Abstract
BackgroundNext generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly.ResultsPreviously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies.ConclusionsOur overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies.
Highlights
Generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates
While assembly results have been shown to be substantially improved by clustering metagenomics data before sequence assembly [13], overlap relationships retained by the assembly overlap graph are lost, leading to the removal of key global read overlap relationships and read similarities
We evaluate our algorithm’s graph coarsening and clustering results and compare them to results obtained by clustering a similar 454 metagenomics read dataset
Summary
Generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. Metagenomics is a field of research that focuses on the sequencing of communities of organisms This adds an additional layer of complexity to the analysis of short reads produced from metagenomics samples containing multiple sources of genetic information. Often these reads must be clustered or binned into their respective genomes before assembly or analysis of the reads can take place to avoid chimeric assembly results [9]. While assembly results have been shown to be substantially improved by clustering metagenomics data before sequence assembly [13], overlap relationships retained by the assembly overlap graph are lost, leading to the removal of key global read overlap relationships and read similarities
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