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Published byMiles Norton Modified over 9 years ago
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Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li and Richard Durbin∗ Members of this presentation: Yunji Wang Sree Devineni Zhen Gao
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Motivation The first generation of hash table-based methods (e.g. MAQ) are: Slow Not support gapped alignment
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Suffix array interval position 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”?
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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”?
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Conclusions Scientists Implemented of Burrows-Wheeler Alignment tool (BWA) which is based on BWT. Thus: Fast Reducing memory Allow gaps
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REFERENCES Heng Li and Richard Durbin (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25, no , pages 1754–1760
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CS 6293: Advanced Topics: Current Bioinformatics A probabilistic framework for aligning paired-end RNA-seq data Members of this presentation: Yunji Wang Sree Devineni Zhen Gao
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A probabilistic framework for aligning paired-end RNA-seq data
Current Biology Method Align RNA-seq reads to the reference genome rather than to a transcript database.
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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)
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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.
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Problem of PER fragment alignment
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Current Tools TopHat TopHat reports the closest end alignment for a PER. SpliceMap SpliceMap considers PERs with ends mapped within bp on the genome.
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Method-Step 1 Mapping the individual reads
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Method-Step 2 Graphical model
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Probabilistic framework
Splice graph, G={V,E} Nodes - individual nucleotides Directed edge types connect adjacent nodes Skips around the sliced-out portion of the genome
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Estimation of alignments
, (Maximize likelihood of PERs over all the putative alignments.)
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EM continued...
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Methods-Step 3 Expectation-maximization algorithm
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Discussion Proposed 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 model Advantageous-higher coverage and specificity than just the alignment of PERs. Capable of detecting trans-chromosome and trans-strand gene fusion events.
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Advantages First, 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.
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Advantages Third, the splice graph accurately captures alternative paths between two end read and the expected mate-pair distance can effectively disambiguate them.
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Thank you
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