Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA14-21421S" target="_blank" >GA14-21421S: Automatic analysis of spatial gene expression patterns</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ADVANCES IN INTELLIGENT DATA ANALYSIS XV - Lecture Notes in Computer Science
ISBN
978-3-319-46348-3
ISSN
0302-9743
e-ISSN
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Number of pages
11
Pages from-to
204-214
Publisher name
Springer
Place of publication
Wien
Event location
Stockholm
Event date
Oct 13, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000388259100018