Part 12 Genome Analysis

1
Part 12 Genome Analysis
2
Outline

• Overview
• Why do comparative genomic analysis?
• Assumptions/Limitations
• Genome Analysis and Annotation Standard Procedure
• General Purposes Databases for Comparative
  Genomics
• Organism Specific Databases
• Genome Analysis Environments
• Genome Sequence Alignment Programs
• Genomic Comparison Visualization Tools

3
Some of the prokaryotic genomes
4
Some of the eukaryotic genomes


Aspergillus fumigatus

Farmers lung

In progress

Dictyostelium discoideum

Soil amoeba

In progress

Amoebic dysentry

In progress

Entamoeba histolitica

Leishmania major
Leishmaniasis

In progress

Plasmodium falciparum

Malaria

In progress


Bilharzia

In progress

Schistosoma mansoni
Schizosaccharomyces pombe
Fission yeast

Complete

Theileria annulata
Veterinary

In progress

Toxoplasma gondii

Toxoplasmosis

In progress

Trypanosoma brucei

Sleeping sickness

In progress



5
Bioinformatics Flow Chart
1a. Sequencing
6. Gene Protein expression data
1b. Analysis of nucleic acid seq.
7. Drug screening
2. Analysis of protein seq.
Ab initio drug design OR Drug compound screening
in database of molecules
3. Molecular structure prediction
4. molecular interaction
8. Genetic variability
5. Metabolic and regulatory networks
6
Genomic DNA
Shearing/Sonication
Subclone and Sequence
Shotgun reads
Assembly
Contigs
Finishing read
Finishing
Complete sequence
7
Genome Sequencing - Review
Strategy
Strategy
Libraries
Libraries
Sequencing
Sequencing
Assembly
Assembly
Closure
Closure
Annotation
Annotation
Release
Release
8
Annotation of eukaryotic genomes
Genomic DNA
ab initio gene prediction
transcription
Unprocessed RNA
RNA processing
Mature mRNA
AAAAAAA
Gm3
Comparative gene prediction
translation
Nascent polypeptide
folding
Active enzyme
Functional identification
Reactant A
Product B
Function
9
Why do comparative genomics?

• Many of the genes encoded in each genome from the
  genome projects had no known or predictable
  function
• Analysis of protein set from completely sequenced
  genomes
• Uniform evolutionary conservation of proteins in
  microbial genomes, 70 of gene products from
  sequenced genomes have homologs in distant
  genomes (Koonin et al., 1997)
• Function of many of these genes can be predicted
  by comparing different genomes of known
  functional annotation and transferring functional
  annotation of proteins from better studied
  organisms to their orthologs in lesser studied
  organisms.
• Cross species comparison to help reveal conserved
  coding regions
• No prior knowledge of the sequence motif is
  necessary
• Complement to algorithmic analysis

10
Assumptions/Limitation

• Homologous genes are relatively well preserved
  while noncoding regions tend to show varying
  degrees of conservation. Conserved noncoding
  regions are believed to be important in
  regulating gene expression, maintaiing structural
  organization of the genome and most likely other
  possible functions.
• Cross species comparative genomics is influenced
  by the evolutionary distance of the compared
  species.

11
Genome Analysis and Annotation General Procedure

• Basic procedure to determine the functional and
  structural annotation of uncharacterized
  proteins
• Use a sequence similarity search programs such as
  BLAST or FASTA to identify all the functional
  regions in the sequence. If greater sensitivity
  is required then the Smith-Waterman algorithm
  based programs are preferred with the trade-off
  greater analysis time.
• Identify functional motifs and structural domains
  by comparing the protein sequence against
  PROSITE, BLOCKS, SMART, CDD, or Pfam.
• Predict structural features of the protein such
  as signal peptides, transmembrane segments,
  coiled-coil regions, and other regions of low
  sequence complexity
• Generate a secondary and tertiary (if possible)
  structure prediction
• Annotation
• Transfer of function information from a
  well-characterized organism to a lesser studied
  organism and/or
• Use phylogenetic patterns (or profiles) and/or
• Use the phylogenetic pattern search tools (e.g.
  through COGs) to perform a systematic formal
  logical operations (AND, OR, NOT) on gene sets --
  differential genome display (Huynen et al., 1997).

12
Genome Analysis and AnnotationOne Possible
Procedure

• Basic procedure to determine the functional and
  structural annotation of uncharacterized
  proteins
• Use a sequence similarity search programs such as
  BLAST or FASTA to identify all the functional
  regions in the sequence. If greater sensitivity
  is required then the Smith-Waterman algorithm
  based programs are preferred with the trade-off
  greater analysis time.
• Identify functional motifs and structural domains
  by comparing the protein sequence against
  PROSITE, BLOCKS, SMART, CDD, or Pfam.
• Predict structural features of the protein such
  as signal peptides, transmembrane segments,
  coiled-coil regions, and other regions of low
  sequence complexity
• Generate a secondary and tertiary (if possible)
  structure prediction
• Transfer of function information from a
  well-characterized organism to a lesser studied
  organism and/or use phylogenetic patterns (or
  profiles) and/or use the phylogenetic pattern
  search tools (e.g. through COGs) to perform a
  systematic formal logical operations (AND, OR,
  NOT) on gene sets -- differential genome display
  (Huynen et al., 1997)..

13
Automated Genome Annotation

• GeneQuiz limited number of searches/day
• MAGPIE outside users cannot submit own seq
• PEDANT commercial version allow for full
  capacity
• SEALS semi automated

14
General Databases Useful for Comparative Genomics

• Locus Link/RefSeq http//www.ncbi.nih.gov/LocusLi
  nk/
• PEDANT -Protein Extraction Description ANalysis
  Tool http//pedant.gsf.de/
• MIPS http//mips.gsf.de/
• COGs - Cluster of Orthologous Groups (of
  proteins) http//www.ncbi.nih.gov/COG/
• KEGG - Kyoto Encyclopedia of Genes and Genomes
  http//www.genome.ad.jp/kegg/
• MBGD - Microbial Genome Database
  http//mbgd.genome.ad.jp/
• GOLD - Genome OnLine Database http//wit.integrate
  dgenomics.com/GOLD/
• TOGA http//www.tigr.org/xxxxx

15
Problems with existing sequence alignments
algorithms for genomic analysis

• Most algorithms were developed for comparing
  single protein sequences or DNA sequences
  containing a single gene
• Most algorithms were based on assigning a score
  to all the possible alignments (usually by the
  sum of the similarity/identity values for each
  aligned residue minus a penalty for the
  introduction of gaps) and then finding the
  optimal or near-optimal alignment based on the
  chosen scoring scheme.
• Unfortunately, most of these programs cannot
  accurately handle long alignments.
• Linear-space type of Smith-Waterman variants are
  too computationally intensive requiring
  specialized hardware (memory-limited) or very
  time-consuming. Higher speed vs increased
  sensitivity.

16
Genome-size comparative alignment tools

• ASSIRC - Accelerated Search for SImilarity
  Regions in Chromosomes
• ftp//ftp.biologie.ens.fr/pub/molbio/ (Vincens et
  al. 1998)
• BLAT
• http//genome.ucsc.edu/cgi-bin/hgBlat?commandstar
  t (Kent xxx)
• DIALIGN - DIagonal ALIGNment
• http//www.gsf.de/biodv/dialign.html (Morgenstern
  et al. 1998 Morgenstern 1999(
• DBA - DNA Block Aligner
• http//www.sanger.ac.uk/Software/Wise2/dba.shtml
  (Jareborg et al. 1999(
• GLASS - GLobal Alignment SyStem
• http//plover.lcs.mit.edu/ (Batzoglou et al.
  2000)
• LSH-ALL-PAIRS - Locality -Sensitve Hashing in ALL
  PAIRS
• Email jbuhler_at_cs.washington.edu (Buhler 2001)
• MegaBlast
• http//www.ncbi.nih.gov/blast/ (Zhang 2000)
• MUMmer - Maximal Unique Match (mer)
• http//www.tigr.org/softlab/ (Delcher et al.
  1999)
• PIPMaker - Percent Identity Plot MAKER
• http//biocse.psu.edu/pipmaker/ (Schwartz et al.
  2000)
• SSAHA Sequence Search and Alignment by Hashing
  Algorithm

17
SSAHA

• Sequence Search and Alignment by Hashing
  Algorithm
• Software tool for very fast matching and
  alignment of DNA sequences.
• Achieves fast search speed by converting sequence
  information into a hash table data structure
  which can then be searched very rapidly for
  matches
• http//www.sanger.ac.uk/Software/analysis/SSAHA/
• Run from the Unix command line
• Need gt 1GB RAM (needs a lot of memory)
• SSAHA algorithm best for application requiring
  exact or almost exact matches between two
  sequences e.g. SNP detection, fast sequence
  assembly, ordering and orientation of contigs

18
Genome Analysis Environment

• MAGPIE - Automated Genome Project Investigation
  Environment
• PEDANT
• SEALS

19
Problems with Visualizing Genomes

• Alignment programs output often were visualized
  by text file, which can be intuitively difficult
  to interpret when comparing genomes.
• Visualization tools needed to handle the
  complexity and volume of data and present the
  information in a comprehensive and comprehensible
  manner to a biologist for interpretation.
• Genome Alignment Visualization tools need to
  provide
• interpretable alignments,
• gene prediction and database homologies from
  different sources
• Interactive features real time capabilities,
  zooming, searching specific regions of homologies
• Represent breaks in synteny
• Multiple alignments display
• Displaying contigs of unfinished genomes with
  finished genomes
• Handle various data formats
• Software availabilty (no black box)

20
Genome Comparison Visualization Tool

• ACT - Artemis Comparison Tool (displays parsed
  BLAST alignments based on Artemis an
  annotation tool)
• http//www.sanger.ac.uk/Software/ACT/
• Alfresco (displays DBA alignments and ...)
• http//www.sanger.ac.uk/Software/Alfresco/
  (Jareborg Durbin 2000)
• PipMaker (displays BlastZ alignments)
• http//bio.cse.psu.edu/pipmaker/ (Schwartz et al.
  2000)
• Enteric/Menteric/Maj (displays Blastz alignments)
• http//glovin.cse.psu.edu/enterix/ (Florea et al.
  2000 McClelland et al. 2000)
• Intronerator (displays WABA alignments and ...)
• http//www.cse.ucsc.edu/kent/intronerator/ (Kent
  Zahler 2000b)
• VISTA (Visualization Tool for Alignment)
  (displays GLASS alignments)
• http//www-gsd.lbl.gov/vista/
• SynPlot (displays DIALIGN and GLASS alignments)
• http//www.sanger.ac.uk/Users/igrg/SynPlot/

21
Artemis Comparison Tool (ACT)

• ACT is a DNA sequence comparison viewer based on
  Artemis
• Can read complete EMBL and GenBank entries or
  sequence in FASTA or raw format
• Additional sequence feature can be in EMBL,
  GenBank, GFF format
• ACT is free software and is distributed under the
  GNU Public License
• Java based software
• Latest release 2.0 better support Eukaryotic
  Genome Comparison
• http//www.sanger.ac.uk/Software/ACT/

22
Salmonella typhi vs. E. coli SPI-2
GC tRNA phage/IS genes Pseudogenes
S.typhi
Blast hits
E.coli
23
Salmonella typhi and Yersinia pestis type III
secretion systems
24
Salmonella typhi vs. E. coli - ACT
SPI-10
SPI-1
SPI-2
SPI-9
SPI-7 Vi
S. typhi
DNA matches
E. coli
25
Neisseria meningitidis - A vs. B comparison - ACT
26
Extra Slides 1
27
ASSIRC

• Accelerated Search for SImilarity Regions in
  Chromosome
• ASSIRC finds regions of similarity in pair-wise
  genomic sequence alignments.
• The method involves three steps
• (i) identification of short exact chains of fixed
  size, called 'seeds', common to both sequences,
  using hashing functions
• (ii) extension of these seeds into putative
  regions of similarity by a 'random walk'
  procedure (i.e. the four bases are associated
• (iii) final selection of regions of similarity by
  assessing alignments of the putative sequences.
• We used simulations to estimate the proportion of
  regions of similarity not detected for particular
  region sizes, base identity proportions and seed
  sizes.
• This approach can be tailored to the user's
  specifications.
• They looked for regions of similarity between two
  yeast chromosomes (V and IX). The efficiency of
  the approach was compared to those of
  conventional programs BLAST and FASTA, by
  assessing CPU time required and the regions of
  similarity found for the same data set.
• http//www.biologie.ens.fr/perso/vincens/assirc.ht
  ml
• ftp//ftp.biologie.ens.fr/pub/molbio/assirc.tar.gz

28
BLAT

• Only DNA sequences of 25,000 or less bases and
  protein or translated sequence of 5000 or less
  letters will be processed. If multiple sequences
  are submitted at the same time, the total limit
  is 50,000 bases or 12,500 letters.
• BLAT on DNA is designed to quickly find sequences
  of 95 and greater similarity of length 40 bases
  or more. It may miss more divergent or shorter
  sequence alignments. It will find perfect
  sequence matches of 33 bases, and sometimes find
  them down to 22 bases. BLAT on proteins finds
  sequences of 80 and greater similarity of length
  20 amino acids or more. In practice DNA BLAT
  works well on primates, and protein blat on land
  vertebrates
• BLAT is not BLAST. DNA BLAT works by keeping an
  index of the entire genome in memory. The index
  consists of all non- overlapping 11-mers except
  for those heavily involved in repeats. The index
  takes up a bit less than a gigabyte of RAM. The
  genome itself is not kept in memory, allowing
  BLAT to deliver high performance on a reasonably
  priced Linux box. The index is used to find areas
  of probable homology, which are then loaded into
  memory for a detailed alignment. Protein BLAT
  works in a similar manner, except with 4-mers
  rather than 11-mers. The protein index takes a
  little more than 2 gigabytes
• BLAT was written by Jim Kent. Like most of Jim's
  software interactive use on this web server is
  free to all. Sources and executables to run batch
  jobs on your own server are available free for
  academic, personal, and non-profit purposes. Non-
  exclusive commercial licenses are also available.
  Contact Jim for details.