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The complex task of choosing a de novo assembly: Lessons from fungal genomes

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dc.creatorGallo, Juan Estebanspa
dc.creatorMuñoz, José Fernandospa
dc.creatorMisas, Elizabethspa
dc.creatorMcEwen, Juan Guillermospa
dc.creatorClay, Oliver Keatingespa
dc.date.accessioned2020-05-26T00:05:10Z
dc.date.available2020-05-26T00:05:10Z
dc.date.created2014spa
dc.description.abstractSelecting the values of parameters used by de novo genomic assembly programs, or choosing an optimal de novo assembly from several runs obtained with different parameters or programs, are tasks that can require complex decision-making. A key parameter that must be supplied to typical next generation sequencing (NGS) assemblers is the k-mer length, i.e., the word size that determines which de Bruijn graph the program should map out and use. The topic of assembly selection criteria was recently revisited in the Assemblathon 2 study (Bradnam et al., 2013). Although no clear message was delivered with regard to optimal k-mer lengths, it was shown with examples that it is sometimes important to decide if one is most interested in optimizing the sequences of protein-coding genes (the gene space) or in optimizing the whole genome sequence including the intergenic DNA, as what is best for one criterion may not be best for the other. In the present study, our aim was to better understand how the assembly of unicellular fungi (which are typically intermediate in size and complexity between prokaryotes and metazoan eukaryotes) can change as one varies the k-mer values over a wide range. We used two different de novo assembly programs (SOAPdenovo2 and ABySS), and simple assembly metrics that also focused on success in assembling the gene space and repetitive elements. A recent increase in Illumina read length to around 150 bp allowed us to attempt de novo assemblies with a larger range of k-mers, up to 127 bp. We applied these methods to Illumina paired-end sequencing read sets of fungal strains of Paracoccidioides brasiliensis and other species. By visualizing the results in simple plots, we were able to track the effect of changing k-mer size and assembly program, and to demonstrate how such plots can readily reveal discontinuities or other unexpected characteristics that assembly programs can present in practice, especially when they are used in a traditional molecular microbiology laboratory with a 'genomics corner'. Here we propose and apply a component of a first pass validation methodology for benchmarking and understanding fungal genome de novo assembly processes. © 2014 Elsevier Ltd. All rights reserved.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1016/j.compbiolchem.2014.08.014
dc.identifier.issn14769271
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23761
dc.language.isoengspa
dc.publisherElsevier Ltdspa
dc.relation.citationEndPage107
dc.relation.citationIssueNo. PA
dc.relation.citationStartPage97
dc.relation.citationTitleComputational Biology and Chemistry
dc.relation.citationVolumeVol. 53
dc.relation.ispartofComputational Biology and Chemistry, ISSN:14769271, Vol.53, No.PA (2014); pp. 97-107spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84908554464&doi=10.1016%2fj.compbiolchem.2014.08.014&partnerID=40&md5=66fd3c29a8b9f784aa0c6941b74970e4spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordComplex taskspa
dc.subject.keywordDe novo assembliesspa
dc.subject.keywordGenome assemblyspa
dc.subject.keywordNext-generation sequencingspa
dc.subject.keywordSpacer DNAspa
dc.subject.keywordAlgorithmspa
dc.subject.keywordContig mappingspa
dc.subject.keywordDNA sequencespa
dc.subject.keywordFungal genomespa
dc.subject.keywordGeneticsspa
dc.subject.keywordHigh throughput sequencingspa
dc.subject.keywordNucleotide repeatspa
dc.subject.keywordOpen reading framespa
dc.subject.keywordParacoccidioidesspa
dc.subject.keywordQuality controlspa
dc.subject.keywordStatistics and numerical dataspa
dc.subject.keywordAlgorithmsspa
dc.subject.keywordBenchmarkingspa
dc.subject.keywordContig Mappingspa
dc.subject.keywordDNAeng
dc.subject.keywordGenomeeng
dc.subject.keywordHigh-Throughput Nucleotide Sequencingspa
dc.subject.keywordOpen Reading Framesspa
dc.subject.keywordParacoccidioidesspa
dc.subject.keywordRepetitive Sequenceseng
dc.subject.keywordSequence Analysiseng
dc.subject.keywordGenome assembly methodsspa
dc.subject.keywordNext-generation sequencingspa
dc.subject.keywordRepetitive DNAspa
dc.titleThe complex task of choosing a de novo assembly: Lessons from fungal genomesspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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