Presentation on theme: "Fast and accurate short read alignment with Burrows–Wheeler transform"— Presentation transcript:
1 Fast and accurate short read alignment with Burrows–Wheeler transform Heng Li and Richard Durbin∗Members of this presentation:Yunji WangSree DevineniZhen Gao
2 MotivationThe first generation of hash table-based methods (e.g. MAQ) are:SlowNot support gapped alignment
3 Suffix array intervalposition of each substring will occur in an interval in the suffix array. (On the right figure)e.g. Suffix interval of pattern “go” is [1, 2].What about “og”?
4 Prefix trie and Inexact string matching Prefix trie of string “GOOGOL”The dashed line shows how to find string ‘LOL’ (1 mismatch allowed)What about “LOG”?
5 ConclusionsScientists Implemented of Burrows-Wheeler Alignment tool (BWA) which is based on BWT. Thus:FastReducing memoryAllow gaps
6 REFERENCESHeng Li and Richard Durbin (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25, no , pages 1754–1760
7 CS 6293: Advanced Topics: Current Bioinformatics A probabilistic framework for aligning paired-end RNA-seq dataMembers of this presentation:Yunji WangSree DevineniZhen Gao
8 A probabilistic framework for aligning paired-end RNA-seq data Current Biology MethodAlign RNA-seq reads to the reference genome rather than to a transcript database.
9 Current Biology Problem A single read:Constitute consecutive nucleotides of a fragment of an mRNA transcript.However, the expected size of mRNA fragments are around 182bp.Paired-end read (PER)protocol sequences two ends of a size-selected fragment of an mRNA.(Double the length of single read)
10 Problem of PER fragment alignment The expected distance between the two end reads within the transcript fragment, know as mate-pair distance.The distance between the two ends when aligned to the genome is quit different with mate-pair distance.
19 DiscussionProposed a probabilistic framework to predict the alignment of each PER fragment to a reference genome.By maximizing the likelihood of all PER alignments through a splice graph modelAdvantageous-higher coverage and specificity than just the alignment of PERs.Capable of detecting trans-chromosome and trans-strand gene fusion events.
20 AdvantagesFirst, the fragment alignments significantly increase coverage of the transcriptome.Reason: The PER contains almost double information of single read.Second, it has higher specificity than the junctions in the individual end reads.Reasons: EM algorithm used the information from the entire set of end read alignments.
21 AdvantagesThird, the splice graph accurately captures alternative paths between two end read and the expected mate-pair distance can effectively disambiguate them.