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.
Highlights
The introduction of the massively parallel next-generation sequencing (NGS) technologies has caused a great increase in the number of reads typically generated by experiments
The whole genome shotgun (WGS) de novo assembly problem is the reconstruction of the genetic sequence information from a set of reads sequenced from the fragments
Discussion and future work In this paper we present methods and implementation techniques for a new clustering-based, graph-conducted assembler, named SeedsGraph, which is efficient and takes advantage of cloud computing for the large dataset of NGS data
Summary
The introduction of the massively parallel next-generation sequencing (NGS) technologies has caused a great increase in the number of reads typically generated by experiments. The shorter read length from NGS and the sheer demand for more scalable assemblers have been an important computational challenge, and the genome assembly continues to represent one of the most difficult and important algorithmic problems in bioinformatics. Software technology and algorithm implementation become critical factors when dealing with terabytes of data. Cloud computing as a brand new way of dealing with an extremely large dataset offers a good chance for bioinformatics data processing. The ability and feasibility for underlying applications have been discussed [1,2]. We design a graph-based method for the NGS reads assembly problem and implement it as a software package, SeedsGraph. In the Background section, the NGS reads assembly problem and the framework for cloud computing are discussed.
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