GeneCodeq: quality score compression and improved genotyping using a Bayesian framework | Zendy

Daniel L. Greenfield | Zendy; Oliver Stegle | Zendy; Alban Rrustemi | Zendy

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GeneCodeq: quality score compression and improved genotyping using a Bayesian framework

Author(s) -

Daniel L. Greenfield,

Oliver Stegle,

Alban Rrustemi

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw385

Subject(s) - computer science , lossy compression , lossless compression , entropy (arrow of time) , data mining , data compression , bayesian probability , algorithm , artificial intelligence , physics , quantum mechanics

The exponential reduction in cost of genome sequencing has resulted in a rapid growth of genomic data. Most of the entropy of short read data lies not in the sequence of read bases themselves but in their Quality Scores-the confidence measurement that each base has been sequenced correctly. Lossless compression methods are now close to their theoretical limits and hence there is a need for lossy methods that further reduce the complexity of these data without impacting downstream analyses.

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