: alignment,deepTools_qc
bwBinSize: 25
fastqc: False
fragmentLength: 200
libraryType: 2
## supported mappers: STAR HISAT2
aligner: STAR
alignerOptions: "--outSAMstrandField intronMotif --outFilterMultimapNmax 1000 --outFilterMismatchNoverLmax 0.1 --outSAMattributes Standard"
verbose: False
plotFormat: png
#### Flag to control the pipeline entry point
fromBAM: False
bamExt: '.bam'
UMIBarcode: False
bcPattern: NNNNCCCCCCCCC #default: 4 base umi barcode, 9 base cell barcode (eg. RELACS barcode)
UMIDedup: False
UMIDedupSep: "_"
UMIDedupOpts: --paired

Apart from the common workflow options (see Running snakePipes), the following parameters are useful to consider:

  • aligner: The only choice at the moment is STAR.

  • alignerOptions: Options to pass on to your chosen aligner. Note that library type and junction definitions don't have to be passed to the aligners as options, as they are handeled either automatically, or via other parameters.

  • plotFormat: You can switch the type of plot produced by all deeptools modules using this option. Possible choices : png, pdf, svg, eps, plotly

Differential expression

Like the other workflows, differential expression can be performed using the --sampleSheet option and supplying a sample sheet like that below:

name    condition
sample1      eworo
sample2      eworo
SRR7013047      eworo
SRR7013048      OreR
SRR7013049      OreR
SRR7013050      OreR


The first entry defines which group of samples are control. This way, the order of comparison and likewise the sign of values can be changed. The DE analysis might fail if your sample names begin with a number. So watch out for that!

Complex designs with blocking factors

If the user provides additional columns between 'name' and 'condition' in the sample sheet, the variables stored there will be used as blocking factors in the order they appear in the sample sheet. Eg. if the first line of your sample sheet looks like 'name batch condition', this will translate into a formula batch + condition. 'condition' has to be the final column and it will be used for any statistical inference.

Multiple pairwise comparisons

The user may specify multiple groups of independent comparisons by providing a 'group' column after the 'condition' column. This will cause the sample sheet to be split into the groups defined in this column, and a corresponding number of independent pairwise comparisons will be run, one for each split sheet, and placed in separate output folders named accordingly. This will be applied to DESeq2 pairwise comparison. Specifying a value of 'All' in the 'group' column will cause that sample group to be used in all pairwise comparisons, e.g. if the same set of controls should be used for several different treatment groups.

An example sample sheet with the group information provided looks like this:

name condition group sample1 Control All sample2 Control All sample3 Treatment Group1 sample4 Treatment Group1 sample5 Treatment Group2 sample6 Treatment Group2

Analysis modes

Following analysis (modes) are possible using the noncoding-RNA-seq workflow:


In this mode, the pipeline uses STAR to create BAM files and TEtranscripts to quantify genes and repeat elements. Differential expression of genes and repeat elements is then performed with DESeq2.


The pipeline provides multiple quality controls through deepTools, which can be triggered using the deepTools_qc mode. It's a very useful add-on with any of the other modes.


Since most deeptools functions require an aligned (BAM) file, the deepTools_qc mode will additionally perform the alignment of the fastq files. However this would not interfere with operations of the other modes.

Understanding the outputs

Assuming the pipline was run with --mode 'alignment,deepTools_qc' on a set of FASTQ files, the structure of the output directory would look like this (files are shown only for one sample)

├── bamCoverage
│   ├──
│   ├──
│   ├──
│   ├──
├── cluster_logs
├── deepTools_qc
│   ├── bamPEFragmentSize
│   │   ├── fragmentSize.metric.tsv
│   │   └── fragmentSizes.png
│   ├── estimateReadFiltering
│   │   └── sample1_filtering_estimation.txt
│   ├── logs
│   │   ├── bamPEFragmentSize.err
│   │   ├── bamPEFragmentSize.out
│   │   ├── multiBigwigSummary.err
│   │   └── plotCorrelation_pearson.err
│   ├── multiBigwigSummary
│   │   └── coverage.bed.npz
│   ├── plotCorrelation
│   │   ├── correlation.pearson.bed_coverage.heatmap.png
│   │   ├── correlation.pearson.bed_coverage.tsv
│   │   ├── correlation.spearman.bed_coverage.heatmap.png
│   │   └── correlation.spearman.bed_coverage.tsv
│   ├── plotEnrichment
│   │   ├── plotEnrichment.png
│   │   └── plotEnrichment.tsv
│   └── plotPCA
│       ├── PCA.bed_coverage.png
│       └── PCA.bed_coverage.tsv
├── DESeq2_sampleSheet
│   ├── DESeq2_report_genes.html
│   ├── DESeq2_report_repeat_class.html
│   ├── DESeq2_report_repeat_family.html
│   ├── DESeq2_report_repeat_name.html
│   ├── DESeq2.session_info.txt
│   ├── genes_counts_DESeq2.normalized.tsv
│   ├── genes_DEresults.tsv
│   ├── genes_DESeq.Rdata
│   ├── repeat_class_counts_DESeq2.normalized.tsv
│   ├── repeat_class_DEresults.tsv
│   ├── repeat_class_DESeq.Rdata
│   ├── repeat_family_counts_DESeq2.normalized.tsv
│   ├── repeat_family_DEresults.tsv
│   ├── repeat_family_DESeq.Rdata
│   ├── repeat_name_counts_DESeq2.normalized.tsv
│   ├── repeat_name_DEresults.tsv
│   └── repeat_name_DESeq.Rdata
├── filtered_bam
│   ├── sample1.filtered.bam
│   ├── sample1.filtered.bam.bai
├── multiQC
├── STAR
└── TEcount
    └── sample1.cntTable


The _sampleSheet suffix for the DESeq2_sampleSheet is drawn from the name of the sample sheet you use. So if you instead named the sample sheet mySampleSheet.txt then the folder would be named DESeq2_mySampleSheet. This facilitates using multiple sample sheets.

Apart from the common module outputs (see Running snakePipes), the workflow would produce the following folders:

  • bamCoverage: This would contain the bigWigs produced by deepTools bamCoverage . Files with suffix are raw coverage files, while the files with suffix are RPKM-normalized coverage files.

  • deepTools_QC: (produced in the mode deepTools_QC) This contains the quality checks specific for mRNA-seq, performed via deepTools. The output folders are names after various deepTools functions and the outputs are explained under deepTools documentation. In short, they show the insert size distribution(bamPEFragmentSize), mapping statistics (estimateReadFiltering), sample-to-sample correlations and PCA (multiBigwigSummary, plotCorrelation, plotPCA), and read enrichment on various genic features (plotEnrichment)

  • DESeq2_[sampleSheet]: (produced only if a sample-sheet is provided.) The folder contains the HTML result reports DESeq2_report_genes.html, DESeq2_report_repeat_name.html, DESeq2_report_repeat_class.html and DESeq2_report_repeat_family.html as we as the annotated output file from DESeq2 (genes_DEresults.tsv, etc.) and normalized counts for all samples, produced via DEseq2 (genes_counts_DESeq2.normalized.tsv, etc.) as well as an Rdata file (genes_DESeq.Rdata, etc.) with the R objects dds <- DESeq2::DESeq(dds) and ddr <- DDESeq2::results(dds,alpha = fdr). Sample name to plotting shape mapping on the PCA plot is limited to 36 samples and skipped otherwise.

  • filtered_bam: This contains sorted and indexed BAM files that have been filtered to remove secondary alignments. This are used by deepTools and are appropriate for use in IGV.

  • multiQC: This folder contains the report produced by MultiQC and summarizes alignment metrics from STAR and possibly various deepTools outputs.

  • STAR: This would contain the output logs of RNA-alignment by STAR. The actual BAM files are removed at the end of the pipeline as they're not compatible with typical visualization programs

  • TEcount: (produced in the alignment mode) This contains the counts files and logs from the TEcount program in the TEtranscripts package. These are used by DESeq2 for differential expression.

Command line options

MPI-IE workflow for noncoding RNA mapping and analysis

usage example:

noncoding-RNA-seq -i input-dir -o output-dir mm10

usage: noncoding-RNA-seq -i INDIR -o OUTDIR [-h] [-v] [--ext EXT]
                         [--reads READS READS] [-c CONFIGFILE]
                         [--clusterConfigFile CLUSTERCONFIGFILE] [-j INT]
                         [--local] [--keepTemp]
                         [--snakemakeOptions SNAKEMAKEOPTIONS] [--DAG]
                         [--version] [--emailAddress EMAILADDRESS]
                         [--smtpServer SMTPSERVER] [--smtpPort SMTPPORT]
                         [--onlySSL] [--emailSender EMAILSENDER]
                         [--smtpUsername SMTPUSERNAME]
                         [--smtpPassword SMTPPASSWORD] [-m MODE]
                         [--downsample INT] [--trim]
                         [--trimmer {cutadapt,trimgalore,fastp}]
                         [--trimmerOptions TRIMMEROPTIONS] [--fastqc]
                         [--bcExtract] [--bcPattern BCPATTERN] [--UMIDedup]
                         [--UMIDedupSep UMIDEDUPSEP]
                         [--UMIDedupOpts UMIDEDUPOPTS] [--bwBinSize BWBINSIZE]
                         [--plotFormat STR] [--aligner ALIGNER]
                         [--alignerOptions ALIGNEROPTIONS]
                         [--sampleSheet SAMPLESHEET] [--fromBAM]
                         [--bamExt BAMEXT] [--singleEnd]

Positional Arguments


Genome acronym of the target organism. Either a yaml file or one of: hg38, mm39_ens106, GRCh38_gencode40, dm6, GRCz10, SchizoSPombe_ASM294v2, mm10_gencodeM19, GRCz11

Required Arguments

-i, --input-dir

input directory containing the FASTQ files, either paired-end OR single-end data

-o, --output-dir

output directory

General Arguments

-v, --verbose

verbose output (default: 'False')


Suffix used by input fastq files (default: '".fastq.gz"').


Suffix used to denote reads 1 and 2 for paired-end data. This should typically be either '_1' '_2' or '_R1' '_R2' (default: '['_R1', '_R2']). Note that you should NOT separate the values by a comma (use a space) or enclose them in brackets.

-c, --configFile

configuration file: config.yaml (default: 'None')


configuration file for cluster usage. In absence, the default options specified in defaults.yaml and workflows/[workflow]/cluster.yaml would be selected (default: 'None')

-j, --jobs

maximum number of concurrently submitted Slurm jobs / cores if workflow is run locally (default: '5')


run workflow locally; default: jobs are submitted to Slurm queue (default: 'False')


Prevent snakemake from removing files marked as being temporary (typically intermediate files that are rarely needed by end users). This is mostly useful for debugging problems.


Snakemake options to be passed directly to snakemake, e.g. use --snakemakeOptions='--dryrun --rerun-incomplete --unlock --forceall'. WARNING! ONLY EXPERT USERS SHOULD CHANGE THIS! THE DEFAULT VALUE WILL BE APPENDED RATHER THAN OVERWRITTEN! (default: '['--use-conda']')


If specified, a file ending in _pipeline.pdf is produced in the output directory that shows the rules used and their relationship to each other.


show program's version number and exit

Email Arguments


If specified, send an email upon completion to the given email address


If specified, the email server to use.


The port on the SMTP server to connect to. A value of 0 specifies the default port.


The SMTP server requires an SSL connection from the beginning.


The address of the email sender. If not specified, it will be the address indicated by --emailAddress


If your SMTP server requires authentication, this is the username to use.


If your SMTP server requires authentication, this is the password to use.


-m, --mode

workflow running modes (available: 'alignment, deepTools_qc') (default: '"alignment,deepTools_qc"')


Downsample the given number of reads randomly from of each FASTQ file (default: 'False')


Activate fastq read trimming. If activated, Illumina adaptors are trimmed by default. Additional parameters can be specified under --trimmerOptions. (default: 'False')


Possible choices: cutadapt, trimgalore, fastp

Trimming program to use: Cutadapt, TrimGalore, or fastp. Note that if you change this you may need to change --trimmerOptions to match! (default: '"fastp"')


Additional option string for trimming program of choice. (default: 'None')


Run FastQC read quality control (default: 'False')


To extract umi barcode from fastq file via UMI-tools and add it to the read name (default: 'False')


The pattern to be considered for the barcode. 'N' = UMI position (required) 'C' = barcode position (optional) (default: '"NNNNCCCCCCCCC"')


Deduplicate bam file based on UMIs via umi_tools dedup that are present in the read name. (default: 'False')


umi separation character that will be passed to umi_tools.(default: '"_"')


Additional options that will be passed to umi_tools.(default: '')


Bin size of output files in bigWig format (default: '25')


Possible choices: png, pdf, None

Format of the output plots from deepTools. Select 'none' for no plots (default: '"png"')


Program used for mapping: STAR (default: '"STAR"'). If you change this, please change --alignerOptions to match.


STAR option string, e.g.: '--twopassMode Basic' (default: '"--sjdbOverhang 100 --outSAMstrandField intronMotif --outFilterMultimapNmax 1000 --outFilterMismatchNoverLmax 0.1 --outSAMattributes Standard --outSAMunmapped Within --outSAMtype BAM Unsorted"')


Information on samples (required for DE analysis); see '' for example. The column names in the tsv files are 'name' and 'condition'. The first entry defines which group of samples are control. This way, the order of comparison and likewise the sign of values can be changed. The DE analysis might fail if your sample names begin with a number. So watch out for that! (default: 'None')


Input folder with bam files. If provided, the analysis will start from this point. If bam files contain single ends, please specify --singleEnd additionally.


Extention of provided bam files, will be substracted from basenames to obtain sample names. (default: '".bam"')


input data is single-end, not paired-end. This is only used if --fromBAM is specified.

code @ github.