nf-core/circrna

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 3 columns, and a header row. See usage docs.

There are two rules for providing the phenotype CSV file. 1) The 'sample' column must match the sample sheets 'sample' column. 2) The response variable containing the phenotype of primary interest in the experiment must have the column name condition. All other columns included in the file are controlled for in the DESeq2 design.

| sample | condition | replicate |
|----------- |----------- |----------- |
| control_1 | ctr | 1 |
| control_2 | ctr | 2 |
| control_3 | ctr | 3 |
| treated_1 | trt | 1 |
| treated_2 | trt | 2 |
| treated_3 | trt | 3 |

The above phenotype file will identify differentially expressed circRNAs/mRNAs between control and treatment cells, whilst controlling for the effect of variation between replicates: ~ replicates + condition

The annotation file should be a CSV file with the following columns: name, file and min_overlap. The name column should contain a unique identifier for the annotation, the file column should contain the path to the BED file and the min_overlap column should contain the minimum overlap required for a circRNA to be considered as overlapping with the annotation. The min_overlap column is optional and defaults to 0.9 if not provided.

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.

Select one or a combination of circRNA quantification tools for the pipeline e.g:
--tool 'circexplorer2, ciriquant, find_circ'

N.B: Selecting more than one circRNA quantification tool will trigger the circRNA filtering parameter --min_tools

This parameter must be a combination of the following values: ciriquant, circexplorer2, find_circ, circrna_finder, mapsplice, dcc, segemehl, ciriquant, circexplorer2, find_circ, circrna_finder, mapsplice, dcc, segemehl

Filter low confidence circRNAs by removing circRNAs with read counts below a specified value. To disable, set the value to 1 (default).

When multiple circRNA quantification tools have been provided to --tool, set a filtering method whereby circRNAs are output if they have been called by at least n quantification tools.

Setting --min_tools to 1 is the same as taking the union, all circRNAs are included in the output.

Setting --min_tools to 2 will output circRNAs that have been called by at least 2 quantification tools and so on.

Filter circRNAs by removing circRNAs detected in fewer samples than the specified value. To disable, set the value to 1 (default).

Select one or a combination of circRNA quantification tools for the pipeline e.g:
--quantification_tools 'ciriquant,psirc,sum,max'

The mirna_min_percentage parameter sets the minimum percentage of samples in which a miRNA must be expressed to pass filtering. The default value is 0.2, which means a miRNA must be detected in at least 20% of the samples to be included in the analysis.

This parameter determines the minimum number of reads that a miRNA must have to pass filtering. The default is 5, meaning a miRNA must have at least 5 reads across the samples to be considered for analysis.

Select the correlation method to be applied in the correlation analysis of miRNAs.

This parameter must be a combination of the following values: pearson, spearman

Select one or a combination of miRNA bindingsite prediction tools for the pipeline e.g:
--mirna_tools 'miranda,targetscan'

Controls the number of votes required for a binding site prediction to be considered valid. If a miRNA binding site was predicted by two different tools (e.g., miRanda and TargetScan), it receives two votes. By specifying additional tools for miRNA binding site prediction (using the 'mirna_min_tools' parameter), you can adjust the number of votes required for a binding site to be considered valid.

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

By using a reference genome build on iGenomes, the gtf, mature, species and index files (bar HISAT2 and segemehl) will be automatically downloaded for you.

See the nf-core website docs for more details.

If you use AWS iGenomes, this has already been set for you appropriately.

This parameter is mandatory if --genome is not specified.

This parameter is mandatory if --genome is not specified. Needs to contain the following attributes: gene_id, transcript_id and gene_name.

Typically this will be the mature.fa file from miRBase. Can be given either as a plain text .fa file or a compressed .gz file.

HiSAT2 requires a huge amount of RAM to build a genome index for larger genomes, if including splice sites and exons e.g. the human genome might typically require 200GB. If you specify less than this threshold for the HISAT2_BUILD process then the splice sites and exons will be ignored, meaning that the process will require a lot less memory. If you are working with a small genome, set this parameter to a lower value to reduce the threshold for skipping this check. If using a larger genome, consider supplying more memory to the HISAT2_BUILD process.

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.

This enables the option 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.