Course Outline

1
Course Outline
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What Happens when HGP is Completed ?

• Known gene number, location, function and
  regulation.
• Chromosome structure and organization.
• Non-coding DNA types, amount, distribution and
  function.
• Gene expression, protein synthesis, and
  post-translation events,
• protein interactions and networks.
• Evolutionary conservation among organisms,
  (structure and function).
• Correlation of SNPs with health and disease.
• Genes involved in complex traits and multi-gene
  diseases.

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The Next Step
Locate all the genes and describe their
function. This will probably take another 15-20
years !
4
Reminder Genome Browsers and Gene Prediction
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Eukaryotes vs Prokaryotes
Typical human bacterial cells drawn to
scale.

• Eukaryotic cells are
• characterized by
• membrane-bound
• compartments,
• which are absent
• in prokaryotes.

BIOS Scientific Publishers Ltd, 1999
6
Gene Prediction is Different for Eukaryotes and
Prokaryotes

• Prokaryotic genes
• Eukaryotic genes

http//csbl.bmb.uga.edu/resources/slides/gene-find
ing-2.ppt275,4,Review
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Eukaryotes Splice Signals
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Simple Prokaryotic Gene Model

• Simple promoter
• Small genomes, high gene density -
• Example Haemophilus influenza genome
• 85 genic
• Operons - One transcript, many genes
• No introns - One gene, one protein
• Open reading frames - One ORF per gene, ORFs
  begin with start, end with stop codon

TIGR http//www.tigr.org/tigr-scripts/CMR2/CMRGen
omes.spl NCBI http//www.ncbi.nlm.nih.gov/PMGifs/
Genomes/micr.html
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Gene finding is simple !
http//tardigrada.cap.ed.ac.uk/teaching/genomics/t
ech2/img2.htm
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Prokaryotic Gene Prediction Tools

• Glimmer
• http//cbcb.umd.edu/software/glimmer/
• GeneMark
• http//exon.gatech.edu/genemark/genemark_prok_gms_
  plus.cgi
• ORNL Annotation Pipeline
• http//compbio.ornl.gov/GP3/pro.html
• FramePlot - protein-coding region prediction tool
  for high GC-content bacteria
• http//www.nih.go.jp/jun/cgi-bin/frameplot.pl

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Eukaryotes

• Complex gene structure.
• Large genomes (0.1 to 3 billion bases).
• Exons and introns (interrupted).
• Low coding density (lt30).
• 2-3 in humans, 25 in Fugu, 60 in yeast
• Alternate splicing (40-60 of all genes).
• Considerable number of pseudogenes.

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Non-Protein Coding Gene Tools

• tRNA
• tRNA-ScanSE (http//www.genetics.wustl.edu/eddy/tR
  NAscan-SE/)
• FAStRNA (http//bioweb.pasteur.fr/seqanal/interfac
  es/fastrna.html)
• snoRNA
• snoRNA database (http//rna.wustl.edu/snoRNAdb/)
• microRNA
• Sfold (http//www.bioinfo.rpi.edu/applications/sfo
  ld/index.pl)
• SIRNA (http//bioweb.pasteur.fr/seqanal/interfaces
  /sirna.html)

http//harlequin.jax.org/GenomeAnalysis/GeneFindin
g04.ppt257,38,Non-protein Coding Gene Tools and
Information
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Approaches to Gene Finding

• Direct Homology based gene prediction
• Exact or near-exact matches (similarity searches)
  of EST,
• cDNA, or proteins from the same, or closely
  related
• organisms (comparative genomics).
• Indirect
• Look for something that looks like one gene
  (homology).
• Look for something that looks like all genes (ab
  initio).
• Hybrid, combining homology and ab initio (and
  perhaps even direct) methods.

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General Things to Remember about
(Protein-Coding) Gene Prediction Software

• Works best on genes that are reasonably similar
  to something seen previously.
• Finds protein coding regions far better than
  non-coding regions. First and last exons are
  difficult to annotate because they contain UTRs.
  Small genes are not statistically significant and
  therefore hard to predict.
• In the absence of external (direct) information,
  alternative forms will not be identified.
• It is imperfect ! (Its biology, after all).

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Finding Eukaryotic Genes Computationally

• Gene finding based on homology evidence BLAST,
  FASTA, BLAT etc.
• Content-based Methods
• CpG islands, GC content, hexamer repeats,
  composition statistics, codon frequencies
• Feature-based Methods
• donor sites, acceptor sites, promoter sites,
  start/stop codons, polyA signals, feature lengths
• Similarity-based Methods
• sequence homology, EST searches
• Pattern-based
• HMMs, Artificial Neural Networks
• Most effective is a combination of all the above !

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Content-Based Methods

• CpG islands (in and near approximately 40 of
  promoters of mammalian genes (about 70 in human
  promoters)
• Very abundant near gene start site
• High GC content found in 5 ends of genes
• Codon Bias
• Some codons are strongly preferred in coding
  regions, others are not
• Positional Bias
• 3rd base tends to be G/C rich in coding regions

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Feature-Based Methods

• Based on identifying gene signals (promoter
  elements, splice sites, start/stop codons, polyA
  sites, etc.)
• Wide range of methods
• Consensus sequences
• Weight matrices
• Neural networks
• Decision trees
• Hidden Markov Models (HMMs)

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Eukaryotic Genomic Hints that a Gene is Nearby

• PolII RNA promoter elements
• GC box, TATA box, CCAAT region.
• Kozak consensus sequence (eukaryotic ribosome
  binding site-RBS consensus (gcc)gccRccAUGG,
  where R is a purine (adenine or guanine) three
  bases upstream of the start codon (AUG).
• Splicing signals donor, acceptor.
• Termination signal.
• Polyadenylation signal.
• Promoter elements.

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Pol II Promoter Elements

• 5 cap region/signal
• (a modified guanine nucleotide that has been
  added to the "front" or 5' end of a eukaryotic
  messenger RNA shortly after the start of
  transcription, important for ribosome
  recognition).
• nCAGTnG

• TATA box (30 bp upstream)
• - TATAAA
• CCAAT box (80 bp upstream)
• - TAGCCAATG
• GC box (200 bp upstream)
• - GGGCGG
• None of these are essential for gene expression
• Each of these may have more than one copy, except
  the TATA box, to produce greater effect
• There are other promoter elements

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Control of Gene Expression Promoter Prediction
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How does it Works Motif Identification

• Exon-Intron Borders Splice Sites

Exon Intron
Exon  gaggcatcagGTttgtagactgtgtttcAG
tgcacccact ccgccgctgaGTgagccgtgtc
tattctAGgacgcgcggg tgtgaattagGTaagaggtt
atatctccAGatggagatca ccatgaggagGTgagtg
ccattatttccAGgtatgagacg
Splice site Splice site
Motif Extraction Programs at http//www-btls.jst.g
o.jp/ Tools for genome analysis
http//www-btls.jst.go.jp/cgi-bin/Tools/index.cgi?
langen
http//www.seas.gwu.edu/simhaweb/cs177/spring2004
/zeeberglecture1Part2.ppt317,25,How it works I
Motif identification
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Prediction of Splice Junction Sites
Splice site prediction tools - 3 splice site
CAG/GT 5 splice
site MAG/GTRAGT
(M is A or C R is A or G). http//l25.itba.mi.cn
r.it/webgene/wwwspliceview.html
http//dot.imgen.bcm.tmc.edu9331/seq-search/gene
-search.html Splice predictor
http//bioinformatics.iastate.edu/cgi-bin/sp.cgi
Splice site prediction by Neural Network
http//www.fruitfly.org/seq_tools/splice.html
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Prediction of Translation Start Site

• Translation start ATG
• How to predict a translation start

ATG
GCCATGGCGA .. ACGATGCTGT . GACATGGTAC
AGGATGGGCT GCGATGTGGC
AUG codon finder tool http//www.cbs.dtu.dk/
services/NetStart/ http//l25.itba.mi.cnr.it/web
gene/wwwaug.html
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Polyadenylation Termination Signals

• Polyadenylation signal
• AA/TTAAA
• Located 20 bp upstream of poly-A cleavage site
• Termination Signal
• AGTGTTCA
• Located 30 bp downstream of poly-A cleavage site

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Why Polyadenylation is Really Useful
Complementary Base Pairing
AAAAAAAAAAA TTTTTTTTTTT
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Links for Gene Finding Software
POLY-A signal prediction - 3 end of most
eukaryotic mRNAs (60-200 residues),
post-transcription added http//dot.imgen.bcm.tmc
.edu9331/seq-search/gene-search.html
http//l25.itba.mi.cnr.it/genebin/wwwHC_POLYA
http//genomic.sanger.ac.uk/gf/gf.shtml http//ru
lai.cshl.org/tools/polyadq/polyadq_form.html
Repeated elements (Repeat Masker) http//ftp.ge
nome.washington.edu/RM/RepeatMasker.html
http//l25.itba.mi.cnr.it/genebin/wwwrepeat.pl
GC rich areas, http//rulai.cshl.org/tools/CpG_p
romoter/
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The Annotation Pipeline

• Mask repeats using RepeatMasker
  (http//www.repeatmasker.org/).
• Run sequence through several gene prediction
  programs.
• Validation
• Take predicted genes and do similarity search
  against ESTs and genes from other organisms.
• Do similarity search for non-coding sequences to
  find ncRNA.

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Eukaryotic Gene Prediction Tools

• Genscan (ab initio), GenomeScan (hybrid)
• (http//genes.mit.edu/)
• (http//genes.mit.edu/genomescan.html)
• Twinscan (hybrid)
• (http//genes.cs.wustl.edu/)
• FGENESH (ab initio)
• (http//www.softberry.com/berry.phtml?topicgfind)
  
• GeneMark.hmm (ab initio)
• (http//opal.biology.gatech.edu/GeneMark/eukhmm.cg
  i)
• MZEF (ab initio)
• (http//rulai.cshl.org/tools/genefinder/)
• GrailEXP (hybrid)
• (http//grail.lsd.ornl.gov/grailexp/)
• GeneID (hybrid)
• (http//www1.imim.es/software/geneid/geneid.html
  )
• FirstEF
• - http//rulai.cshl.org/tools/FirstEF/

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Gene Finding Software Promoter Prediction
Promoter Prediction McPromoter
http//genes.mit.edu/McPromoter.html Human
Promoter Prediction http//www.softberry.com/berr
y.phtml?topicfpromgroupprogramssubgrouppromot
er DNA Motif search (in TRANSFAC)
http//motif.genome.jp/ TFBind http//tfbind.im
s.u-tokyo.ac.jp/ CONSITE (predict TFBS for 1 or
2 sequences) http//mordor.cgb.ki.se/cgi-bin/CO
NSITE/consite Promoter prediction - DNA region
that RNA polymerase binds before initiating
transcription (TATA box prediction) http//www.fr
uitfly.org/seq_tools/promoter.html Transcription
factor binding sites prediction http//www.cbil.u
penn.edu/tess/index.html
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Example - gene finding tool http//genes.mit.e
du/genomescan.html
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When selecting ORF, you can blast the amino
acid sequence and get a protein.
http//www.ncbi.nlm.nih.gov/gorf/gorf.html
6 frames
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GENSCAN output for sequence
Optimal exon
Initial exon
Internal exon
Terminal exon
Single Exon gene
Sub-Optimal exon
http//genes.mit.edu/
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Gene Prediction Pipeline

• Get a genomic sequence from genome browser
• BRCA1 genomic NG_005905 Fasta format.
• Apply to gene prediction tools provided
• GENSCAN http//genes.mit.edu/GENSCAN.html
• ORFinder http//www.ncbi.nlm.nih.gov/gorf/go
  rf.html
• Also, worth trying
• GeneBuilder http//l25.itba.mi.cnr.it/webgene/g
  enebuilder.html
• Compare results.
• Try CONSITE example for Transcription factor
  recognition
• (http//mordor.cgb.ki.se/cgi-bin/CONSITE/consite)

Step-by-step practical exercise for gene
annotation http//www1.imim.es/eblanco/seminars/
docs/Reus2005/exercise1/index.html
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Annotation of Putative Genes
Annotation category

• Matches known protein sequence
• Strong similarity to protein sequence
• Similar to known protein
• Similar to unknown protein
• Similar to EST (i.e., putative protein)
• No EST or protein matches (i.e., hypothetical
  protein)

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Sequencing and Assembling a Genome

• To sequence a genome, the first task is to cut it
  into many small, overlapping pieces.
• Then clone each piece.

http//media.wiley.com/assets/1312/61/chapter05.pp
t317,15,Sequencing and Assembling a Genome (I)
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Sequencing and Assembling a Genome

• Each piece must be sequenced.
• Sequencing machines cannot do an entire sequence
  at once.
• They can only produce short sequences smaller
  than 1 Kb.
• These pieces are called reads.
• It is necessary to assemble the reads into
  contigs.

http//media.wiley.com/assets/1312/61/chapter05.pp
t322,16,Sequencing and Assembling a Genome (II)
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Cleaning DNA Sequences

• In order to sequence genomes, DNA sequences are
  often cloned in a vector (plasmid, YAC, or
  cosmide).
• Sequences of the vector can be mixed with your
  DNA sequence.
• Before working with your DNA sequence, you should
  always clean it with VecScreen

http//www.ncbi.nlm.nih.gov/VecScreen/VecScreen.ht
ml
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Gene Assembly
Usually genes are found in fragments, and need to
be assembled Simple example input
ACCGT CGTGC TTAC TACCGT
Output --ACCGT-- ----CGTGC
TTAC----- -TACCGT--
Spaces are ignored. Fragments are
assembled according to overlapping areas.
___________ TTACCGTGC
A multiple alignment of the fragments result in
consensus sequence.
Assembled piece 9 bases
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Sequence Assembly - Real life is more complicated
Errors in sequencing Example
Output --ACCGT--
----CGTGC TTAC-----
-TGCCGT--
Input ACCFT CGTGC TTAC TGCCGT
TTACCGTGC
The consensus is still correct because of
majority voting.
Insertion error Example
Output
--ACC-GT-- ----CAGTGC TTAC------ -TACC-GT--
Input ACCGT CAGTGC TTAC TACCGT
TTACC?GTGC
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Gene Assembly - Real life is more complicated
Deletion error Example Input ACCGT Output
--ACCGT-- CGTGC
----CGTGC TTAC TTAC----- TACGT
-TAC_GT--
Repeats
TTACCGTGC
x
x
x
Lack of coverage
Target DNA
Uncovered area
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Sequence Assembly
CAP3 http//pbil.univ-lyon1.fr/cap3.php http//bi
oweb.pasteur.fr/seqanal/interfaces/cap3.html
http//deepc2.psi.iastate.edu/aat/cap/cap.html
Example sequences (seq) http//genome.cs.mtu.edu/
cap/data/
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Compute a Restriction Map
NEBcutter V2.0 http//tools.neb.com/NEBcutter2/in
dex.php http//bioinformatics.org/sms2/rest_map.ht
ml WatCut An on-line tool for restriction
analysis, silent mutation scanning, and SNP-RFLP
analysis http//watcut.uwaterloo.ca/watcut/watcut
/template.php Sequence Extractor generates a
clickable restriction map and PCR primer map of a
DNA sequence. Protein translations and
intron/exon boundaries are also shown
http//bioinformatics.org/seqext/
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Perform PCR Using a Computer

• Polymerase Chain Reaction (PCR) is a method for
  amplifying DNA.
• PCR is used for many applications, including
• Gene cloning
• Forensic analysis and paternity tests
• PCR amplifies the DNA between two anchors called
  PCR primers.
• Hydrogen bonding of single-stranded nucleic acids
  is referred to as "annealing two complementary
  sequences will form hydrogen bonds between their
  complementary bases (G to C, and A to T or U) and
  form a stable double-stranded molecule.

primers
http//media.wiley.com/assets/1312/61/chapter05.pp
t309,6,Making PCR with a Computer
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Annealing Temperature and Primer Design
The melting temperature (Tm) of a DNA duplex
increases both with its length, and with
increasing (GC) content and may be calculated
Tm 4(G C) 2(A T)oC. Degenerate Primers
are used for amplification of sequences from
different organisms a set of primers which have
a number of options at several positions in the
sequence so as to allow annealing to and
amplification of a variety of related sequences.
http//www.mcb.uct.ac.za/pcroptim.htm
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PCR Primer Design Rules
1.  Primer length should be 17-28 bases in
length   2.  Base composition determines DNA
stability (50-60 GC content higher GC makes
more stable DNA)   3.  Primers should end (3')
in a G or C, or CG or GC this prevents
"breathing" of ends and increases efficiency of
priming   4.  Primer Tms (melting point)
between 55-80oC are preferred   5.  3'-ends of
primers should not be complementary (ie. base
pair), as otherwise primer dimers will be
synthesized preferentially to any other
product   (adapted from Innis and
Gelfand,1991 http//bioweb.uwlax.edu/GenWeb/Mole
cular/Seq_Anal/Primer_Design/primer_design.htm )
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Primer Design
6.  Primer self-complementarity (ability to form
secondary structures such as hairpins) should be
avoided   7.  Runs of three or more Cs or Gs at
the 3'-ends of primers may promote mis-priming at
G or C-rich sequences (because of stability of
annealing), and should be avoided.   8. Target
sequence Amplicon length. 9. Check cross
homology with related genes and possible
pseudo-genes. Amplicon location (distance from
3'end). 10. Intron spanning.
Read about primer design http//www.mcb.uct.ac.za
/manual/pcroptim.htm
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Primer Design Tools
Primer3 http//biotools.umassmed.edu/bioapps/pri
mer3_www.cgi PrimerQuest (also option for
RT-PCR) http//www.idtdna.com/Scitools/Applicatio
ns/PrimerQuest PCR primers based upon
multialignments http//cgi-www.daimi.au.dk/cgi-ch
ili/PriFi/main?config.x101config.y30 PCR
primers designed from protein multiple sequence
alignments http//bioinformatics.weizmann.ac.il/bl
ocks/codehop.html GeneFisher input single or
multiple sequence(s), either nucleotide or amino
acid. For multiple sequences a multiple alignment
will be calculated (or upload already aligned
sequences). http//bibiserv.techfak.uni-bielefel
d.de/genefisher2/submission.html (example
sequences in web-site). Web Primer
http//seq.yeastgenome.org/cgi-bin/web-primer
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Special Features Primer Design
Create overlapping PCR products in large
sequences. http//www2.eur.nl/fgg/kgen/primer/Over
lapping_Primers.html Creating primers around
exons in genomic DNA. http//www2.eur.nl/fgg/kgen/
primer/Genomic_Primers.html Creating primers
around SNPs in genomic DNA. http//www2.eur.nl/fg
g/kgen/primer/SNP_Primers.html Creating primers
around the Open Reading Frame of cDNAs.
http//www2.eur.nl/fgg/kgen/primer/cDNA_Primers.h
tml
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Examine Primer Properties
NetPrimer http//molbiol-tools.ca/PCR.htm Blast
primer sequence against NCBI and provide primer
propertieshttp//www.idtdna.com/analyzer/Applica
tions/OligoAnalyzer/ OligoCalculator
Oligonucleotide properties calculator http//www.
basic.northwestern.edu/biotools/oligocalc.html
OligoAnalyzer http//www.idtdna.com/analyzer/Ap
plications/OligoAnalyzer/
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Other Primer Utilities
UCSC In-Silico PCR UCSC http//genome.ucsc.ed
u/cgi-bin/hgPcr?commandstart Electronic PCR
(e-PCR) is computational procedure that is used
to identify sequence tagged sites(STSs), within
DNA sequences http//www.ncbi.nlm.nih.gov/sutils/
e-pcr/ In silico simulation of molecular
biology experiments http//insilico.ehu.es/
http//insilico.ehu.es/PCR/
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Primers Design Pipeline

• Use only accurate sequence data !
• Restrict primer search to regions that best
  reflect goals
• Locate candidate primers
• Discard candidate primers that show
  undesirable self-hybridization
• Verify the site-specificity of the primer

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Primers Design Pipeline

• Question
• Hybrid cells are formed containing human BRCA1
  cDNA in mouse cells.
• We want to identify cells which had incorporated
  the human cDNA, using PCR primers.
• Steps
• Look for mouse and human BRCA1 in NCBI/GENE
  databse.
• Locate BRCA1 cDNA sequence (is there only 1
  transcript ?).
• If there are more than 1 transcripts, compare
  between them (lets take only 3 for this example)
  and find the region that would identify all
  transcripts (hint use http//www.ebi.ac.uk/Tools
  /clustalw/index.html).
• Find primer-pairs for PCR (http//frodo.wi.mit.e
  du/primer3/input.htm).
• Check primer specificity (human only, not
  mouse)
• UCSC http//genome.ucsc.edu/cgi-bin/hgPcr?comman
  dstart
• NCBI (BLAST) http//genome.ucsc.edu/cgi-bin/hgPc
  r?commandstart
• Check primer properties (http//www.basic.north
  western.edu/biotools/oligocalc.html)

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Bioinformatics - Past and Present
ORTHOLOG GENES (Taxonomy)
SEQUENCE ALIGNMENT
CODING REGIONS
CONSERVED DOMAINS
SEQUENCES LITERATURE
3-D STRUCTURE
GENE FAMILIES
SIGNAL PEPTIDE
MUTATIONS POLYMORPHISM
GENOME MAPS
CELLULAR LOCATION
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Bioinformatics - Present and Future
ORTHOLOG GENES (Taxonomy)
SEQUENCE ALIGNMENT
CODING REGIONS
CONSERVED DOMAINS
GENE EXPRESSION, GENES FUNCTION, DRUG PERSONAL
THERAPY
3-D STRUCTURE
GENE FAMILIES
SIGNAL PEPTIDE
MUTATIONS POLYMORPHISM
GENOME MAPS
CELLULAR LOCATION
57
Are We Done ?
Now this is not the end. It is not even the
beginning of the end. But it is perhaps, the
end of the beginning. Winston Churchill, 1942
(3 years into WW2)
http//www.globecartoon.com/neweconomy/10.html
58
Thank-you !
Dr. Metsada Pasmanik-Chor Bioinformatics Unit,
001 Sherman Bldg. Faculty of Life Science,
TAU Tel x 6992 E-mail metsada_at_bioinfo.tau.ac.il
Bioinfo. Unit webpage http//www.tau.ac.il/lifes
ci/bioinfo/