MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets

摘要

AkeychallengeinanalyzingmetagenomicsdatapertainstoassemblyofsequencedDNAfragments(i.e.reads)originatingfromvariousmicrobesinagivenenvironmentalsample.Severalexistingmethodologiescanassemblereadsoriginatingfromasinglegenome.However,thesemethodologiescannotbeappliedforefficientassemblyofmetagenomicsequencedatasets.Inthisstudy,wepresentMetaCAAmdash;aclustering-aidedmethodologywhichhelpsinimprovingthequalityofmetagenomicsequenceassembly.MetaCAAinitiallygroupssequencesconstitutingagivenmetagenomeintosmallerclusters.Subsequently,sequencesineachclusterareindependentlyassembledusingCAP3,anexistingsinglegenomeassemblyprogram.Contigsformedineachoftheclustersalongwiththeunassembledreadsarethensubjectedtoanotherroundofassemblyforgeneratingthefinalsetofcontigs.Validationusingsimulatedandreal-worldmetagenomicdatasetsindicatesthatMetaCAAaidsinimprovingtheoverallqualityofassembly.AsoftwareimplementationofMetaCAAisavailableatspanid="ir0045"class="interref"data-locatorType="https"data-locatorKey="//metagenomics.atc.tcs.com/MetaCAA"https://metagenomics.atc.tcs.com/MetaCAA/a/span./p/div