rnacounter 1.1.2

Estimate abundances of genomic features from read densities

Welcome!

Rnacounter estimates abundances of genes and their different transcripts from read densities. Exons and introns can also be quantified. It requires a genome-level BAM file and a GTF/GFF file describing the exon structure, such as those provided by Ensembl or GenRep. The method used is described in [<ref>].

It is not meant to be used as a library, but through its command-line tool “rnacounter”.

The code is under GPL-2 license.

Usage:

See “rnacounter –help” and the tutorial at http://bbcf.epfl.ch/bbcflib/tutorial_rnacounter.html, also available in the doc/ folder.

Minimal example:

rnacounter test.bam test.gtf        # Python 2.7 version
rnacounter3 test.bam test.gtf       # Python 3 version

Installation:

First ensure that you have setuptools>=7.0 and numpy installed.

With easy_install:

sudo easy_install rnacounter

or better yet, with pip:

sudo pip install rnacounter

Use “easy_install3”, “pip3” respectively to install the python3 version and run it in command-line with “rnacounter3”. The code is fully compatible with Python 2.7, but uses Python3’s syntax. In particular, things that became iterators in Python3 will be treated as lists if Python2.7 is used (which may increase speed at the expense of the memory used).

It installs as a standard Python library but includes the executable and puts it somewhere in your $PATH. Dependencies should be added automatically.

To uninstall with pip:

sudo pip uninstall rnacounter

Build from source:

Clone or download the repository from https://github.com/delafont/rnacounter . From where rnacounter.pyx lies (rnacounter/rnacounter/), run:

sudo python setup.py build_ext

It will recompile to create rnacounter.c, and build it. Then add the executable (rnacounter/rnacounter/rnacounter) to your $PATH, or install from the package root (rnacounter/):

sudo python setup.py install

Dependencies:

Tests run with the library versions below, but may work with earlier versions.

• setuptools 7.0+

• pysam 0.7.5+

• numpy 1.6.2+

• scipy 0.9.0+

• docopt 0.6.1+

• cython 0.18+

Testing:

Testing files in the testfiles/ folder: - gapdhKO.bam: alignment on mm9 with only Gapdh covered. - mm9_3genes_renamed.gtf: extract of the Ensembl GTF with Gapdh, the gene before and the gene after it. - mm9_Gapdh_renamed.gtf: extract of the Ensembl GTF with Gapdh only.

Example:

rnacounter testfiles/gapdhKO.bam testfiles/mm9_3genes_renamed.gtf

The BAM contains 4041 reads all aligning perfectly on Gapdh (ENSMUSG00000057666) exons, mostly on ENSMUSE00000487077 but also ENSMUSE00000751942 and ENSMUSE00000886744. Nothing on other exons, which makes it a good example of badly conditioned input data…

The least squares method returns counts on the following transcripts: ENSMUST00000117757, ENSMUST00000118875, ENSMUST00000147954 and nothing on ENSMUST00000073605, ENSMUST00000144205, ENSMUST00000144588 .

Returns a count of 2459.62 (1091.71 RPK) for the gene.