mattravenhall / SV-Pop

Upscaling SV detection to a multi-population level.

SV-Pop

SV-Pop performs post-discovery SV analysis and visualisation; it contains two modules for these purposes. Both modules should work out of the box, but it's a good idea to run preflightchecks.py (in Analysis/) to check that all dependencies are installed, and to optionally add SVPop to your PATH.

Extended documentation, including specifics regarding input files, is present on this repo's wiki.

Analysis Module

Quick start:

SVPop -h

Functions

• DEFAULT: Process individual vcf files to population-level lists.
• CONVERT: Convert a variant output file into a window file.
• FILTER: Filter a variant output file by a range of factors.
• MERGE-CHR: Merge per-chromosome variants files into one file.
• MERGE-MODEL: Merge by-model variants files into one file.
• SUBSET: Create a subset of a given variant or window file.
• STATS: Produce summary statistics for a variant or window files.
• PREPROCESS: Process analysis output files for visualisation.

Expanded help can be found on the wiki.

Visualisation Module

Quick start:

SVPop --PREPROCESS --variantFile=YOUR_PREFIX
Rscript easyRun.r

Expected Input

The visualisation module will expect the following files in Visualisation/Files/:

• <model>_Variants.csv: Reformatted variants file.
• <model>_Windows.csv: Reformatted windows file.
• <model>_AllIndex.csv: Locations of all variants, for faster indexing.
• <model>_FrqIndex.csv: Subset of AllIndex for 'frequent' (>5%) variants only.
• annotation.txt: The annotation file used for your SVPop Analysis run (in tsv format).

These files can be created from a default SVPop Analysis run with SVPop --PREPROCESS --variantFile=YOUR_PREFIX.

Expanded help can be found on the wiki.

Citation

Matt Ravenhall, Susana Campino and Taane G. Clark. BMC Bioinformatics 2019 20:136. https://doi.org/10.1186/s12859-019-2718-4