Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00304251" target="_blank" >RIV/68407700:21230/16:00304251 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-46349-0_18" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-46349-0_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-46349-0_18" target="_blank" >10.1007/978-3-319-46349-0_18</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data

Popis výsledku v původním jazyce

Clustering biological sequences is a central task in bioinformatics. The typical result of new-generation sequencers is a set of short substrings (“reads”) of a target sequence, rather than the sequence itself. To cluster sequences given only their read-set representations, one may try to reconstruct each one from the corresponding read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. This approach is however problematic and 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. It avoids the NP-hard problem of sequence assembly and in empirical experiments it results in a better approximation of the true sequence similarities and consequently in better clustering, in comparison to the first-assemble-then-cluster approach.

Název v anglickém jazyce

Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data

Popis výsledku anglicky

Clustering biological sequences is a central task in bioinformatics. The typical result of new-generation sequencers is a set of short substrings (“reads”) of a target sequence, rather than the sequence itself. To cluster sequences given only their read-set representations, one may try to reconstruct each one from the corresponding read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. This approach is however problematic and 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. It avoids the NP-hard problem of sequence assembly and in empirical experiments it results in a better approximation of the true sequence similarities and consequently in better clustering, in comparison to the first-assemble-then-cluster approach.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GA14-21421S" target="_blank" >GA14-21421S: Automatická analýza prostorových vzorů genové exprese</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

ADVANCES IN INTELLIGENT DATA ANALYSIS XV - Lecture Notes in Computer Science

ISBN

978-3-319-46348-3

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

204-214

Název nakladatele

Springer

Místo vydání

Wien

Místo konání akce

Stockholm

Datum konání akce

13. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000388259100018