Applied Bioinformatics - Course Description | BIT 150, Assignments of Bioinformatics

Download Applied Bioinformatics - Course Description | BIT 150 and more Assignments Bioinformatics in PDF only on Docsity! Applied Bioinformatics. BIT150 COURSE DESCRIPTION: • Requirements: ECS 10 or 15 or AMR 21; Biological Sciences 101 and 104; AMR 120 or Statistics 13 or 100. • Lecture: T & R 10:00 am to 10:50 am. • Laboratory: R 3:10 pm to 5:00 pm. 1137 PES (door code 51330) • TA office hours: Wednesday 3-5:00 pm • My office hours: Friday 2:00 pm - 4:00 pm. 281 Hunt Hall. Phone: 752-5159 BOOK: Bioinformatics. A practical guide to the analysis of genes and proteins. A.D. Baxevanis & B.F.F. Ouellette. Wiley-Interscience. Third Edition. 2005. GRADING %: Laboratory problem sets and homework (20% Dubcovsky’s section, 20% Neale’s section), a mid-term examination Dubcovsky’s section (30%) and a final examination of Neal’s section (30%). • This course will introduce the concepts and programs needed to apply bioinformatics in molecular biology and biotechnology research. Computational Biology and Bioinformatics • Computational biology – Development of algorithms to solve problems in biology • Bioinformatics – Application of computational biology to the analysis and management of biological data • Applied bioinformatics – Intelligent use of tools to navigate the sequence space Better experiments can be designed by a careful Bioinformatics analysis before the bench work Bioinformatics: 13,700,000 hits Sequence reading of fluorescently labeled reactions • Fluorescently labeled reactions scanned by laser as particular point is passed • Color picked up by detector • Output sent directly to computer In its simplest form a sequence can be represented as a string of nucleotides with a basic tag or identifier after a greater than character “>”: FASTA format Definition line (commonly called “def line”) >U54469.1 CGGTTGCTTGGGTTTTATAACATCAGTCAGTGACAGGCATTTCCAGAGTTGCCCTGTTCAACAATCGATA GCTGCCTTTGGCCACCAAAATCCCAAACTTAATTAAAGAATTAAATAATTCGAATAATAATTAAGCCCAG TAACCTACGCAGCTTGAGTGCGTAACCGATATCTAGTATACATTTCGATACATCGAAATCATGGTAGTGT TGGAGACGGAGAAGGTAAGACGATGATAGACGGCGAGCCGCATGGGTTCGATTTGCGCTGAGCCGTGGCA GGGAACAACAAAAACAGGGTTGTTGCACAAGAGGGGAGGCGATAGTCGAGCGGAAAAGAGTGCAGTTGGC GTGGCTACATCATCATTGTGTTCACCGATTATTTTTTGCACAATTGCTTAATATTAATTGTACTTGCACG CTATTGTCTACGTCATAGCTATCGCTCATCTCTGTCTGTCTCTATCAAGCTATCTCTCTTTCGCGGTCAC TCGTTCTCTTTTTTCTCTCCTTTCGCATTTGCATACGCATACCACACGTTTTCAGTGTTCTCGCTCTCTC TCTCTTGTCAAGACATCGCGCGCGTGTGTGTGGGTGTGTCTCTAGCACATATACATAAATAGGAGAGCGG More information can be be added to the FASTA definition line >gb|U54469.1|DMU54469 Drosophila melanogaster eukaryotic initiation factor 4E (eIF4E) gene. GenBank Accession.version Locus Descritpion Primer walking to sequence stretches of few kb M13_F F1 F2 F3 R2 R1 M13_R VectorVector Animation sequencing: http://www.jgi.doe.gov/education/how/how30minflash.html Editing Errors \ Query: 644 atgcatatggtggtacgaagtaangeccaaggaagecgacatgaacatgagaagtaatca PEPEEEEEEEEPEE EEE EEE PEEP ee Sbjct: 11 atgcatatggtggtacgaagtaa-geccaaggaagecgacatgaacatgagaagtaatca Query: 704 tgggaaatagactctgtatcttgacticttcagtatcccrcttgatectactitgtaaga PEPEEE PEEP DEEP EP EEE Sbjct: 70 tgggaaatagactctgtatcttgacttcttcagtatcectcttgatectactttgtaaga Query: 764 caatattgetgcattctiatgtacatgccatatataatlacacattttcagcacaac-at PEPEEEIEEEEP EEE PEEP EEE Sbjct: 130 castattgetgeattctratgtacatgccatatataattacacattttcagcacaacaat Query: 823 cggcaacctaagataatcccasatctataattictagattgtttgaag-cecacgattctt PEPEEEIEEEEPEE PEEP EEE EEE Pee Sbjct: 190 cqgcaacctaagataatcccaaatctataattctagatigtttgaagececacgattete Query: 882 a-ttaatgtgtaatgcacgtgeacccattgascagacactttgte 925 POPPPEIPEPEP EE PEPE Sbjct: 250 atttaatgtgtaatgcacgtgcacccattgaacagacactttgie 294 : >I Find: foccacgattctt Find First | | Find Next | Close | TTT Gan @ BeeaeceatrertatTtT ant erery: a aFindNet | Close | 24 [GAA GeCREcecacgcatTTertTartTTaAatTetetar ue? 6 Find: [occacaattctt 703 69 763 12g 822 163 ee1 249 Find: [gcccaaggaagecgacatga whe 2A2GtMI3) | FindFist | FindNex | Close | i8 x TAAGTAAAGBCCE C AA GEA AGCOCEGACATGAAL Find [geccasqasaccegscataa oo ee Find Neat Close 30 AA GTA ABEOCCAA GG AARGCCEACATG AACA Present and Future of sequencing • Sequencing costs – Dropping each year • Opens possibility of sequencing genomes of individuals • Greatly facilitates comparative genomics. • New technologies 1/100th of cost GenBank 2008 NAR article (see link) 190 billion bp from 76 million sequences and 260,000 organisms We need databases! EST (Expressed Sequence TAGs) 25 billion bp GSS (Genome Survey Sequences) BAC ends 13.5 billion bp WGS (Whole Genome Shotgun) 4letter 6 digit 101 billion bp HTG (High throughput Genomics) 18 billion bp (unfinished in transition) Top 10 species in submitted bp Species billions of bp 01 Homo sapiens 12.9 02 Mus musculus 8.3 03 Rattus norvegicus 5.8 04 Bos taurus (cattle) 3.8 05 Zea mays 3.6 06 Danio rerio (Zebrafish) 2.8 07 Sus scrofa (pig) 1.9 08 Oryza sativa 1.5 Sequence databases • Examples of sequence databases – Primary databases (archival) • GenBank • EMBL (European Molecular Biology Laboratory) • DDBJ (DNA Data Bank of Japan) – Secondary databases (curated) • RefSeq • EMBL Genome Reviews • Protein databases • TPA (Third party annotations) • What is a database? – An indexed set of records – Records retrieved using a query language Web Browser BLAST Search Engine Database Web Server Biologist query The client–server model has made access to sequence databases fast and easy GenBank EMBL DDBJ Data flow of submissions between primary databases (Chp. 1) GenBank EMBL DDBJ •Submissions •Updates (Sequin) NCBI Entrez NIH National Institute of Health (USA) •Submissions •Updates EBI Ensambl EMBL European Molecular Biology Laboratory •Submissions •Updates (Sakura) NIG National Institute of Genetics (JAPAN) International Nucleotide Sequence Database Collaboration Updated every 24 hs National Center for Biotechnology Information DNA Data Bank of Japan European Bioinformatics Institute CIB Getentry Center for Information Biology http://getentry.ddbj.nig.a c.jp/getstart-e.html Integrated information retrieval system Is an interface not a database http://www.ensembl.org/index.html http://www.ensembl.org/i ndex.html Secondary databases Third Party Annotation (TPA) – Includes • Reannotations, • Combinations of novel and existing primary entries • Annotations of trace archives • Whole genome Shotgun data – Provides • GenBank accession. Version numbers and nucleotide locations for all primary entries to which the TPA sequence relates EMBL Genome reviews – Includes • Add information from UniProt knowledgebase, Gene Ontology Annotation, InterPro, and others • Curated versions of entries representing complete genomes • Standardize annotations Protein databases • GenPept: translations of all CDS. Not curated • Uniprot (Swiss-Prot/TrEMBL/PIR-PSD) – UniParc: most comprehensive, public nonredundant protein database • Swiss-Prot (manual)/TrEMBL(computer)/PIR-PSD • GenBank, Patents, Int. Pr. Index (IPI) • Protein Data Bank – UniProt Knowledgebase: curated subset of UniParc • Function Postranslational modifications • Domains Catalytic sites • Structures Associated diseases • Pathways Etc. – UniRef: UniProt nonredundant reference database: 95%, 90% and 50% sets. • Functional groups – Pfam – Prosite – IternPro Merge 95% = Merge 90% = Merge 50% = All predicted coding regions http://www.ebi.ac.uk/blast2/index.html?UniProt