NPLB

No Promoter Left Behind - learn de novo promoter architectures from genome-wide TSSs

NPLB comes with a set of two commands: promoterLearn and promoterClassify. promoterLearn is used on a fasta file containing a list promoter sequences. It finds the optimal number of promoter architectures(PAs) each with their own set of promoter elements(PEs). promoterClassify can be used explore new PAs in a different dataset based on an existing model found by promoterLearn. It is also available as a web based application at http://nplb.ncl.res.in.

Prerequisites

Python 2.6+ (Not compatible with Python 3.x)
python-numpy
python-ctypes
python-multiprocessing
python-re
python-pickle
gnuplot 4.6+
Truetype fonts
ImageMagick
psutils

Download

NPLB can be downloaded from https://github.com/computationalBiology/NPLB/archive/v1.0.0.tar.gz. It can be run on any Mac or Linux system.

Installation

In order to download and install NPLB, execute the following steps:

wget https://github.com/computationalBiology/NPLB/archive/v1.0.0.tar.gz
tar -xvf v1.0.0.tar.gz
cd NPLB-1.0.0/NPLB/
make

Publication

Mitra S. and Narlikar L., No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites, Bioinformatics, 32(5):779-781, 2016. [ Full Text ]

Contact

The complete Documentation can be downloaded from here.
For further details
Email: leelavati AT iiserpune.ac.in

License

GNU General Public License v3.0