Jacques'van'Heldenulb'ac'be

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Genome analysis

• Bioinformatics

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Contents

• Genome annotation
• Comparative genomics
• Phylogenetic profiles
• Gene fusion analysis
• Phylogenetic footprinting

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From sequences to genomes

• Bioinformatics

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From sequences to genomes

• Before the 1990s, DNA sequencing represented an
  important investment in terms of human work. A
  PhD student could spend a significant fraction of
  his thesis to sequence a single gene.
• Genome projects stimulated the development of
  automatic sequencing methods, and led to
  important technological improvement.
• There are currently (2008) several hundreds of
  publicly available fully sequenced genomes.
• The NCBI genome distribution (ftp//ftp.ncbi.nih.g
  ov/genomes/) contains
• gt650 prokaryotes (Bacteria and Archaea)
• Insects (Drosophila melanogaster, Apis mellifera)
• Plants (Arabidopsis thaliana, rice, maize)
• A worm (Caenorhabditis elegans)
• Some fungi (Saccharomyces cerevisiae,
  Schizosaccharomyces pombe, )
• Some mammals (Homo sapiens, Mus musculus, Rattus
  norvegicus)
• Other genome centres give acces to other genomes.
• ENSEMBL (http//www.ensembl.org/) maintains many
  vertebrate genomes
• UCSC (http//genome.ucsc.edu/) maintains genomes
  of metazoan insects
• Sanger Institute (http//www.sanger.ac.uk/genbiol/
  )
• Integr8 800 of genomes in 2008.
• Many other genomes were sequenced by commercial
  companies, and are not available to the public.

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Gene organization
Source Mount (2000)
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Gene function
gtPHO4,SPBC428.03C THIAMINE-REPRESSIBLE ACID
PHOSPHATASE PRECURSOR Q01682Q9UU70 Length
463 Score 161 bits (408), Expect 1e-40
Identities 138/473 (29), Positives 223/473
(46), Gaps 47/473 (9) Query 9
ILAASLVNAGTIPLGKLSDIDKIGTQTEIFPFLGGSGPYYSFPGDYGISR
DLPESCEMKQ 68 LAASVAG S
LG Y P G PESC KQ Sbjct 10
LLAASIVHAGK------SQFEAFENEFYFKDHLGTISVYHE-PYFNGPTT
SFPESCAIKQ 62 Query 69 VQMVGRHGERYPT-------VSKAK
SIMTTWYKLSNYTGQFSGALSFLNDDYEFFIRDTK 121
V RHG R PT VS A I KL N G S
F T Sbjct 63 VHLLQRHGSRNPTGDDTATDVSSAQYI
DIFQNKLLN--GSIPVNFSYPENPLYFVKHWTP 120 Query 122
NLEMETTLANSVNVLNPYTGEMNAKRHARDFLAQYGYMVENQTSFAVFTS
NSNRCHDTAQ 181 E S G
R Y Y T R DA Sbjct 121
VIKAENADQLSSS------GRIELFDLGRQVFERY-YELFDTDVYDINTA
AQERVVDSAE 173 Query 182 YFIDGL-GDKFN--ISLQTISEAE
SAGANTLSAHHSCPAWDDDVNDDILKK-----YDTK 233
F G GD E SAGANL SCP D D
Sbjct 174 WFSYGMFGDDMQNKTNFIVLPEDDSA
GANSLAMYYSCPVYEDNNIDENTTEAAHTSWRNV 233 Query 234
YLSGIAKRLNKE-NKGLNLTSSDANTFFAWCAYEINARGYSDICNIFTKD
ELVRFSYGQD 292 L IA RLNK G NLT SD
C YEI R SD CFT E F Y D Sbjct 234
FLKPIANRLNKYFDSGYNLTVSDVRSLYYICVYEIALRDNSDFCSLFTPS
EFLNFEYDSD 293 Query 293 LETYYQTGPGYDVVRSVGANLFNA
SVKLLKE--SEVQDQKVWLSFTHDTDILNYLTTIGI 350
L Y GP G N L
DKVLFTHD I G Sbjct 294
LDYAYWGGPASEWASTLGGAYVNNLANNLRKGVNNASDRKVFLAFTHDSQ
IIPVEAALGF 353 Query 351 IDDKNNLTAEH-VPFMENTF----
HRSWYVPQGARVYTEKFQCS-NDTYVRYVINDAVVP 404
D T EH P N F S VP TE F CS N
YVRN V P Sbjct 354 FPD---ITPEHPLPTDKNIFTYSLKT
SSFVPFAGNLITELFLCSDNKYYVRHLVNQQVYP 410 Query 405
IETCSTGPGFS----CEINDFYDYAEKRVAGTDFLKVCNVSSVSNSTELT
FFW 453 C GP CE
N ST T Sbjct 411
LTDCGYGPSGASDGLCELSAYLNSSVRVNSTSNGIANFNSQCQAHSTNVT
VYY 463

• After having localized genes on the sequence, we
  have to predict their function.
• Some genes have already been characterized before
  the genome project, but these are generally a
  minority of those found in the genome.
• For the majority of the genes, one tries to
  predict function on the basis of similarities
  between the sequence of the newly sequenced gene
  and some previously known genes (function
  assignation by sequence similarity).
• Example yeast genome (1996) there are still
  2500 genes (39) whose function is completely
  unknown. However
• Yeast is among the best known model organisms
  (genetics, molecular biology).
• The full genome is available since 1996.
• When the first traft of the Human genome has been
  published, 60 of the predicted genes were of
  unknwown function.

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Some milestones
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Genes and genome size

• In prokaryotes, the number of genes increases
  linearly with genome size
• In eukaryotes, this is not the case the genome
  size increases faster than the number of genes

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Genes and genome size

• Beware the axes are logarithmic.
• This plot represents the same data as the
  previous one, but in logarithmic scale, in order
  to see Mammals as well.

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Gene spacing

• Gene spacing increases considerably with the
  complexity off the organisms.
• Note the X axis si logarithmic, not the Y axis
  -gt the increase seems grossly exponential.

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Proportion of intergenic regions

• Beware the X axis is logarithmic.
• The proportion of intergenic regions increases
  with the complexity of an organism.
• In addition (not shown here), introns represent
  an increasing fraction of the genome.
• For example, the exonic fraction represents lt5
  of the human genome.

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Protein size versus genome size

• Protein sequences are shorter in prokaryotes than
  in eukaryotes.
• Among eukaryotes, the increase in genome size is
  not correlated to an increase in protein size
• higher eukaryotes have a much larger genome than
  fungi, without increase in protein size

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Genome annotation

• Bioinformatics

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Gene prediction

• Starting from a completely sequenced genome,
  predict the positions of genes
• Elements of prediction
• Open Reading Frames
• Start and stop codons, separated by a a
  continuous set of non-stop codons.
• Region content
• Hexanucleotide composition
• Codon adaptation index (CAI).
• Signals
• In prokaryotes Shine-Delgarno boxes.
• In eukaryotes intron/exon boundary elements
  (splicing signals).
• Similarity with known genes.

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Gene prediction - limitations

• Typical problems
• Gene prediction programs are trained for a
  specific organism, and can give very bad results
  with other organisms (e.g., the first rounds of
  annotations of A.thaliana were done with programs
  trained for mammals).
• Any gene prediction program will unavoidably
  predict false genes, and miss some true genes.
• The prediction of intron/exon boundaries is
  particularly difficult.
• For prokaryotes, the predicted start codons are
  sometimes imprecise.
• Example genome of the yeast Saccharomyces
  cerevisiae
• For the yeast genomes, the gene detection
  protocol used in 1996 was over-predictive.
• The program essentially relied on ORF, and
  predicted 6400 gene.
• Some researchers estimated that 1,000 ORFs might
  be false predictions.
• Since 1996, the reality of the predicted genes
  has been tested by combining several methods of
  functional genomics (expression studies, mutant
  phenotypes, comparative genomics between closely
  related species, ).
• A few hundreds of the initially predicted genes
  have been removed from the annotations.

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Non-coding genes

• There are many types of non-coding genes
• tRNA transfer RNA
• rRNA ribosomial RNA
• snRNA small nuclear RNA (elements of spliceosome)
• snoRNA methylation guides
• ...
• Detection of non-coding RNA
• generally transcribed by polymerase I and III and
  have different promoters

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Annotation of gene function

• Once a genomic region has been predicted to
  contain a gene, the next step is to predict the
  function of this gene.
• The translated product is compared with all known
  proteins, and a putative function can be assigned
  on the basis of high similarity matches.
• Problems
• Sequence similarity is not always sufficient to
  confer the same function
• Where to put the threshold ?
• Some proteins might have similar function with
  different sequences (convergent evolution).
• Once a gene has been assigned some putative
  function, this will be used to assign the same
  function to other genes ? expansion of errors.
• We should thus be aware that gene annotations
  have to be taken with caution.

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Genes with unknown function

• When genomes of model organisms were sequenced,
  about 40 of the predicted genes could not be
  associated to any known function
• These genes are annotated as "hypothetical
  proteins".
• Note
• In the yeast genome, many of these hypothetical
  proteins have been removed from the annotations
  since 1996, because they were false predictions.

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Comparative genomics

• Bioinformatics

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Phylogenetic footprinting

• One of the main reasons for sequencing the mouse
  genome was to detect conserved regions between
  mouse and human, which will reveal exons and
  regulatory regions.
• The fact that an unknown gene is found in
  different genomes gives more confidence in the
  existence of this gene.
• Another important goal was to detect conserved
  regions in non-coding regions.
• On the basis of a few known cases, it has been
  shown that conserved non-coding regions contain a
  high concentration in regulatory elements.
• The detection of conserved non-coding sequences
  gives thus indications about regions potentially
  involved in regulation.
• Such conserved regions are called phylogenetic
  footprints.

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Phylogenetic profiles

• For each gene of the query genome (e.g. E.coli),
  orthologs are searched in all the sequenced
  genomes
• Each gene is characterized by a profile of
  presence/absence in all the sequenced genomes
• Groups of genes having similar phylogenetic
  profiles are likely to be functionally related

Pellegrini et al. (1999). Proc Natl Acad Sci U S
A 96(8), 4285-8.
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Gene fusion analysis

• It is quite frequent to observe that two genes of
  a given organism are fused into a single gene in
  another organism.
• Fusions between more than 2 genes are
  occasionally observed.
• Fused genes are likely to be functionally related.

References Marcotte, et al. (1999). Science
285(5428), 751-3. Marcotte, et al. (1999).
Nature 402(6757), 83-6. Enright, et al.
(1999). Nature 402(6757), 86-90.
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Conclusion

• Bioinformatics

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The genome challenge

• Despite the availability of several hundreds of
  genomes, we are far from understanding the
  organization and function of a single genome.
• In particular, a lot of work remains to be done
  to decipher genomes of higher organisms.
• Genome sequence by itself is far from sufficient
  for this.
• Since 1997, several high-throughput methods have
  been invented to give complementary information
  about gene function (see courses on
  transcriptome, proteome and interactome).

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Quelques jalons