RT Journal Article
SR Electronic
T1 Graph regularized, semi-supervised learning improves annotation of <em>de novo</em> transcriptomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 089417
DO 10.1101/089417
A1 Laraib I. Malik
A1 Shravya Thatipally
A1 Nikhil Junneti
A1 Rob Patro
YR 2016
UL http://biorxiv.org/content/early/2016/11/25/089417.abstract
AB We present a new method, GRASS, for improving an initial annotation of de novo transcriptomes. GRASS makes the shared-sequence relationships between assembled contigs explicit in the form of a graph, and applies an algorithm that performs label propagation to transfer annotations between related contigs and modifies the graph topology iteratively. We demonstrate that GRASS increases the completeness and accuracy of the initial annotation, allows for improved differential analysis, and is very efficient, typically taking 10s of minutes.