1. 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

2. Motivation
The first generation of hash table-based methods (e.g. MAQ) are:
Slow
Not support gapped alignment

3. 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”?

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. Conclusions
Scientists Implemented of Burrows-Wheeler Alignment tool (BWA) which is based on BWT. Thus:
Fast
Reducing memory
Allow gaps

6. REFERENCES
Heng 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 data
Members of this presentation:
Yunji Wang
Sree Devineni
Zhen Gao

8. 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.

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.

11. Problem of PER fragment alignment

12. Current Tools
TopHat
TopHat reports the closest end alignment for a PER.
SpliceMap
SpliceMap considers PERs with ends mapped within bp on the genome.

13. Method-Step 1
Mapping the individual reads

14. Method-Step 2
Graphical model

15. 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

16. Estimation of alignments,
(Maximize likelihood of PERs
over all the putative alignments.)

17. EM continued...

18. Methods-Step 3
Expectation-maximization algorithm

19. 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.

20. 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.

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

22. Thank you