1. Need & Emergence of the Field Speaker Shashi Shekhar Head of computational Section Biowits Life Sciences
2. The marriage between computer science and molecular biology ◦ The algorithm and techniques of computer science are being used to solve the problems faced by molecular biologists ‘Information technology applied to the management and analysis of biological data’ ◦ Storage and Analysis are two of the important functions – bioinformaticians build tools for each.
4. The need for bioinformatics has arisen from the recent explosion of publicly available genomic information, such as resulting from the Human Genome Project. Gain a better understanding of gene analysis, taxonomy, & evolution. To work efficiently on the rational drug designs and reduce the time taken for the development of drug manually.
5. To uncover the wealth of Biological information hidden in the mass of sequence, structure, literature and biological data. It is being used now and in the foreseeable future in the areas of molecular medicine. It has environmental benefits in identifying waste and clean up bacteria. In agriculture, it can be used to produce high yield, low maintenance crops.
7. In Experimental Molecular Biology In Genetics and Genomics In generating Biological Data Analysis of gene and protein expression Comparison of genomic data Understanding of evolutionary aspect of Evolution Understanding biological pathways and networks in System Biology In Simulation & Modeling of DNA, RNA & Protein
8. e.g. homology searches Bioinformatics lecture March 5, 2002organisation of knowledge(sequences, structures,functional data)
9. Prediction of structure from sequence ◦ secondary structure ◦ homology modelling, threading ◦ ab initio 3D prediction Analysis of 3D structure ◦ structure comparison/ alignment ◦ prediction of function from structure ◦ molecular mechanics/ molecular dynamics ◦ prediction of molecular interactions, docking Structure databases (RCSB)
10. Sequence Similarity Tools used for sequence similarity searching There uses in biology or to us Databases Different types of databases
11. One could align the sequence so that many corresponding residues match. Strong similarity between two sequences is a strong argument for their homology. Homology: Two(or more) sequences have a common ancestor. Similarity: Two(or more) sequences are similar by some criterion, and it does not refer to any historical process.
12. To find the relatedness of the proteins or gene, if they have a common ancestor or not. Mutation in the sequences, brings the changes or divergence in the sequences. Can also reveal the part of the sequence which is crucial for the functioning of gene or protein.
13. Optimal Alignment: The alignment that is the best, given a defined set of rules and parameter values for comparing different alignments. Global Alignment: An alignment that assumes that the two proteins are basically similar over the entire length of one another. The alignment attempts to match them to each other from end to end. Local Alignment: An alignment that searches for segments of the two sequences that match well. There is no attempt to force entire sequences into an alignment, just those parts that appear to have good similarity. (contd.)
14. Gaps & Insertions: In an alignment, one may achieve much better correspondence between two sequences if one allows a gap to be introduced in one sequence. Equivalently, one could allow an insertion in the other sequence. Biologically this corresponds to an mutation event. Substitution matrix: A Substitution matrix describes the two residue types would mutate to each other in evolutionary time. This is used to estimate how well two residues of given types would match if they were aligned in a sequence alignment. Gap Penalty: The gap penalty is used to help decide whether or not to accept a gap or insertion in an alignment when it is possible to achieve a good alignment residue to residue at some other neighboring point in the sequence.
15. Similarity indicates conserved function Human and mouse genes are more than 80% similar at sequence level But these genes are small fraction of genome Most sequences in the genome are not recognizably similar Comparing sequences helps us understand function ◦ Locate similar gene in another species to understand your new gene
17. We want to find alignments that are evolutionarily likely. Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGAT-------ATAGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT AC-T-TGA--CG-CGT-TA-TCTATCT We can achieve this by penalizing more for a new gap, than for extending an existing gap
19. Alignment scoring and substitution matrices Aligning two sequences ◦ Dotplots ◦ The dynamic programming algorithm ◦ Significance of the results Heuristic methods ◦ FASTA ◦ BLAST ◦ Interpreting the output
20. Examples: Staden: simple text file, lines <= 80 characters FASTA: simple text file, lines <= 80 characters, one line header marked by ">" GCG: structured format with header and formatted sequence Sequence format descriptions e.g. on http://www.infobiogen.fr/doc/tutoriel/formats.html
21. Local sequence comparison: assumption of evolution by point mutations ◦ amino acid replacement (by base replacement) ◦ amino acid insertion ◦ amino acid deletion scores: ◦ positive for identical or similar ◦ negative for different ◦ negative for insertion in one of the two sequences
22. Simple comparison without alignment Similarities between sequences show up in 2D diagram
23. identity (i=j)similarity of sequencewith other parts of itself
24. The 1st alignment: highly significant The 2nd: plausible The 3rd: spurious Distinguish by alignment score Similarities increase score substitution matrix Mismatches decrease score Gaps decrease score gap penalties
25. Substitution matrix weights replacement of one residue by another: ◦ Similar -> high score (positive) ◦ Different -> low score (negative) Simplest is identity matrix (e.g. for nucleic acids) A C G T A 1 0 0 0 C 0 1 0 0 G 0 0 1 0 T 0 0 0 1
26. PAM matrix series (PAM1 ... PAM250): ◦ Derived from alignment of very similar sequences ◦ PAM1 = mutation events that change 1% of AA ◦ PAM2, PAM3, ... extrapolated by matrix multiplication e.g.: PAM2 = PAM1*PAM1; PAM3 = PAM2 * PAM1 etc Problems with PAM matrices: ◦ Incorrect modelling of long time substitutions, since conservative mutations dominated by single nucleotide change ◦ e.g.: L <–> I, L <–> V, Y <–> F long time: any Amino Acid change
27. positive and negative valuesidentity score depends on residue
28. BLOSUM series (BLOSUM50, BLOSUM62, ...) derived from alignments of distantly related sequence BLOCKS database: ◦ ungapped multiple alignments of protein families at a given identity BLOSUM50 better for gapped alignments BLOSUM62 better for ungapped alignments
29. Blosum62 substitution matrix
30. Significance of alignment: Depends critically on gap penalty Need to adjust to given sequence Gap penalties influenced by knowledge of structure etc. Simple rules when nothing is known (linear or affine)
31. Dynamic programming = build up optimal alignment using previous solutions for optimal alignments of subsequences. The dynamic programming relies on a principle of optimality. This principle states that in an optimal sequence of decisions or choices, each subsequence must also be optimal. The principle can be related as follows: the optimal solution to a problem is a combination of optimal solutions to some of its sub-problems.
32. Construct a two-dimensional matrix whose axes are the two sequences to be compared. The scores are calculated one row at a time. This starts with the first row of one sequence, which is used to scan through the entire length of the other sequence, followed by scanning of the second row. The scanning of the second row takes into account the scores already obtained in the first round. The best score is put into the bottom right corner of an intermediate matrix. This process is iterated until values for all the cells are filled.
35. The results are traced back through the matrix in reverse order from the lower right-hand corner of the matrix toward the origin of the matrix in the upper left- hand corner. The best matching path is the one that has the maximum total score. If two or more paths reach the same highest score, one is chosen arbitrarily to represent the best alignment. The path can also move horizontally or vertically at a certain point, which corresponds to introduction of a gap or an insertion or deletion for one of the two sequences.
36. Global alignment (ends aligned) ◦ Needleman & Wunsch, 1970 Local alignment (subsequences aligned) ◦ Smith & Waterman, 1981 Searching for repetitions Searching for overlap
37. Multi-step approach to find high-scoring alignments Exact short word matches Maximal scoring ungapped extensions Identify gapped alignments
39. FASTA also uses E-values and bit scores. The FASTA output provides one more statistical parameter, the Z-score. This describes the number of standard deviations from the mean score for the database search. Most of the alignments with the query sequence are with unrelated sequences, the higher the Z-score for a reported match, the further away from the mean of the score distribution, hence, the more significant the match. For a Z-score > 15, the match can be considered extremely significant, with certainty of a homologous relationship. If Z is in the range of 5 to 15, the sequence pair can be described as highly probable homologs. If Z < 5, their relationships is described as less certain.
40. Multi-step approach to find high-scoring alignments List words of fixed length (3AA) expected to give score larger than threshold For every word, search database and extend ungapped alignment in both directions New versions of BLAST allow gaps
42. The E-value provides information about the likelihood that a given sequence match is purely by chance. The lower the E- value, the less likely the database match is a result of random chance and therefore the more significant the match is. If E < 1e − 50 (or 1 × 10−50), there should be an extremely high confidence that the database match is a result of homologous relationships. If E is between 0.01 and 1e − 50, the match can be considered a result of homology. If E is between 0.01 and 10, the match is considered not significant, but may hint at a tentative remote homology relationship. Additional evidence is needed. If 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.
43. Various versions: Blastn: nucleotide sequences Blastp: protein sequences tBlastn: protein query - translated database Blastx: nucleotide query - protein database tBlastx: nucleotide query - translated database
44. Very fast growth of biological data Diversity of biological data: ◦ Primary sequences ◦ 3D structures ◦ Functional data Database entry usually required for publication ◦ Sequences ◦ Structures Database entry may replace primary publication ◦ Genomic approaches
45. Nucleic Acid ProteinEMBL (Europe) PIR - Protein Information ResourceGenBank (USA) MIPSDDBJ (Japan) SWISS-PROT University of Geneva, now with EBI TrEMBL A supplement to SWISS- PROT NRL-3D
46. Three databanks exchange data on a daily basis Data can be submitted and accessed at either location GenBank ◦ www.ncbi.nlm.nih.gov/Genbank/GenbankOverview.html EMBL ◦ www.ebi.ac.uk/embl/index.html DNA Databank of Japan (DDBJ) ◦ www.nig.ac.jp/home.html
47. As there are many databases which one to search? Some are good in some aspects and weak in others? Composite databases is the answer – which has several databases for its base data Search on these databases is indexed and streamlined so that the same stored sequence is not searched twice in different databases.
48. OWL has these as their primary databases. ◦ SWISS PROT (top priority) ◦ PIR ◦ GenBank ◦ NRL-3D
49. Store secondary structure info or results of searches of the primary databases. Composite Primary Source Databases PROSITE SWISS-PROT PRINTS OWL
50. We have sequenced and identified genes. So we know what they do. The sequences are stored in databases. So if we find a new gene in the human genome we compare it with the already found genes which are stored in the databases. Since there are large number of databases we cannot do sequence alignment for each and every sequence So heuristics must be used again.
51. Applications:- Bioinformatics joins mathematics, statistics, and computer science and information technology to solve complex biological problems. Sequence Analysis:- The application of sequence analysis determines those genes which encode regulatory sequences or peptides by using the information of sequencing. These computers and tools also see the DNA mutations in an organism and also detect and identify those sequences which are related. Special software is used to see the overlapping of fragments and their assembly. Contd.
52. Prediction of Protein Structure:- It is easy to determine the primary structure of proteins in the form of amino acids which are present on the DNA molecule but it is difficult to determine the secondary, tertiary or quaternary structures of proteins. Tools of bioinformatics can be used to determine the complex protein structures. Genome Annotation:- In genome annotation, genomes are marked to know the regulatory sequences and protein coding. It is a very important part of the human genome project as it determines the regulatory sequences.
53. Comparative Genomics:- Comparative genomics is the branch of bioinformatics which determines the genomic structure and function relation between different biological species. For this purpose, intergenomic maps are constructed which enable the scientists to trace the processes of evolution that occur in genomes of different species. Health and Drug discovery:- The tools of bioinformatics are also helpful in drug discovery, diagnosis and disease management. Complete sequencing of human genes has enabled the scientists to make medicines and drugs which can target more than 500 genes.