BioInformatics - What and Why? The following power point presentation is designed to give some background information on Bioinformatics. This presentation is modified from information supplied by Dr. Bruno Gaeta, and with permission from eBioInformatics Pty Ltd (c) Copywright

The need for bioinformaticists. The number of entries in data bases of gene sequences is increasing exponentially. Bioinformaticians are needed to understand and use this information GenBank growth

Genome sequencing projects, including the human genome project are producing vast amounts of information. The challenge is to use this information in a useful way COMPLETE/PUBLIC Aquifex aeolicus Pyrococcus horikoshii Bacillus subtilis Treponema pallidum Borrelia burgdorferi Helicobacter pylori Archaeoglobus fulgidus Methanobacterium thermo. Escherichia coli Mycoplasma pneumoniae Synechocystis sp. PCC6803 Methanococcus jannaschii Saccharomyces cerevisiae Mycoplasma genitalium Haemophilus influenzae COMPLETE/PENDING PUBLICATION Rickettsia prowazekii Pseudomonas aeruginosa Pyrococcus abyssii Bacillus sp. C-125 Ureaplasma urealyticum Pyrobaculum aerophilum ALMOST/PUBLIC Pyrococcus furiosus Mycobacterium tuberculosis H37Rv Mycobacterium tuberculosis CSU93 Neisseria gonorrhea Neisseria meningiditis Streptococcus pyogenes Terry Gaasterland, Siv Andersson, Christoph Sensen Publically available genomes (April 1998)

”..We must hook our individual computers into the worldwide network that gives us access to daily changes in the databases and also makes immediate our communications with each other. The programs that display and analyze the material for us must be improved - and we must learn to use them more effectively. Like the purchased kits, they will make our life easier, but also like the kits, we must understand enough of how they work to use them effectively…” Walter Gilbert (1991) “Towards a paradigm shift in biology” Nature News and Views 349:99 “Towards a paradigm shift in biology” Nature News and Views 349:99 Bioinformatics impacts on all aspects of biological research.

Promises of genomics and bioinformatics  Medicine  Knowledge of protein structure facilitates drug design  Understanding of genomic variation allows the tailoring of medical treatment to the individual’s genetic make-up  Genome analysis allows the targeting of genetic diseases  The effect of a disease or of a therapeutic on RNA and protein levels can be elucidated  The same techniques can be applied to biotechnology, crop and livestock improvement, etc...

What is bioinformatics?  Application of information technology to the storage, management and analysis of biological information  Facilitated by the use of computers

What is bioinformatics?  Sequence analysis  Geneticists/ molecular biologists analyse genome sequence information to understand disease processes  Molecular modeling  Crystallographers/ biochemists design drugs using computer-aided tools  Phylogeny/evolution  Geneticists obtain information about the evolution of organisms by looking for similarities in gene sequences  Ecology and population studies  Bioinformatics is used to handle large amounts of data obtained in population studies  Medical informatics  Personalised medicine

Sequence analysis: overview Nucleotide sequence file Search databases for similar sequences Sequence comparison Multiple sequence analysis Design further experiments Restriction mapping PCR planning Translate into protein Search for known motifs RNA structure prediction non-coding coding Protein sequence analysis Search for protein coding regions Manual sequence entry Sequence database browsing Sequencing project management Protein sequence file Search databases for similar sequences Sequence comparison Search for known motifs Predict secondary structure Predict tertiary structure Create a multiple sequence alignment Edit the alignment Format the alignment for publication Molecular phylogeny Protein family analysis Nucleotide sequence analysis Sequence entry

Gene Sequencing: Automated chemcial sequencing methods allow rapid generation of large data banks of gene sequences

Database similarity searching: The BLAST program has been written to allow rapid comparison of a new gene sequence with the 100s of 1000s of gene sequences in data bases Sequences producing significant alignments: (bits) Value gnl|PID|e (Z74911) ORF YOR003w [Saccharomyces cerevisiae] 112 7e-26 gi| (U18795) Prb1p: vacuolar protease B [Saccharomyces ce e-24 gnl|PID|e (X59720) YCR045c, len:491 [Saccharomyces cerevi e-13 gnl|PID|e (Z71514) ORF YNL238w [Saccharomyces cerevisiae] gnl|PID|e (Z71603) ORF YNL327w [Saccharomyces cerevisiae] gnl|PID|e (Z71554) ORF YNL278w [Saccharomyces cerevisiae] gnl|PID|e (Z74911) ORF YOR003w [Saccharomyces cerevisiae] Length = 478 Score = 112 bits (278), Expect = 7e-26 Identities = 85/259 (32%), Positives = 117/259 (44%), Gaps = 32/259 (12%) Query: 2 QSVPWGISRVQAPAAHNRG LTGSGVKVAVLDTGIST-HPDLNIRGG-ASFV 50 + PWG+ RV G G GV VLDTGI T H D R + + Sbjct: 174 EEAPWGLHRVSHREKPKYGQDLEYLYEDAAGKGVTSYVLDTGIDTEHEDFEGRAEWGAVI 233 Query: 51 PGEPSTQDGNGHGTHVAGTIAALNNSIGVLGVAPSAELYXXXXXXXXXXXXXXXXXQGLE 110 P D NGHGTH AG I + + GVA G+E Sbjct: 234 PANDEASDLNGHGTHCAGIIGSKH-----FGVAKNTKIVAVKVLRSNGEGTVSDVIKGIE 288

Sequence comparison: Gene sequences can be aligned to see similarities between gene from different sources 768 TT....TGTGTGCATTTAAGGGTGATAGTGTATTTGCTCTTTAAGAGCTG 813 || || || | | ||| | |||| ||||| ||| ||| 87 TTGACAGGTACCCAACTGTGTGTGCTGATGTA.TTGCTGGCCAAGGACTG AGTGTTTGAGCCTCTGTTTGTGTGTAATTGAGTGTGCATGTGTGGGAGTG 863 | | | | |||||| | |||| | || | | 136 AAGGATC TCAGTAATTAATCATGCACCTATGTGGCGG AAATTGTGGAATGTGTATGCTCATAGCACTGAGTGAAAATAAAAGATTGT 913 ||| | ||| || || ||| | ||||||||| || |||||| | 173 AAA.TATGGGATATGCATGTCGA...CACTGAGTG..AAGGCAAGATTAT 216

Restriction mapping: Genes can be analysed to detect gene sequences that can be cleaved with restriction enzymes

PCR Primer Design: Oligonucleotides for use in the polymerisation chain reaction can be designed using computer based prgrams OPTIMAL primer length --> 20 MINIMUM primer length --> 18 MAXIMUM primer length --> 22 OPTIMAL primer melting temperature --> MINIMUM acceptable melting temp --> MAXIMUM acceptable melting temp --> MINIMUM acceptable primer GC% --> MAXIMUM acceptable primer GC% --> Salt concentration (mM) --> DNA concentration (nM) --> MAX no. unknown bases (Ns) allowed --> 0 MAX acceptable self-complementarity --> 12 MAXIMUM 3' end self-complementarity --> 8 GC clamp how many 3' bases --> 0

Plot created using codon preference (GCG) Gene discovery: Computer program can be used to recognise the protein coding regions in DNA 01,0002,0003,0004,000 3,0002,0001,

RNA structure prediction: Structural features of RNA can be predicted G G A C A G G A G G A U A C C G C G G U C C U G C C G G UC C U C A C U U GGAC U U A G U A U CA U C A G U C UG CGC A AU A G G U AA C G C G U

Protein structure prediction: Particular structural features can be recognised in protein sequences KD Hydrophobicity Surface Prob. Flexibility Antigenic Index CF Turns CF Alpha Helices CF Beta Sheets GOR Alpha Helices GOR Turns GOR Beta Sheets Glycosylation Sites

Protein Structure : the 3-D structure of proteins is used to understand protein function and design new drugs

Multiple sequence alignment: Sequences of proteins from different organisms can be aligned to see similarities and differences Alignment formatted using MacBoxshade

Phylogeny inference: Analysis of sequences allows evolutionary relationships to be determined E.coli C.botulinum C.cadavers C.butyricum B.subtilis B.cereus Phylogenetic tree constructed using the Phylip package

Large scale bioinformatics: genome projects  Mapping Identifying the location of clones and markers on the chromosome by genetic linkage analysis and physical mapping  Sequencing Assembling clone sequence reads into large (eventually complete) genome sequences  Gene discovery Identifying coding regions in genomic DNA by database searching and other methods  Function assignment Using database searches, pattern searches, protein family analysis and structure prediction to assign a function to each predicted gene Data mining Searching for relationships and correlations in the information  Genome comparison Comparing different complete genomes to infer evolutionary history and genome rearrangements

Challenges in bioinformatics  Explosion of information  Need for faster, automated analysis to process large amounts of data  Need for integration between different types of information (sequences, literature, annotations, protein levels, RNA levels etc…)  Need for “smarter” software to identify interesting relationships in very large data sets  Lack of “bioinformaticians”  Software needs to be easier to access, use and understand  Biologists need to learn about the software, its limitations, and how to interpret its results