CSE182-L12 Gene Finding

Silly Quiz Who are these people, and what is the occasion?

Gene Features 5’ UTR intron exon 3’ UTR Acceptor Donor splice site ATG 5’ UTR intron exon 3’ UTR Acceptor Donor splice site Transcription start Translation start

DNA Signals 5’ UTR intron exon 3’ UTR Acceptor Donor splice site Coding versus non-coding Splice Signals Translation start ATG 5’ UTR intron exon 3’ UTR Acceptor Donor splice site Transcription start Translation start

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 > 1200 donor sites

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

MDD method Choose the position i which has the highest correlation score. Split sequences into two: those which have the consensus at position i, and the remaining. Recurse until <Terminating conditions>

MDD for Donor sites

Gene prediction: Summary 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

How many genes do we have? Nature Science

Alternative splicing

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.

Comparative gene finding tools Genscan/Genie Procrustes/Sim4: mRNA vs. genomic Genewise: proteins versus genomic CEM: genomic versus genomic Twinscan: Combines comparative and de novo approach.

Databases RefSeq and other databases maintain sequences of full-length transcripts. We can query using sequence.

De novo Gene prediction: Summary 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

How many genes do we have? Nature Science

Alternative splicing

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.

Comparative gene finding tools Procrustes/Sim4: mRNA vs. genomic Genewise: proteins versus genomic CEM: genomic versus genomic Twinscan: Combines comparative and de novo approach.

Course Sequence Comparison (BLAST & other tools) Protein Motifs: Profiles/Regular Expression/HMMs Protein Sequence Identification via Mass Spec. Discovering protein coding genes Gene finding HMMs DNA signals (splice signals)

Genome Assembly

DNA Sequencing DNA is double-stranded The strands are separated, and a polymerase is used to copy the second strand. Special bases terminate this process early.

A break at T is shown here. Measuring the lengths using electrophoresis allows us to get the position of each T The same can be done with every nucleotide. Color coding can help separate different nucleotides

Automated detectors ‘read’ the terminating bases. The signal decays after 1000 bases.

Sequencing Genomes: Clone by Clone Clones are constructed to span the entire length of the genome. These clones are ordered and oriented correctly (Mapping) Each clone is sequenced individually

Shotgun Sequencing Shotgun sequencing of clones was considered viable However, researchers in 1999 proposed shotgunning the entire genome.

Library Create vectors of the sequence and introduce them into bacteria. As bacteria multiply you will have many copies of the same clone.

Sequencing

Questions Algorithmic: How do you put the genome back together from the pieces? Will be discussed in the next lecture. Statistical? How many pieces do you need to sequence, etc.? The answer to the statistical questions had already been given in the context of mapping, by Lander and Waterman.

Lander Waterman Statistics Island L G

LW statistics: questions As the coverage c increases, more and more areas of the genome are likely to be covered. Ideally, you want to see 1 island. Q1: What is the expected number of islands? Ans: N exp(-c) The number increases at first, and gradually decreases.

Analysis: Expected Number Islands Computing Expected # islands. Let Xi=1 if an island ends at position i, Xi=0 otherwise. Number of islands = ∑i Xi Expected # islands = E(∑i Xi) = ∑i E(Xi)

Prob. of an island ending at i E(Xi) = Prob (Island ends at pos. i) =Prob(clone began at position i-L+1 AND no clone began in the next L-T positions)

LW statistics Pr[Island contains exactly j clones]? Consider an island that has already begun. With probability e-c, it will never be continued. Therefore Pr[Island contains exactly j clones]= Expected # j-clone islands

Expected # of clones in an island Why?

Expected length of an island