Gene & genome organisation. Computational gene identification

Transcription

1 Gene & genome organisation Computational gene identification

2 Eubacterial gene

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6 Eukaryotic gene Regulatory elements Promoter Translation start Introns polya signal Transcription stop DNA Transcription start Exons Translation stop

7 Promoters in eukaryotic DNA 1. TATA box 2. Initiators 5 Y Y A +1 N [T,A] Y Y Y 3 3. CpG islands

8 EMBOSS CpGPlot

9 Splicing RNA (primary transcript) polya tail RNA (spliced) Translation Protein

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14 Regulated splicing Exons may be combined differently during splicing. One gene can in this way give rise to multiple forms of a protein. Primary RNA transcript Splice variants

15 Alternative splicing of the? -tropomyosin pre-mrna a 13 Nonmuscle Smooth muscle Striated muscle Striated muscle' Hepatoma Brain a 13

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17 Higher eukaryote genomes contain a substantial amount of non-coding sequences Genome No of genes Genes / MB Homo sapiens 3000 Mb ~40,000? ~13 Mycoplasma genitalium 0.6 MB ~600 ~1000

18 Repetitive DNA ~50 % of human genomic DNA mobile elements - - viral retrotransposons common in yeast & Drosophila - non-viral retrotransposons common in mammals LINES SINES LINES long interspersed elements 6-7 kb LINE1 : 600,000 copies in human genome = 15 % of genomic DNA

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20 SINES short interspersed elements ~300 bp Sequence conservation ~80 % within the same species Alu sequence the most abundant class of SINE 1 million copies = 10 % of genomic DNA Many Alu sequences have cleavage sites for the restriction enzyme AluI, (AGCT), hence the name Originally derived from SRP RNA by reverse transcription

21 Repetitive sequences Mammals % genome size ~ 3 GB Fugu : < 15 % ~365 MB

22 Why so many repetitive elements in higher mammals? Mobile elements probably had a significant influence on evolution of higher organisms : Novel genes and new controls on gene expression were created because mobile elements have served as sites for recombination, leading to gene duplications and other gene rearrangements (exon shuffling).

23 Detection of repetitive DNA Dotplot analysis

24 Detection of repetitive DNA RepeatMasker RepeatMasker is a program that screens DNA sequences for interspersed repeats known to exist in mammalian genomes as well as for low complexity DNA sequences. The output of the program is a detailed annotation of the repeats that are present in the query sequence as well as a modified version of the query sequence in which all the annotated repeats have been masked (replaced by Ns). On average, over 40% of a human genomic DNA sequence is masked by the program. Sequence comparisons in RepeatMasker are performed by the program cross_match, an efficient implementation of the Smith-Waterman-Gotoh algorithm developed by Phil Green HSU (22462) + MER7A DNA/MER2_type (109) HSU (21523) C TIGGER1 DNA/MER2_type (0) HSU (21222) C AluSx SINE/Alu (4) HSU (20544) C TIGGER1 DNA/MER2_type (943) HSU (20243) C AluSg SINE/Alu (0) HSU (19548) C TIGGER1 DNA/MER2_type (1608) HSU (19427) + MER7A DNA/MER2_type (1)

25 Human genome contains a substantial number of pseudogenes - non-functional gene variants Non-processed pseudogene Gene duplication has resulted in new copy of gene Copy has mutated to become non-functional Processed pseudogenes Non-functional genomic copies of mrnas. Often contain multiple mutations

26 Human genome: * variation of GC content * longer introns in AT-rich regions

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28 Gene prediction methods - Ab initio, pattern recognition - Database searching Identification of ORFs Finding long ORFs Stop codon expected every 64/3 = 21 codons number of stop codons=3 (UAA, UAG, UGA) Average proteins are much longer Disadvantages: short genes are not detected some ORFs are false positives not suitable for eukaryotes

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30 LOCUS AAB aa BCT 03-MAR-1995 DEFINITION aeph=putative exoenzyme production regulatory peptide [Erwinia carotovora, carotovora, Peptide, 47 aa]. ACCESSION AAB32243 PID g VERSION AAB GI: DBSOURCE locus S74077 accession S KEYWORDS. SOURCE Pectobacterium carotovorum carotovora. ORGANISM Pectobacterium carotovorum Bacteria; Proteobacteria; gamma subdivision; Enterobacteriaceae; Pectobacterium. REFERENCE 1 (residues 1 to 47) AUTHORS Murata,H., Chatterjee,A., Liu,Y. and Chatterjee,A.K. TITLE Example of awful ORF prediction Regulation of the production of extracellular pectinase, cellulase, and protease in the soft rot bacterium Erwinia carotovora subsp. carotovora: evidence that aeph of E. carotovora subsp. carotovora 71 activates gene expression in E. carotovora subsp. car JOURNAL Appl. Environ. Microbiol. 60 (9), (1994) MEDLINE REMARK GenBank staff at the National Library of Medicine created this entry [NCBI gibbsq ] from the original journal article. This sequence comes from Fig. 2A. COMMENT Method: conceptual translation supplied by author. FEATURES Location/Qualifiers source /organism="pectobacterium carotovorum" /db_xref="taxon:554" Protein /product="aeph" /name="putative exoenzyme production regulatory peptide" CDS /gene="aeph+" /coded_by="s : " /note="author translates GTG start as Val" ORIGIN 1 vgqepkgies rkiqdghvrk kvgrqqglwv rttkkekfsr msrdanv

31 Compositional bias in coding regions Codon usage for enteric bacterial (highly expressed) genes 7/19/83 AmAcid Codon Number /1000 Fraction.. Gly GGG Gly GGA Gly GGU Gly GGC Glu GAG Glu GAA Asp GAU Asp GAC Val GUG Val GUA Val GUU Val GUC Ala GCG Ala GCA Ala GCU Ala GCC Arg AGG Arg AGA Ser AGU Ser AGC

32 CodonPreference Codon preference plot is constructed by calculating a codon preference statistic for each position of each of three reading frames. The statistic is calculated over a window of length w and window moved along the sequence in increments of three bases, maintainin the reading frame. The magnitude of the codon preference statistic is a measure of the likeness of particular window of codons to a predetermined preferred usage. p = preference parameter = relative likelihood of a codon being found in a gene as opposed to a random sequence f ABC /F ABC p = r ABC /R ABC f frequency of codon ABC(found in frequency table) F sum of frequencies for all codons that are members of ABCs synonymous family r frequency of codon ABC in a random sequence R sum of frequencies of ABCs synonymous family in a random sequence Codon preference statistic P (sum logp i /w) P = e w is between 25 and 50

33 CodonPreference is a frame-specific gene finder that tries to recognize protein coding sequences by virtue of the similarity of their codon usage to a codon frequency table or by the bias of their composition (usually GC) in the third position of each codon.

34 Compositional bias of exons K-tuple method from Bishop ed., Guide to Human Genome Computing Consider a sequence S = {s i } of length L. It can be transformed into a sequence of k-tuples (i.e oligonucleotides of length k) : W = {W k,i } (i = 1,, L - k + (1) ; W k,i??? Here? = {W k,i } is the set of all the possible oligonucleotides W k of length k. In this way it is possible to construct a table F with the occurrence frequency F(W k ) for all possible k-tuples of the set of sequences {S} having the function of interest. Consider two sets of sequences {S (1) } and {S (2) } with mutually exclusive functions, for instance intron and exon. It is possible to calculate the k-tuple frequency tables F 1 and F 2 for these two sets of sequences. The difference in frequencies between these tables can be used for discrimination. To analyze the test sequence using the F 1 and F 2 tables, calculate the local discriminant index for the ith position: d(i) = F 1 (S k,i ) / (F 1 ( S k,i ) + F 2 ( S k,i )) d(i) is smoothed using an averaging window of 2w+1 consecutive positions i + w?d??????????d(i) j = i - w k= 6 is often used

35 Local sites (=signals) used in the prediction of genes Promoters Terminators of transcription Start and stop codons Splice sites Branch points Polyadenylation sites Signal sensors = methods for detecting signals Content sensors Hexamer counts to discriminate between exons and introns Gene finding methods: Combination of signal and content sensors

36 Gene prediction methods Ab initio: HMM methods Genscan HMMGene Genie GeneMark.hmm FGENEH GeneID Ab initio: Neural network methods GRAIL NetGene2 Homology based Blast Procrustes Genewise

37 Limitations Non-coding parts, 5 and 3 UTRs, and non-coding RNAs are not detected Lack of suitable training sets of very long genomic sequences Methods are conservative - they have been trained on typical genes