/geofetch

Builds a PEP from SRA or GEO accessions

Primary LanguagePython

geofetch

Given a GEO or SRA accessions, geofetch can 1) download either raw or processed data from either GEO or SRA and 2) produce a standaridized PEP sample annotation sheet of public metadata. This makes it really easy to run looper-compatible pipelines on public datasets by handling data aquisition and metadata formatting and standaridazation for you.

This project is still pre-release, but it is completely functional. However, some things may change in the near future.

Overview

geofetch has two components:

  1. geofetch/geofetch.py - A python script that downloads metadata and produces PEP-compatible sample annotation files, and downloads .sra files (or processed data from GEO if requested).
  2. sra_convert/sra_convert.py - A pypiper pipeline that converts SRA files into BAM files.

How to build a PEP from SRA or GEO data

  1. Set environment variables for $SRARAW (where .sra files will live) and $SRABAM (where .bam files will live). geofetch will use these environment variables to automatically know where to store the .sra and .bam files.

  2. Download SRA data using geofetch.py. You run it like:

    geofetch.py -i GSE#####

    This will download all .sra files into your $SRARAW folder. To see full options, see the help menu with:

    geofetch.py -h

    This will also produce a sample annotation sheet (currently called annocomb_GSE#####.csv in your $SRAMETA folder), which is what you will use as part of your PEP.

  3. With .sra data downloaded, we now need to convert these files into a more usable format (.bam). Build a configuration file (see sra_convert/example/project_config.yaml for example) and point the sample_annotation to the annotation file produced by earlier geofetch.py.

  4. Run the sra_convert pipeline using looper by running this command:

looper run project_config.yaml --lump