Bioinformatics Guide to bio-computing and the Internet Copyright

Introduction: What is bioinformatics? Can be defined as the body of tools, algorithms needed to handle large and complex biological information. Bioinformatics is a new scientific discipline created from the interaction of biology and computer. The NCBI defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline”

Genomics era: High-throughput DNA sequencing The first high-throughput genomics technology was automated DNA sequencing in the early 1990. Baker’s yeast, Saccharomyces cerevisiae (15 million bp), was the first eukaryotic genome to be sequenced. In September 1999, Celera Genomics completed the sequencing of the Drosophila genome. The 3 -billion-bp human genome sequence was generated in a competition between the publicly funded Human Genome Project and Celera

High-throughput DNA sequencing Top image: confocal detection by the Mega. BACE sequencer of fluorescently labeled DNA Bottom image: computer image of sequence read by automated sequencer

Genomics: Completed genomes as 2002 Currently the genome of over 600 organisms are sequenced: Whole-genome shotgun Map-based 15 million + – – + C. elegans (roundworm) 100 million – + Drosophila (fruitfly) 120 million + – Arabidopsis (thale cress) 130 million – + Rice 435 million – + 3 billion + + Fugu (puffer fish) 365 million + – Anopheles (malaria-carrying mosquito) 278 million + – Organism 54 Bacteria Yeast Human Base pairs 0. 8 -6 million This generates large amounts of information to be handled by individual computers.

The trend of data growth 21 st century is a century of biotechnology: Genomics: New sequence information is being produced at increasing rates. (The contents of Gen. Bank double every year) Microarray: Global expression analysis: RNA levels of every gene in the genome analyzed in parallel. Proteomics: Global protein analysis generates by large mass spectra libraries. Metabolomics: Global metabolite analysis: 25, 000 secondary metabolites characterized Glycomics: Global sugar metabolism analysis

How to handle the large amount of information? Drew Sheneman, New Jersey--The Newark Star Ledger Answer: bioinformatics and Internet

Bioinformatics history In 1960 s: the birth of bioinformatics IBM 7090 computer Margaret Oakley Dayhoff created: The first protein database The first program for sequence assembly There is a need for computers and algorithms that allow: Access, processing, storing, sharing, retrieving, visualizing, annotating…

Why do we need the Internet? “omics” projects and the information associated with involve a huge amount of data that is stored on computers all over the world. Because it is impossible to maintain up-to-date copies of all relevant databases within the lab. Access to the data is via the internet.

Database storage lt su s Re t q es u You are here ou Y e rr

The Commercial Market Current bioinformatics market is worth 300 million / year (Half software) Prediction: $2 ~50 billion / year in 5 -6 years Bioinformatics companies: Genomatrix Software, Genaissance Pharmaceuticals, Lynx, Lexicon Genetics, De. Code Genetics, Cura. Gen, Alpha. Gene, Bionavigation, Pangene, Infor. Max, Time. Logic, Gene. Codes, Lab. On. Web. com, Darwin, Celera, Incyte, Bio. Research Online, Bio. Tools, Oxford Molecular, Genomica, Net. Genics, Rosetta, Lion Bio. Science, Double. Twist, e. Bioinformatics, Prospect Genomics, Neomorphic, Molecular Mining, Gene. Logic, Gene. Formatics, Molecular Simulations, Bioinformatics Solutions….

Scope of this lab The lab will touch on the following computational tasks: Similarity search Sequence comparison: Alignment, multiple alignment, retrieval Sequences analysis: Signal peptide, transmembrane domain, … Protein folding: secondary structure from sequence Sequence evolution: phylogenetic trees Make you familiar with bioinformatics resources available on the web to do these tasks.

Applying algorithms to analyze genomics data -Accession #? -Annotation? Is it already in databases? Protein characteristics? -Sub-localization -Soluble? -3 D fold Is there conserved regions? -Alignments? -Domains? Other information? You have just cloned a gene Is there similar sequences? -% identity? -Family member? -Expression profile? -Mutants? Evolutionary relationship? -Phylogenetic tree A critical failure of current bioinformatics is the lack of a single software package that can perform all of these functions.

DNA (nucleotide sequences) databases They are big databases and searching either one should produce similar results because they exchange information routinely. -Gen. Bank (NCBI): http: //www. ncbi. nlm. nih. gov -DDBJ (DNA Data. Base of Japan): http: //www. ddbj. nig. ac. jp -TIGR: http: //tigr. org/tdb/tgi -Yeast: http: //yeastgenome. org -E. coli: http: //colibase. bham. ac. uk/blast/ Specialized databases: Tissues, species… -ESTs (Expressed Sequence Tags) ~at NCBI http: //www. ncbi. nlm. nih. gov/db. EST ~at TIGR http: //tigr. org/tdb/tgi -. . . many more!

Protein (amino acid) databases They are big databases too: -Swiss-Prot (very high level of annotation) http: //au. expasy. org/ -PIR (protein identification resource) the world's most comprehensive catalog of information on proteins http: //www. pir. uniprot. org/ Translated databases: -TREMBL (translated EMBL): includes entries that have not been annotated yet into Swiss-Prot. http: //www. ebi. ac. uk/trembl/access. html -Gen. Pept (translation of coding regions in Gen. Bank) -pdb (sequences derived from the 3 D structure Brookhaven PDB) http: //www. rcsb. org/pdb/

Database homology searching Use algorithms to efficiently provide mathematical basis of searches that can be translated to statistical significance. Assumes that sequence, structure, and function are inter-related. All similarity searching methods rely on the concepts of alignment and distance between sequences. A similarity score is calculated from a distance: the number of DNA bases or amino acids that are different between two sequences.

Calculating alignment scores Scoring system: Uses scoring matrices that allow biologists to quantify the quality of sequence alignments. The raw score S is calculated by summing the scores for each aligned position and the scores for gaps. Gap creation/extension scores are inherent to the scoring system in use (BLAST, FASTA…) The score for an identity or a mismatch is given by the specified substitution matrix (e. g. , BLOSUM 62).

Devising a scoring system Some popular scoring matrices are: PAM (Percent Accepted Mutation): for evolutionary studies. For example in PAM 1, 1 accepted point mutation per 100 amino acids is erquired. BLOSUM (BLOcks amino acid SUbstitution Matrix): for finding common motifs. For example in BLOSUM 62, the alignment is created using sequences sharing no more than 62% identity. How the matrices were created: Very similar sequences were aligned. From these alignments, the frequency of substitution between each pair of amino acids was calculated and then PAM 1 was built. After normalizing to log-odds format, the full series of PAM matrices can be calculated by multiplying the PAM 1 matrix by itself.

Devising a scoring system Importance: Scoring matrices appear in all analysis involving sequence comparison. The choice of matrix can strongly influence the outcome of the analysis. Understanding theories underlying a given scoring matrix can aid in making proper choice: -Some matrices reflect similarity: good for database searching -Some reflect distance: good for phylogenies Log-odds matrices, a normalisation method for matrix values: S is the probability that two residues, i and j, are aligned by evolutionary descent and by chance. qij are the frequencies that i and j are observed to align in sequences known to be related. pi and pj are their frequencies of occurrence in the set of sequences.

Database search methods: Sequence Alignment Two broad classes of sequence alignments exist: QKESGPSSSYC Global alignment: Local alignment: VQQESGLVRTTC not sensitive ESG The ESG faster most widely used local similarity algorithms are: Smith-Waterman (http: //www. ebi. ac. uk/MPsrch/) Basic Local Alignment Search Tool (BLAST, http: //www. ncbi. nih. gov) Fast Alignment (FASTA, http: //fasta. genome. jp; http: //www. ebi. ac. uk/fasta 33/; http: //www. arabidopsis. org/cgi-bin/fasta/nph-TAIRfasta. pl)

Which algorithm to use for database similarity search? Speed: BLAST > FASTA > Smith-Waterman (It is VERY SLOW and uses a LOT OF COMPUTER POWER) Sensitivity/statistics: FASTA is more sensitive, misses less homologues Smith-Waterman is even more sensitive. BLAST calculates probabilities FASTA more accurate for DNA-DNA search then BLAST

-tuple methods provide optimal alignments These methods are faster and excellent in comparing sequences. BLAST and FASTA programs are based on -tuple algorithms: 1 -Using query sequence, derive a list of words of length w (e. g. , 3) 2 -Keep high-scoring words using a scoring matrix(e. g. BLOSUM 62) 3 -High-scoring words are compared with database sequences 4 -Sequences with many matches to high-scoring words are used for final alignments

Tools to search databases The dilemma: DNA or protein? Search by similarity Using nucleotide seq. Using amino acid seq. Is the comparison of two nucleotide sequences accurate? By translating into amino acid sequence, are we losing information? The genetic code is degenerate (Two or more codons can represent the same amino acid) Very different DNA sequences may code for similar protein sequences We certainly do not want to miss those cases!

Reasons for translating Comparing DNA sequences give more random matches: A good alignment with end-gaps A very poor alignment Almost 50% identity! Conservation of protein in evolution (DNA similarity decays faster!) Conclusion: It is almost always better to compare coding sequences in their amino acid form, especially if they are very divergent. Very highly similar nucleotide sequences may give better results.

BLAST and FASTA variants FASTA: Compares a DNA query to DNA database, or a protein query to protein database FASTX: Compares a translated DNA query to a protein database TFASTA: Compares a protein query to a translated DNA database BLASTN: Compares a DNA query to DNA database. BLASTP: Compares a protein query to protein database. BLASTX: Compares the 6 -frame translations of DNA query to protein database. Compares a protein query to the 6 -frame translations of a DNA database. TBLASTN: TBLASTX: Compares the 6 -frame translations of DNA query to the 6 -frame translations of a DNA database (each sequence is comparable to BLASTP searches!) PSI-BLAST: Performs iterative database searches. The results from each round are incorporated into a 'position specific' score matrix, which is used for further searching

A practical example of sequence alignment http: //www. ncbi. nlm. nih. gov BLAST results

Detailed BLAST results E value: is the expectation value or probability to find by chance hits similar to your sequence. The lower the E, the more significant the score.

Database searching tips Use latest database version. Use BLAST first, then a finer tool (FASTA, …) Search both strands when using FASTA. Translate Search sequences where relevant 6 -frame translation of DNA database E < 0. 05 is statistically significant, usually biologically interesting. If the query has repeated segments, delete them and repeat search

Most widely used sites for sequence analysis Sites for alignment of 2 sequences: T-COFFEE (http: //www. ch. embnet. org/software/TCoffee. html): more accurate than Clustal. W for sequences with less than 30% identity. Clustal. W (http: //www. ch. embnet. org/software/Clustal. W. html; http: //align. genome. jp) bl 2 sequ (http: //www. ncbi. nlm. nih. gov/blast/bl 2 seq/wblast 2. cgi) LALIGN (http: //www. ch. embnet. org/software/LALIGN_form. html ) Multi. ALIGN (http: //prodes. toulouse. inra. fr/multalin. html) Sites for DNA to protein translation: These algorithms can translate DNA sequences in any of the 3 forward or three reverse sense frames. Translate (http: //au. expasy. org/tools/dna. html) Translate a DNA sequence: (http: //www. vivo. colostate. edu/molkit/translate/index. html) Transeq (http: //www. ebi. ac. uk/emboss/transeq)