cdpipelines 0.0.7

Description:

cdpipelines 0.0.7

This repository holds various bioinformatics pipelines.

Dependencies
Many of the pipeline dependencies can be obtained using the prepare
submodule (see below). Additionally, a working Python environment is
needed along with some of the common scientific python packages. I
recommend using Anaconda
python since it includes
most of the needed packages. If you are using Anaconda, I’d recommend
making new environments for different projects. Besides the default
Anaconda packages, you will need the following (available through
conda or pip):

HTSeq
pandas
pysam (this is available through conda but currently it’s an old
version so you have to get it using pip)
PyVCF


rpy2
Installing rpy2 can be tricky. Different versions of R and
rpy2 don’t work well together, so it’s recommended to make a local
installation of R using the prepare submodule and compile
rpy2 against this installation. You can install R using
prepare.download_r and install rpy2 using
prepare.download_install_rpy2. prepare.download_install_rpy2
will prompt you to set your PATH, LDFLAGS, and LD_LIBRARY_PATH to
correctly install rpy2. After installing rpy2, you need to set
your PATH and LD_LIBRARY_PATH using these commands for every bash
session where you want to use this rpy2. I’d recommend putting the
commands in a file that you source every time you load the project’s
Anaconda environment.



Submodules

general
general contains methods used in multiple pipelines. Some pipelines
use similar but different versions of some methods, so the pipelines
will have their own versions of those methods. Sometimes it may make
sense to add options to a particular method that is used in multiple
pipelines (where each pipeline has slightly different versions) and add
the method into general.


prepare
The prepare module contains functions for downloading various
software and reference files needed for the different pipelines.


rnaseq
This pipeline currently starts from fastq files and has two steps. For
detailed information on each step, so the docstrings for each method.
The first step is align_and_sort which (optionally) removes
duplicates, aligns the reads, and makes coverage bigwig files for use
with the UCSC genome browser or IGV. The read alignments are output in
both genomic and transcriptomic coordinates. The second step is
get_counts which counts reads overlapping genes for gene
differential expression and exonic bins for use with DEXSeq.