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Annotering av sekvensen

Kromosom 16: et av de minste

Finding genesWhat are we looking for?Proteins encoded in mRNANon-coding RNA (ncRNA) genes

Where are we looking?ProkaryotesEukaryotes (often introns)

Classes of RNAfRNA: Functional RNA — essentially synonymous with non-

coding RNAmRNA: Messenger RNA — coding for proteinsmiRNA: MicroRNA — putative translational regulatory gene familyncRNA: Non-coding RNA — all RNAs other than mRNArRNA: Ribosomal RNAsiRNA: Small interfering RNA — active molecules in RNA

interferencesnRNA: Small nuclear RNA — includes spliceosomal RNAssnmRNA: Small non-mRNA — essentially

synonymous with small ncRNAssnoRNA:Small nucleolar RNA — usually involved in rRNA

modificationstRNA: Small temporal RNA — e.g. lin-4 and let-7 in C. eleganstRNA: Transfer RNA

Source: Eddy SR (2001) Nature Reviews in Genetics

Informasjon i sekvensen som kan brukes for å finne gener

”Signaler” i sekvensen: Spleisesignaler, promotere, termineringssignaler, polyA-signaler, CpG-øyer (Gene search by signal)

”Innholdet” i sekvensen: ORFs, kodonstatistikk osv.(Gene search by content)

Likhet med kjente gener (Gene search by similarity)

Fra gen til protein: så lett for cellen, så vanskelig for oss

Simple protein findingExamine all 6 possible reading frames 3 frames on forward strand 3 frame on reverse strand

Plot positions of Initiation (start) (Methionine) codon: ATG Termination (stop) codons: TAA, TAG, TGA

Look for long stretches without stop codons after a start codon

Source: http://cwx.prenhall.com/horton/medialib/media_portfolio/

Standard Genetic Code

The standard genetic code is used in most organisms

Another code is use din mitochondria and some organismsOverview of gentic codes in various organisms:http://www.ncbi.nlm.nih.gov/htbin-post/Taxonomy/wprintgc?mode=c

Start and stop codon distribution

Distribution of start codons (short lines) and stop codons (long lines) in the six reading frames along a genomic sequence (lacZ operon in E.coli)There is an open reading frame (lacZ) in frame +3 from position 1284 to 4355.Created by DNA STRIDER.

Prokaryotic promotor regions

Source: http://cwx.prenhall.com/horton/medialib/media_portfolio/

Transcription termination

Shine-Dalgarno (SD) sequenceThe 16S rRNA ribosomal protein binding site

Transcription and translationGenomic DNA

Primary transcript

Spliced mRNA

Protein

Promotor

Cap

5’UTR 3’UTR

Exon1 Exon2 Exon3

StartAUG

StopTAA/TAG/TGA

GU…AG

M

GU…AG

Intron1 Intron2

Terminator

AAAA…

Gene, exon and intron number for whole ExInt and subdivisions

Gene number Exon number Intron number

Whole ExInt 94 615 518 169 525 870

Non-redundant ExInt 15 271 113 457 128 065

Rattus norvegicus 835 4889 7191

Homo sapiens 8287 60 499 43 127

Mus musculus 3044 18 920 15 407

Drosophila melanogaster 15 220 64 271 89 969

Caenorhabditis elegans 18 924 121 708 108 803

Arabidopsis thaliana 25 216 158 629 127 386

Saccharomyces cerevisiae 589 1695 1438

Fordeling av eksonstørrelser i ExInt

Fordeling av intronstørrelser i ExInt

0 1 2

All ExInt257 713 (49%)

147 625 (28%)

120 532 (23%)

Non-redundant60 979 (48%)

35 438 (28%)

31 608 (24%)

Rattus norvegicus 2842 (39%) 2365 (33%) 1384 (28%)

Mus musculus 6703 (44%) 5921 (38%) 2783 (18%)

Caenorhabditis elegans51 251 (47%)

28 553 (26%)

28 999 (27%)

Homo sapiens19 102 (44%)

15 423 (36%) 8602 (20%)

Arabidopsis thaliana71 958 (56%)

28 178 (22%)

27 250 (22%)

Drosophila melanogaster

38 101 (42%)

28 896 (32%)

22 972 (26%)

Saccharomyces cerevisiae 641 (45%) 428 (30%) 369 (25%)

Intron phase 0 1 2

Intron-fase: ekson/intron-overganger mellom kodoner eller i dem

Hvordan finne spleisesignaler og eksoner?

Vektsmatriser: Hvordan er fordelingen av nukleotider rundt spleiseseter?”Weight array matrices” hvor det tas hensyn til nabonukleotider”Maximal dependence decomposition”: Korrelasjoner med ikke-nabonukleotiderSkjulte Markov-modellerNeurale nettverk: En mønstergjenkjenningsteknikk som ”lærer”

Slik lages en vektmatrise

Og slik brukes den

Konsensus-sekvenser for ekson/intron-overganger

Forskjellige klasser av eksoner som må oppdages på forskjellige måter

Innledende eksoner: Begynner med et startkodon og slutter med et spleisedonorsete

Interne eksoner: Begynner med et akseptorsete og slutter med et donorsete

Terminale eksoner: Begynner med et akseptorsete og slutter med et stoppkodon

Enkelteksongener: Begynner med et startkodon og slutter med et stoppkodon

Integrert genfinning: Hva følger etter hva?

Neuronnettverk: et eksempel

The Grail II system for finding exons in eukaryotic genes (Uberbacher and Mural 1991; Uberbacher et al. 1996). The method uses a neural network to identify patterns characteristic of coding sequences. The network includes three layers, an input layer for the data with the data coming from a candidate exon sequence, and a hidden layer for discerning relationships among the input data. An output layer comprising one neuron indicates whether or not the region is likely to be an exon. Each neuron receives information from a set in the layer above, some

with a positive value and others with a negative value; sums these values; and then converts them to an output of approximately 0 or 1.

The system is trained using a set of known coding sequences, and as each sequence is utilized, the strengths and types of connections (positive or negative) between the neurons are adjusted, decreasing or increasing the signal to the next neuron in a manner that produces the correct output. The major difference between neural networks for exon and secondary structure prediction is that the exon prediction uses sequence pattern information as input whereas secondary structure prediction uses a window of amino acid sequence in the protein. In Grail II, a candidate sequence is evaluated by calculating pattern frequencies in the sequence and applying these values to the neural network. If the output is close to a value of 1, then the region is predicted to be an exon.

Sekvens”innhold”: Forskjeller mellom den ekte leserammen og de to andre

Ramme 1 er den ekte, og inneholder kodoner som koder for et protein med gjennomsnittlig aminosyresammensetning

Kodonbruk i de tre leserammene

Basefordeling på de tre kodonposisjonene

Å skille mellom kodende og ikkekodende sekvenser ut fra basesammensetningen av de

tre kodonposisjonene

Antall ganger en base forekommer i hver av de tre kodonposisjonene i vinduet = Nij.

Forventet verdi for hver base i hver av de tre kodonposisjoneneEij=(Ni1+Ni2+Ni3)/3

Divergensen D=Σ|Eij-Nij|Vindu: 67 kodonerEMBL-databasen 1984

Codon usage in the E.coli genome

Source: http://www.kazusa.or.jp/codon/

Escherichia coli [gbbct]: 11865 CDS's (3662594 codons)

fields: [triplet] [amino acid] [fraction] [frequency: per thousand] ([number])

UUU F 0.58 22.1 ( 80995) UCU S 0.17 10.4 ( 38027) UAU Y 0.59 17.5 ( 63937) UGU C 0.46 5.2 ( 19138) UUC F 0.42 16.0 ( 58774) UCC S 0.15 9.1 ( 33430) UAC Y 0.41 12.2 ( 44631) UGC C 0.54 6.1 ( 22188) UUA L 0.14 14.3 ( 52382) UCA S 0.14 8.9 ( 32715) UAA * 0.61 2.0 ( 7356) UGA * 0.30 1.0 ( 3623) UUG L 0.13 13.0 ( 47500) UCG S 0.14 8.5 ( 31146) UAG * 0.08 0.3 ( 989) UGG W 1.00 13.9 ( 50991) CUU L 0.12 11.9 ( 43449) CCU P 0.18 7.5 ( 27340) CAU H 0.57 12.5 ( 45879) CGU R 0.36 20.0 ( 73197) CUC L 0.10 10.2 ( 37347) CCC P 0.13 5.4 ( 19666) CAC H 0.43 9.3 ( 34078) CGC R 0.36 19.7 ( 72212) CUA L 0.04 4.2 ( 15409) CCA P 0.20 8.6 ( 31534) CAA Q 0.34 14.6 ( 53394) CGA R 0.07 3.8 ( 13844) CUG L 0.47 48.4 (177210) CCG P 0.49 20.9 ( 76644) CAG Q 0.66 28.4 (104171) CGG R 0.11 5.9 ( 21552) AUU I 0.49 29.8 (109072) ACU T 0.19 10.3 ( 37842) AAU N 0.49 20.6 ( 75436) AGU S 0.16 9.9 ( 36097) AUC I 0.39 23.7 ( 86796) ACC T 0.40 22.0 ( 80547) AAC N 0.51 21.4 ( 78443) AGC S 0.24 15.2 ( 55551) AUA I 0.11 6.8 ( 24984) ACA T 0.17 9.3 ( 33910) AAA K 0.74 35.3 (129137) AGA R 0.07 3.6 ( 13152) AUG M 1.00 26.4 ( 96695) ACG T 0.25 13.7 ( 50269) AAG K 0.26 12.4 ( 45459) AGG R 0.04 2.1 ( 7607) GUU V 0.28 19.8 ( 72584) GCU A 0.18 17.1 ( 62479) GAU D 0.63 32.7 (119939) GGU G 0.35 25.5 ( 93325) GUC V 0.20 14.3 ( 52439) GCC A 0.26 24.2 ( 88721) GAC D 0.37 19.2 ( 70394) GGC G 0.37 27.1 ( 99390) GUA V 0.17 11.6 ( 42420) GCA A 0.23 21.2 ( 77547) GAA E 0.68 39.1 (143353) GGA G 0.13 9.5 ( 34799) GUG V 0.35 24.4 ( 89265) GCG A 0.33 30.1 (110308) GAG E 0.32 18.7 ( 68609) GGG G 0.15 11.3 ( 41277)

Coding GC 50.58% 1st letter GC 57.71% 2nd letter GC 40.68% 3rd letter GC 53.36% Genetic code 1: Standard

Codon usage in the human genome

Source: http://www.kazusa.or.jp/codon/

Homo sapiens [gbpri]: 44580 CDS's (19894411 codons)

fields: [triplet] [amino acid] [fraction] [frequency: per thousand] ([number])

UUU F 0.45 16.9 (336562) UCU S 0.18 14.6 (291040) UAU Y 0.44 12.0 (239268) UGU C 0.45 9.9 (197293) UUC F 0.55 20.4 (406571) UCC S 0.22 17.4 (346943) UAC Y 0.56 15.6 (310695) UGC C 0.55 12.2 (243685) UUA L 0.07 7.2 (143715) UCA S 0.15 11.7 (233110) UAA * 0.28 0.7 ( 14322) UGA * 0.50 1.3 ( 25383) UUG L 0.13 12.6 (249879) UCG S 0.06 4.5 ( 89429) UAG * 0.22 0.5 ( 10915) UGG W 1.00 12.8 (255512) CUU L 0.13 12.8 (253795) CCU P 0.28 17.3 (343793) CAU H 0.41 10.4 (207826) CGU R 0.08 4.7 ( 93458) CUC L 0.20 19.4 (386182) CCC P 0.33 20.0 (397790) CAC H 0.59 14.9 (297048) CGC R 0.19 10.9 (217130) CUA L 0.07 6.9 (138154) CCA P 0.27 16.7 (331944) CAA Q 0.25 11.8 (234785) CGA R 0.11 6.3 (126113) CUG L 0.41 40.3 (800774) CCG P 0.11 7.0 (139414) CAG Q 0.75 34.6 (688316) CGG R 0.21 11.9 (235938) AUU I 0.36 15.7 (313225) ACU T 0.24 12.8 (255582) AAU N 0.46 16.7 (331714) AGU S 0.15 11.9 (237404) AUC I 0.48 21.4 (426570) ACC T 0.36 19.2 (382050) AAC N 0.54 19.5 (387148) AGC S 0.24 19.4 (385113) AUA I 0.16 7.1 (140652) ACA T 0.28 14.8 (294223) AAA K 0.42 24.0 (476554) AGA R 0.20 11.5 (228151) AUG M 1.00 22.3 (443795) ACG T 0.12 6.2 (123533) AAG K 0.58 32.9 (654280) AGG R 0.20 11.4 (227281) GUU V 0.18 10.9 (216818) GCU A 0.26 18.6 (370873) GAU D 0.46 22.3 (443369) GGU G 0.16 10.8 (215544) GUC V 0.24 14.6 (290874) GCC A 0.40 28.5 (567930) GAC D 0.54 26.0 (517579) GGC G 0.34 22.8 (453917) GUA V 0.11 7.0 (139156) GCA A 0.23 16.0 (317338) GAA E 0.42 29.0 (577846) GGA G 0.25 16.3 (325243) GUG V 0.47 28.9 (575438) GCG A 0.11 7.6 (150708) GAG E 0.58 40.8 (810842) GGG G 0.25 16.4 (326879)

Coding GC 52.65% 1st letter GC 56.26% 2nd letter GC 42.37% 3rd letter GC 59.31% Genetic code 1: Standard

Codon usage diagram

Usage of various codons along the sequence of lacZO: Optimal codon usageS: Suboptimal codon usageR: Rare codon usageU: Unique codon usage

Created by DNA STRIDER.

Comparative genomics methods

Gene finding by sequence comparison to sequences known to be transcribed or translatedCompare the genomic sequence to sequence databases Proteins mRNA sequences EST sequences (mRNA)

Both exact matches and approximate matches are interestingConserved sequences between speciesProgram: Procrustes

Et eksempel på et resultat med søkeprogrammet Genscan

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