Fast and accurate short read alignment with Burrows–Wheeler

transformHeng Li and Richard Durbin∗

Members of this presentation:Yunji WangSree DevineniZhen Gao

Motivation

The first generation of hash table-based methods (e.g. MAQ) are:SlowNot support gapped alignment

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”?

Prefix trie and Inexact string matchingPrefix trie of string “GOOGOL”

The dashed line shows how to find string ‘LOL’ (1 mismatch allowed)

What about “LOG”?

ConclusionsScientists Implemented of Burrows-Wheeler Alignment tool (BWA) which is based on BWT. Thus:FastReducing memoryAllow gaps

REFERENCESHeng Li and Richard Durbin (2009) Fast and

accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25, no. 14 2009, pages 1754–1760

CS 6293: Advanced Topics: Current Bioinformatics

A probabilistic framework for aligning paired-end RNA-seq data

Members of this presentation:Yunji WangSree DevineniZhen Gao

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.

Current Biology Problem

• A single read:

Constitute 35-100 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)

Problem of PER fragment alignment

• Problem:

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.

Problem of PER fragment alignment

Current Tools

• TopHat• TopHat reports the closest end alignment for a

PER.

• SpliceMap• SpliceMap considers PERs with ends mapped

within 400 000bp on the genome.

Method-Step 1

• Mapping the individual reads

Method-Step 2

• Graphical model

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

Estimation of alignments,

(Maximize likelihood of PERsover all the putative alignments.)

EM continued...

Methods-Step 3

• Expectation-maximization algorithm

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.

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.

Advantages

• Third, the splice graph accurately captures alternative paths between two end read and the expected mate-pair distance can effectively disambiguate them.

Thank you