Fast ℓ1 minimization for genomewide analysis of mRNA lengths

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2006 IEEE International Workshop on Genomic Signal Processing and Statstics, GENSIPS 2006
Pages19-20
Number of pages2
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006 - College Station, TX, United States
Duration: May 28 2006May 30 2006

Publication series

Name2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006

Other

Other2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
CountryUnited States
CityCollege Station, TX
Period5/28/065/30/06

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Statistics and Probability

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