Bayexer: An accurate and fast Bayesian demultiplexer for Illumina sequences

Bioinformatics

Volumn 31, Issue 24, 2015, Pages 4000-4002

  Yi, Haisi ^a,b   Li, Zhe ^c   Li, Tao ^a   Zhao, Jindong ^a,d  

^a INSTITUTE OF HYDROBIOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c INSTITUTE OF BOTANY   (China)

^d PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BAYES THEOREM; COMPUTER PROGRAM; DNA SEQUENCE; HIGH THROUGHPUT SEQUENCING; HUMAN; PROCEDURES;

ALGORITHMS; BAYES THEOREM; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 84950275156     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv501     Document Type: Article

References (6)

1. Dohm, J.C. et al. (2008) Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res., 36, e105.
2. Harismendy, O. et al. (2009) Evaluation of next generation sequencing platforms for population targeted sequencing studies. Genome Biol., 10, R32.
3. Hoffmann, S. et al. (2009) Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput. Biol., 5, e1000502.
4. Kircher, M. et al. (2009) Improved base calling for the Illumina genome analyzer using machine learning strategies. Genome Biol., 10, R83.
5. Nakamura, K. et al. (2011) Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res., 39, e90.
6. Renaud, G. et al. (2015) deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics, 31, 770-772.