Statistical Measures in Gene Prediction

1. Statistical Measures in Gene Prediction

2. Complications in Gene Prediction The problem of gene identification is complicated in case of eukaryotes by the vast variation that is found in the structure of genes. On an average, a vertebrate gene is 30Kb long. Of this, the coding region is only about 1Kb. The coding region typically consists of 6 exons, each about 150bp long. These are average statistics.

3. Complications in Gene Prediction Huge variations from the average are observed Biggest human gene, dystrophin is 2.4Mb long. Blood coagulation human factor VIII gene is ~186Kb. It has 26 exons with sizes varying from 69 bp to 3106 bpand its 25 introns range in size from 207 to 32,400 bp. An average 5’ UTR is 750bp long, but it can be longer and span several exons (for e.g., in MAGE family). On an average, the 3’ UTR is about 450bp long, but for e.g., in case of the gene for Kallman’s syndrome, the length exceeds 4Kb

4. Some facts about human genes • Comprise about 3% of the genome • Average gene length: ~ 8,000 bp • Average of 5-6 exons/gene • Average exon length: ~200 bp • Average intron length: ~2,000 bp • ~ 8% genes have a single exon • Some exons can be as small as 1 or 3 bp

5. Complications in Gene Prediction In higher eukaryotes the gene finding becomes far more difficult because it is now necessary to combine multiple ORFs to obtain a spliced coding region. Alternative splicing is not uncommon, exons can be very short, and introns can be very long. Given the nature of genomic sequence in humans, where large introns are known to exist, there is definitely a need for highly specific gene finding algorithms.

6. Finding Open Reading Frames (ORFs) Prokaryotic vs eukaryotic sequences Frequency of stop codons Frame-shift errors Homology Search DNA vs. Protein Searches Specificity and Sensitivity of the Search Tools Signal-based methods: CpG islands Finding promoter regions, poly adenylation sites, intron/exon splice sites Identifying Coding Regions

7. Content-based methods: Coding statistics, viz., codon usage bias, periodicity in base occurrence, etc. Integration of these methods Web-based Tools Evaluation of Gene Prediction Methods Identifying Coding Regions

8. Owing to thehigher gene densityand theabsence of intronsin prokaryotes, most open reading frames (ORFs) longer than some reasonablethresholdare most likely to correspond to genes. Drawbacks: Addition / deletion of one or two bases will cause all the codons scanned to be different - sensitive to frame shift errors Fails to identify very small coding regions Fails to identify the occurrence of overlapping long ORFs on opposite DNA strands (genes and ‘shadow genes’) Identifying ORFs in Prokaryotes

9. The problem complicates in eukaryotic DNA: due to the existence of interweaving exons and introns – stop codons may exist in intronic regions making it difficult to identify correct ORF In general, in prokaryotic organisms, a DNA region will encode only one gene, which is not necessarily true in eukaryotes. Identifying ORFs in Eukaryotes Not all ORFs are Genes

10. True coding regions have specific sequences in the upstream / downstream regions Sequence similarity to known gene sequences Non-random use of codons, 3-base periodicity, higher G+C content, etc. Identifying ORFs in Eukaryotes

11. ORF Finder (NCBI) http://www.ncbi.nih.gov/gorf/gorf.html EMBOSS getorf - Finds and extracts open reading frames plotorf - Plot potential open reading frames showorf - Pretty output of DNA translations Sixpack - Display a DNA sequence with 6-frame translation and ORFs http://www.hgmp.mrc.ac.uk/Software/EMBOSS/Apps/getorf.html Web-based tools

12. Once a long ORF/ all ORFs above a certain threshold are identified, - these ORF sequences are called putative coding sequences - translate each ORF using the Universal Genetic code to obtain amino acid sequence - search against the protein database for homologs Homology Search

13. There are three main search tools for database searching: FastA BLAST Smith-Waterman (SW) algorithm FastA – is sequence comparison software that uses the method of Pearson and Lipman. It can be very specific when identifying long regions of low similarity especially for highly diverged sequences Available at EBI: http://www.ebi.ac.uk/fasta33/ Homology Search

14. BLAST(Basic Local Alignment Search Tool) BLAST programs designed for fast database searching, uses a heuristic search algorithm by Karlin and Altschul. Available at NCBI: http://www.ncbi.nlm.nih.gov/BLAST/ Smith-Waterman (SW) algorithm– is the Smith-Waterman algorithm for pair-wise comparisons – based on dynamic programming algorithm. Since SW searching is very exhaustive, it is also the slowest method Homology Search

15. Signal – a string of DNA recognized by the cellular machinery Signal-based Methods GT AG

16. Signals for gene identification There are many signals associated with genes, each of whichsuggestsbut does not provethe existence of a gene CpG Islands– identify the 2% of the genome that codes for proteins (rich in CG dinucleotides) Start & Stop Codons– signifies the start & end of a coding region Transcription Start Site – to identify the start of coding region Donor & Acceptor Sites- signifies the start & end of intronic regions Cap Site– found in the 5’ UTR region

17. Signalsfor gene identification Promoters– short sequences that initiate/regulate transcription(found in 5’ UTR region) Enhancers– regulates gene expression, (found in 5’ or 3’ UTR regions, intronic regions, or up to few Kb away from the gene) Motifs – short DNA sequences where proteins bind to initiate transcription /translation process Poly-A Site – identify the end of coding region (found in 3’ UTR region) Most of these signals can be modeled by position specific scoring matrices (PSSM), or Hidden Markov models (HMM)

18. Gene Prediction • At the core of all gene identification programs • – there exist one or more coding measures • These programs also rely on additional information – • potential sequence signals, • database similarity searches, and • use complex frameworks for its integration. • A good knowledge of core coding statistics is important to understand how gene identification programs work.

19.  Classification of Coding Measures • Coding statistic – is a function that computes the likelihood that the sequence is coding for a protein. • Coding statistics measure • codon usage bias • base compositional bias between codon positions • periodicity in base occurrence • Main distinction is between • measures dependentof a model of coding DNA • measures independent of such a model

20.  Classification of Coding Measures • The model of coding DNA is probabilistic • Given a query sequence, compute the probability of the sequence under • - the model of coding DNA, and • - an alternative model or non-coding (random) DNA • The logarithm of the ratio of these two probabilities, the log-likelihood ratio • - is the score of the coding statistic in the query sequence.

21.  Classification of Coding Measures Model dependent coding statistics capture the specific features of coding DNA Require a representative sample of coding DNA from the species under consideration to estimate the model's parameters Complex models are more powerful in discriminating coding from non-coding DNA. Model independent coding statistics capture only the “universal” features of coding DNA. They do not require a sample of coding DNA

22. Table – I: Human codon usage & codon preference table

23. Measures dependent on a Model of Coding DNA Codon usage table is used to compute the coding potential of a nucleotide sequence Pi(S) - the probability of the sequence of nucleotides S, given that S is coding in frame i (i=1,2,3) P0(S) - the probability of S given a model of non-coding DNA. The coding potential of sequence S in frame i, is measured by the log-likelihood ratio:

24.  Measures dependent on a Model of Coding DNA LPi(S) > 0 the probability that Sis coding in frame i is higher than Sis non-coding in frame i. Compute the log-likelihood ratio in the three frames. If the sequence is coding, the log-likelihood ratio will be larger for one of the frames. Non-coding DNA - is random DNA with nucleotide equiprobability and independence between positions.

25.  Measures dependent on a Model of Coding DNA • Measures may be based on • Oligonucleotide counts • Base compositional bias between codon positions • Dependence between nucleotide positions

26.   Measures based on oligonucleotide counts • Unequal usage of codons in the coding regions is a universal feature of the genomes. This bias occurs mainly due to: • the uneven usage of the amino acids in the existing proteins, • the uneven usage of synonymous codons • Measures based on oligonucleotide counts: • Codon Usage • Amino Acid Usage • Codon Preference • Hexamer Usage

27.  Codon Usage Let F(C) - frequency of codon C in the genes of the species under consideration (from codon usage table) For a given sequence of codons C= C1C2…Cm the probability of the sequence of codons C coding for a protein is given by P(C) = F(C1)F(C2)…F(Cm)

28.  Codon Usage For e.g., if S is the sequence S= AGGACG, when read in frame 1, it results in the sequence Substitute appropriate values from Table-I to compute Pi(C), i = 1, 2,3.

29.  Codon Usage Probability of finding sequence Sif C is non-coding: F0(C)- frequency of codon c in a non-coding sequence, and for all codons, Assuming the random model of DNA, the probability, P0 for the above sequence of codons C would be

30.  Codon Usage The log-likelihood ratio for Scoding in frame 1, LP1, is The log-likelihood ratio for Scoding in frames 2 & 3 are computed in a similar fashion

31.  Codon Usage • To locate the (usually small) coding regions within large genomic sequences: • compute the value of a coding statistic in successive (usually overlapping) sliding windows • record the value of the statistic for each window • This generates a profile along the sequence in which peaks correspond to the coding regions and valleys to the non-coding ones.

32.  Codon Usage • Codon usage bias is a mixture of both: • bias in the usage of amino acids, and • bias in the usage of synonymous codons. • One can measure these effects separately.