A single pass approach to reducing sampling variation, removing errors, and scaling {\em de novo} assembly of shotgun sequences

Details A single pass approach to reducing sampling variation, removing errors, and scaling {\em de novo} assembly of shotgun sequences A single pass approach to reducing sampling variation, removing errors, and scaling {\em de novo} assembly of shotgun sequences

2012-03-21

arxiv.org/abs/1203.4802v1

Biology

Quantitative Biology

Genomics

Scientific paper

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes enable the sensitive investigation of a wide range of biological phenomena. However, it is increasingly difficult to deal with the volume of data emerging from deep short-read sequencers, in part because of random and systematic sampling variation as well as a high sequencing error rate. These challenges have led to the development of entire new classes of short-read mapping tools, as well as new {\em de novo} assemblers. Even newer assembly strategies for dealing with transcriptomes, single-cell genomes, and metagenomes have also emerged. Despite these advances, algorithms and compute capacity continue to be challenged by the continued improvements in sequencing technology throughput. We here describe an approach we term digital normalization, a single-pass computational algorithm that discards redundant data and both sampling variation and the number of errors present in deep sequencing data sets. Digital normalization substantially reduces the size of data sets and accordingly decreases the memory and time requirements for {\em de novo} sequence assembly, all without significantly impacting content of the generated contigs. In doing so, it converts high random coverage to low systematic coverage. Digital normalization is an effective and efficient approach to normalizing coverage, removing errors, and reducing data set size for shotgun sequencing data sets. It is particularly useful for reducing the compute requirements for {\em de novo} sequence assembly. We demonstrate this for the assembly of microbial genomes, amplified single-cell genomic data, and transcriptomic data. The software is freely available for use and modification.

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