Alta-Cyclic: a self-optimizing base caller for next-generation sequencing

Abstract

Next-generation sequencing is limited to short read lengths and by high error rates. We systematically analyzed sources of noise in the Illumina Genome Analyzer that contribute to these high error rates and developed a base caller, Alta-Cyclic, that uses machine learning to compensate for noise factors. Alta-Cyclic substantially improved the number of accurate reads for sequencing runs up to 78 bases and reduced systematic biases, facilitating confident identification of sequence variants.

Keywords

Identification (biology)Computational biologyComputer scienceNoise (video)DNA sequencingBase (topology)GenomeBiologyGeneticsArtificial intelligenceGeneMathematics

MeSH Terms

AnimalsDatabasesNucleic AcidHumansResearch DesignSensitivity and SpecificitySequence AnalysisDNASoftware

Affiliated Institutions

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Publication Info

Year: 2008
Type: article
Volume: 5
Issue: 8
Pages: 679-682
Citations: 207
Access: Closed

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APA Style

                            
                                
                                    Yaniv Erlich, 
                                
                                    Partha P. Mitra, 
                                
                                    Melissa delaBastide
                                
                                et al.
                            
                            (2008). 
                            Alta-Cyclic: a self-optimizing base caller for next-generation sequencing. 
                            Nature Methods
                            , 5
                            (8)
                            , 679-682.
                            https://doi.org/10.1038/nmeth.1230
                        

Identifiers

DOI: 10.1038/nmeth.1230
PMID: 18604217
PMCID: PMC2978646

Data Quality

Data completeness: 86%