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Lecture 1 Introduction to Bioinformatics BCH 433.ppt

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KelechiChukwuemeka

Bioinformatics involves using computational tools to analyze large datasets from molecular biology, including DNA, RNA, proteins, and genetic information. It combines biology, computer science, and information technology. Key applications include processing and visualizing data from genomics, transcriptomics, and proteomics projects. Challenges include educating biologists in computational tools, developing databases to store biological data, and creating efficient search engines for complex databases. The document provides examples of molecular data like DNA sequences, protein sequences, and describes genomic and proteomic analysis.

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Introduction to Bioinformatics BCH 433 Lecture 1 Dr. J. Ikwebe
What is Bioinformatics?
Molecular Bioinformatics Molecular Bioinformatics involves the use of computational tools to discover new information in complex data sets (from the one-dimensional information of DNA through the two-dimensional information of RNA and the three-dimensional information of proteins, to the four-dimensional information of evolving living systems).
Bioinformatics (Oxford English Dictionary): The branch of science concerned with information and information flow in biological systems, especially the use of computational methods in genetics and genomics.
What is bioinformatics? • The application of computational tools on molecular data, including the means to acquire, analyse, or visualize such data. • Key tools to handle and analyze the large amount of data generated by large-scale DNA, RNA and protein characterization projects (genomics -transcriptomics - proteomics).
Biologists collect molecular data: DNA & Protein sequences, gene expression, etc. Computer scientists (+Mathematicians, Statisticians, etc.) Develop tools, softwares, algorithms to store and analyze the data. Bioinformaticians Study biological questions by analyzing molecular data The field of science in which biology, computer science and information technology merge into a single discipline
.... • Bioinformatics uses computers, computing technology and software to manage large amounts of biological data and enable their analysis. • At the end of this course students will be expected to: – understand biological data and data management and integration – have a broad knowledge of computing and biological methods in bioinformatics – understand genomes, genome sequencing, genomic structure and comparison – know about the technology used in modern post-genomic biology, the data produced and the software to manage it.
Introduction Large databases that can be accessed and analyzed with sophisticated tools have become central to biological research and education. The information content in the genomes of organisms, in the molecular dynamics of proteins, and in population dynamics, to name but a few areas, is enormous.  Biologists are increasingly finding that the management of complex data sets is becoming a bottleneck for scientific advances. Therefore, bioinformatics is rapidly becoming a key technology in all fields of biology.
The present bottlenecks in bioinformatics include; the education of biologists in the use of advanced computing tools, the recruitment of computer scientists into this evolving field, the limited availability of developed databases of biological information, the need for more efficient and intelligent search engines for complex databases. Bottlenecks
The hereditary information of all living organisms, with the exception of some viruses, is carried by deoxyribonucleic acid (DNA) molecules. 2 purines: 2 pyrimidines: adenine (A) cytosine (C) guanine (G) thymine (T) two rings one ring
Eukaryotes may have up to 3 subcellular genomes: 1. Nuclear 2. Mitochondrial 3. Plastid Bacteria have either circular or linear genomes and may also carry plasmids The entire complement of genetic material carried by an individual is called the genome. Human chromosomes Circular genome
Central dogma: DNA makes RNA makes Protein Modified dogma: DNA makes DNA and RNA, RNA makes DNA, RNA an Protein
Amino acids - The protein building blocks
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Any region of the DNA sequence can, in principle, code for six different amino acid sequences, because any one of three different reading frames can be used to interpret each of the two strands.
Protein folding A human Haemoglobin
Some basic definitions • Genomics---- Genome: The total genetic content contained in a haploid set of chromosomes in eukaryotes, in a single chromosome in bacteria, or in the DNA or RNA of viruses. • Transcriptomics---- Transcriptome: the complete set of genes encoded on a genome that can be transcribed. • Proteomics---- Proteome: the complete set of proteins encoded on a genome that can be expressed and modified by a cell, tissue, or organism (Etymology: Protein+genome). – Sub-cellular proteome: the complete set of proteins for a given membrane or organelle (e.g. mitochondrial proteome). – Membranome: the complete set of membranes from a cell. – Metabolome: The metabolic products of the cell, that is, all the metabolites – Secretome: The secreted proteins of a cell? – The phosphome:Total phosphorylated proteins of a cell?
How does it all look like on a computer monitor?
A cDNA sequence >gi|14456711|ref|NM_000558.3| Homo sapiens hemoglobin, alpha 1 (HBA1), mRNA ACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCG CCTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACC ACCAAGACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGA CGCGCTGACCAACGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACA AGCTTCGGGTGGACCCGGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCC GAGTTCACCCCTGCGGTGCACGCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCG TTAAGCTGGAGCCTCGGTGGCCATGCTTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGT ACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGGCGGC
A cDNA sequence (reading frame) A protein sequence >gi|14456711|ref|NM_000558.3| Homo sapiens hemoglobin, alpha 1 (HBA1), mRNA ACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCC GCCTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCAC CACCAAGACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCG ACGCGCTGACCAACGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCAC AAGCTTCGGGTGGACCCGGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGC CGAGTTCACCCCTGCGGTGCACGCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACC GTTAAGCTGGAGCCTCGGTGGCCATGCTTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCC GTACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGGCGGC >gi|4504347|ref|NP_000549.1| alpha 1 globin [Homo sapiens] MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAH VDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR
ACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCGCCTGG GGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACCACCAAGACCT ACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGACGCGCTGACCAACGC CGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGGACCCGGTC AACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGTGCACGCCT CCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCCATGCTTCT TGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGGCG GCACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCGCCT GGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACCACCAAGAC CTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGACGCGCTGACCAAC GCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGGACCCGG TCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGTGCACGC CTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCCATGCTT CTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGG CGGCACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCGC CTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACCACCAAG ACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGACGCGCTGACCA ACGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGGACCC GGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGTGCAC GCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCCATGC TTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTGAGTG GGCGGCGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGG ACCCGGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGT GCACGCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCC ATGCTTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTG AGTGGGCGGCACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAA GGCCGCCTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACC ACCAAGACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCG... And, a whole genome…
E. coli 4.6 x 106 nucleotides – Approx. 4,000 genes Yeast 15 x 106 nucleotides – Approx. 6,000 genes Human 3 x 109 nucleotides – Approx. 30,000 genes Smallest human chromosome 50 x 106 nucleotides How big are whole genomes?
What do we actually do with bioinformatics?
From DNA to Genome Watson and Crick DNA model Sanger sequences insulin protein Sanger dideoxy DNA sequencing PCR (Polymerase Chain Reaction) 1955 1960 1965 1970 1975 1980 1985 ARPANET (early Internet) PDB (Protein Data Bank) Sequence alignment GenBank database Dayhoff’s Atlas
1995 1990 2000 SWISS-PROT database NCBI World Wide Web BLAST FASTA EBI Human Genome Initiative First human genome draft First bacterial genome Yeast genome
The first protein sequence reported was that of bovine insulin in 1956, consisting of 51 residues. Origin of bioinformatics and biological databases: Nearly a decade later, the first nucleic acid sequence was reported, that of yeast tRNAalanine with 77 bases.
In 1965, Dayhoff gathered all the available sequence data to create the first bioinformatic database (Atlas of Protein Sequence and Structure). The Protein DataBank followed in 1972 with a collection of ten X-ray crystallographic protein structures. The SWISSPROT protein sequence database began in 1987.
Nucleotides
as of August 2011: Eukaryotes 37 Prokaryotes 1708 Total 1745 Complete Genomes
CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA TAT GGA CAA TTG GTT TCT TCT CTG AAT ...... .............. TGAAAAACGTA
CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA TAT GGA CAA TTG GTT TCT TCT CTG AAT ................................. .............. TGAAAAACGTA TF binding site promoter Ribosome binding Site ORF = Open Reading Frame CDS = Coding Sequence Transcription Start Site
Biological databases
What is a Database? A structured collection of data held in computer storage; esp. one that incorporates software to make it accessible in a variety of ways; transf., any large collection of information. database management: the organization and manipulation of data in a database. database management system (DBMS): a software package that provides all the functions required for database management. database system: a database together with a database management system. Oxford Dictionary
What is a database? • A collection of data – structured – searchable (index) -> table of contents – updated periodically (release) -> new edition – cross-referenced (hyperlinks) -> links with other db • Includes also associated tools (software) necessary for access, updating, information insertion, information deletion…. • Data storage management: flat files, relational databases…
Database or databank? Initially • Databank (in UK) • Database (in the USA) Solution • The abbreviation db
Why biological databases? • Exponential growth in biological data. • Data (genomic sequences, 3D structures, 2D gel analysis, MS analysis, Microarrays….) are no longer published in a conventional manner, but directly submitted to databases. • Essential tools for biological research. The only way to publish massive amounts of data without using all the paper in the world.
Distribution of sequences • Books, articles 1968 -> 1985 • Computer tapes 1982 -> 1992 • Floppy disks 1984 -> 1990 • CD-ROM 1989 -> • FTP 1989 -> • On-line services 1982 -> 1994 • WWW 1993 -> • DVD 2001 ->
Some statistics • More than 1000 different ‘biological’ databases • Variable size: <100Kb to >20Gb – DNA: > 20 Gb – Protein: 1 Gb – 3D structure: 5 Gb – Other: smaller • Update frequency: daily to annually to seldom to forget about it. • Usually accessible through the web (some free, some not)
International nucleotide data banks EMBL Europe EMBL EBI GenBank USA NLM NCBI DDBJ Japan NIG CIB International Advisory Meeting Collaborative Meeting TrEMBL NRDB
Databases • NCBI (National Centre for Biotechnology Information): http://www.ncbi.nlm.nih.gov/ • EBI: http://www.ebi.ac.uk/ • DDBJ: http://www.ddbj.nig.ac.jp/ • InterPro: http://www.ebi.ac.uk/interpro/ • InterPro is a database of protein families, domains and functional sites in which identifiable features found in known proteins can be applied to unknown protein sequences • b) Search and analytical tools • ORFFinder: http://www.ncbi.nlm.nih.gov/gorf/gorf.html • It is an analysis tool which finds all open reading frames in a user's sequence or in a sequence already in the database. • InterProScan server: http://www.ebi.ac.uk/InterProScan/ • InterProScan is used to search various protein domain/motifs/functional sites databases and can combine other analyses such as the identification of potential transmembrane domains and signal peptides.
…… • PSORT: http://www.psort.org/ • This cite provides links to the PSORT family of programs for subcellular localization prediction as well as other datasets and resources relevant to localization prediction. • SignalP v3.0 Server: http://www.cbs.dtu.dk/services/SignalP/ • SignalP aims at identifying signal peptides in eukaryotes and bacteria query proteins. • TMHMM v2.0 server: http://www.cbs.dtu.dk/services/TMHMM/ • TMHMM aims at identifying trans-membrane domains in proteins (eukaryotic or prokaryotic).
 Some databases in the field of molecular biology… AATDB, AceDb, ACUTS, ADB, AFDB, AGIS, AMSdb, ARR, AsDb, BBDB, BCGD, Beanref, Biolmage, BioMagResBank, BIOMDB, BLOCKS, BovGBASE, BOVMAP, BSORF, BTKbase, CANSITE, CarbBank, CARBHYD, CATH, CAZY, CCDC, CD4OLbase, CGAP, ChickGBASE, Colibri, COPE, CottonDB, CSNDB, CUTG, CyanoBase, dbCFC, dbEST, dbSTS, DDBJ, DGP, DictyDb, Picty_cDB, DIP, DOGS, DOMO, DPD, DPlnteract, ECDC, ECGC, EC02DBASE, EcoCyc, EcoGene, EMBL, EMD db, ENZYME, EPD, EpoDB, ESTHER, FlyBase, FlyView, GCRDB, GDB, GENATLAS, Genbank, GeneCards, Genline, GenLink, GENOTK, GenProtEC, GIFTS, GPCRDB, GRAP, GRBase, gRNAsdb, GRR, GSDB, HAEMB, HAMSTERS, HEART-2DPAGE, HEXAdb, HGMD, HIDB, HIDC, HlVdb, HotMolecBase, HOVERGEN, HPDB, HSC-2DPAGE, ICN, ICTVDB, IL2RGbase, IMGT, Kabat, KDNA, KEGG, Klotho, LGIC, MAD, MaizeDb, MDB, Medline, Mendel, MEROPS, MGDB, MGI, MHCPEP5 Micado, MitoDat, MITOMAP, MJDB, MmtDB, Mol-R-Us, MPDB, MRR, MutBase, MycDB, NDB, NRSub, 0-lycBase, OMIA, OMIM, OPD, ORDB, OWL, PAHdb, PatBase, PDB, PDD, Pfam, PhosphoBase, PigBASE, PIR, PKR, PMD, PPDB, PRESAGE, PRINTS, ProDom, Prolysis, PROSITE, PROTOMAP, RatMAP, RDP, REBASE, RGP, SBASE, SCOP, SeqAnaiRef, SGD, SGP, SheepMap, Soybase, SPAD, SRNA db, SRPDB, STACK, StyGene,Sub2D, SubtiList, SWISS-2DPAGE, SWISS-3DIMAGE, SWISS- MODEL Repository, SWISS-PROT, TelDB, TGN, tmRDB, TOPS, TRANSFAC, TRR, UniGene, URNADB, V BASE, VDRR, VectorDB, WDCM, WIT, WormPep, YEPD, YPD, YPM, etc .................. !!!!
Categories of databases for Life Sciences • Sequences (DNA, protein) • Genomics • Mutation/polymorphism • Protein domain/family • Proteomics (2D gel, Mass Spectrometry) • 3D structure • Metabolic networks • Regulatory networks • Bibliography • Expression (Microarrays,…) • Specialized
Bookshelf: A collection of searchable biomedical books linked to PubMed. PubMed: Allows searching by author names, journal titles, and a new Preview/Index option. PubMed database provides access to over 12 million MEDLINE citations back to the mid-1960's. It includes History and Clipboard options which may enhance your search session. PubMed Central: The U.S. National Library of Medicine digital archive of life science journal literature. OMIM: Online Mendelian Inheritance in Man is a database of human genes and genetic disorders (also OMIA). Literature Databases:
..... • BLAST is… Basic Local Alignment Search Tool • NCBI's sequence similarity search tool • supports analysis of DNA and protein databases • 80,000 searches per day
Why use BLAST? • BLAST searching is fundamental to understanding the relatedness of any favourite query sequence to other known proteins or DNA sequences. • Applications include: – identifying orthologs and paralogs – discovering new genes or proteins – discovering variants of genes or proteins – investigating expressed sequence tags (ESTs) – exploring protein structure and function
.... • TaxBrowser is… • browser for the major divisions of living organisms (archaea, bacteria, eukaryota, viruses). • taxonomy information such as genetic codes. • molecular data on extinct organisms.
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
What is an accession number? • An accession number is a label that is used to identify a sequence. It is a unique string of letters and/or numbers that corresponds to a given molecular sequence. • Examples:  DNA AF492453 GenBank genomic sequence (same at EBI)  Protein AAM97590 GenBank protein Q8MV55 SwissProt protein Non Protein Data Bank structure record  Publication 12192407 PubMed ID - Williams et al. Nature 418: 865-9 (2002).
PubMed (Medline) • MEDLINE covers the fields of medicine, nursing, dentistry, veterinary medicine, public health, and preclinical sciences • Contains citations from approximately 5,200 worldwide journals in 37 languages; 60 languages for older journals. • Contains over 20 million citations since 1948 • Contains links to biological db and to some journals • New records are added to PreMEDLINE daily!
Type in a Query term • Enter your search words in the query box and hit the “Go” button http://www.ncbi.nlm.nih.gov/entrez/query/static/help/helpdoc.html#Searching
The Syntax … 1. Boolean operators: AND, OR, NOT must be entered in UPPERCASE (e.g., promoters OR response elements). The default is AND. 2. Entrez processes all Boolean operators in a left-to-right sequence. The order in which Entrez processes a search statement can be changed by enclosing individual concepts in parentheses. The terms inside the parentheses are processed first. For example, the search statement: g1p3 OR (response AND element AND promoter). 3. Quotation marks: The term inside the quotation marks is read as one phrase (e.g. “public health” is different than public health, which will also include articles on public latrines and their effect on health workers). 4. Asterisk: Extends the search to all terms that start with the letters before the asterisk. For example, dia* will include such terms as diaphragm, dial, and diameter.
Refine the Query • Often a search finds too many (or too few) sequences, so you can go back and try again with more (or fewer) keywords in your query • The “History” feature allows you to combine any of your past queries. • The “Limits” feature allows you to limit a query to specific organisms, sequences submitted during a specific period of time, etc. • [Many other features are designed to search for literature in MEDLINE]
The OMIM (Online Mendelian Inheritance in Man) – Genes and genetic disorders – Edited by team at Johns Hopkins – Updated daily
MIM Number Prefixes * gene with known sequence + gene with known sequence and phenotype # phenotype description, molecular basis known % mendelian phenotype or locus, molecular basis unknown no prefix other, mainly phenotypes with suspected mendelian basis
Searching OMIM • Search Fields – Name of trait, e.g., hypertension – Cytogenetic location, e.g., 1p31.6 – Inheritance, e.g., autosomal dominant – Gene, e.g., coagulation factor VIII
OMIM search tags All Fields [ALL] Allelic Variant [AV] or [VAR] Chromosome [CH] or [CHR] Clinical Synopsis [CS] or [CLIN] Gene Map [GM] or [MAP] Gene Name [GN] or [GENE] Reference [RE] or [REF]
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Online Literature databases 1. Google Scholar 2. Google Books 3. Web of Science
4. Google Scholar http://www.scholar.google.com/
Enables you to search specifically for scholarly literature, including peer-reviewed papers, theses, books, preprints, abstracts and technical reports from all broad areas of research. What is Google Scholar?
Use Google Scholar to find articles from a wide variety of academic publishers, professional societies, preprint repositories and universities, as well as scholarly articles available across the web.
Google Scholar orders your search results by how relevant they are to your query, so the most useful references should appear at the top of the page This relevance ranking takes into account the: full text of each article. the article's author, the publication in which the article appeared and how often it has been cited in scholarly literature.
What other DATA can we retrieve from the record?
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
5. Google Book Search
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
6. Web of science http://http://apps.webofknowledge.com.ezproxy.lib.uh.edu/WOS_GeneralSearch_input.do?product =WOS&search_mode=GeneralSearch&SID=4FB7LbbLgDMhG9fDiLh&preferencesSaved=
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Areas in Bioinformatics…
Genomics • Because of the multicellular structure, each cell type does gene expression in a different way –although each cell has the same content as far as the genetic constitution. • i.e. All the information for a liver cell to be a liver cell is also present on nose cell, so gene expression is the only thing that differentiates
Genomics - Finding Genes • Gene in sequence data – needle in a haystack • However as the needle is different from the haystack genes are not diff from the rest of the sequence data • Is whole array of nt we try to find and border mark a set o nt as a gene • This is one of the challenges of bioinformatics • Neural networks and dynamic programming are being employed
Organism Genome Size (Mb) bp * 1,000,000 Gene Number Web Site Yeast 13.5 6,241 http://genome- www.stanford.edu /Saccharomyces Fruit Flies 180 13,601 http://flybase.bio. indiana.edu Homo Sapiens 3,000 45,000 http://www.ncbi.n lm.nih.gov/genom e/guide
Proteomics • Proteome is the sum total of an organisms proteins • More difficult than genomics – 4 20 – Simple chemical makeup complex – Can duplicate can’t • We are entering into the ‘post genome era’ • Meaning much has been done with the Genes – not that it’s a over
Proteomics….. • The relationship between the RNA and the protein it codes are usually very different • After translation proteins do change – So aa sequence do not tell anything about the post translation changes • Proteins are not active until they are combined into a larger complex or moved to a relevant location inside or outside the cell • So aa only hint in these things • Also proteins must be handled more carefully in labs as they tend to change when in touch with an inappropriate material
Protein Structure Prediction • Is one of the biggest challenges of bioinformatics and esp. biochemistry • No algorithm is there now to consistently predict the structure of proteins
Structure Prediction methods • Comparative Modeling – Target proteins structure is compared with related proteins – Proteins with similar sequences are searched for structures
Phylogenetics • The taxonomical system reflects evolutionary relationships • Phylogenetics trees are things which reflect the evolutionary relationship thru a picture/graph • Rooted trees where there is only one ancestor • Un rooted trees just showing the relationship • Phylogenetic tree reconstruction algorithms are also an area of research
Applications….
Medical Implications • Pharmacogenomics – Not all drugs work on all patients, some good drugs cause death in some patients – So by doing a gene analysis before the treatment the offensive drugs can be avoided – Also drugs which cause death to most can be used on a minority to whose genes that drug is well suited – volunteers wanted! – Customized treatment • Gene Therapy – Replace or supply the defective or missing gene – E.g: Insulin and Factor VIII or Haemophilia • BioWeapons (??)
Diagnosis of Disease • Diagnosis of disease – Identification of genes which cause the disease will help detect disease at early stage e.g. Huntington disease - • Symptoms – uncontrollable dance like movements, mental disturbance, personality changes and intellectual impairment • Death in 10-15 years • The gene responsible for the disease has been identified • Contains excessively repeated sections of CAG • So once analyzed the couple can be counseled
Drug Design • Can go up to 15 yrs and $700 million • One of the goals of bioinformatics is to reduce the time and cost involved with it. • The process – Discovery • Computational methods can improves this – Testing
Discovery Target identification – Identifying the molecule on which the germs relies for its survival – Then we develop another molecule i.e. drug which will bind to the target – So the germ will not be able to interact with the target. – Proteins are the most common targets
Discovery… • For example HIV produces HIV protease which is a protein and which in turn eat other proteins • This HIV protease has an active site where it binds to other molecules • So HIV drug will go and bind with that active site – Easily said than done!
Discovery… • Lead compounds are the molecules that go and bind to the target protein’s active site • Traditionally this has been a trial and error method • Now this is being moved into the realm of computers
Restriction Analysis of DNA • Special enzymes termed restriction enzymes have been discovered in many different bacteria and other single-celled organisms. These enzymes act as chemical scissors to cut λ DNA into pieces. • They are able to scan along a length of DNA looking for a particular sequence of bases that they recognize. • This recognition site or sequence is generally from 4 to 6 base pairs in length. Once it is located, the enzyme will attach to the DNA molecule and cut each strand of the double helix- the first step in a process called restriction mapping. • The restriction enzyme will continue to do this along the full length of the DNA molecule which will then break into fragments. The size of these fragments is measured in base pairs or kilobase (1000 bases) pairs. • Since the recognition site or sequence of base pairs is known for each restriction enzyme, we can use this to form a detailed analysis of the sequence of bases in specific regions of the DNA in which we are interested. • This procedure is one of the most important in modern biology.
.... Restriction analysis • In the presence of specific DNA repair enzymes, DNA fragments will re-anneal or stick themselves to other fragments with cut ends that are complimentary to their own end sequence. • It doesn’t matter if the fragment that matches the cut end comes from the same organism or from a different one. • This ability of DNA to repair itself has been utilized by scientists to introduce foreign DNA into an organism. • This DNA may contain genes that allow the organism to exhibit a new function or process. This would include transferring genes that will result in a change in the nutritional quality of a crop or perhaps allow a plant to grow in a region that is colder than its usual preferred area.
Example: Restriction Digestion and Analysis of DNA from Bacteriophage λ • This small virus is 48,502 base pairs in length which is very small compared with the human genome of approximately 3 billion base pairs. • Since the whole sequence of λ is already known we can predict where each restriction enzyme will cut and thus the expected size of the fragments that will be produced. • If the virus DNA is exposed to the restriction enzyme for only a short time, then not every restriction site will be cut by the enzyme. • This will result in fragments ranging in size from the smallest possible (all sites are cut) to in-between lengths (some of the sites are cut) to the longest (no sites are cut). This is termed a partial restriction digestion.
..... • After overnight digestion, the reaction is stopped by addition of a loading buffer. • The DNA fragments are separated by electrophoresis, a process that involves application of an electric field to cause the DNA fragments to migrate into an agarose gel. • The gel is then stained with a methylene blue stain to visualize the DNA bands and may be photographed.
..... • The movement of the fragments during electrophoresis will always be towards the positive electrode because DNA is a negatively charged molecule. • The fragments move through the gel at a rate that is determined by their size and shape, with the smallest moving the fastest. • DNA cannot be seen as it moves through the gel. That is why a loading dye must be added to each of the samples before it is pipetted into the wells. • The progress of the dye can be seen in the gel. It will initially appear as a blue band, eventually resolving into two bands of different colours.
...... • Restriction enzymes cut at specific sites along the DNA. These sites are determined by the sequence of bases which usually form palindromes. • Palindromes are groups of letters that read the same in both the forward and backwards orientation. • In the case of DNA the letters are found on both the forward and the reverse strands of the DNA. • For example, the 5’ to 3’ strand may have the sequence GAATTC. The complimentary bases on the opposite strand will be CTTAAG, which is the same as reading the first strand backwards! • Many enzymes recognize these types of sequences and will attach to the DNA at this site and then cut the strand between two of the bases. In this example, the DNA was digested with BamHI, EcoRI and HindIII restriction enzymes, and their sequences are as follows, with the cut site indicated by the arrow.
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
Lecture 1 Introduction to Bioinformatics BCH 433.ppt
λ cut with EcoRI λ cut with HindIII λ cut with BamHI
Restriction map
Assignment: Using the graph in next slide, address the following • Calculate the size the resulting fragments will be after digestion and write them on the map. • How many fragments would you expect to see for each of the maps A, B and C? • Draw these fragments onto the graph in the next slide. • Now compare the size of the fragments that you have calculated with the bands shown in the photographs of the gels and determine which of the enzymes, BamHI, EcoRI and HindIII were used to cut A, B and C. • How many times does the sequence GAATTC occur in the λ DNA sequence? What about AAGCTT and GGATCC?
Lecture 1 Introduction to Bioinformatics BCH 433.ppt

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Lecture 1 Introduction to Bioinformatics BCH 433.ppt

  • 1. Introduction to Bioinformatics BCH 433 Lecture 1 Dr. J. Ikwebe
  • 3. Molecular Bioinformatics Molecular Bioinformatics involves the use of computational tools to discover new information in complex data sets (from the one-dimensional information of DNA through the two-dimensional information of RNA and the three-dimensional information of proteins, to the four-dimensional information of evolving living systems).
  • 4. Bioinformatics (Oxford English Dictionary): The branch of science concerned with information and information flow in biological systems, especially the use of computational methods in genetics and genomics.
  • 5. What is bioinformatics? • The application of computational tools on molecular data, including the means to acquire, analyse, or visualize such data. • Key tools to handle and analyze the large amount of data generated by large-scale DNA, RNA and protein characterization projects (genomics -transcriptomics - proteomics).
  • 6. Biologists collect molecular data: DNA & Protein sequences, gene expression, etc. Computer scientists (+Mathematicians, Statisticians, etc.) Develop tools, softwares, algorithms to store and analyze the data. Bioinformaticians Study biological questions by analyzing molecular data The field of science in which biology, computer science and information technology merge into a single discipline
  • 7. .... • Bioinformatics uses computers, computing technology and software to manage large amounts of biological data and enable their analysis. • At the end of this course students will be expected to: – understand biological data and data management and integration – have a broad knowledge of computing and biological methods in bioinformatics – understand genomes, genome sequencing, genomic structure and comparison – know about the technology used in modern post-genomic biology, the data produced and the software to manage it.
  • 8. Introduction Large databases that can be accessed and analyzed with sophisticated tools have become central to biological research and education. The information content in the genomes of organisms, in the molecular dynamics of proteins, and in population dynamics, to name but a few areas, is enormous.  Biologists are increasingly finding that the management of complex data sets is becoming a bottleneck for scientific advances. Therefore, bioinformatics is rapidly becoming a key technology in all fields of biology.
  • 9. The present bottlenecks in bioinformatics include; the education of biologists in the use of advanced computing tools, the recruitment of computer scientists into this evolving field, the limited availability of developed databases of biological information, the need for more efficient and intelligent search engines for complex databases. Bottlenecks
  • 10. The hereditary information of all living organisms, with the exception of some viruses, is carried by deoxyribonucleic acid (DNA) molecules. 2 purines: 2 pyrimidines: adenine (A) cytosine (C) guanine (G) thymine (T) two rings one ring
  • 11. Eukaryotes may have up to 3 subcellular genomes: 1. Nuclear 2. Mitochondrial 3. Plastid Bacteria have either circular or linear genomes and may also carry plasmids The entire complement of genetic material carried by an individual is called the genome. Human chromosomes Circular genome
  • 12. Central dogma: DNA makes RNA makes Protein Modified dogma: DNA makes DNA and RNA, RNA makes DNA, RNA an Protein
  • 13. Amino acids - The protein building blocks
  • 15. Any region of the DNA sequence can, in principle, code for six different amino acid sequences, because any one of three different reading frames can be used to interpret each of the two strands.
  • 16. Protein folding A human Haemoglobin
  • 17. Some basic definitions • Genomics---- Genome: The total genetic content contained in a haploid set of chromosomes in eukaryotes, in a single chromosome in bacteria, or in the DNA or RNA of viruses. • Transcriptomics---- Transcriptome: the complete set of genes encoded on a genome that can be transcribed. • Proteomics---- Proteome: the complete set of proteins encoded on a genome that can be expressed and modified by a cell, tissue, or organism (Etymology: Protein+genome). – Sub-cellular proteome: the complete set of proteins for a given membrane or organelle (e.g. mitochondrial proteome). – Membranome: the complete set of membranes from a cell. – Metabolome: The metabolic products of the cell, that is, all the metabolites – Secretome: The secreted proteins of a cell? – The phosphome:Total phosphorylated proteins of a cell?
  • 18. How does it all look like on a computer monitor?
  • 19. A cDNA sequence >gi|14456711|ref|NM_000558.3| Homo sapiens hemoglobin, alpha 1 (HBA1), mRNA ACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCG CCTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACC ACCAAGACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGA CGCGCTGACCAACGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACA AGCTTCGGGTGGACCCGGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCC GAGTTCACCCCTGCGGTGCACGCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCG TTAAGCTGGAGCCTCGGTGGCCATGCTTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGT ACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGGCGGC
  • 20. A cDNA sequence (reading frame) A protein sequence >gi|14456711|ref|NM_000558.3| Homo sapiens hemoglobin, alpha 1 (HBA1), mRNA ACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCC GCCTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCAC CACCAAGACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCG ACGCGCTGACCAACGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCAC AAGCTTCGGGTGGACCCGGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGC CGAGTTCACCCCTGCGGTGCACGCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACC GTTAAGCTGGAGCCTCGGTGGCCATGCTTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCC GTACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGGCGGC >gi|4504347|ref|NP_000549.1| alpha 1 globin [Homo sapiens] MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAH VDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR
  • 21. ACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCGCCTGG GGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACCACCAAGACCT ACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGACGCGCTGACCAACGC CGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGGACCCGGTC AACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGTGCACGCCT CCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCCATGCTTCT TGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGGCG GCACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCGCCT GGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACCACCAAGAC CTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGACGCGCTGACCAAC GCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGGACCCGG TCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGTGCACGC CTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCCATGCTT CTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTGAGTGGG CGGCACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAAGGCCGC CTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACCACCAAG ACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCGACGCGCTGACCA ACGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGGACCC GGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGTGCAC GCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCCATGC TTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTGAGTG GGCGGCGCCGTGGCGCACGTGGACGACATGCCCAACGCGCTGTCCGCCCTGAGCGACCTGCACGCGCACAAGCTTCGGGTGG ACCCGGTCAACTTCAAGCTCCTAAGCCACTGCCTGCTGGTGACCCTGGCCGCCCACCTCCCCGCCGAGTTCACCCCTGCGGT GCACGCCTCCCTGGACAAGTTCCTGGCTTCTGTGAGCACCGTGCTGACCTCCAAATACCGTTAAGCTGGAGCCTCGGTGGCC ATGCTTCTTGCCCCTTGGGCCTCCCCCCAGCCCCTCCTCCCCTTCCTGCACCCGTACCCCCGTGGTCTTTGAATAAAGTCTG AGTGGGCGGCACTCTTCTGGTCCCCACAGACTCAGAGAGAACCCACCATGGTGCTGTCTCCTGCCGACAAGACCAACGTCAA GGCCGCCTGGGGTAAGGTCGGCGCGCACGCTGGCGAGTATGGTGCGGAGGCCCTGGAGAGGATGTTCCTGTCCTTCCCCACC ACCAAGACCTACTTCCCGCACTTCGACCTGAGCCACGGCTCTGCCCAGGTTAAGGGCCACGGCAAGAAGGTGGCCG... And, a whole genome…
  • 22. E. coli 4.6 x 106 nucleotides – Approx. 4,000 genes Yeast 15 x 106 nucleotides – Approx. 6,000 genes Human 3 x 109 nucleotides – Approx. 30,000 genes Smallest human chromosome 50 x 106 nucleotides How big are whole genomes?
  • 23. What do we actually do with bioinformatics?
  • 24. From DNA to Genome Watson and Crick DNA model Sanger sequences insulin protein Sanger dideoxy DNA sequencing PCR (Polymerase Chain Reaction) 1955 1960 1965 1970 1975 1980 1985 ARPANET (early Internet) PDB (Protein Data Bank) Sequence alignment GenBank database Dayhoff’s Atlas
  • 25. 1995 1990 2000 SWISS-PROT database NCBI World Wide Web BLAST FASTA EBI Human Genome Initiative First human genome draft First bacterial genome Yeast genome
  • 26. The first protein sequence reported was that of bovine insulin in 1956, consisting of 51 residues. Origin of bioinformatics and biological databases: Nearly a decade later, the first nucleic acid sequence was reported, that of yeast tRNAalanine with 77 bases.
  • 27. In 1965, Dayhoff gathered all the available sequence data to create the first bioinformatic database (Atlas of Protein Sequence and Structure). The Protein DataBank followed in 1972 with a collection of ten X-ray crystallographic protein structures. The SWISSPROT protein sequence database began in 1987.
  • 29. as of August 2011: Eukaryotes 37 Prokaryotes 1708 Total 1745 Complete Genomes
  • 31. CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA TAT GGA CAA TTG GTT TCT TCT CTG AAT ................................. .............. TGAAAAACGTA TF binding site promoter Ribosome binding Site ORF = Open Reading Frame CDS = Coding Sequence Transcription Start Site
  • 33. What is a Database? A structured collection of data held in computer storage; esp. one that incorporates software to make it accessible in a variety of ways; transf., any large collection of information. database management: the organization and manipulation of data in a database. database management system (DBMS): a software package that provides all the functions required for database management. database system: a database together with a database management system. Oxford Dictionary
  • 34. What is a database? • A collection of data – structured – searchable (index) -> table of contents – updated periodically (release) -> new edition – cross-referenced (hyperlinks) -> links with other db • Includes also associated tools (software) necessary for access, updating, information insertion, information deletion…. • Data storage management: flat files, relational databases…
  • 35. Database or databank? Initially • Databank (in UK) • Database (in the USA) Solution • The abbreviation db
  • 36. Why biological databases? • Exponential growth in biological data. • Data (genomic sequences, 3D structures, 2D gel analysis, MS analysis, Microarrays….) are no longer published in a conventional manner, but directly submitted to databases. • Essential tools for biological research. The only way to publish massive amounts of data without using all the paper in the world.
  • 37. Distribution of sequences • Books, articles 1968 -> 1985 • Computer tapes 1982 -> 1992 • Floppy disks 1984 -> 1990 • CD-ROM 1989 -> • FTP 1989 -> • On-line services 1982 -> 1994 • WWW 1993 -> • DVD 2001 ->
  • 38. Some statistics • More than 1000 different ‘biological’ databases • Variable size: <100Kb to >20Gb – DNA: > 20 Gb – Protein: 1 Gb – 3D structure: 5 Gb – Other: smaller • Update frequency: daily to annually to seldom to forget about it. • Usually accessible through the web (some free, some not)
  • 39. International nucleotide data banks EMBL Europe EMBL EBI GenBank USA NLM NCBI DDBJ Japan NIG CIB International Advisory Meeting Collaborative Meeting TrEMBL NRDB
  • 40. Databases • NCBI (National Centre for Biotechnology Information): http://www.ncbi.nlm.nih.gov/ • EBI: http://www.ebi.ac.uk/ • DDBJ: http://www.ddbj.nig.ac.jp/ • InterPro: http://www.ebi.ac.uk/interpro/ • InterPro is a database of protein families, domains and functional sites in which identifiable features found in known proteins can be applied to unknown protein sequences • b) Search and analytical tools • ORFFinder: http://www.ncbi.nlm.nih.gov/gorf/gorf.html • It is an analysis tool which finds all open reading frames in a user's sequence or in a sequence already in the database. • InterProScan server: http://www.ebi.ac.uk/InterProScan/ • InterProScan is used to search various protein domain/motifs/functional sites databases and can combine other analyses such as the identification of potential transmembrane domains and signal peptides.
  • 41. …… • PSORT: http://www.psort.org/ • This cite provides links to the PSORT family of programs for subcellular localization prediction as well as other datasets and resources relevant to localization prediction. • SignalP v3.0 Server: http://www.cbs.dtu.dk/services/SignalP/ • SignalP aims at identifying signal peptides in eukaryotes and bacteria query proteins. • TMHMM v2.0 server: http://www.cbs.dtu.dk/services/TMHMM/ • TMHMM aims at identifying trans-membrane domains in proteins (eukaryotic or prokaryotic).
  • 42.  Some databases in the field of molecular biology… AATDB, AceDb, ACUTS, ADB, AFDB, AGIS, AMSdb, ARR, AsDb, BBDB, BCGD, Beanref, Biolmage, BioMagResBank, BIOMDB, BLOCKS, BovGBASE, BOVMAP, BSORF, BTKbase, CANSITE, CarbBank, CARBHYD, CATH, CAZY, CCDC, CD4OLbase, CGAP, ChickGBASE, Colibri, COPE, CottonDB, CSNDB, CUTG, CyanoBase, dbCFC, dbEST, dbSTS, DDBJ, DGP, DictyDb, Picty_cDB, DIP, DOGS, DOMO, DPD, DPlnteract, ECDC, ECGC, EC02DBASE, EcoCyc, EcoGene, EMBL, EMD db, ENZYME, EPD, EpoDB, ESTHER, FlyBase, FlyView, GCRDB, GDB, GENATLAS, Genbank, GeneCards, Genline, GenLink, GENOTK, GenProtEC, GIFTS, GPCRDB, GRAP, GRBase, gRNAsdb, GRR, GSDB, HAEMB, HAMSTERS, HEART-2DPAGE, HEXAdb, HGMD, HIDB, HIDC, HlVdb, HotMolecBase, HOVERGEN, HPDB, HSC-2DPAGE, ICN, ICTVDB, IL2RGbase, IMGT, Kabat, KDNA, KEGG, Klotho, LGIC, MAD, MaizeDb, MDB, Medline, Mendel, MEROPS, MGDB, MGI, MHCPEP5 Micado, MitoDat, MITOMAP, MJDB, MmtDB, Mol-R-Us, MPDB, MRR, MutBase, MycDB, NDB, NRSub, 0-lycBase, OMIA, OMIM, OPD, ORDB, OWL, PAHdb, PatBase, PDB, PDD, Pfam, PhosphoBase, PigBASE, PIR, PKR, PMD, PPDB, PRESAGE, PRINTS, ProDom, Prolysis, PROSITE, PROTOMAP, RatMAP, RDP, REBASE, RGP, SBASE, SCOP, SeqAnaiRef, SGD, SGP, SheepMap, Soybase, SPAD, SRNA db, SRPDB, STACK, StyGene,Sub2D, SubtiList, SWISS-2DPAGE, SWISS-3DIMAGE, SWISS- MODEL Repository, SWISS-PROT, TelDB, TGN, tmRDB, TOPS, TRANSFAC, TRR, UniGene, URNADB, V BASE, VDRR, VectorDB, WDCM, WIT, WormPep, YEPD, YPD, YPM, etc .................. !!!!
  • 43. Categories of databases for Life Sciences • Sequences (DNA, protein) • Genomics • Mutation/polymorphism • Protein domain/family • Proteomics (2D gel, Mass Spectrometry) • 3D structure • Metabolic networks • Regulatory networks • Bibliography • Expression (Microarrays,…) • Specialized
  • 44. Bookshelf: A collection of searchable biomedical books linked to PubMed. PubMed: Allows searching by author names, journal titles, and a new Preview/Index option. PubMed database provides access to over 12 million MEDLINE citations back to the mid-1960's. It includes History and Clipboard options which may enhance your search session. PubMed Central: The U.S. National Library of Medicine digital archive of life science journal literature. OMIM: Online Mendelian Inheritance in Man is a database of human genes and genetic disorders (also OMIA). Literature Databases:
  • 45. ..... • BLAST is… Basic Local Alignment Search Tool • NCBI's sequence similarity search tool • supports analysis of DNA and protein databases • 80,000 searches per day
  • 46. Why use BLAST? • BLAST searching is fundamental to understanding the relatedness of any favourite query sequence to other known proteins or DNA sequences. • Applications include: – identifying orthologs and paralogs – discovering new genes or proteins – discovering variants of genes or proteins – investigating expressed sequence tags (ESTs) – exploring protein structure and function
  • 47. .... • TaxBrowser is… • browser for the major divisions of living organisms (archaea, bacteria, eukaryota, viruses). • taxonomy information such as genetic codes. • molecular data on extinct organisms.
  • 50. What is an accession number? • An accession number is a label that is used to identify a sequence. It is a unique string of letters and/or numbers that corresponds to a given molecular sequence. • Examples:  DNA AF492453 GenBank genomic sequence (same at EBI)  Protein AAM97590 GenBank protein Q8MV55 SwissProt protein Non Protein Data Bank structure record  Publication 12192407 PubMed ID - Williams et al. Nature 418: 865-9 (2002).
  • 51. PubMed (Medline) • MEDLINE covers the fields of medicine, nursing, dentistry, veterinary medicine, public health, and preclinical sciences • Contains citations from approximately 5,200 worldwide journals in 37 languages; 60 languages for older journals. • Contains over 20 million citations since 1948 • Contains links to biological db and to some journals • New records are added to PreMEDLINE daily!
  • 52. Type in a Query term • Enter your search words in the query box and hit the “Go” button http://www.ncbi.nlm.nih.gov/entrez/query/static/help/helpdoc.html#Searching
  • 53. The Syntax … 1. Boolean operators: AND, OR, NOT must be entered in UPPERCASE (e.g., promoters OR response elements). The default is AND. 2. Entrez processes all Boolean operators in a left-to-right sequence. The order in which Entrez processes a search statement can be changed by enclosing individual concepts in parentheses. The terms inside the parentheses are processed first. For example, the search statement: g1p3 OR (response AND element AND promoter). 3. Quotation marks: The term inside the quotation marks is read as one phrase (e.g. “public health” is different than public health, which will also include articles on public latrines and their effect on health workers). 4. Asterisk: Extends the search to all terms that start with the letters before the asterisk. For example, dia* will include such terms as diaphragm, dial, and diameter.
  • 54. Refine the Query • Often a search finds too many (or too few) sequences, so you can go back and try again with more (or fewer) keywords in your query • The “History” feature allows you to combine any of your past queries. • The “Limits” feature allows you to limit a query to specific organisms, sequences submitted during a specific period of time, etc. • [Many other features are designed to search for literature in MEDLINE]
  • 55. The OMIM (Online Mendelian Inheritance in Man) – Genes and genetic disorders – Edited by team at Johns Hopkins – Updated daily
  • 56. MIM Number Prefixes * gene with known sequence + gene with known sequence and phenotype # phenotype description, molecular basis known % mendelian phenotype or locus, molecular basis unknown no prefix other, mainly phenotypes with suspected mendelian basis
  • 57. Searching OMIM • Search Fields – Name of trait, e.g., hypertension – Cytogenetic location, e.g., 1p31.6 – Inheritance, e.g., autosomal dominant – Gene, e.g., coagulation factor VIII
  • 58. OMIM search tags All Fields [ALL] Allelic Variant [AV] or [VAR] Chromosome [CH] or [CHR] Clinical Synopsis [CS] or [CLIN] Gene Map [GM] or [MAP] Gene Name [GN] or [GENE] Reference [RE] or [REF]
  • 60. Online Literature databases 1. Google Scholar 2. Google Books 3. Web of Science
  • 62. Enables you to search specifically for scholarly literature, including peer-reviewed papers, theses, books, preprints, abstracts and technical reports from all broad areas of research. What is Google Scholar?
  • 63. Use Google Scholar to find articles from a wide variety of academic publishers, professional societies, preprint repositories and universities, as well as scholarly articles available across the web.
  • 64. Google Scholar orders your search results by how relevant they are to your query, so the most useful references should appear at the top of the page This relevance ranking takes into account the: full text of each article. the article's author, the publication in which the article appeared and how often it has been cited in scholarly literature.
  • 65. What other DATA can we retrieve from the record?
  • 68. 5. Google Book Search
  • 70. 6. Web of science http://http://apps.webofknowledge.com.ezproxy.lib.uh.edu/WOS_GeneralSearch_input.do?product =WOS&search_mode=GeneralSearch&SID=4FB7LbbLgDMhG9fDiLh&preferencesSaved=
  • 74. Genomics • Because of the multicellular structure, each cell type does gene expression in a different way –although each cell has the same content as far as the genetic constitution. • i.e. All the information for a liver cell to be a liver cell is also present on nose cell, so gene expression is the only thing that differentiates
  • 75. Genomics - Finding Genes • Gene in sequence data – needle in a haystack • However as the needle is different from the haystack genes are not diff from the rest of the sequence data • Is whole array of nt we try to find and border mark a set o nt as a gene • This is one of the challenges of bioinformatics • Neural networks and dynamic programming are being employed
  • 76. Organism Genome Size (Mb) bp * 1,000,000 Gene Number Web Site Yeast 13.5 6,241 http://genome- www.stanford.edu /Saccharomyces Fruit Flies 180 13,601 http://flybase.bio. indiana.edu Homo Sapiens 3,000 45,000 http://www.ncbi.n lm.nih.gov/genom e/guide
  • 77. Proteomics • Proteome is the sum total of an organisms proteins • More difficult than genomics – 4 20 – Simple chemical makeup complex – Can duplicate can’t • We are entering into the ‘post genome era’ • Meaning much has been done with the Genes – not that it’s a over
  • 78. Proteomics….. • The relationship between the RNA and the protein it codes are usually very different • After translation proteins do change – So aa sequence do not tell anything about the post translation changes • Proteins are not active until they are combined into a larger complex or moved to a relevant location inside or outside the cell • So aa only hint in these things • Also proteins must be handled more carefully in labs as they tend to change when in touch with an inappropriate material
  • 79. Protein Structure Prediction • Is one of the biggest challenges of bioinformatics and esp. biochemistry • No algorithm is there now to consistently predict the structure of proteins
  • 80. Structure Prediction methods • Comparative Modeling – Target proteins structure is compared with related proteins – Proteins with similar sequences are searched for structures
  • 81. Phylogenetics • The taxonomical system reflects evolutionary relationships • Phylogenetics trees are things which reflect the evolutionary relationship thru a picture/graph • Rooted trees where there is only one ancestor • Un rooted trees just showing the relationship • Phylogenetic tree reconstruction algorithms are also an area of research
  • 83. Medical Implications • Pharmacogenomics – Not all drugs work on all patients, some good drugs cause death in some patients – So by doing a gene analysis before the treatment the offensive drugs can be avoided – Also drugs which cause death to most can be used on a minority to whose genes that drug is well suited – volunteers wanted! – Customized treatment • Gene Therapy – Replace or supply the defective or missing gene – E.g: Insulin and Factor VIII or Haemophilia • BioWeapons (??)
  • 84. Diagnosis of Disease • Diagnosis of disease – Identification of genes which cause the disease will help detect disease at early stage e.g. Huntington disease - • Symptoms – uncontrollable dance like movements, mental disturbance, personality changes and intellectual impairment • Death in 10-15 years • The gene responsible for the disease has been identified • Contains excessively repeated sections of CAG • So once analyzed the couple can be counseled
  • 85. Drug Design • Can go up to 15 yrs and $700 million • One of the goals of bioinformatics is to reduce the time and cost involved with it. • The process – Discovery • Computational methods can improves this – Testing
  • 86. Discovery Target identification – Identifying the molecule on which the germs relies for its survival – Then we develop another molecule i.e. drug which will bind to the target – So the germ will not be able to interact with the target. – Proteins are the most common targets
  • 87. Discovery… • For example HIV produces HIV protease which is a protein and which in turn eat other proteins • This HIV protease has an active site where it binds to other molecules • So HIV drug will go and bind with that active site – Easily said than done!
  • 88. Discovery… • Lead compounds are the molecules that go and bind to the target protein’s active site • Traditionally this has been a trial and error method • Now this is being moved into the realm of computers
  • 89. Restriction Analysis of DNA • Special enzymes termed restriction enzymes have been discovered in many different bacteria and other single-celled organisms. These enzymes act as chemical scissors to cut λ DNA into pieces. • They are able to scan along a length of DNA looking for a particular sequence of bases that they recognize. • This recognition site or sequence is generally from 4 to 6 base pairs in length. Once it is located, the enzyme will attach to the DNA molecule and cut each strand of the double helix- the first step in a process called restriction mapping. • The restriction enzyme will continue to do this along the full length of the DNA molecule which will then break into fragments. The size of these fragments is measured in base pairs or kilobase (1000 bases) pairs. • Since the recognition site or sequence of base pairs is known for each restriction enzyme, we can use this to form a detailed analysis of the sequence of bases in specific regions of the DNA in which we are interested. • This procedure is one of the most important in modern biology.
  • 90. .... Restriction analysis • In the presence of specific DNA repair enzymes, DNA fragments will re-anneal or stick themselves to other fragments with cut ends that are complimentary to their own end sequence. • It doesn’t matter if the fragment that matches the cut end comes from the same organism or from a different one. • This ability of DNA to repair itself has been utilized by scientists to introduce foreign DNA into an organism. • This DNA may contain genes that allow the organism to exhibit a new function or process. This would include transferring genes that will result in a change in the nutritional quality of a crop or perhaps allow a plant to grow in a region that is colder than its usual preferred area.
  • 91. Example: Restriction Digestion and Analysis of DNA from Bacteriophage λ • This small virus is 48,502 base pairs in length which is very small compared with the human genome of approximately 3 billion base pairs. • Since the whole sequence of λ is already known we can predict where each restriction enzyme will cut and thus the expected size of the fragments that will be produced. • If the virus DNA is exposed to the restriction enzyme for only a short time, then not every restriction site will be cut by the enzyme. • This will result in fragments ranging in size from the smallest possible (all sites are cut) to in-between lengths (some of the sites are cut) to the longest (no sites are cut). This is termed a partial restriction digestion.
  • 92. ..... • After overnight digestion, the reaction is stopped by addition of a loading buffer. • The DNA fragments are separated by electrophoresis, a process that involves application of an electric field to cause the DNA fragments to migrate into an agarose gel. • The gel is then stained with a methylene blue stain to visualize the DNA bands and may be photographed.
  • 93. ..... • The movement of the fragments during electrophoresis will always be towards the positive electrode because DNA is a negatively charged molecule. • The fragments move through the gel at a rate that is determined by their size and shape, with the smallest moving the fastest. • DNA cannot be seen as it moves through the gel. That is why a loading dye must be added to each of the samples before it is pipetted into the wells. • The progress of the dye can be seen in the gel. It will initially appear as a blue band, eventually resolving into two bands of different colours.
  • 94. ...... • Restriction enzymes cut at specific sites along the DNA. These sites are determined by the sequence of bases which usually form palindromes. • Palindromes are groups of letters that read the same in both the forward and backwards orientation. • In the case of DNA the letters are found on both the forward and the reverse strands of the DNA. • For example, the 5’ to 3’ strand may have the sequence GAATTC. The complimentary bases on the opposite strand will be CTTAAG, which is the same as reading the first strand backwards! • Many enzymes recognize these types of sequences and will attach to the DNA at this site and then cut the strand between two of the bases. In this example, the DNA was digested with BamHI, EcoRI and HindIII restriction enzymes, and their sequences are as follows, with the cut site indicated by the arrow.
  • 97. λ cut with EcoRI λ cut with HindIII λ cut with BamHI
  • 99. Assignment: Using the graph in next slide, address the following • Calculate the size the resulting fragments will be after digestion and write them on the map. • How many fragments would you expect to see for each of the maps A, B and C? • Draw these fragments onto the graph in the next slide. • Now compare the size of the fragments that you have calculated with the bands shown in the photographs of the gels and determine which of the enzymes, BamHI, EcoRI and HindIII were used to cut A, B and C. • How many times does the sequence GAATTC occur in the λ DNA sequence? What about AAGCTT and GGATCC?