Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Improved base calling for the Illumina Genome Analyzer using machine learning strategies. | Open Access Software Improved base calling for the Illumina Genome Analyzer using machine learning strategies Martin Kircher Udo Stenzel and Janet Kelso Address Department of Evolutionary Genetics Max Planck Institute for Evolutionary Anthropology Deutscher Platz 04103 Leipzig Germany. Correspondence Janet Kelso. Email kelso@ Published 14 August 2009 Genome Biology 2009 10 R83 doi gb-2009- 10-8-r83 The electronic version of this article is the complete one and can be found online at http 2009 10 8 R83 Received 9 April 2009 Revised 9 July 2009 Accepted 14 August 2009 2009 Kircher et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract The Illumina Genome Analyzer generates millions of short sequencing reads. We present Ibis Improved base identification system an accurate fast and easy-to-use base caller that significantly reduces the error rate and increases the output of usable reads. Ibis is faster and more robust with respect to chemistry and technology than other publicly available packages. Ibis is freely available under the GPL from http Ibis . Rationale Recent advances in high-throughput sequencing have revolutionized genomics making it possible for even single research groups to generate large amounts of sequence data very rapidly and at substantially lower costs than traditional Sanger sequencing. This puts the ability to perform deep transcriptome sequencing and transcript quantification whole genome sequencing and resequencing into the hands of many more researchers. However while cost and time have been greatly reduced the error profiles of next-generation platforms differ significantly to those of previous approaches. By addressing this issue the .
