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Transcript modeling

Brent lab

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Overview Of Entertainment

Gene prediction Jeltje van Baren

Improving gene prediction with tiling arrays Aaron Tenney

Validating predicted genes Laura Langton

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How gene finders work in 3 easy steps

A computational gene finder annotates a sequence by:

1. Identifying valid gene predictions2. Assigning a probability to each gene

prediction3. Selecting the gene prediction with the

highest probability

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TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

Defining valid gene predictions

Start codon Stop codon

Canonical splice sites

No in-frame stops

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Assigning probabilities to valid gene predictions

0.05

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Probabilities based on “sequence submodels” trained on examples of real genes

TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

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Picking optimal gene prediction

0.05

0.20

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Viterbi a

lgorithmTGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

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Alignment.....ATGACTGGGGT-TACAGTTAA.....GTACGATGT-ATTGCT............................GATAACCTAA....

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TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

Adding external information

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0.60

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0.05

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Types of external informationDNA s

eque

nce

Alig

ned

trans

crip

ts

Evolutionary conservation

Tiling array data

Gene predictions

•Conservation: D. erecta and D. pseudoobscura

•Transcripts: ESTs and mRNA

•Tiling arrays: Affymetrix and Aaron

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PASA

Assembly 1Assembly 2

Brian J. Haas et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. NAR 2003 31: 5654–5666.

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Adding the cDNA information

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0.60

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TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

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Creating an annotation

We can use PASA to “update” gene predictions

Assembly 1Assembly 2

Alternative splice

Gene prediction

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Difficult: dscam

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Difficult: dscam

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Quirks

In-frame stop codons (selenocysteines?) Uncommon splice sites

Non GT/AG GC/AG or AT/AC Genome rearrangements Dicistronic genes (androcam) Trans-splicing (mod(mdg4)) More?

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Storing the data DNA s

eque

nceEvolutionary conservation

Tiling array

data

Gene predictions

GenomeDB

(Brian Koebbe)

PASA

clusters

Manual annotation

Genome annotation

Alig

ned

trans

crip

ts

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Tiling arrays

Aaron Tenney

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Goal

Combine computational gene finding and tiling array analysis Improve prediction accuracy on protein

coding genes Predict different forms of genes in

different hybridization conditions

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Tiling arrays complement other information sources

Tiling arrays vs. DNA sequence No explicit use of sequence, not as biased by

genes in training set Easier to find atypical novel genes (odd splice

sites or codon usage) Tiling arrays vs. Evolutionary conservation

Much conserved sequence is not transcribed Tiling array will help sift out conserved but non-

transcribed sequence Tiling arrays vs. aligned ESTs

Similar to information from aligned ESTs Less biased to high copy number transcripts and

3’ ends More complete view of transcriptome

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Tiling arrays complement other information sources

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Challenges

Most literature on analysis of oligonucleotide arrays is about expression arrays Sets of 10-20 probes designed to query specific

genes Analysis of tiling arrays is different

Determining which probes are hybridizing instead of estimating expression levels

Looser probe design criteria, noiser data

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Low level analysis questions

Individual probe intensities Normalization Probe sequence specific corrections Cross hybridization

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Data integration questions

Adding tiling arrays to information we already use

Resolution vs. noise reduction tradeoff Sequence representation / feature

functions Modeling entities of interest in the

genome Protein and non protein coding genes Non-genes, “Dark matter”

Correlations to DNA / conservation / EST signals

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Validation experiments

Laura Langton

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Prediction Validation

Which predictions to validate? Filter predictions for exon overlap with existing

experimental evidence. Evidence = mRNAs and PASA clustered ESTs

Classify predictions into 3 major categories: Known Partially verified Novel

Feature of interest = splice sites

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Known Gene

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Known Gene

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Partial

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Novel

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Novel

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Categories not currently tested

Alternative splices Single exon genes UTRs Structural disagreements

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Structural disagreements

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Alternate Splice

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Design primers to span one or more unverified introns.

Reverse transcribe RNA from whole fly or cell lines.

PCR 650 bp amplicons. Directly sequence. Align resulting ESTs to genome

Experimental Validation - RT PCR

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Sequence Data

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RT - PCR

Our EST data[RTDB]

DNA seq

uenc

eAlig

ned

ES

TsEvolutionary conservation

Tiling array data

Gene predictions

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RT Database

Types of data in RTDB (examples) Traces, reads, quality values Primers, amplicons Predictions, genome version Experiment information

Accessible to collaborators Schema available on request

Charles Comstock

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Preliminary results

Novel 176 predictions tested, 51% hit rate

Partial 442 predictions tested, 74% hit rate