Bioinformatics Tools

1. Bioinformatics Tools Stuart M. Brown, Ph.D Dept of Cell Biology NYU School of Medicine

2. Stuart M. Brown, Ph.D Dept of Cell Biology NYU School of Medicine Bioinformatics Tools

3. Overview This lecture will summarize a huge amount of bioinformatics material that is usually presented as a full 12 week course. • Data management and analysis of sequences from the HGP • A quick look at GenBank and ENTREZ. • Gene finding and translation • Similarity searching and alignment (BLAST) • Protein structure and function

4. Data Management and Analysis • The Human Genome Project has generated huge quantities of DNA sequence data. • This data will lead to many medial advances. • But a great deal of analysis and research will be needed.

5. Organize the genome data & provide access for scientists • Use the Internet • The data is public, so anyone can access it. Access to the Data

6. GenBank • All Genome Project data is stored in a database called GenBank managed by the National Center for Biotechnology Information (NCBI) • The NCBI is a branch of the National Library of Medicine, which is part of the NIH (National Institutes of Health). • http://ncbi.nlm.nih.gov

8. GenBank Sections In addition to DNA sequences of genes GenBank has a number of other sections including: • Protein sequences (translated from DNA) • Short RNA fragments (ESTs) • Cancer Genome Anatomy Project (CGAP) gene expression profiles of normal, pre-cancer, and cancer cells from a wide variety of tissue types • Single Nucleotide Polymorphisms (SNPs) which represent genetic variations in the human population • Online Mendelian Inheritance in Man (OMIM) a database of human genetic disorders

9. Finding Genes • GenBank contains approximately 13 billion bases in 12 million sequence records (as of August 2001). • These billions of G, A, T, and C letters would be almost useless without descriptions of what genes they contain, the organisms they come from, etc. • All of this information is contained in the "annotation" part of each sequence record.

11. Entrez is a Tool for Finding Sequences • NCBI has created a Web-based tool called Entrez for finding sequences in GenBank. • Each sequence in GenBank has a unique “accession number”. • Entrez can also search for keywords such as gene names, protein names, and the names of orgainisms or biological functions

13. Entrez has links to Medline • Entrez is much more than just a tool for finding sequences by keywords. • It contains links to PubMed/Medline • Entrez also contains all known protein sequences and 3-D protein structures.

15. Entrez is Internally Cross-linked • DNA and protein sequences are linked to other similar sequences • Medline citations are linked to other citations that contain similar keywords • 3-D structures are linked to similar structures

17. These relationships might include genes in a multi-gene family, related journal articles, or other proteins in the same biochemical pathway • This potential for horizontal movement through the linked databases makes Entrez a dynamic tool. • You can start with only a vague set of keywords or a sequence from the laboratory and rapidly access a set of relevant literature and related database sequences.

18. Similarity Searching • There are a variety of computer programs that are used for making comparisons between DNA sequences. • The most popular is known as BLAST (Basic Local Alignment Search Tool) • BLAST is free at the NCBI website

20. BLAST Searches GenBank The NCBI BLAST web server lets you compare your query sequence to various sections of GenBank • nr = non-redundant (main sections) • month = new sequences from the past few weeks • ESTs • human, drososphila, yeast, or E.coli genomes • proteins (by automatic translation) • This is a VERY fast and powerful computer.

21. BLAST is Complex • Similarity searching relies on the concepts of alignment and distance between pairs of sequences. • Distances can only be measured between aligned sequences (match vs. mismatch at each position). • A similarity search is a process of testing the best alignment of a query sequence with every sequence in a database.

22. Search with Protein not DNA 1) 4 DNA bases vs. 20 amino acids - less random similarity 2) Can have varying degrees of similarity between different AAs - # of mutations, chemical similarity, PAM matrix 3) Protein databanks are much smaller than DNA databanks.

23. BLAST has Automatic Translation • BLASTX makes automatic translation (in all 6 reading frames) of your DNA query sequence to compare with protein databanks • TBLASTN makes automatic translation of an entire DNA database to compare with your protein query sequence • Only make a DNA-DNA search if you are working with a sequence that does not code for protein.

24. >gb|BE588357.1|BE588357 194087 BARC 5BOV Bos taurus cDNA 5'. • Length = 369 • Score = 272 bits (137), Expect = 4e-71 • Identities = 258/297 (86%), Gaps = 1/297 (0%) • Strand = Plus / Plus • Query: 17 aggatccaacgtcgctccagctgctcttgacgactccacagataccccgaagccatggca 76 • |||||||||||||||| | ||| | ||| || ||| | |||| ||||| ||||||||| • Sbjct: 1 aggatccaacgtcgctgcggctacccttaaccact-cgcagaccccccgcagccatggcc 59 • Query: 77 agcaagggcttgcaggacctgaagcaacaggtggaggggaccgcccaggaagccgtgtca 136 • |||||||||||||||||||||||| | || ||||||||| | ||||||||||| ||| || • Sbjct: 60 agcaagggcttgcaggacctgaagaagcaagtggagggggcggcccaggaagcggtgaca 119 • Query: 137 gcggccggagcggcagctcagcaagtggtggaccaggccacagaggcggggcagaaagcc 196 • |||||||| | || | ||||||||||||||| ||||||||||| || |||||||||||| • Sbjct: 120 tcggccggaacagcggttcagcaagtggtggatcaggccacagaagcagggcagaaagcc 179 • Query: 197 atggaccagctggccaagaccacccaggaaaccatcgacaagactgctaaccaggcctct 256 • ||||||||| | |||||||| |||||||||||||||||| |||||||||||||||||||| • Sbjct: 180 atggaccaggttgccaagactacccaggaaaccatcgaccagactgctaaccaggcctct 239 • Query: 257 gacaccttctctgggattgggaaaaaattcggcctcctgaaatgacagcagggagac 313 • || || ||||| || ||||||||||| | |||||||||||||||||| |||||||| • Sbjct: 240 gagactttctcgggttttgggaaaaaacttggcctcctgaaatgacagaagggagac 296

25. Understand the Statistics! • BLAST produces an E-value for every match • This is the same as the P value in a statistical test • A match is generally considered significant if the E-value < 0.05 (smaller numbers are more significant) • Very low E-values (e-100) are homologs or identical genes • Moderate E-values are related genes • Long regions of moderate similarity are more important than short regions of high identity.

26. BLAST is Approximate • BLAST makes similarity searches very quickly because it takes shortcuts. • looks for short, nearly identical “words” (11 bases) • It also makes errors • misses some important similarities • makes many incorrect matches • easily fooled by repeats or skewed composition

27. Bad Genome Annotation • Gene finding is at best only 90% accurate. • New sequences are automatically annotated with BLAST scores. • Bad annotations propagate • Its going to take us 10-20 years or more to sort this mess out!

28. Protein Function • The ultimate goal of the HGP is to identify all of the genes and determine their functions • Genes function by being translated into proteins: • structural • enzymes • regulatory • signalling

29. Translation • Once we have found the DNA sequence of a gene, we can decode the amino acid sequence of the corresponding protein . • The “Genetic Code” is actually quite simple.

30. Chemical Properties Some chemical properties of a protein can be calculated from its amino acid sequence: • molecular weight • charge/pH • hydrophobicity

31. Patterns in Proteins

32. Conserved Domains • Proteins are built out of functional units know as domains (or motifs) • These domains have conserved sequences • Often much more similar than their respective proteins • Exon splicing theory (W. Gilbert) • Exons correspond to folding domains which in turn serve as functional units • Unrelated proteins may share a single similar exon (i.e.. ATPase or DNA binding function)

33. Simple Structures Some motifs form structures that can be recognized as simple sequence patterns: • transmembrane domains • coiled coils • helix-turn-helix • signal peptides

34. Functional Motifs • Other functional portions of proteins can be recognized by their sequence, even if their 3-D structure is not known. • There are many databases of protein motifs/domains: ProSite, Pfam, ProDom, etc.

35. Tools for Finding Motifs • Define a motif from a set of known proteins that share a similar sequence and function. • A pattern is a list of amino acids that can occur at each position in the motif. • A profile is a matrix that assigns a value to every amino acid at every position in the motif. • A HMM is a more complex profile based on pairs of amino acids.

37. Protein 3-D Structure

38. Structure = Function • Proteins function by 3-D interactions with other molecules (i.e. physical chemistry). • So for a protein, 3-D structure is function. • But we can’t accurately determine 3-D structure from gene sequence.

39. Structure Prediction Predicting a protein’s 3-D structure from its amino acid sequence is incredibly complex. • proteins are polypeptides (long chains of amino acids) • can fold and rotate around bonds within each amino acid as well as the bonds between them • it is not possible to evaluate every possible folding pattern for an amino acid sequence

40. Secondary Structure • The local structure of the amino acids in a protein can also be predicted to some extent. • Each amino acid has a tendency to form either an alpha helix or a beta sheet

41. Threading • Rather than computing a 3-D structure from scratch, it may be possible to find a similar structure. • Must have ~25% aa sequence identity. • Uses a process called threading to create a new structure based on a known structure. • This still requires HUGE amounts of computer power.

43. Protein Data Base • There is a database of all known protein structures called the PDB. • These have been determined by X-ray crystalography and/or NMR. • Anyone download and view these structures with a PDB viewer program.

44. RasMol RasMol is the simplest PDB viewer. http://www.umass.edu/microbio/rasmol/ It can work together with a web browser to let you view the structure of any sequence found with Entrez that has a known 3-D structure.

45. Gene Finding & Translation • How can we find genes on chromosomes? • Genome project data is just huge chunks of DNA. • Does automatic annotation work?

46. Raw Genome Data:

47. Finding Genes is Not Easy • Perhaps 1% of human DNA encodes functional genes. • Genes are interspersed among long stretches of non-coding DNA. • Repeats, pseudo-genes, and introns confound matters

48. Pattern Finding Tools It is possible to use DNA sequence patterns to predict genes: • Promoters • translational start and stop codes (ORFs) • intron splice sites • codon usage

50. Similarity to Known Genes • It is also possible to scan new DNA sequence for known genes • Can look for annotated genes/proteins • Or just for RNAs (ESTs)