Bioinformatics Tools

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Bioinformatics Tools

• Stuart M. Brown, Ph.D
• Dept of Cell Biology
• NYU School of Medicine

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• Bioinformatics Tools

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

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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.

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• Access to the Data

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

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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

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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

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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.

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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

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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.

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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

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• 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.

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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

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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.

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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.

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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.

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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.

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• gtgbBE588357.1BE588357 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 aggatccaacgtcgctccagctgctcttgacgactccac
  agataccccgaagccatggca 76
• 
  
• Sbjct 1 aggatccaacgtcgctgcggctacccttaaccact-cgc
  agaccccccgcagccatggcc 59
• 
  
• Query 77 agcaagggcttgcaggacctgaagcaacaggtggagggg
  accgcccaggaagccgtgtca 136
• 
  
• Sbjct 60 agcaagggcttgcaggacctgaagaagcaagtggagggg
  gcggcccaggaagcggtgaca 119
• 
  
• Query 137 gcggccggagcggcagctcagcaagtggtggaccaggcc
  acagaggcggggcagaaagcc 196
• 
  
• Sbjct 120 tcggccggaacagcggttcagcaagtggtggatcaggcc
  acagaagcagggcagaaagcc 179
• 
  
• Query 197 atggaccagctggccaagaccacccaggaaaccatcgac
  aagactgctaaccaggcctct 256

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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 lt 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.

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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

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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!

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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

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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.

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Chemical Properties

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

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Patterns in Proteins
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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)

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Simple Structures

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

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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.

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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.

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Protein 3-D Structure
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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 cant accurately determine 3-D structure
  from gene sequence.

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Structure Prediction

• Predicting a proteins 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

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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

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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.

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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.

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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.

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Gene Finding Translation

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

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Raw Genome Data
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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

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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

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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)