grothlab/crepas

You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 5 columns, and a header row. See usage docs.

See the SAM format specification. Valid values: CAPILLARY, DNBSEQ (MGI/BGI), ELEMENT, HELICOS, ILLUMINA, IONTORRENT, LS454, ONT (Oxford Nanopore), PACBIO (Pacific Biosciences), SINGULAR, SOLID, and ULTIMA. This field should be omitted when the technology is not in this list (though the PM field may still be present in this case) or is unknown.

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.

If you don't have the appropriate alignment index available this will be generated for you automatically based on this file. Combine with --save_reference to save alignment index for future runs.

Effective genome size parameter required by MACS3.

If provided, alignments that overlap with the regions in this file will be filtered out (see ENCODE blacklists). The file should be in BED format.

Defines the annotation file of 'sparse segments' (genomic regions) from the complement of an 'inflated' BED file of known active regions. See https://github.com/nolan-h-hamilton/Consenrich/blob/5e7f287d81ac4a9bc098ae29e825a5ded07af3b8/consenrich/consenrich.py#L58C54-L58C174

Defines the annotation file of 'active regions' (genomic regions) for Consenrich. See https://github.com/nolan-h-hamilton/Consenrich/blob/5e7f287d81ac4a9bc098ae29e825a5ded07af3b8/consenrich/consenrich.py#L67

CSV file containing chromosome-specific optimization parameters. Each supported genome has a custom --params file packaged with ROCCO, but a custom file has to be manually passed to ROCCO within this pipeline. Consider using this argument to set chromosome-specific budget and gamma values custom to your data and preferences. See the ROCCO documentation for more details.

If using HISAT2 as the aligner, you can provide a list of known splice sites, which HISAT2 makes use of to align reads with small anchors. If this file is not provided, it will be generated from the GTF file.

If the BWA index is generated by the pipeline use this parameter to save it to your results folder. These can then be used for future pipeline runs, reducing processing times.

Set this parameter to true to skip indexing the GTF file with tabix. This may be useful if your GTF file is already indexed or if you encounter issues with tabix.

More details can be found in the UMI-tools documentation.

More details can be found in the UMI-tools documentation.

BAMs are splitted by chromosome, deduplicated, and then merged back to improve parallelization of the deduplication step. Set this parameter to true to skip this splitting step. This may be useful if you encounter issues with the splitting or merging steps or if your BAM files are small and you don't think that parallelization will speed up the deduplication step.

The default (false) setting will mean that the auto-detect function of Trim Galore will be used. See https://github.com/FelixKrueger/TrimGalore/blob/master/Docs/Trim_Galore_User_Guide.md#adapter-auto-detection

This enables the Cutadapt --nextseq-trim=3'CUTOFF option via Trim Galore, which will set a quality cutoff (that is normally given with -q instead), but qualities of G bases are ignored. This trimming is in common for the NextSeq- and NovaSeq-platforms, where basecalls without any signal are called as high-quality G bases. Read the Cutadapt documentation for more details.

In other words, to reduce the number of falsely trimmed bases, the alignment algorithm in Trim Galore requires that at least this number of bases of the adapter are aligned to the read. This parameter corresponds to the --stringency option in Trim Galore and the --overlap option in Cutadapt.

Use this if your input FastQ files have already been trimmed outside of the workflow or if you're very confident that there is no adapter contamination in your data.

By default, trimmed FastQ files will not be saved to the results directory. Specify this flag (or set to true in your config file) to copy these files to the results directory when complete.

By default, intermediate BAM files will not be saved. The final BAM files created after the appropriate filtering step are always saved to limit storage usage. Set this parameter to also save other intermediate BAM files.

This may either be in the form of FastQ or BAM files depending on the options available for that particular tool.

Single-end: Requires a user specified value for the final fragment length. Reads that already exceed this fragment length will not be extended. Paired-end: Reads with mates are always extended to match the fragment size defined by the two read mates. Unmated reads, mate reads that map too far apart (>4x fragment length) or even map to different chromosomes are treated like single-end reads. The input of a fragment length value is optional. If no value is specified, it is estimated from the data (mean of the fragment size of all mate reads). Read the deepTools documentation for more details.

The min_endo_across method will downsample all IPs and inputs of the same antibody to the minimum number of endogenous reads across all IPs and inputs of the same antibody. The min_exo_across method will downsample all IPs and inputs of the same antibody to the minimum number of exogenous reads across all IPs and inputs of the same antibody. The min_endo_by_type method will downsample all IPs to the minimum number of endogenous reads across all IPs of the same antibody, and all the inputs to the minimum number of endogenous reads across all inputs of the same antibody. The min_exo_by_type method will downsample all IPs to the minimum number of exogenous reads across all IPs of the same antibody, and all the inputs to the minimum number of exogenous reads across all inputs of the same antibody.

If you are confident you have good reproducibility amongst your replicates then you can increase the value of this parameter to create a 'reproducible' set of consensus peaks. For example, a value of 2 will mean peaks that have been called in at least 2 replicates will contribute to the consensus set of peaks, and as such peaks that are unique to a given replicate will be discarded. A value of -1 means that a peak must have been called in all replicates to contribute to the consensus set of peaks.

By default, MACS3 considers 1000 bp for small local region (--slocal). You can tweak this according to your project. Remember that if the region is set too small, a sharp spike in the input data may kill a significant peak. See MACS3 documentation for more details.

By default, MACS3 considers 10000 bp for large local region (--llocal) which captures the bias from a long-range effect like an open chromatin domain. You can tweak this according to your project. See MACS3 documentation for more details.

See DESeq2 docs.