Assignment 2: Papers read for this assignment

1. Assignment 2: Papers read for this assignment • Paper 1:PALMA: mRNA to Genome Alignments using Large Margin Algorithms • Paper 2: Optimal spliced alignments of short sequence reads • Badil Elhady, Michael Chan

2. Paper 1: PALMA: mRNA to Genome Alignments using Large Margin Algorithms

3. Motivation • Question for the study? • The correct alignment of mRNA sequences to genomic DNA is still a challenging task. ( Due to the presence of sequencing errors, micro-exons, alternative splicing)

4. Method • Splice Site Prediction • SVM with large margin, decided under convex optimization • Intron Length Model • Dynamic Programming is used to maximize the scoring function, leading to Optimal Alignment. (Smith-Waterman Alignments with Intron Model) • This leads to: • Tuning the parameters of scoring function leads to. • A larger score • Other alignment would score lower

5. Slice site prediction • Accurately differentiates the exon-intron boundaries • Compartmentalize the local alignment of EST. • Claim: • Robust to mutations, insertions and deletions, as well as noise levels in accurately identifying intron boundaries as well as boundaries of the optimal local alignment.

6. Splice Site Predictions • From a set of ETS, sequences were extracted of confirmed donor and acceptor slice sites. • To recognize acceptor and donor slice sites, 2 SVM classifiers were trained. Using “ weighted degree ” kernel. • kernel computes the similarity between sequences s and s’.

7. Intron Length Model • The main idea of the algorithm is to compute a local alignment by determining the maximum over all alignments of all prefixes • SE (1 : i) :=(SE(1), . . . , SE(i)) • SD(1 : j) := (SD(1), . . . , SD(j)) • SE EST Sequences • SD  DNA Sequences • Running time is O(m*n*L) • mlength of SE • n length of SD • Smith-Waterman does not distinguish between exons and introns.

8. Scoring Function

9. In generalizing the Smith-Waterman algorithm by including an intron model taking splice site predictions as well as intron length into account. • The information is then used to optimize the parameters used for alignment. Smith-Waterman Alignments with Intron Model Splice site prediction assisting params

10. Experimental setup • Evaluating PALMA vs. exalin, sim4, and blat. • Alignment of mRNA seq. artificially shorting the middle exon (3-50)nt as shown.

11. Experimental setup cont. • Artificially generating the data : as a control to know exactly what the correct alignment has to be. • Add varying amounts of noise (p ¼ 0 ,1 ,5 and 10% of random mutations, deletions or insertions) to the query sequence. • Replace a part of the DNA or mRNA sequence at its terminal ends with random sequence leading to a shortened correct alignment.

12. PALMA vs. exalin, sim4, and blat. Add noise

13. PALMA vs. exalin, sim4, and blat. Varying lengths

14. Conclusion • Motivation: high sensitivity detection of short exons in the midst of noise. • Principles • Splice Site Prediction • Intron Length Model • maximize scoring function, for Optimal Alignment. • Results: PALMA detects short exons while exalin, blat, etc, are unsuccessful

15. Further Topics vmatch svm convex optimization

16. Paper 2: Optimal spliced alignments of short sequence reads

17. Situation • NGS has short length and inherent high error rate even compared to Sanger. It is fast but the accuracy? • Many methods are efficient and accurate if the sequence blocks (exons) are sufficiently long and are highly similar to the genomic sequence. • Reads from NG sequencing techniques do not have either of 2 properties.

18. Motivation • Objective to be able to accurately align the sequence reads over intron boundaries. • QPALMA takes the read’s quality information as well as computational splice site predictions to compute accurate spliced alignments.

19. Principles • Learn, in a supervised manner, how to score quality information, splice site predictions and sequence identity based on a representative set of sequence reads with known alignments. • Extended Smith-Waterman algorithm: • Extension 1: Quality Scores • Extension 2: Splice Sites • Extension 3: Non-affine Intron Length Model

20. Splice site prediction • Need to know acceptor and donor splice sites as well as suitable decoy sequences. • Extension 1: Quality Scores • Extension 2: Splice Sites • Extension 3: Non-affine Intron Length Model

21. Extension 1: Quality Scores • same computational complexity as the original Smith–Waterman algorithm (O(mn)). However, it uses a more complex scoring that may depend on the sequencing technology used. Constant

22. Extension 2: Splice Sites • (O(mnL))operations, where L is the maximal length of the intron. • The idea is to maintain an additional recurrence matrix W used to keep track of the intron boundaries.

23. Extension 3: Non-affine Intron Length Model • Recurrence can be implemented as follows • For long introns this approach seem computationally infeasible.

25. An alignment pipeline against whole genomes • !!! optimal alignments is time consuming • => use vmatch(multi-step approach on enhanced suffix arrays) + high quality splice site detection

26. [Optional]An alignment pipeline against whole genomes • vmatch (1st round) finds global alignments of all short reads (max 2 mismatches) against the genome to identify large fraction of unspliced reads. • If there are reads that cannot be aligned (leftover reads) – spliced or low quality reads • Yet, there is possibility that the boundary of the reads are the spliced sites • Check with QPALMA scoring function as a filter to quickly decide whether the read is spliced or not. • all combinations of putative donor splice sites within the read and acceptor splice sites ≤2000 ntdownstream of the read, and • all combinations of putative acceptor splice sites within the read and donor splice sites ≤2000 nt upstream of the read.

27. [Optional]An alignment pipeline against whole genomes • leftover reads + spliced (predicted to be by QPALMA) used as seeds for vmatch (2nd round) and localize the splice sites with a ‘window’

28. Results

29. Conclusion • Motivation: NGS is inaccurate. • Principles • 3 extentions to PALMA • Vmatch pipelining, for boundary precision. • Results: lower error • QPALMA + vmatch pipelining =PALMA + 3extentions – {SVM, large marigin}

30. References • A Tutorial on Support Vector Machines for Pattern Recognition • NCBINational Center forBiotechnology Information http://www.ncbi.nlm.nih.gov/About/primer/genetics_genome.html • BioInfoBank Libraryhttp://lib.bioinfo.pl/ • High Throughput Short Read Alignment via Bi-directional BWT

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