CSE182-L10

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CSE182-L10

• MS Spec Applications Gene Finding Projects

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Relative abundance computation
run

• Once we have features matched across runs, we
  have data identical to microarrays .
• Features can be identified in separate MS2
  experiments

feature
intensity
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Structural genomics via MS
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Cross-linking

• Cross-links are fixed length that bind to
  amino-acids.
• How can they help predict structure?
• Protocol
• Cross-link native protein
• Denature, digest
• MS/MS (identify cross-linked peptides)
• Potentially valuable, but not widely used

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Identifying Cross-linked peptides

• Identify all peptide pairs, whose mass explains
  the parent mass.
• Given a list of peptide pairs, find the pair, and
  the linked position that best explains the MS2
  data.
• What is the number of possible candidate pairs.
• Fragmentation in the presence of linkers is
  poorly understood
• How do you separate cross-linked peptides from
  singly linked, and non-cross-linked peptides?

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Identifying cross-linked peptides

• Use isotopically labeled cross-linking agents.
• Cross-linked peptides will show up as pairs
  separated by a small mass.
• Non cross-linked peptides appear at one position
  only.

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MS application Protein-protein interaction

• Proteins combine to form functional complexes.
• An antibody is a special kind of protein that can
  recognize a specific protein
• Use an antibody to recognize a protein in a
  complex. Isolate Purify the complex that binds
  to the antibody.
• Identify all the proteins in the complex via mass
  spectrometry.

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Mass Spectrometry conclusion

• Mass Spectrometry can be used to identify
  peptides, modifications, quantitation, protein
  structure, protein-protein interaction (complex
  formation)
• Each of these poses significant computational
  challenges.

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Proteomic Databases/Tools
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Eukaryotic Gene Prediction
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Eukaryotic gene structure
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Translation
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Gene Features
ATG
5 UTR
3 UTR
exon
intron
Translation start
Acceptor
Donor splice site
Transcription start
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Gene identification

• Eukaryotic gene definitions
• Location that codes for a protein
• The transcript sequence(s) that encodes the
  protein
• The protein sequence(s)
• Suppose you want to know all of the genes in an
  organism.
• This was a major problem in the 70s. PhDs, and
  careers were spent isolating a single gene
  sequence.
• All of that changed with the development of high
  throughput methods like EST sequencing

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EST Sequencing

• Suppose we could collect all of the mRNA.
• However, mRNA is unstable
• An enzyme called reverse transcriptase is used to
  make a DNA copy of the RNA.
• Use DNA polymerase to get a complementary DNA
  strand.
• Sequence the (stable) cDNA from both ends.
• This leads to a collection of transcripts/expresse
  d sequences (ESTs).
• Many might be from the same gene

AAAA
TTTT
AAAA
TTTT
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EST Sequencing

• Often, reverse transcriptase breaks off early.
  Why is this a good thing?
• The 3 end may not have a much coding sequence.
• We can assemble the 5 end to get more of the
  coding sequence

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Project 2

• EST assembly
• Given a collection of EST (3) sequences, your
  goal is to cluster all ESTs from the same gene,
  and produce a consensus.
• How would you do it if we also had 5 EST
  sequences?

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Project 1

• Goal Look for signals in the UTR.
• The UTR is not boring. It often folds into a 2 D
  structure and subsequently affects
  transcription/translation of genes.
• What are Riboswitches?
• miRNA?

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Project 3

• Goal is to predict expressed genes using
  ESTs/proteins and mass spectrometry.

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Project guidelines

• 4 Checkpoints.
• The first is mainly to identify a project,
  project partners, and answer a few simple
  questions to get started.
• Deadline 11/3/05.

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Gene Finding The 1st generation

• Given genomic DNA, does it contain a gene (or
  not)?
• Key idea The distributions of nucleotides is
  different in coding (translated exons) and
  non-coding regions.
• Therefore, a statistical test can be used to
  discriminate between coding and non-coding
  regions.

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Coding versus Non-coding

• You are given a collection of exons, and a
  collection of intergenic sequence.
• Count the number of occurrences of ATGATG in
  Introns and Exons.
• Suppose 1 of the hexamers in Exons are ATGATG
• Only 0.01 of the hexamers in Intons are ATGATG
• How can you use this idea to find genes?

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Generalizing
I
E
AAAAAA AAAAAC AAAAAG AAAAAT
Compute a frequency count for all hexamers. Use
this to decide whether a sequence is an
exon/intron
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Coding versus non-coding

• Fickett and Tung (1992) compared various measures
• Measures that preserve the triplet frame are the
  most successful.
• Genscan 5th order Markov Model
• Conservation across species

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Coding vs. non-coding regions
Compute average coding score (per base) of exons
and introns, and take the difference. If the
measure is good, the difference must be biased
away from 0.
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Coding differential for 380 genes
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Other Signals
ATG
AG
GT
Coding
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Coding region can be detected

• Plot the coding score using a sliding window of
  fixed length.
• The (large) exons will show up reliably.
• Not enough to predict gene boundaries reliably

Coding
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Other Signals

• Signals at exon boundaries are precise but not
  specific. Coding signals are specific but not
  precise.
• When combined they can be effective

ATG
AG
GT
Coding
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The second generation of Gene finding

• Ex Grail II. Used statistical techniques to
  combine various signals into a coherent gene
  structure.
• It was not easy to train on many parameters.
  Guigo Bursett test revealed that accuracy was
  still very low.
• Problem with multiple genes in a genomic region

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(No Transcript)
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HMMs and gene finding

• HMMs allow for a systematic approach to merging
  many signals.
• They can model multiple genes, partial genes in a
  genomic region, as also genes on both strands.

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The Viterbi Algorithm
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HMMs and gene finding

• The Viterbi algorithm (and backtracking) allows
  us to parse a string through the states of an HMM
• Can we describe Eukaryotic gene structure by the
  states of an HMM?
• This could be a solution to the GF problem.

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An HMM for Gene structure
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Generalized HMMs, and other refinements

• A probabilistic model for each of the states (ex
  Exon, Splice site) needs to be described
• In standard HMMs, there is an exponential
  distribution on the duration of time spent in a
  state.
• This is violated by many states of the gene
  structure HMM. Solution is to model these using
  generalized HMMs.

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Length distributions of Introns Exons
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Generalized HMM for gene finding

• Each state also emits a duration for which it
  will cycle in the same state. The time is
  generated according to a random process that
  depends on the state.

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Forward algorithm for gene finding
qk
j
i
Duration Prob. Probability that you stayed in
state qk for j-i1 steps
Emission Prob. Probability that you emitted
Xi..Xj in state qk (given by the 5th order
markov model)
Forward Prob Probability that you emitted I
symbols and ended up in state qk
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HMMs and Gene finding

• Generalized HMMs are an attractive model for
  computational gene finding
• Allow incorporation of various signals
• Quality of gene finding depends upon quality of
  signals.

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DNA Signals

• Coding versus non-coding
• Splice Signals
• Translation start

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Splice signals

• GT is a Donor signal, and AG is the acceptor
  signal

GT
AG
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PWMs
321123456 AAGGTGAGT CCGGTAAGT GAGGTGAGG TAGGTAAGG

• Fixed length for the splice signal.
• Each position is generated independently
  according to a distribution
• Figure shows data from gt 1200 donor sites

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MDD

• PWMs do not capture correlations between
  positions
• Many position pairs in the Donor signal are
  correlated

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• Choose the position which has the highest
  correlation score.
• Split sequences into two those which have the
  consensus at position I, and the remaining.
• Recurse until ltTerminating conditionsgt

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MDD for Donor sites
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De novo Gene prediction Sumary

• Various signals distinguish coding regions from
  non-coding
• HMMs are a reasonable model for Gene structures,
  and provide a uniform method for combining
  various signals.
• Further improvement may come from improved signal
  detection

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How many genes do we have?
Nature
Science
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Alternative splicing
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Comparative methods

• Gene prediction is harder with alternative
  splicing.
• One approach might be to use comparative methods
  to detect genes
• Given a similar mRNA/protein (from another
  species, perhaps?), can you find the best parse
  of a genomic sequence that matches that target
  sequence
• Yes, with a variant on alignment algorithms that
  penalize separately for introns, versus other
  gaps.

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Comparative gene finding tools

• Procrustes/Sim4 mRNA vs. genomic
• Genewise proteins versus genomic
• CEM genomic versus genomic
• Twinscan Combines comparative and de novo
  approach.