1. Last lecture summary

3. Window size? Stringency? Color mapping? Frame shifts?

4. Limits of detection of alignment Homology, similarity Twilight zone

5. Statistical significance Key question – Constitutes a given alignment evidence for homology? Or did it occur just by chance? The statistical significance of the alignment (i.e. its score) can be tested by statistical hypotheses testing. What are H 0 and H a ? Significance of local alignment gapless gapped Significance of global alignment

6. Gumbel distribution wikipedia.org

7. New stuff

8. Database similarity searching

9. BLAST Basic Local Alignment Search Tool (BLAST) – Google of the sequence world. Compare a protein or DNA sequence to other sequences in various databases, main tool of NCBI. Why to search database Determine what orthologs and paralogs are known for a particular sequence. Determine what proteins or genes are present in a particular organism. Determining the identity of a DNA or protein sequence. Determining what variants have been described for a particular gene or protein. Investigating ESTs. Exploring amino acid residues that are important in the function and/or structure of a protein (multiple alignment of BLAST results, conserved residues).

10. Database searching requirements I query sequence, perform pairwise alignments between the query and the whole database (target) Typically, this means that millions of alignments are analyzed in a BLAST search, and only the most closely related matches are returned. We are usually more interested in identifying locally matching regions such as protein domains. Global alignment (Needlman-Wunsch) is not often used. Smith-Watermann is too computationally intensive. Instead, heuristic is utilized, significant speed up.

11. Database searching requirements II sensitivity – the ability to find as many correct hits (TP) as possible selectivity (specificity) – ability to exclude incorrect hits (FP) speed ideally: high sensitivity, high specificity, high speed reality: increase in sensitivity leads to decrease in specificity, improvement in speed often comes at the cost of lowered sensitivity and selectivity

12. Types of algorithms exhaustive uses a rigorous algorithm to find the best or exact solution for a particular problem by examining all mathematical combinations example: DP heuristic computational strategy to find an empirical or near optimal solution by using rules of thumb this type of algorithms take shortcuts by reducing the search space according to some criteria the shortcut strategy is not guaranteed to find the best or most accurate solution

13. Heuristic algorithms Perform faster searches because they examine only a fraction of the possible alignments examined in regular dynamic programming currently, there are two major algorithms: FASTA BLAST Not guaranteed to find the optimal alignment or true homologs, but are 50–100 times faster than DP. The increased computational speed comes at a moderate expense of sensitivity and specificity of the search, which is easily tolerated by working molecular biologists.

14. BLAST Parts of algorithm list, scan, extend BLAST uses word method for pairwise alignment Find short stretches of identical (or nearly identical) letters in two sequences – words (similar to window in dot plot) Basic assumption: two related sequences must have at least one word in common By first identifying word matches, a longer alignment can be obtained by extending similarity regions from the words. Once regions of high sequence similarity are found, adjacent high-scoring regions can be joined into a full alignment.

15. BLAST - list Compile a list of “words” of a fixed length w that are derived from the query sequence. protein searches – word size = 3, NA searches = 11 A threshold value T is established for the score of aligned words (true for proteins, for NAs exact matches are used). Those words either at or above the threshold are collected and used to identify database matches; those words below threshold are not further pursued. The threshold score T can be lowered to identify more initial pairwise alignments. This will increase the time required to perform the search and may increase the sensitivity

17. BLAST - scan After compiling a list of word pairs at or above threshold T, the BLAST algorithm scans a database for hits. This requires BLAST to search an index of the database to find entries that correspond to words on the compiled list.

18. BLAST - extend Extend hits to find alignments called high-scoring segment pairs (HSPs). Extend in both directions (ungapped originally, gapped BLAST is newer), count the alignment score. The extension process is terminated when a score falls below a cutoff.

19. BLAST strategy Compare a protein or DNA query sequence to each database entry and form pairwise alignments (HSPs). When the threshold parameter is raised, the speed of the search is increased, but fewer hits are registered, and so distantly related database matches may be missed. When the threshold parameter is lowered, the search proceeds more slowly, but many more word hits are evaluated, and thus sensitivity is increased.

20. Recent improvement – gapped BLAST Variants BLASTN – nucleotide sequences BLASTP – protein sequences BLASTX – uses nucleotide sequences as queries and translates them in all six reading frames to produce translated protein sequences, which are used to query a protein sequence database TBLASTN – queries protein sequences to a nucleotide sequence database with the sequences translated in all six reading frames TBLASTX – uses nucleotide sequences, which are translated in all six frames, to search against a nucleotide sequence database that has all the sequences translated in six frames.

21. Which sequence to search? The choice of the type of sequences also influences the sensitivity of the search. Clear advantage of using protein sequences in detecting homologs If the input sequence is a protein-encoding DNA sequence, use BLASTX (six open reading frames before sequence comparisons) If you’re looking for protein homologs encoded in newly sequenced genomes, you may use TBLASTN. This may help to identify protein coding genes that have not yet been annotated. If a DNA sequence is to be used as the query, a protein- level comparison can be done with TBLASTX. TBLASTN, TBLASTX are very computationally intensive and the search process can be very slow.

22. BLAST Statistics Gumbel distribution

25. E-value Karlin S., Altschul S. F. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. PNAS 87, 2264–2268, 1990

26. Properties of E-value I Decreases exponentially with increasing S. Thus, a high score corresponds to a low E-value. As E approaches zero, the probability that the alignment occurred by chance approaches zero. The expected score for aligning a random pair of amino acids must be negative. Otherwise, very long alignments of two sequences could accumulate large positive scores and appear to be significantly related when they are not. The size of the database that is searched influences the likelihood that particular alignments will occur by chance. Consider a result with an E = 1. This value indicates that in a database of this particular size one match with a similar score is expected to occur by chance. If the database were twice as big, there would be twice the likelihood of finding a score equal to or greater than S by chance.

27. Properties of E-value II

28. Bit score

29. Relation between E and p values I

30. Relation between E and p values II While BLAST reports E values rather than p values, the two measures are nearly identical, especially for very small values associated with strong database matches. An advantage of using E values is that it is easier to think about E values of 5 versus 10 rather than 0.99326205 versus 0.99995460. A p-value below 0.05 is usually used to define statistical significance (what does it mean?) Thus, an E value of 0.05 or less may be considered significant.

31. Multiple comparisons correction I

32. Multiple comparisons correction II

33. E-value interpretation E < 10 -50 … extremely high confidence that the database match is a result of homologous relationships E is from (10 -50, 0.01) … the match can be considered a result of homology E is from (0.01, 10) … the match is considered not significant, but may hint tentative remote homology E > 10 … the sequences under consideration are either unrelated or related by extremely distant relationships that fall below the limit of detection with the current method. E-value is proportional to the database size, as database grows E-value for a given sequence match increases. However, the evolutionary relationship between two sequences remains constant. As the db grows, one may lose previously detected homologs.