LSTrAP: efficiently combining RNA sequencing data into co-expression networks.

Sebastian ProostAgnieszka KrawczykSebastian Proost

Published in: BMC bioinformatics (2017)

LSTrAP combines the most popular and performant methods to construct co-expression networks from RNA-Seq data into a single workflow. This allows large amounts of expression data, required to construct co-expression networks, to be processed efficiently and consistently across hundreds of samples. LSTrAP is implemented in Python 3.4 (or higher) and available under MIT license from https://github.molgen.mpg.de/proost/LSTrAP.

Keyphrases

poor prognosis
rna seq
single cell
electronic health record
big data
machine learning
deep learning
data analysis
artificial intelligence