20100516 bioinformatics kapushesky_lecture08
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20100516 bioinformatics kapushesky_lecture08
- 1. Bioinformatics for High-Throughput Sequencing Misha Kapushesky St. Petersburg Russia 2010 Slides: Nicolas Delhomme, Simon Anders, EMBL-EBI
- 14. Solexa Pipeline
- 17. FASTQ format @HWI-EAS225:3:1:2:854#0/1 GGGGGGAAGTCGGCAAAATAGATCCGTAACTTCGGG +HWI-EAS225:3:1:2:854#0/1 a`abbbbabaabbababb^`[aaa`_N]b^ab^``a @HWI-EAS225:3:1:2:1595#0/1 GGGAAGATCTCAAAAACAGAAGTAAAACATCGAACG +HWI-EAS225:3:1:2:1595#0/1 a`abbbababbbabbbbbbabb`aaabababa_`
- 32. Courtesy of Heng Li (Welcome Trust Sanger Institute)
- 35. Courtesy of Heng Li (Welcome Trust Sanger Institute)
- 46. Alignment
- 48. Example
- 49. 4-mer parsing
- 51. Bubble Removal
Hinweis der Redaktion
- In contrast to other sequencing systems, SOLiD data are not collected directly as DNA sequences, but instead are recorded in ‘color space’, in which the individual values (colors) within a read provide information about (but not a definite identification of) two adjacent bases. Without a decoding step, in which color data are converted to sequence data, they cannot be mapped to a reference genome using conventional alignment tools. Direct conversion of color data to sequence data, however, has a significant drawback—reads that contain sequencing errors cannot be converted accurately (in translating a color space string, all bases after a sequencing error will be translated incorrectly).
- Algorithms based on spaced-seed indexing, such as Maq, index the reads as follows: each position in the reference is cut into equal-sized pieces, called 'seeds' and these seeds are paired and stored in a lookup table. Each read is also cut up according to this scheme, and pairs of seeds are used as keys to look up matching positions in the reference. Because seed indices can be very large, some algorithms (including Maq) index the reads in batches and treat substrings of the reference as queries. (b) Algorithms based on the Burrows-Wheeler transform, such as Bowtie, store a memory-efficient representation of the reference genome. Reads are aligned character by character from right to left against the transformed string. With each new character, the algorithm updates an interval (indicated by blue 'beams') in the transformed string. When all characters in the read have been processed, alignments are represented by any positions within the interval. Burrows-Wheeler–based algorithms can run substantially faster than spaced seed approaches, primarily owing to the memory efficiency of the Burrows-Wheeler search. Chr., chromosome.