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2015-07-30Zeitschriftenartikel DOI: 10.1186/s12859-015-0644-7
SuRankCo: supervised ranking of contigs in de novo assemblies
dc.contributor.authorKuhring, Mathias
dc.contributor.authorDabrowski, Piotr Wojtek
dc.contributor.authorPiro, Vitor C.
dc.contributor.authorNitsche, Andreas
dc.contributor.authorRenard, Bernhard Y.
dc.date.accessioned2018-05-07T18:23:22Z
dc.date.available2018-05-07T18:23:22Z
dc.date.created2015-08-06
dc.date.issued2015-07-30none
dc.identifier.otherhttp://edoc.rki.de/oa/articles/reuMFUyptp8s/PDF/26EWOLwHWwpXo.pdf
dc.identifier.urihttp://edoc.rki.de/176904/2108
dc.description.abstractBackground: Evaluating the quality and reliability of a de novo assembly and of single contigs in particular is challenging since commonly a ground truth is not readily available and numerous factors may influence results. Currently available procedures provide assembly scores but lack a comparative quality ranking of contigs within an assembly. Results: We present SuRankCo, which relies on a machine learning approach to predict quality scores for contigs and to enable the ranking of contigs within an assembly. The result is a sorted contig set which allows selective contig usage in downstream analysis. Benchmarking on datasets with known ground truth shows promising sensitivity and specificity and favorable comparison to existing methodology. Conclusions: SuRankCo analyzes the reliability of de novo assemblies on the contig level and thereby allows quality control and ranking prior to further downstream and validation experiments.eng
dc.language.isoeng
dc.publisherRobert Koch-Institut
dc.subjectAlgorithmseng
dc.subjectSoftwareeng
dc.subjectEscherichia coli/geneticseng
dc.subjectQuality controleng
dc.subjectDe novo assemblyeng
dc.subjectGenome assemblyeng
dc.subjectNext generation sequencingeng
dc.subjectContigseng
dc.subjectMachine learningeng
dc.subjectRandom foresteng
dc.subjectEscherichia coli/metabolismeng
dc.subjectContig Mapping/methodseng
dc.subjectROC Curveeng
dc.subject.ddc610 Medizin
dc.titleSuRankCo: supervised ranking of contigs in de novo assemblies
dc.typeperiodicalPart
dc.identifier.urnurn:nbn:de:0257-10040193
dc.identifier.doi10.1186/s12859-015-0644-7
dc.identifier.doihttp://dx.doi.org/10.25646/2033
local.edoc.container-titleBMC Bioinformatics
local.edoc.fp-subtypeArtikel
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttp://www.biomedcentral.com/1471-2105/16/240
local.edoc.container-publisher-nameBioMedCentral
local.edoc.container-volume16
local.edoc.container-issue240
local.edoc.container-year2015

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