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Fast l1 Minimization for Genomewide Analysis of mRNA Lengths

Iddo Drori; Victoria C. Stodden; Evan H. Hurowitz

Title:
Fast l1 Minimization for Genomewide Analysis of mRNA Lengths
Author(s):
Drori, Iddo
Stodden, Victoria C.
Hurowitz, Evan H.
Date:
Type:
Articles
Department:
Statistics
Book/Journal Title:
2006 IEEE International Workshop on Genomic Signal Processing and Statistics: College Station, TX, 28-30 May 2006
Publisher:
IEEE
Publisher Location:
Piscataway, N.J.
Abstract:
Application of the virtual northern method to human mRNA allows us to systematically measure transcript length on a genome-wide scale [1]. Characterization of RNA transcripts by length provides a measurement which complements cDNA sequencing. We have robustly extracted the lengths of the transcripts expressed by each gene for comparison with the Unigene, Refseq, and H-Invitational databases [2, 3]. Obtaining an accurate probability for each peak requires performing multiple bootstrap simulations, each involving a deconvolution operation which is equivalent to finding the sparsest non-negative solution of an underdetermined system of equations. This process is computationally intensive for a large number of simulations and genes. In this contribution we present an efficient approximation method which is faster than general purpose solvers by two orders of magnitude, and in practice reduces our processing time from a week to hours.
Subject(s):
Genetics
Statistics
Publisher DOI:
http://dx.doi.org/10.1109/GENSIPS.2006.353135
Item views:
120
Metadata:
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