Robustness of Massively Parallel Sequencing Platforms

Kavak, Pınar; Yüksel, Bayram; Aksu, Soner; Külekçi, M. Oğuzhan; Güngör, Tunga; Hach, Faraz; Şahinalp, S. Cenk; Alkan, Can; Sağıroğlu, Mahmut Şamil

doi:10.1371/journal.pone.0138259

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an illustration, consider a pipeline including read mapping, variant calling and genotyping, phasing and association testing. Substantial uncertainty and sequence composition biases are already inherent to the input data generated by next-generation sequencing [ 157 ]. The following read alignment step adds ambiguity in read placement, leading to uncertain coverage and fragment lengths.…”

Section: Computational Challengesmentioning

confidence: 99%

Computational pan-genomics: status, promises and challenges

2016

Brief Bioinform

174

105

View full text Add to dashboard Cite

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

show abstract

Section: Computational Challengesmentioning

confidence: 99%

Computational pan-genomics: status, promises and challenges

2016

Brief Bioinform

174

105

View full text Add to dashboard Cite

show abstract

“…As an illustration, consider a pipeline including read mapping, variant calling and genotyping, phasing and association testing. Substantial uncertainty and sequence composition biases are already inherent to the input data generated by next-generation sequencing [156]. The following read alignment step adds ambiguity in read placement, leading to uncertain coverage and fragment lengths.…”

Section: Data Uncertainty Propagationmentioning

confidence: 99%

Computational Pan-Genomics: Status, Promises and Challenges

Marschall¹,

Marz²,

Abeel³

et al. 2016

Preprint

Self Cite

View full text Add to dashboard Cite

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic datasets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this paper, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies, and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains. * The Computational Pan-Genomics Consortium formed at a workshop held June 8-12, 2015, at the Lorentz Center in Leiden, the Netherlands, with the purpose of providing a cross-disciplinary overview of the emerging discipline of Computational Pan-Genomics. Members are listed at the end of this article.

show abstract

“…We recently showed that resequencing the same DNA library with the same model HTS instrument twice and analyzing the data with the same algorithms may lead to different variation call sets (Kavak et al, 2015).…”

Section: Introductionmentioning

confidence: 99%