Estimating sequence similarity from read sets for clustering next-generation sequencing data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00322805" target="_blank" >RIV/68407700:21230/19:00322805 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s10618-018-0584-8" target="_blank" >https://doi.org/10.1007/s10618-018-0584-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10618-018-0584-8" target="_blank" >10.1007/s10618-018-0584-8</a>

Result language

angličtina

Original language name

Estimating sequence similarity from read sets for clustering next-generation sequencing data

Original language description

Computing mutual similarity of biological sequences such as DNA molecules is essential for significant biological tasks such as hierarchical clustering of genomes. Current sequencing technologies do not provide the content of entire biological sequences; rather they identify a large number of small substrings called reads, sampled at random places of the target sequence. To estimate similarity of two sequences from their read-set representations, one may try to reconstruct each one first from its read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. Due to the nature of data, sequence assembly often cannot provide a single putative sequence that matches the true DNA. Therefore, we propose instead to estimate the similarities directly from the read sets. Our approach is based on an adaptation of the Monge-Elkan similarity known from the field of databases, avoiding the sequence assembly step. For low-coverage (i.e. small) read set samples, it yields a better approximation of the true sequence similarities. This in turn results in better clustering in comparison to the first-assemble-then-cluster approach. Put differently, for a fixed estimation accuracy, our approach requires smaller read sets and thus entails reduced wet-lab costs.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Data Mining and Knowledge Discovery

ISSN

1384-5810

e-ISSN

1573-756X

Volume of the periodical

33

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

1-23

UT code for WoS article

000455608400001

EID of the result in the Scopus database

2-s2.0-85051561012