title: SeedsGraph: an efficient assembler for next-generation sequencing data.
abstract: DNA sequencing technology has been rapidly evolving, and produces a large number of short reads with a fast rising tendency. This has led to a resurgence of research in whole genome shotgun assembly algorithms. We start the assembly algorithm by clustering the short reads in a cloud computing framework, and the clustering process groups fragments according to their original consensus long-sequence similarity. We condense each group of reads to a chain of seeds, which is a kind of substring with reads aligned, and then build a graph accordingly. Finally, we analyze the graph to find Euler paths, and assemble the reads related in the paths into contigs, and then lay out contigs with mate-pair information for scaffolds. The result shows that our algorithm is efficient and feasible for a large set of reads such as in next-generation sequencing technology.
authors.forename: Chunyu
authors.initials: C
authors.lastname: Wang
authors.forename: Maozu
authors.initials: M
authors.lastname: Guo
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Liu
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
authors.forename: Quan
authors.initials: Q
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D012242:Rhodobacter sphaeroides / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*; D013211:Staphylococcus aureus / Q000235:genetics