title: Parallel short sequence assembly of transcriptomes.
abstract: BACKGROUND
  The de novo assembly of genomes and transcriptomes from short sequences is a challenging problem. Because of the high coverage needed to assemble short sequences as well as the overhead of modeling the assembly problem as a graph problem, the methods for short sequence assembly are often validated using data from BACs or small sized prokaryotic genomes.
  
  
  RESULTS
  We present a parallel method for transcriptome assembly from large short sequence data sets. Our solution uses a rigorous graph theoretic framework and tames the computational and space complexity using parallel computers. First, we construct a distributed bidirected graph that captures overlap information. Next, we compact all chains in this graph to determine long unique contigs using undirected parallel list ranking, a problem for which we present an algorithm. Finally, we process this compacted distributed graph to resolve unique regions that are separated by repeats, exploiting the naturally occurring coverage variations arising from differential expression.
  
  
  CONCLUSIONS
  We demonstrate the validity of our method using a synthetic high coverage data set generated from the predicted coding regions of Zea mays. We assemble 925 million sequences consisting of 40 billion nucleotides in a few minutes on a 1024 processor Blue Gene/L. Our method is the first fully distributed method for assembling a non-hierarchical short sequence data set and can scale to large problem sizes.
authors.affiliation: Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA. zbbrox@iastate.edu
authors.forename: Benjamin G
authors.initials: BG
authors.lastname: Jackson
authors.forename: Patrick S
authors.initials: PS
authors.lastname: Schnable
authors.forename: Srinivas
authors.initials: S
authors.lastname: Aluru
mesh_terms: D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D018745:Genome, Plant; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D003313:Zea mays / Q000235:genetics