Title: CSE182-L10
1CSE182-L10
- MS Spec Applications Gene Finding Projects
2Relative 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
3Structural genomics via MS
4Cross-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
5Identifying 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?
6Identifying 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.
7MS 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.
8Mass 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.
9Proteomic Databases/Tools
10Eukaryotic Gene Prediction
11Eukaryotic gene structure
12Translation
13Gene Features
ATG
5 UTR
3 UTR
exon
intron
Translation start
Acceptor
Donor splice site
Transcription start
14Gene 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
15EST 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
16EST 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
17Project 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?
18Project 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?
19Project 3
- Goal is to predict expressed genes using
ESTs/proteins and mass spectrometry.
20Project 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.
21Gene 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.
22Coding 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?
23Generalizing
I
E
AAAAAA AAAAAC AAAAAG AAAAAT
Compute a frequency count for all hexamers. Use
this to decide whether a sequence is an
exon/intron
24Coding 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
25Coding 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.
26Coding differential for 380 genes
27Other Signals
ATG
AG
GT
Coding
28Coding 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
29Other 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
30The 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
31(No Transcript)
32HMMs 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.
33The Viterbi Algorithm
34HMMs 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.
35An HMM for Gene structure
36Generalized 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.
37Length distributions of Introns Exons
38Generalized 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.
39Forward 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
40HMMs 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.
41DNA Signals
- Coding versus non-coding
- Splice Signals
- Translation start
42Splice signals
- GT is a Donor signal, and AG is the acceptor
signal
GT
AG
43PWMs
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
44MDD
- PWMs do not capture correlations between
positions - Many position pairs in the Donor signal are
correlated
45- 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
46MDD for Donor sites
47De 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
48How many genes do we have?
Nature
Science
49Alternative splicing
50Comparative 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.
51Comparative gene finding tools
- Procrustes/Sim4 mRNA vs. genomic
- Genewise proteins versus genomic
- CEM genomic versus genomic
- Twinscan Combines comparative and de novo
approach.