1. 1 Exercise: BIOINFORMATIC DATABASES and BLAST

2. 2 Outline  NCBI and Entrez  Pubmed  Google scholar  RefSeq  Swissprot  Fasta format  PDB: Protein Data Bank  Organism specific databases  Summary  Pairwise Sequence Alignment and BLAST  Overview  Query type: DNA or Protein

3. 3 What’s in a database?  Sequences – genes, proteins, etc  Full genomes  Annotation – information about genes/proteins: - function - cellular location - chromosomal location - introns/exons - protein structure - phenotypes, diseases  Publications

4. 4 NCBI and Entrez National center for biotechnology information  One of the largest and most comprehensive databases belonging to the NIH (national institute of health) The primary Federal agency for conducting and supporting medical research in the USA The primary Federal agency for conducting and supporting medical research in the USA  Entrez is the search engine of NCBI  Search for : genes, proteins, genomes, structures, diseases, publications and more  http://www.ncbi.nlm.nih.gov/

5. 5 PubMed: search for published papers  Yang X, Kurteva S, Ren X, Lee S, Sodroski J. “Subunit stoichiometry of human immunodeficiency virus type 1 envelope glycoprotein trimers during virus entry into host cells “, J Virol. 2006 May;80(9):4388-95.

6. 6 Use fields! Yang[AU] AND glycoprotein[TI] AND 2006[DP] AND J virol[TA] For the full list of field tags: go to help -> Search Field Descriptions and TagsSearch Field Descriptions and Tags

7. 7 Exercise  Retrieve all publications in which the first author is: Pe'er I and the last author is: Shamir R

8. 8 Using limits Retrieve the publications of Friedman N, in the journals: Bioinformatics and Journal of Computational Biology, in the last 5 years

9. 9 Google scholar http://scholar.google.com/

10. 10

11. 11 NCBI gene & protein databases: GenBank  GenBank is an annotated collection of all publicly available DNA sequences (and their amino-acid translations)  Holds 99 billion bases (2008)

12. 12 Searching NCBI for the protein human CD4 Search demonstration

13. 13

14. 14 Using field descriptions, qualifiers, and boolean operators  Cd4[GENE] AND human[ORGN] Or Cd4[gene name] AND human[organism]  List of field codes: http://www.ncbi.nlm.nih.gov/entrez/query/static/help/Summary_Matrices.html#Search_Fields_and_Qualifiers http://www.ncbi.nlm.nih.gov/entrez/query/static/help/Summary_Matrices.html#Search_Fields_and_Qualifiers Boolean Operators: AND OR NOT Boolean Operators: AND OR NOT Note: do not use the field Protein name [PROT], only GENE!

15. 15 This time we directly search in the protein database

16. 16 RefSeq  RefSeq: sub-collection of NCBI databases with only non-redundant, highly annotated entries (genomic DNA, transcript (RNA), and protein products)

17. 17

18. 18 An explanation on GenBank records

19. 19 Accession Numbers Two letters followed by six digits, e.g.: AY123456 One letter followed by five digits, e.g.: U12345 GenBankEMBL RefSeq accession numbers can be distinguished from GenBank accessions by their prefix: 2 characters+underscore], e.g.: NP_015325 NM_: mRNA, NP_: protein Refseq All are six characters: Character/Format 1 [O,P,Q] 2 [0-9] 3 [A-Z,0-9] 4 [A-Z,0-9] 5 [A-Z,0-9] 6 [0-9] e.g.:P12345 and Q9JJS7 SWISSPROT (another protein database) one digit followed by three letters, e.g.: 1hxw PDB (Protein Data Bank – structure database)

20. 20 Swissprot  A protein sequence database which strives to provide a high level of annotation: * the function of a protein * domains structure * post-translational modifications * variants  One entry for each protein

21. 21

22. 22 GenBank Vs. Swissprot GenBank results Swiss-Prot results

23. 23 Fasta format > gi|10835167|ref|NP_000607.1| CD4 antigen precursor [Homo sapiens] MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIK ILGNQGSFLTKGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQL LVFGLTANSDTHLLQGQSLTLTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSG TWTCTVLQNQKKVEFKIDIVVLAFQKASSIVYKKEGEQVEFSFPLAFTVEKLTGSGELWW QAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPLHLTLPQALPQYAGSGNLTLA LEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAKVSKREKAVWV LNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCV RCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI Save accession numbers for future use (makes searching quicker): RefSeq accession number: NP_000607.1 header ID/accession description sequence

24. 24 Downloading

25. 25 PDB: Protein Data Bank  Main database of 3D structures  Includes ~56,000 entries (proteins, nucleic acids, others)  Proteins organized in groups, families etc  Is highly redundant different conformations (e.g., ligand dependent) different conformations (e.g., ligand dependent)  http://www.rcsb.org

26. 26 Human CD4 in complex with HIV gp120 gp120 CD4 PDB ID 1G9M

27. 27  Model organisms have independent databases: Organism specific databases HIV database http://hiv-web.lanl.gov/content/index http://gmod.org/wiki/Main_Page?q=node/71

28. 28 Summary  General and comprehensive databases: NCBI, EMBL, DDBJ NCBI, EMBL, DDBJ  Genome specific databases: ENSEMBL, UCSC genome browser ENSEMBL, UCSC genome browser  Highly annotated databases: Proteins: Proteins: Swissprot, RefSeqSwissprot, RefSeq Structures: Structures: PDBPDB

29. 29 And always remember: 1. Google (or any search engine) 2. RTFM - Read the manual!!! (/help/FAQ)

30. 30 Pairwise Sequence Alignment and BLAST

31. 31 What is sequence alignment? Alignment: Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences. MVNLTSDEKTAVLALWNKVDVEDCGGE || || ||||| ||| || || || MVHLTPEEKTAVNALWGKVNVDAVGGE

32. 32 Local vs. Global  Global alignment – finds the best alignment across the whole two sequences.  Local alignment – finds regions of high similarity in parts of the sequences.  Local alignment – finds regions of high similarity in parts of the sequences. ADLGAVFALCDRYFQ |||| |||| | ADLGRTQN-CDRYYQ ADLG CDRYFQ |||| |||| | ADLG CDRYYQ

33. 33 In the course of evolution, the sequences changed from the ancestral sequence by random mutations Three types of mutations: 1. Insertion - AAGA  AAGTA 2. Deletion - AAGA  AGA 3. Substitution - AAGA  AACA Evolutionary changes in sequences Insertion + Deletion  Indel

34. 34 Scoring scheme  Match/mismatch scores: substitution matrices Nucleic acids: Nucleic acids: Transition-transversionTransition-transversion Amino acids: Amino acids: Evolution (empirical data) based: (PAM, BLOSUM)Evolution (empirical data) based: (PAM, BLOSUM) Physico-chemical properties based (Grantham, McLachlan)Physico-chemical properties based (Grantham, McLachlan)  Gap penalty

35. 35 Computation time: How do we search a database?  If each pairwise alignment takes 1/10 of a second, and if the database contains 10 7 sequences, it will take 10 6 seconds = 11.5 days to complete one search.  150,000 searches (at least!!) are performed per day. >82,000,000 sequence records in GenBank.

36. 36 Conclusion  Using the exact comparison pairwise alignment algorithm between the query and all DB entries – too slow

37. 37 Heuristic  Definition: a heuristic is a design to solve a problem that does not provide an exact solution (but is not too bad) but reduces the time complexity of the exact solution

38. 38 BLAST  BLAST - Basic Local Alignment and Search Tool  A heuristic for searching a database for similar sequences  The heuristic based on restrictions of the similarity (such as using ungapped word matching instead of single character matching).

39. 39 Query:DNAProtein Database:DNAProtein Query type: DNA or Protein  All types of searches are possible blastn – nuc vs. nuc blastp – prot vs. prot blastx – translated query vs. protein database tblastn – protein vs. translated nuc. DB tblastx – translated query vs. translated database

40. 40 Query type  Information content in the letters: Nucleotides: 4 letter alphabet Nucleotides: 4 letter alphabet Amino acids: 20 letter alphabet Amino acids: 20 letter alphabet Two random DNA sequences will, on average, have 25% identity Two random protein sequences will, on average, have 5% identity The amino-acid sequence is often preferable for homology search  Selection (and hence conservation) works (mostly) at the protein level

41. 41 E-value  The number of times we will theoretically find an alignment with a score ≥ Y of a random sequence vs. a random database Theoretically, we could trust any result with an E-value ≤ 1 In practice – BLAST uses estimations. E-values of 10 -4 and lower indicate a significant homology. E-values between 10 -4 and 10 -2 should be checked (similar domains, maybe non-homologous). E-values between 10 -2 and 1 do not indicate a good homology

42. 42 Filtering low complexity  Low complexity regions : e.g., Proline rich areas (in proteins), Alu repeats (in DNA)  Regions of low complexity generate high scores of alignment, BUT – this does not indicate homology

43. 43 BLAST 2 sequences at NCBI Produces the local alignment of two given sequences using BLAST (Basic Local Alignment Search Tool) engine for local alignment BLAST  Does not use an optimal algorithm but a heuristic

44. 44 Back to NCBI

45. 45 BLAST – bl2seq

46. 46 blastn – nucleotide blastp – protein Bl2Seq - query

47. 47 Bl2seq results

48. 48 Bl2seq results Match Dissimilarity Gaps Similarity Low complexity

49. 49 BLAST – programs Query:DNAProtein Database:DNAProtein

50. 50 BLAST – Blastp

51. 51 Blastp - results

52. 52 Blastp – results (cont’)

53. 53 Blastp – acquiring sequences

54. 54 Blastp – acquiring sequences (cont’)

55. 55 Fasta format – multiple sequences >gi|4504351|ref|NP_000510.1| delta globin [Homo sapiens] MVHLTPEEKTAVNALWGKVNVDAVGGEALGRLLVVYPWTQRFFESFGDLSSPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFSQLSELHCDKLHVDPENFRLLGNVLVCVLARNFGKEFTPQMQAAYQKVVAGVAN ALAHKYH >gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens] MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH >gi|4885393|ref|NP_005321.1| epsilon globin [Homo sapiens] MVHFTAEEKAAVTSLWSKMNVEEAGGEALGRLLVVYPWTQRFFDSFGNLSSPSAILGNPKVKAHGKKVLT SFGDAIKNMDNLKPAFAKLSELHCDKLHVDPENFKLLGNVMVIILATHFGKEFTPEVQAAWQKLVSAVAI ALAHKYH >gi|6715607|ref|NP_000175.1| G-gamma globin [Homo sapiens] MGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAIMGNPKVKAHGKKVLT SLGDAIKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVLAIHFGKEFTPEVQASWQKMVTGVAS ALSSRYH >gi|28302131|ref|NP_000550.2| A-gamma globin [Homo sapiens] MGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAIMGNPKVKAHGKKVLT SLGDATKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVLAIHFGKEFTPEVQASWQKMVTAVAS ALSSRYH