NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly

TitleNeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly
Publication TypeJournal Article
Year of Publication2014
AuthorsMcCorrison JM, Venepally P, Singh I, Fouts DE, Lasken RS, Methé BA
JournalBMC Bioinformatics
Volume15
Issue1
Pagination357
Date Published2014 Nov 19
ISSN1471-2105
Abstract

BackgroundDeep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases.ResultsHere we introduce NeatFreq, a software tool that reduces a data set to more uniform coverage by clustering and selecting from reads binned by their median kmer frequency (RMKF) and uniqueness. Previous algorithms normalize read coverage based on RMKF, but do not include methods for the preferred selection of (1) extremely low coverage regions produced by extremely variable sequencing of random-primed products and (2) 2-sided paired-end sequences. The algorithm increases the incorporation of the most unique, lowest coverage, segments of a genome using an error-corrected data set. NeatFreq was applied to bacterial, viral plaque, and single-cell sequencing data. The algorithm showed an increase in the rate at which the most unique reads in a genome were included in the assembled consensus while also reducing the count of duplicative and erroneous contigs (strings of high confidence overlaps) in the deliverable consensus. The results obtained from conventional Overlap-Layout-Consensus (OLC) were compared to simulated multi-de Bruijn graph assembly alternatives trained for variable coverage input using sequence before and after normalization of coverage. Coverage reduction was shown to increase processing speed and reduce memory requirements when using conventional bacterial assembly algorithms.ConclusionsThe normalization of deep coverage spikes, which would otherwise inhibit consensus resolution, enables High Throughput Sequencing (HTS) assembly projects to consistently run to completion with existing assembly software. The NeatFreq software package is free, open source and available at https://github.com/bioh4x/NeatFreq.

DOI10.1186/s12859-014-0357-3
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/25407910?dopt=Abstract
PMCPMC4245761
Alternate JournalBMC Bioinformatics
PubMed ID25407910