Database Searching
- 2. BIOLOGICAL DATABASE A BIOLOGICAL DATABASE is a large, organized body of persistent data, usually associated with computerized software designed to update, query, and retrieve components of the data stored within the system. A simple database might be a single file containing many records, each of which includes the same set of information.
- 3. FOR EXAMPLE A few popular databases are GenBank from NCBI (National Center for Biotechnology Information), SwissProt from the Swiss Institute of Bioinformatics and PIR from the Protein Information Resource. GenBank: GenBank (Genetic Sequence Databank) is one of the fastest growing repositories of known genetic sequences. EMBL: The EMBL Nucleotide Sequence Database is a comprehensive database of DNA and RNA sequences collected from the scientific literature and patent applications and directly submitted from researchers and sequencing groups.
- 4. DATABASE SEARCHING Searching is done to find the relatedness between the query and the entries in the database. For nucleic acids and proteins, the relatedness is defined by “homology”. A ‘Query’ sequence is used to search against each entry, a ‘subject’ in a database. Two sequences are said to be homologous when they possess sequence identity above a certain threshold. Thresholds can be defined by length, percentage identity, E- value, Bit-score, etc., or a combination of one or more of these, depending on the objective of the search.
- 5. Basic Elements in Searching Biological Databases Sensitivity versus Specificity/selectivity Scoring Scheme, Gap penalties Distance/Substitution Matrices (PAM, BLOSSUM Series) Search Parameters (E-value, Bit score) Handling Data Quality Issues (Filtering, Clustering) Type of Algorithm (Smith-Waterman, Needleman-Wunsch)
- 6. Sensitivity vs. Specificity Sensitivity: Attempts to report ALL true positives Sensitivity = True Positives / (True Positives + False Negatives) = TP/(TP+FN) (1-sensitivity) gives false negative rate Specificity: Attempts to report ALL true negatives Specificity = True Negatives / (True Negatives + False Positives) = TN/(TN+FP) (1-specificity) gives false positive rate
- 7. Scoring Scheme Match - Match between identical letters or letters of the same group Mismatch – Match between letters of different groups Gap – Match between a letter and a gap Alignment score is the sum of match, mismatch and gap penalty scores Say, you are aligning two sequences A and B Sequence A : PQVNNTVNRT / Sequence B : PVNRT
- 8. 1 2 3 PQVNNTVNRT PQVNNTVNRT PQVNNTVNRT P-VNRT---- P-V-N---RT P-----VNRT
- 9. Distance/Substitution Matrices Unitary matrices/minimum distance matrices PAM (Percent Accepted Mutations) BLOSUM (BLOcks SUbstitution Matrix)
- 10. PAM (Percent Accepted Mutations) Developed by Dayhoff and co-workers PAM 30, 60, 100, 200, 250 Built from globally aligned, closely related sequences (85% similarity) A database of 1572 changes in 71 groups of closely related proteins PAM 1 matrix incorporates amino acid replacements that would be expected if one mutation had occurred per 100 amino acids of sequence i.e., corresponds to roughly one percent divergence in a protein
- 11. BLOSUM (BLOCKS SUBSTITUTION Matrix) Developed by Henikoff and Henikoff (1992) Blosum 30, 62, 80 Built from BLOCKS database From the most conserved regions of aligned sequences 2000 blocks from 500 families Blosum 62 is the most popular. Here, 62 means that the sequences used in creating the matrix are at least 62% identical
- 12. E-value (Expectation value) The number of equal or higher scores expected at random for a given High Scoring Pair (HSP) E-value of 10 for a match means, in a database of current size, one might expect to see 10 matches with a similar or better score, simply by chance E-value is the most commonly used threshold in database searches. Only those matches with E-values smaller than the set threshold will be reported in the output E-value ranges between 0 to higher, lower the E-value, better the reliability of a match.
- 13. Bit Score Raw scores have no meaning without the knowledge of the scoring scheme used. Raw score are normalized to get Bit scores by incorporating information about the scoring scheme used and the search space used (size of database) Bit score is normalized score and hence it is independent of the size of the database, while Evalues are very sensitive to the database size. Generally bit scores of 40 are higher are considered reliable
- 14. Filtering low complexity sequences Filters out short repeats and low complexity regions from the query sequences before searching the database. Filtering helps to obtain statistically significant results and reduce the background noise resulting from matches with repeats and low complexity regions. The output shows which regions of the query sequence were masked.
- 15. Choice of the Searching Algorithm An ideal algorithm should have • Good specificity and sensitivity • Should be fast running • Should not use too much memory Greedy algorithms are very sensitive, but very slow. Heuristic algorithms are relatively fast, but loose some sensitivity. Itʼs always a challenge for a programmer to develop algorithms that fulfill both of these requirements
- 16. Needleman-Wunsch algorithm (JMB 48:443-53, 1970) • Very greedy algorithm, so very sensitive • Implements Dynamic programming • Provides global alignment between the two sequences Smith-Waterman algorithm (JMB 147:195-97, 1981) • A set of heuristics were applied to the above algorithm to make it less greedy, so it is less sensitive but runs faster • Implements Dynamic programming • Provide local alignment between two sequences • Both BLAST and FASTA use this algorithm with varying heuristics applied in each case
- 17. FASTA (FAST Algorithm) The first step is application of heuristics and the second step is using dynamic programming • First, the query sequence and the database sequence are cut into defined length words and a word matching is performed in all-to-all combinations • Word size is 2 for proteins and 6 for nucleic acids • If the initial score is above a threshold, the second score is computed by joining fragments and using gaps of less than some maximum length • If this second score is above some threshold, Smith- Waterman alignment is performed within the regions of high identities (known as high-scoring pairs)
- 18. BLAST (Basic Local Alignment Search Tool) The fist step is application of heuristics and the second step is using dynamic programming • First, the query sequence and the database sequence are cut into defined length words and a word matching is performed in all combinations. • Words that score above a threshold are used to extend the word list. • Several High Scoring Segments are found, with the maximum scoring segment used to define a band in the path graph • Smith-Waterman algorithm is performed on several possible segments to obtain optimal alignment • The word size for Protein is 3 and for Nucleic acid is 11.
- 19. THANK YOU