Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds
Identifieur interne : 000E80 ( Main/Exploration ); précédent : 000E79; suivant : 000E81Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds
Auteurs : Laurent NoéSource :
- Algorithms for Molecular Biology : AMB [ 1748-7188 ] ; 2017.
Abstract
We expand the scope of work of Mak and Benson on single and multiple seeds by considering the Hit Integration model of Chung and Park (BMC Bioinform 11:31, 2010), demonstrate that the same dominance definition can be applied, and that a parameter-free study can be performed without any significant additional cost. We also consider two new discrete models, namely the Heaviside and the Dirac models, where lossless seeds can be integrated. From a theoretical standpoint, we establish a generic framework on all the proposed models, by applying a
Url:
DOI: 10.1186/s13015-017-0092-1
PubMed: 28289437
PubMed Central: 5310094
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p><italic>Spaced seeds</italic>
, also named <italic>gapped q-grams, gapped k-mers, spaced q-grams</italic>
, have been proven to be more sensitive than contiguous seeds (<italic>contiguous q-grams, contiguous k-mers</italic>
) in nucleic and amino-acid sequences analysis. Initially proposed to detect sequence similarities and to anchor sequence alignments, spaced seeds have more recently been applied in several <italic>alignment-free</italic>
related methods. Unfortunately, spaced seeds need to be initially designed. This task is known to be time-consuming due to the number of spaced seed candidates. Moreover, it can be altered by a set of <italic>arbitrary chosen</italic>
parameters from the probabilistic alignment models used. In this general context, <italic>Dominant seeds</italic>
have been introduced by Mak and Benson (Bioinformatics 25:302–308, 2009) on the Bernoulli model, in order to reduce the number of spaced seed candidates that are further processed in a <italic>parameter-free</italic>
calculation of the sensitivity.</p>
</sec>
<sec><title>Results</title>
<p>We expand the scope of work of Mak and Benson on single and multiple seeds by considering the Hit Integration model of Chung and Park (BMC Bioinform 11:31, 2010), demonstrate that the same dominance definition can be applied, and that a parameter-free study can be performed without any significant additional cost. We also consider two new discrete models, namely the Heaviside and the Dirac models, where lossless seeds can be integrated. From a theoretical standpoint, we establish a generic framework on all the proposed models, by applying a <italic>counting semi-ring</italic>
to quickly compute large polynomial coefficients needed by the <italic>dominance</italic>
filter. From a practical standpoint, we confirm that <italic>dominant seeds</italic>
reduce the set of, either single seeds to thoroughly analyse, or multiple seeds to store. Moreover, in <ext-link ext-link-type="uri" xlink:href="http://bioinfo.cristal.univ-lille.fr/yass/iedera%5fdominance">http://bioinfo.cristal.univ-lille.fr/yass/iedera_dominance</ext-link>
, we provide a full list of spaced seeds computed on the four aforementioned models, with one (continuous) parameter left free for each model, and with several (discrete) alignment lengths.</p>
</sec>
</div>
</front>
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<tree><noCountry><name sortKey="Noe, Laurent" sort="Noe, Laurent" uniqKey="Noe L" first="Laurent" last="Noé">Laurent Noé</name>
</noCountry>
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