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Reference-guided de novo assembly approach improves genome reconstruction for related species.

Abstract：

BACKGROUND:The development of next-generation sequencing has made it possible to sequence whole genomes at a relatively low cost. However, de novo genome assemblies remain challenging due to short read length, missing data, repetitive regions, polymorphisms and sequencing errors. As more and more genomes are sequenced, reference-guided assembly approaches can be used to assist the assembly process. However, previous methods mostly focused on the assembly of other genotypes within the same species. We adapted and extended a reference-guided de novo assembly approach, which enables the usage of a related reference sequence to guide the genome assembly. In order to compare and evaluate de novo and our reference-guided de novo assembly approaches, we used a simulated data set of a repetitive and heterozygotic plant genome. RESULTS:The extended reference-guided de novo assembly approach almost always outperforms the corresponding de novo assembly program even when a reference of a different species is used. Similar improvements can be observed in high and low coverage situations. In addition, we show that a single evaluation metric, like the widely used N50 length, is not enough to properly rate assemblies as it not always points to the best assembly evaluated with other criteria. Therefore, we used the summed z-scores of 36 different statistics to evaluate the assemblies. CONCLUSIONS:The combination of reference mapping and de novo assembly provides a powerful tool to improve genome reconstruction by integrating information of a related genome. Our extension of the reference-guided de novo assembly approach enables the application of this strategy not only within but also between related species. Finally, the evaluation of genome assemblies is often not straight forward, as the truth is not known. Thus one should always use a combination of evaluation metrics, which not only try to assess the continuity but also the accuracy of an assembly.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Lischer HEL,Shimizu KK

doi

10.1186/s12859-017-1911-6

subject

Has Abstract

pub_date

2017-11-10 00:00:00

pages

474

issue

1

issn

1471-2105

pii

10.1186/s12859-017-1911-6

journal_volume

18

pub_type

杂志文章

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