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A bioinformatic filter for improved base-call accuracy and polymorphism detection using the Affymetrix GeneChip® whole-genome resequencing platform
Author(s) -
Gagan A. Pandya,
Michael H. Holmes,
Sirisha Sunkara,
Andrew B. Sparks,
Yun Bai,
Kathleen Verratti,
Kelly Saeed,
Pratap Venepally,
Behnam Jarrahi,
Robert Fleischmann,
Scott N. Peterson
Publication year - 2007
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkm918
Subject(s) - biology , genotyping , genome , computational biology , genetics , reference genome , dna microarray , false positive paradox , population , genotype , gene , computer science , gene expression , demography , machine learning , sociology
DNA resequencing arrays enable rapid acquisition of high-quality sequence data. This technology represents a promising platform for rapid high- resolution genotyping of microorganisms. Tradi- tional array-based resequencing methods have relied on the use of specific PCR-amplified frag- ments from the query samples as hybridization targets. While this specificity in the target DNA population reduces the potential for artifacts caused by cross-hybridization, the subsampling of the query genome limits the sequence coverage that can be obtained and therefore reduces the techni- que's resolution as a genotyping method. We have developed and validated an Affymetrix Inc. GeneChip array-based, whole-genome resequen- cing platform for Francisella tularensis, the causative agent of tularemia. A set of bioinformatic filters that targeted systematic base-calling errors caused by cross-hybridization between the whole- genome sample and the array probes and by deletions in the sample DNA relative to the chip reference sequence were developed. Our approach eliminated 91% of the false-positive single- nucleotide polymorphism calls identified in the SCHU S4 query sample, at the cost of 10.7% of the true positives, yielding a total base-calling accuracy of 99.992%.

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