GenomeTester4: a toolkit for performing basic set operations - union, intersection and complement on k-mer lists

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Abstract

Background

K-mer-based methods of genome analysis have attracted great interest because they do not require genome assembly and can be performed directly on sequencing reads. Many analysis tasks require one to compare k-mer lists from different sequences to find words that are either unique to a specific sequence or common to many sequences. However, no stand-alone k-mer analysis tool currently allows one to perform these algebraic set operations.

Findings

We have developed the GenomeTester4 toolkit, which contains a novel tool GListCompare for performing union, intersection and complement (difference) set operations on k-mer lists. We provide examples of how these general operations can be combined to solve a variety of biological analysis tasks.

Conclusions

GenomeTester4 can be used to simplify k-mer list manipulation for many biological analysis tasks.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0097-y) contains supplementary material, which is available to authorized users.

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Most cited references 8

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GAGE: A critical evaluation of genome assemblies and assembly algorithms.

S. L. Salzberg, A. M. Phillippy, A. Zimin … (2012)

New sequencing technology has dramatically altered the landscape of whole-genome sequencing, allowing scientists to initiate numerous projects to decode the genomes of previously unsequenced organisms. The lowest-cost technology can generate deep coverage of most species, including mammals, in just a few days. The sequence data generated by one of these projects consist of millions or billions of short DNA sequences (reads) that range from 50 to 150 nt in length. These sequences must then be assembled de novo before most genome analyses can begin. Unfortunately, genome assembly remains a very difficult problem, made more difficult by shorter reads and unreliable long-range linking information. In this study, we evaluated several of the leading de novo assembly algorithms on four different short-read data sets, all generated by Illumina sequencers. Our results describe the relative performance of the different assemblers as well as other significant differences in assembly difficulty that appear to be inherent in the genomes themselves. Three overarching conclusions are apparent: first, that data quality, rather than the assembler itself, has a dramatic effect on the quality of an assembled genome; second, that the degree of contiguity of an assembly varies enormously among different assemblers and different genomes; and third, that the correctness of an assembly also varies widely and is not well correlated with statistics on contiguity. To enable others to replicate our results, all of our data and methods are freely available, as are all assemblers used in this study.

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Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

Rob Patro, Stephen Mount, Carl Kingsford (2014)

We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.

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RefSeq microbial genomes database: new representation and annotation strategy

Tatiana Tatusova, Stacy Ciufo, Boris Fedorov … (2013)

The source of the microbial genomic sequences in the RefSeq collection is the set of primary sequence records submitted to the International Nucleotide Sequence Database public archives. These can be accessed through the Entrez search and retrieval system at http://www.ncbi.nlm.nih.gov/genome. Next-generation sequencing has enabled researchers to perform genomic sequencing at rates that were unimaginable in the past. Microbial genomes can now be sequenced in a matter of hours, which has led to a significant increase in the number of assembled genomes deposited in the public archives. This huge increase in DNA sequence data presents new challenges for the annotation, analysis and visualization bioinformatics tools. New strategies have been developed for the annotation and representation of reference genomes and sequence variations derived from population studies and clinical outbreaks.

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Author and article information

Contributors

Lauris Kaplinski: lauris.kaplinski@ut.ee

Maarja Lepamets: maarja.lepamets@ut.ee

Maido Remm: maido.remm@ut.ee

Journal

Journal ID (nlm-ta): Gigascience

Journal ID (iso-abbrev): Gigascience

Title: GigaScience

Publisher: BioMed Central (London )

ISSN (Electronic): 2047-217X

Publication date (Electronic): 3 December 2015

Publication date PMC-release: 3 December 2015

Publication date Collection: 2015

Volume: 4

Electronic Location Identifier: 58

Affiliations

[ ]Department of Bioinformatics, University of Tartu, Riia 23, Tartu, 51010 Estonia

[ ]Estonian Biocentre, Riia 23B, Tartu, 51010 Estonia

Article

Publisher ID: 97

DOI: 10.1186/s13742-015-0097-y

PMC ID: 4669650

PubMed ID: 26640690

SO-VID: dab4c078-c0e7-46c5-a38d-da50fa5e7685

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 17 April 2015

Date accepted : 11 November 2015

Custom metadata

Keywords: k-mers,sequence analysis,next-generation sequencing

Data availability:

Keywords: k-mers, sequence analysis, next-generation sequencing

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GenomeTester4: a toolkit for performing basic set operations - union, intersection and complement on k-mer lists

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Abstract

Background

Findings

Conclusions

Electronic supplementary material

Related collections

Arabidopsis genomics

Most cited references 8

GAGE: A critical evaluation of genome assemblies and assembly algorithms.

Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

RefSeq microbial genomes database: new representation and annotation strategy

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Contributors

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Article

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Cited by 19

Most referenced authors 179