Bioinformatics

1. Bioinformatics Neha Barve Lecturer, Bioinformatics School of Biotechnology, DAVV Indore

2. Topics covered • Introduction to Bioinformatics • Area of Bioinformatics • Fields • Biological databases • Sequence analysis

3. What Is Bioinformatics? • Bioinformatics is the unified discipline formed from the combination of: • Biology, • Computer science, and • Information technology. • "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.“ –Frank Tekaia

4. Area • Algorithms, • Databases and information systems, • Artificial intelligence and soft computing, • Information and computation theory, • Modeling and simulation, • Generating new knowledge of biology and medicine, and improving & discovering new models of computation (e.g. DNA computing, neural computing, evolutionary computing, immuno-computing, cellular-computing).

5. Related fields of Bioinformatics

6. Related Fields:Computational Biology • The study and application of computing methods for classical biology • Primarily concerned with evolutionary, population and theoretical biology, rather than the cellular or molecular level

7. Related Fields:Medical Informatics • The study and application of computing methods to improve communication, understanding, and management of medical data • Generally concerned with how the data is manipulated rather than the data itself

8. Related Fields:Cheminformatics • The study and application of computing methods, along with chemical and biological technology, for drug design and development

9. Related Fields:Genomics • Analysis and comparison of the entire genome of a single species or of multiple species • A genome is the set of all genes possessed by an organism • Genomics existed before any genomes were completely sequenced, but in a very primitive state

10. Related Fields:Proteomics • Study of how the genome is expressed in proteins, and of how these proteins function and interact • Concerned with the actual states of specific cells, rather than the potential states described by the genome

11. Related Fields:Pharmacogenomics • The application of genomic methods to identify drug targets • For example, searching entire genomes for potential drug receptors, or by studying gene expression patterns in tumors

12. Related Fields:Pharmacogenetics • The use of genomic methods to determine what causes variations in individual response to drug treatments • The goal is to identify drugs that may be only be effective for subsets of patients, or to tailor drugs for specific individuals or groups

13. Genomics • Classic Genomics • Post Genomic era • Comparative Genomics • Functional Genomics • Structural Genomics

14. What is Genomics? • Genome • complete set of genetic instructions for making an organism • Genomics • any attempt to analyze or compare the entire genetic complement of a species • Early genomics was mostly recording genome sequences

15. What next? • Post Genomic era • Comparative Genomics • Functional Genomics • Structural Genomics

16. What Is Proteomics? • Proteomics is the study of the proteome—the “PROTEin complement of the genOME” • More specifically, "the qualitative and quantitative comparison of proteomes under different conditions to further unravel biological processes"

17. What Makes Proteomics Important? • All cells in an organism contain the same DNA. • This DNA encodes every possible cell type in that organism—muscle, bone, nerve, skin, etc. • If we want to know about the type and state of a particular cell, the DNA does not help us, in the same way that knowing what language a computer program was written in tells us nothing about what the program does.

18. What Makes Proteomics Important? • There are more than 160,000 genes in each cell, only a handful of which actually determine that cell’s structure. • Many of the interesting things about a given cell’s current state can be deduced from the type and structure of the proteins it expresses. • Changes in, for example, tissue types, carbon sources, temperature, and stage in life of the cell can be observed in its proteins.

19. Proteomics In Disease Treatment • Nearly all major diseases—more than 98% of all hospital admissions—are caused by an particular pattern in a group of genes. • Isolating this group by comparing the hundreds of thousands of genes in each of many genomes would be very impractical. • Looking at the proteomes of the cells associated with the disease is much more efficient.

20. Proteomics In Disease Treatment • Many human diseases are caused by a normal protein being modified improperly. This also can only be detected in the proteome, not the genome. • The targets of almost all medical drugs are proteins. By identifying these proteins, proteomics aids the progress of pharmacogenetics.

21. Examples • What do these have in common? • Alzheimer's disease • Cystic fibrosis • Mad Cow disease • An inherited form of emphysema • Even many cancers

22. Biological databases • Data: any fact and statistics which has some useful information and used for analysis. • Biological system has tremendous data and it is difficult to maintain this much amount in paper • So need of a system to store and manipulate it.

23. Definition • A biological database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. The activity of preparing a database can be divided in to: • · Collection of data in a form which can be easily accessed • · Making it available to a multi-user system ( always available for the user) • The biological information of nucleic acids is available as sequences while the data of proteins is available as sequences and structures. Sequences are represented in single dimension where as the structure contains the three dimensional data of sequences.

24. Biological databases • It is a library of scientific information. • Which type of data? • DNA (sequence) • RNA • Protein (sequence, structure, functions, annotations, localiztion etc) • Chemical (structure, reactions, assays) • Literature • Enzymes (Functions) • Signaling pathways (prok, euk.) • Evolutionary (Phylogenetics) • Clinical

25. From where this data come from: • Experimentations (genome sequencing, structure determination, chemical reactions) • Literatures (scientific research papers) • Computational analysis (predictions) • Need: • Make it available to researcher all over the world • Development / storage • updation • To make biological data available in computer-readable form.

26. Types • Primary • Secondary • Based on type of data • Sequence databases (protein/ nucleic acid) • Structure database • Enzyme database • Signaling pathway • Interaction databases (PPI/PDI)

27. Different classifications of databases…. • Primary or derived databases • Primary databases: experimental results directly into database • Secondary databases: results of analysis of primary databases • Aggregate of many databases • Links to other data items • Combination of data • Consolidation of data

28. Data types: • Relationships between • sequence 3D structure protein functions • Properties and evolution of genes, genomes, proteins, metabolic pathways in cells • Use of this knowledge for prediction, modelling, and design TDQAAFDTNIVTLTRFVMEQGRKARGTGEMTQLLNSLCTAVKAISTAVRKAGIAHLYGIAGSTNVTGDQVKKLDVLSNDLVINVLKSSFATCVLVTEEDKNAIIVEPEKRGKYVVCFDPLDGSSNIDCLVSIGTIFGIYRKNSTDEPSEKDALQPGRNLVAAGYALYGSATMLV

29. Nucleic acid • EMBL • GenBank • DDBJ (DNA Data Bank of Japan)

30. Examples of databases Primary: (Nucleic acid) • DDBJ • EMBL • GeneBank (Protein) • PIR • MIPS • SWISS-PROT • TrEMBL Secondary: • PROSITE • Pfam • TrEMBL • FSSP • HSSP • Etc………

31. Genebank • GenBank is the NIH genetic sequence database of all publicly available DNA and derived protein sequences, with annotations describing the biological information these records contain.

32. EMBL/GenBank/DDJB • These 3 dbcontainmainly the same information (few differences in the format and syntax) • Serve as archives containing all sequences (single genes, ESTs, complete genomes, etc.)derived from: • Genomeprojects and sequencingcenters • Individualscientists • Patent offices (i.e. USPTO, EPO) • Non-confidential data are exchanged daily • There are approximately 126,551,501,141 bases in 135,440,924 sequence records in the traditional GenBank divisions and 191,401,393,188 bases in 62,715,288 sequence records in the WGS division as of April 2011. • Sequencesfrom > 50,000 differentspecies;

33. EBI Entrez NIH NCBI GenBank • Submissions • Updates • Submissions • Updates EMBL DDBJ CIB NIG • Submissions • Updates SRS EMBL getentry

34. Each database store data in specific file format. • For e.g. genbank file format • Embl file format • Ddbj file format

35. Title • Taxonomy • Citation Header Features (AA seq) DNA Sequence GenBank Flat File (GBFF) LOCUS MUSNGH 1803 bp mRNA ROD 29-AUG-1997 DEFINITION Mouse neuroblastoma and rat glioma hybridoma cell line NG108-15 cell TA20 mRNA, complete cds. ACCESSION D25291 NID g1850791 KEYWORDS neurite extension activity; growth arrest; TA20. SOURCE Murinae gen. sp. mouse neuroblastma-rat glioma hybridoma cell_line:NG108-15 cDNA to mRNA. ORGANISM Murinae gen. sp. Eukaryotae; mitochondrial eukaryotes; Metazoa; Chordata; Vertebrata; Mammalia; Eutheria; Rodentia; Sciurognathi; Muridae; Murinae. REFERENCE 1 (sites) AUTHORS Tohda,C., Nagai,S., Tohda,M. and Nomura,Y. TITLE A novel factor, TA20, involved in neuronal differentiation: cDNA cloning and expression JOURNAL Neurosci. Res. 23 (1), 21-27 (1995) MEDLINE 96064354 REFERENCE 3 (bases 1 to 1803) AUTHORS Tohda,C. TITLE Direct Submission JOURNAL Submitted (18-NOV-1993) to the DDBJ/EMBL/GenBank databases. Chihiro Tohda, Toyama Medical and Pharmaceutical University, Research Institute for Wakan-yaku, Analytical Research Center for Ethnomedicines; 2630 Sugitani, Toyama, Toyama 930-01, Japan (E-mail:CHIHIRO@ms.toyama-mpu.ac.jp, Tel:+81-764-34-2281(ex.2841), Fax:+81-764-34-5057) COMMENT On Feb 26, 1997 this sequence version replaced gi:793764. FEATURES Location/Qualifiers source 1..1803 /organism="Murinae gen. sp." /note="source origin of sequence, either mouse or rat, has not been identified" /db_xref="taxon:39108" /cell_line="NG108-15" /cell_type="mouse neuroblastma-rat glioma hybridoma" misc_signal 156..163 /note="AP-2 binding site" GC_signal 647..655 /note="Sp1 binding site" TATA_signal 694..701 gene 748..1311 /gene="TA20" CDS 748..1311 /gene="TA20" /function="neurite extensiion activity and growth arrest effect" /codon_start=1 /db_xref="PID:d1005516" /db_xref="PID:g793765" /translation="MMKLWVPSRSLPNSPNHYRSFLSHTLHIRYNNSLFISNTHLSRR KLRVTNPIYTRKRSLNIFYLLIPSCRTRLILWIIYIYRNLKHWSTSTVRSHSHSIYRL RPSMRTNIILRCHSYYKPPISHPIYWNNPSRMNLRGLLSRQSHLDPILRFPLHLTIYY RGPSNRSPPLPPRNRIKQPNRIKLRCR" polyA_site 1803 BASE COUNT 507 a 458 c 311 g 527 t ORIGIN 1 tcagtttttt tttttttttt tttttttttt tttttttttt tttttttttg ttgattcatg 61 tccgtttaca tttggtaagt tcacaggcct cagtcaacac aattggactg ctcaggaaat 121 cctccttggt gaccgcagta tacttggcct atgaacccaa gccacctatg gctaggtagg 181 agaagctcaa ctgtagggct gactttggaa gagaatgcac atggctgtat cgacatttca 241 catggtggac ctctggccag agtcagcagg ccgagggttc tcttccgggc tgctccctca 301 ctgcttgact ctgcgtcagt gcgtccatac tgtgggcgga cgttattgct atttgccttc 361 cattctgtac ggcattgcct ccatttagct ggagagggac agagcctggt tctctagggc 421 gtttccattg gggcctggtg acaatccaaa agatgagggc tccaaacacc agaatcagaa 481 ggcccagcgt atttgtaaaa acaccttctg gtgggaatga atggtacagg ggcgtttcag 541 gacaaagaac agcttttctg tcactcccat gagaaccgtc gcaatcactg ttccgaagag 601 gaggagtcca gaatacacgt gtatgggcat gacgattgcc cggagagagg cggagcccat 661 ggaagcagaa agacgaaaaa cacacccatt atttaaaatt attaaccact cattcattga 721 cctacctgcc ccatccaaca tttcatcatg atgaaacttt gggtcccttc taggagtctg 781 cctaatagtc caaatcatta caggtctttt cttagccata cactacacat cagatacaat 841 aacagccttt tcatcagtaa cacacatttg tcgagacgta aattacgggt gactaatccg 901 atatatacac gcaaacggag cctcaatatt ttttatttgc ttattccttc atgtcggacg 961 aggcttatat tatggatcat atacatttat agaaacctga aacattggag tacttctact 1021 gttcgcagtc atagccacag catttatagg ctacgtcctt ccatgaggac aaatatcatt 1081 ctgaggtgcc acagttatta caaacctcct atcagccatc ccatatattg gaacaaccct 1141 agtcgaatga atttgagggg gcttctcagt agacaaagcc accttgaccc gattcttcgc 1201 tttccacttc atcttaccat ttattatcgc ggccctagca atcgttcacc tcctcttcct 1261 ccacgaaaca ggatcaaaca acccaacagg attaaactca gatgcagata aaattccatt 1321 tcacccctac tatacatcaa agatatccta ggtatcctaa tcatattctt aattctcata 1381 accctagtat tatttttccc agacatacta ggagacccag acaactacat accagctaat 1441 ccactaaaca ccccacccca tattaaaccc gaatgatatt tcctatttgc atacgccatt 1501 ctacgctcaa tccccaataa actaggaggt gtcctagcct taatcttatc tatcctaatt 1561 ttagccctaa tacctttcct tcatacctca aagcaacgaa gcctaatatt ccgcccaatc 1621 acacaaattt tgtactgaat cctagtagcc aacctactta tcttaacctg aattgggggc 1681 caaccagtag acacccattt attatcattg gccaactagc ctccatctca tacttctcaa 1741 tcatcttaat tcttatacca atctcaggaa ttatcgaaga caaaatacta aaattatatc 1801 cat //

36. GenBank file format

37. LOCUS ACCESSION gi Accession.version PID protein gi Protein_id LOCUS, Accession, gi and PID LOCUS HSU40282 1789 bp mRNA PRI 21-MAY-1998 DEFINITION Homo sapiens integrin-linked kinase (ILK) mRNA, complete cds. ACCESSION U40282 VERSION U40282.1 GI:3150001 LOCUS: HSU40282 ACCESSION: U40282 VERSION: U40282.1 GI: 3150001 PID: g3150002 Protein gi: 3150002 protein_id: AAC16892.1 CDS 157..1515 /gene="ILK" /note="protein serine/threonine kinase" /codon_start=1 /product="integrin-linked kinase" /protein_id="AAC16892.1" /db_xref="PID:g3150002" /db_xref="GI:3150002"

38. Which Tool? • BankIt: Web based tool which is simple, easy to use, great for simple submissions, but not ideal for complicated ones. • Sakura (DDBJ) • WebIn (EMBL) • Sequin: Client that you need to d/l to your computer, a little harder to learn, but has great documentation, and ideal for complicated, large, multiple submissions. • tbl2asn: ideal for batch records, command line, scriptable, can work with sequin

39. Which tool? mRNA Genomic STS/ GSS EST Other Other HTGS dbEST Simple • Better control of annotations • pop/phylo • segmented sets Simple dbSTSdbGSS Customized software or tbl2asn E-mail or FTP WWW BankIt Sequin or tbl2asn WWW BankIt E-mail or FTP E-mail or FTP E-mail

40. Protein databases • SWISS-PROT: Annotated Sequence Database • TrEMBL: Database of EMBL nucleotide translated sequences • InterPro:Integrated resource for protein families, domains • and functional sites. • CluSTr:Offers an automatic classification of SWISS-PROT • and TrEMBL. • Etc………..

41. Swiss prot • It is a high quality annotated and non-redundant protein sequence database, which brings together experimental results, computed features and scientific conclusions.Since 2002, it is maintained by the UniProt consortium and is accessible via the UniProt website. • Statistics: Release 2011_12 of 14-Dec-11 of UniProtKB/Swiss-Prot contains 0.5 million sequence entries, comprising 189261966 amino acids abstracted from 205244 references. 629 sequences have been added since release 2011_11, the sequence data of 146 existing entries has been updated and the annotations of 182184 entries have been revised.

43. Structure database • PDB An Information Portal to Biological Macromolecular Structures • As of Tuesday Dec 20, 2011 there are 78020 Structures • NMR • X-ray • Electron microscopy

44. HEADER LEUCINE ZIPPER 15-JUL-93 1DGC 1DGC 2 COMPND GCN4 LEUCINE ZIPPER COMPLEXED WITH SPECIFIC 1DGC 3 COMPND 2 ATF/CREB SITE DNA 1DGC 4 SOURCE GCN4: YEAST (SACCHAROMYCES CEREVISIAE); DNA: SYNTHETIC 1DGC 5 AUTHOR T.J.RICHMOND 1DGC 6 REVDAT 1 22-JUN-94 1DGC 0 1DGC 7 JRNL AUTH P.KONIG,T.J.RICHMOND 1DGC 8 JRNL TITL THE X-RAY STRUCTURE OF THE GCN4-BZIP BOUND TO 1DGC 9 JRNL TITL 2 ATF/CREB SITE DNA SHOWS THE COMPLEX DEPENDS ON DNA 1DGC 10 JRNL TITL 3 FLEXIBILITY 1DGC 11 JRNL REF J.MOL.BIOL. V. 233 139 1993 1DGC 12 JRNL REFN ASTM JMOBAK UK ISSN 0022-2836 0070 1DGC 13 REMARK 1 1DGC 14 REMARK 2 1DGC 15 REMARK 2 RESOLUTION. 3.0 ANGSTROMS. 1DGC 16 REMARK 3 1DGC 17 REMARK 3 REFINEMENT. 1DGC 18 REMARK 3 PROGRAM X-PLOR 1DGC 19 REMARK 3 AUTHORS BRUNGER 1DGC 20 REMARK 3 R VALUE 0.216 1DGC 21 REMARK 3 RMSD BOND DISTANCES 0.020 ANGSTROMS 1DGC 22 REMARK 3 RMSD BOND ANGLES 3.86 DEGREES 1DGC 23 REMARK 3 1DGC 24 REMARK 3 NUMBER OF REFLECTIONS 3296 1DGC 25 REMARK 3 RESOLUTION RANGE 10.0 - 3.0 ANGSTROMS 1DGC 26 REMARK 3 DATA CUTOFF 3.0 SIGMA(F) 1DGC 27 REMARK 3 PERCENT COMPLETION 98.2 1DGC 28 REMARK 3 1DGC 29 REMARK 3 NUMBER OF PROTEIN ATOMS 456 1DGC 30 REMARK 3 NUMBER OF NUCLEIC ACID ATOMS 386 1DGC 31 REMARK 4 1DGC 32 REMARK 4 GCN4: TRANSCRIPTIONAL ACTIVATOR OF GENES ENCODING FOR AMINO 1DGC 33 REMARK 4 ACID BIOSYNTHETIC ENZYMES. 1DGC 34 REMARK 5 1DGC 35 REMARK 5 AMINO ACIDS NUMBERING (RESIDUE NUMBER) CORRESPONDS TO THE 1DGC 36 REMARK 5 281 AMINO ACIDS OF INTACT GCN4. 1DGC 37 REMARK 6 1DGC 38 REMARK 6 BZIP SEQUENCE 220 - 281 USED FOR CRYSTALLIZATION. 1DGC 39 REMARK 7 1DGC 40 REMARK 7 MODEL FROM AMINO ACIDS 227 - 281 SINCE AMINO ACIDS 220 - 1DGC 41 REMARK 7 226 ARE NOT WELL ORDERED. 1DGC 42 REMARK 8 1DGC 43 REMARK 8 RESIDUE NUMBERING OF NUCLEOTIDES: 1DGC 44 REMARK 8 5' T G G A G A T G A C G T C A T C T C C 1DGC 45 REMARK 8 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 1DGC 46 REMARK 9 1DGC 47 REMARK 9 THE ASYMMETRIC UNIT CONTAINS ONE HALF OF PROTEIN/DNA 1DGC 48 REMARK 9 COMPLEX PER ASYMMETRIC UNIT. 1DGC 49 REMARK 10 1DGC 50 REMARK 10 MOLECULAR DYAD AXIS OF PROTEIN DIMER AND PALINDROMIC HALF 1DGC 51 REMARK 10 SITES OF THE DNA COINCIDES WITH CRYSTALLOGRAPHIC TWO-FOLD 1DGC 52 REMARK 10 AXIS. THE FULL PROTEIN/DNA COMPLEX CAN BE OBTAINED BY 1DGC 53 REMARK 10 APPLYING THE FOLLOWING TRANSFORMATION MATRIX AND 1DGC 54 REMARK 10 TRANSLATION VECTOR TO THE COORDINATES X Y Z: 1DGC 55 REMARK 10 1DGC 56 REMARK 10 0 -1 0 X 117.32 X SYMM 1DGC 57 REMARK 10 -1 0 0 Y + 117.32 = Y SYMM 1DGC 58 REMARK 10 0 0 -1 Z 43.33 Z SYMM 1DGC 59 SEQRES 1 A 62 ILE VAL PRO GLU SER SER ASP PRO ALA ALA LEU LYS ARG 1DGC 60 SEQRES 2 A 62 ALA ARG ASN THR GLU ALA ALA ARG ARG SER ARG ALA ARG 1DGC 61 SEQRES 3 A 62 LYS LEU GLN ARG MET LYS GLN LEU GLU ASP LYS VAL GLU 1DGC 62 SEQRES 4 A 62 GLU LEU LEU SER LYS ASN TYR HIS LEU GLU ASN GLU VAL 1DGC 63 SEQRES 5 A 62 ALA ARG LEU LYS LYS LEU VAL GLY GLU ARG 1DGC 64 SEQRES 1 B 19 T G G A G A T G A C G T C 1DGC 65 SEQRES 2 B 19 A T C T C C 1DGC 66 HELIX 1 A ALA A 228 LYS A 276 1 1DGC 67 CRYST1 58.660 58.660 86.660 90.00 90.00 90.00 P 41 21 2 8 1DGC 68 ORIGX1 1.000000 0.000000 0.000000 0.00000 1DGC 69 ORIGX2 0.000000 1.000000 0.000000 0.00000 1DGC 70 ORIGX3 0.000000 0.000000 1.000000 0.00000 1DGC 71 SCALE1 0.017047 0.000000 0.000000 0.00000 1DGC 72 SCALE2 0.000000 0.017047 0.000000 0.00000 1DGC 73 SCALE3 0.000000 0.000000 0.011539 0.00000 1DGC 74 ATOM 1 N PRO A 227 35.313 108.011 15.140 1.00 38.94 1DGC 75 ATOM 2 CA PRO A 227 34.172 107.658 15.972 1.00 39.82 1DGC 76 ATOM 842 C5 C B 9 57.692 100.286 22.744 1.00 29.82 1DGC 916 ATOM 843 C6 C B 9 58.128 100.193 21.465 1.00 30.63 1DGC 917 TER 844 C B 9 1DGC 918 MASTER 46 0 0 1 0 0 0 6 842 2 0 7 1DGC 919 END 1DGC 920 PDB • HEADER • COMPND • SOURCE • AUTHOR • DATE • JRNL • REMARK • SEQRES • ATOM COORDINATES

45. Other databases • Pathway databases • Enzyme databases • Ligand databases • Motif, domain databases etc…………..

46. Sequence analysis • term sequence analysis refers to the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution.

47. Need • The comparison of sequences in order to find similarity often to infer if they are related (homologous) • Identification of intrinsic features of the sequence such as active sites, post translational modification sites, gene-structures, reading frames, distributions of introns and exons and regulatory elements • Identification of sequence differences and variations such as point mutations and single nucleotide polymorphism (SNP) in order to get the genetic marker. • Revealing the evolution and genetic diversity of sequences and organisms • Identification of molecular structure from sequence alone