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Gene finding pipelines for automatic annotation of new eukaryotic and bacterial genomes Victor Solovyev Professor of computer science, Royal Holloway,

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Presentation on theme: "Gene finding pipelines for automatic annotation of new eukaryotic and bacterial genomes Victor Solovyev Professor of computer science, Royal Holloway,"— Presentation transcript:

1 Gene finding pipelines for automatic annotation of new eukaryotic and bacterial genomes
Victor Solovyev Professor of computer science, Royal Holloway, University of London Chairman, Softberry Inc.

2 New genomes sequencing
Human, Mouse, Rat, Cow, Sheep, Cat, Dog, Pig, Chicken, Drosophila, Bee, Zebrafish, Fugu, Nematodes Arabidopsis, Rice, Medicago, Soybean, Barley, Poplar, Tomato, Oat, Wheat, Corn S.cerevisiae, S.pombe, Aspergillus nidulans, Coprinus cinereus Cryptococcus neoformans, Fusarium graminearum Magnaporthe grisea Neurospora crassa Ustilago maydis Anopheles, P. falciparum, E. cuniculi, Chlamy, Ciona, Diatom, White rot, P. sojae Bacterial & bacterial communities

3 Expression stages and structural organization of typical eukaryotic protein-coding gene
Translation Enhancer 3’- 5’- Core promoter Start of transcription Transcription, 5’-Capping and 3’-polyadenilation Splicing (removing of intron sequences) Pre-mRNA mRNA Protein 5’-non-coding exon 3’-non-coding Poly-A signal Internal exons Introns ATG-codon Stop-codon

4 Ab initio multiple gene prediction approaches
Genescan (Burge, Karlin,1997) HMMgene (Krogh, 1977) Fgenesh (Salamov, Solovyev,1998) Genie (Reese et al., 2000) GeneID (Guigo at al. 1992) Neural networks Probabilistic Pattern recognition Fgenes (Solovyev,1997) Discriminant functions Likelihoods of gene components, HMM Flexible combinations of any discriminative features Balanced score as production of likelihoods, simple features

5 E5 I0 I1 I2 E0 E1 E2 EL 3’- 5’- EF N E3 I5 I3 Hidden Markov model of
PolyA Pr N E3 I5 I3 Hidden Markov model of multiple eukaryotic genes Used in Genescan and Fgenesh programs Ei and Ii are different exon and intron states, respectively (i=0,1,2 reflect 3 possible different ORF). E 5/3 marks non-coding exons and I5/I3 are 5’- and 3’-introns adjacent to non-coding exons.

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7 Signal differences: start of translation

8 Signal differences: donor splice site

9 Importance of good specific parameters: Rice example Fgenesh with Monocot gene-finding parametres .

10 Strategy to make gene-finding parameters for new genomes
Using GeneBank genes for close organisms Using new genomic sequence a) Having known mRNA/cDNA sequences Map mRNA by EST_MAP program on genomic sequence Extract genes and use them as learning set b) Using ab initio gene-prediction Predict genes, Select genes with protein support c) Using a database of known proteins (NR) that can be mapped on genome by Prot_map program with reconstructing gene-structure In addition find protein coding ORF by BESTORF program in a set of ESTs and use them in learning of coding parameters

11 Learning parameters using GeneBank genes for close organisms
Select GeneBank organism class having enough known genes Create Infogene database with reconstructed genes running Infog program (some genes might be described in several GeneBank entries) Run GetGenes program to extract genes from Infogen to use in learning programs (with cleaning genes with errors in annotation in ORF and splice sites) Run Efeature program to create: set of coding regions (usually significantly bigger than set of genes) set of non-coding regions Run scripts/programs of learning coding parameters (might be several GC zones) Run scripts/programs of learning splice sites parameters Run scripts/programs to create exon length distributions and other statistics Check parameters of initial probabilities (exons/introns/noncoding) depending on gene density in genome and gene structure Test and edit parameters to select the best variant. Repeat learning on bigger or smaller organism classes and select the best learning set.

12 Developed parameters for fgenesh group of programs:
Human, Mouse, Drosophila, C. elegans, Fish (WUSTL, Baylor, CSHL, JGI) Dicots (Arabidopsis), Nicotiana tabacum, Monocots (Corn, Rice, Wheat, Barley) (TIGR, Rutgers University) Algae, Plasmodium falciparum, Anopheles gambiae Schizosaccharomyces pombe, Neurospora crassa, Aspergillus nidulans, Coprinus cinereus, Cryptococcus neoformans, Fusarium graminearum, Magnaporthe grisea, Ustilago maydis (MIT/Broad Institute) Medicago (University of Minnesota) Brugie malayi (TIGR)

13 FGENESH++: AUTOMATIC EUKARYOTIC GENOME ANNOTATION PIPELINE
RefSeq mRNA mapping by Est_map program - mapped genes are excluded from further gene prediction process. Map all known proteins (NR) on genome by Prot_map program with gene structure reconstruction (find regions occupied by genes) Run Fgenesh+ using mapped proteins and selected genome sequences Run ab initio Fgenesh gene prediction on the rest of genome. Search for protein homologs (by BLAST) of all products of predicted genes in NR. Run Fgenesh+ gene prediction on sequences (from stage 4) having protein homologs. Second run of Fgenesh in regions free from genes selected on stages 1,3,5. Run of Fgenesh gene predictions in large introns of known and predicted genes. Special variant of FGENESH++ can take into account synteny (human-mouse, for example) using FGENESH-2 program that predicts genes using 2 similar genomic sequences from different species.

14 Components of Fgenesh++ automatic pipeline:
Gene-finding group of program have mostly common components and working with the same organism-specific parameters Fgenesh – ab initio gene prediction. Run on whole chromosomes (~300MB). FAST: The Human genome of 3 GB sequences is processed for ~ 4 hours Fgenesh+ This derivative of Fgenesh uses information on homologous proteins to improve accuracy of gene prediction, if such homologs can be found. Fgenesh-2 Variant of Fgenesh that uses homology between two genomic DNAsequences, such as human and mouse, as an extra factor for more accurate gene prediction. Fgenesh_C uses information on homologous mRNA/EST to improve accuracy of gene prediction. Can be used to reconstruct alternatively spliced genes.

15 Components of Fgenesh++ automatic pipeline:
Programs for mapping known mRNA/Est or proteins with reconstruction of gene structure Est_map a program for fast mapping of a set of mRNAs/ESTs to a chromosome sequence. It takes into account splice site weight matrices for accurate mapping (important for accurate mapping very small exons). Prot_map is used for fast mapping a database of protein sequences to genome with accounting for splice sites (useful for genomes with a few known genes and to search for pseudogenes).

16 Example of Prot_map mapping of a protein sequence to genome
First sequence Chr19 [cut: ] [DD] Sequence: ( ), S: , L:1739 IPI:IPI |SWISS-PROT:Q8TEK3-1 Summ of block lengths: 1468, Alignment bounds: On first sequence: start , end , length 21213 On second sequence: start , end , length 1477 Blocks of alignment: 19 1 E: [ca GT] P: L: , G: , W: , S: 2 E: [AG GT] P: L: , G: , W: , S: 3 E: [AG GT] P: L: , G: , W: , S: 4 E: [AG GT] P: L: , G: , W: , S: 5 E: [AG GT] P: L: , G: , W: , S: 6 E: [AG GT] P: L: , G: , W: , S: 7 E: [AG GT] P: L: , G: , W: , S: 8 E: [AG GT] P: L: , G: , W: , S: 9 E: [AG GT] P: L: , G: , W: , S: 10 E: [AG GT] P: L: , G: , W: , S: 11 E: [AG GT] P: L: , G: , W: , S: 12 E: [AG GT] P: L: , G: , W: , S: 13 E: [AG GT] P: L: , G: , W: , S: 14 E: [AG GT] P: L: , G: , W: , S: 15 E: [AG GT] P: L: , G: , W: , S:40.932 16 E: [AG GC] P: L: , G: , W: , S: 17 E: [AG GT] P: L: , G: , W: , S: 18 E: [AG GT] P: L: , G: , W: , S: 19 E: [AG ta] P: L: , G: , W: , S:

17 Prot_map example of alignment
gatcacagaggctgg(..)agtgtctgtgtttca?[GGRIVSSKPFAPLNFRINSRNLSg (..)evdhqlkerfanmke GGRIVSSKPFAPLNFRINSRNLS- ]gtaagaaactctcat(..)ctgtggctcctgcag[acIGTIMRVVELSPLKGSVSWTGK (..) dIGTIMRVVELSPLKGSVSWTGK PVSYYLHTIDRTI]gtgagtatctcgctg(..)ctttcttctttttag[LENYFSSLKNP PVSYYLHTIDRTI (..) LENYFSSLKNP KLR]gtaagtttgtgtgtt(..)ctgctctccttccag[EEQEAARRRQQRESKSNAATP KLR (..) EEQEAARRRQQRESKSNAATP TKGPEGKVAGPADAPM]gtaaggccccagcct(..)ccttgtgtcctccag[DSGAEEEK TKGPEGKVAGPADAPM (..) DSGAEEEK AGAATVKKPSPSKARKKKLNKKGRKMAGRKRGRPKKMNTANPERKPKKNQTALDALHAQT

18 Analysis of gene-finding accuracy and running time
Test on 83 small (< bp) human genes using mouse homolog: Prot_map: Sne= 73.7 Sn_pe Spe Sn_n= 93.9 Sp_n= 88.6 C= Time ~ 1 min Genewise: Sne= 76.4 Sn_pe Spe Sn_n= 94.9 Sp_n=89.4 C= Time ~ 90 min Fgenesh: Test on 8 big (> bp) human genes using mouse homolog: Prot_map: Sne= 87.9 Sn_pe Spe Sn_n= 94.3 Sp_n= 96.0 C= Time ~ 1 min Genewise: Sne= 91.9 Sn_pe Spe Sn_n= 95.1 Sp_n= 97.0 C= Time ~ 1200 min Prot_map mapping of Human protein set of proteins on chromosome 19 (~59 MB) takes 90 min (best hit for each protein) and 148 min (all significant hits for each protein)  Can be used for fast finding of an initial gene set in new genome mapping all known proteins  Used for pseudogenes finding as mapping with frameshifts damaging ORFs

19 New Fgenesh+ and Genewise
1) 700 genes with 6508 exons having similar protein with > 90% similarity GeneWISE: Sne= 94.1 Sn_pe Spe Sn_n= 98.9 Sp_n= 99.6 C=0.992 FGENESH+: Sne= 96.9 Sn_pe= 98.5 Spe= 97.9 Sn_n= 99.0 Sp_n= 99.5 C=0.992 2) 18 genes with 116 exons having similar Drosophila protein with identity 28-70% GeneWISE: Sne= 40.5 Sn_pe Spe Sn_n= 68.3 Sp_n= 99.7 C=0.813 Fgenesh+: Sne= 70.7 Sn_pe= 84.5 Spe= 82.0 Sn_n= 84.8 Sp_n= 96.9 C=0.898 5’-exon Observed - 18 Predicted - 14 Correct - 2 (11 by Fgenesh+) Intr: Observed - 80 Predicted - 43 Correct - 38 (59 by Fgenesh+) 3’-exon: Observed - 18 Predicted - 14 Correct - 7 (12 by Fgenesh+) Run time: Fgenesh+ 50 – 1000 times faster than GeneWise

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21 Automated Gene Calling at Center for Genome Research MIT
Sequencing: 2003/2004 – 6 new yeast genomes 2004/2005 ~ 20 new yeast genomes Gene structures are predicted using a combination of FGENESH, FGENESH+, and GENEWISE (Sanger Institute). the protein used in the previous had >90% amino acid identity to the translated genome (cumulative across sub-alignments), then the GENEWISE call, if valid, was favored over the FGENESH+ call, and was used as the EVIDENCE_GENE If this protein had >80% but less than 90% amino acid identity to the translated genome (cumulative across sub-alignments), then the FGENESH+ call, if valid, was favored over the GENEWISE call, and was used as the EVIDENCE_GENE

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26 Examples of usage Fgenesh suit in genome annotations
         Grimwood J, Gordon LA, Olsen A, .., Salamov A., Solovyev V., ..., Lukas S. (2004) The DNA sequence and biology of human chromosome 19. Nature, 428(6982), Using Fgenesh, Fgenesh+, est_map to annotate genes in Himan cjromosome 19. annotation. ·         Heiliget al. (2003) The DNA sequence and analysis of human chromosome 14. Nature 421, FGENESH used for human chromosome 14 annotation. ·         Hillier et al. (2003) The DNA sequence of human chromosome 7. Nature 424, Extensive use of FGENESH-2 for human chromosome 7 annotation. ·         Feng et al. (2002) Sequence and analysis of rice chromosome 4. Nature 420,   FGENESH used for annotation of rice chromosome 4. Galagan et al. (2003) The genome sequence of the filamentous fungus Neurospora crassa. Nature 422: Neurospora genome annotation based on FGENESH and FGENESH+. ·         Lander et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, Original paper on sequencing human genome by public consortium also reports use of FGENESH genefinder for genome annotation. ·         Deloukas et al. (2001) The DNA sequence and comparative analysis of human chromosome 20. Nature 414, Use of FGENESH for annotation of human chromosome 20. ·         Yu et al. (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296: Rice genome sequencing and annotation project used FGENESH as primary source of gene predictions. ·         Holt et al. (2002) The Genome Sequence of the Malaria Mosquito Anopheles gambiae. Science 298: Use of FGENESH for annotation of Anopheles genome.

27 Canonical and Non-canonical splice sites
SpliceDB (Burset, Seledtsov, Solovyev, NAR 1999,2000) GT-AG: 99.24% GC-AG: 0.69% AT-AC: 0.05% other sites: 0.02% GT-AG group (canonical splice sites): examples M70A60G80|GTR95A71G81T46 Y73Y75Y78Y79Y80Y79Y78Y81Y86Y86NC71AG|G52 b) GC-AG group: 126 examples M83A89G98|GCA87A84G97T71 c) AT-AC group: 8 annotated examples + 2 examples recovered from annotation errors S90|ATA100T100C100C100T100T90T70  T70G50C70NC60AC|A60T60 Gene prediction is usually done with only standard splice sites

28 Additional sources of genes
Identified with synteny data help Non canonical splice sites Alternatively spliced Alternative promoters Alternative poly-A Additional studies of the above topics will update the current gene collections

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30 Exon-based syntheny Run Gene-finding annotation pipeline for each genome Select chains of similar exons between 2 genomes comparing coding exons by Blast 95% in agreement with filtered genome alignments Brudno et al.(2004) Automated Whole-Genome Multiple Alignment of Rat, Mouse, and Human Genome Research Journal, 14(4):

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32 Pseudogene finding using Prot_map
nnnnnnnn(..)nnnnnnnnnnnnnag[KEFDFESANAQFNKEEMGREFHNKLKLKEDKL (..) KDFDFESANAQFNKEEIDREFHNKLKLKEDKL EKEEKPVNGEDKGDSGVDTQNSEGHADEEDALGPNCFYDQTKSSFDNISGDDNRERRPTW EKQEKPVNGEDKGDSGVDTQNSEGNADEEDPLGPNCYYDKTKSFFDNISCDDNRERRPTW AEGRRLNAETFGIPLCPNRGHGGYRGRGaGLGFHGGRGRg]gtggcagaagtggta(..) AEERRLNAETFGIPLRPNRGRGGYRGRG-GLGFRGGRGR (..)

33 Pseudogene finder Generation pseudogene candidates:
Run script finding genes having almost identical coding proteins (or part of them) with lesser number introns (or without introns). Run prot_map mapping Human (mammalian) proteins and selecting damaged ones Selecting pseudogenes using additional features: like poly_A tail, ratio ks/kn

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35 Development of eukaryotic promoter recognizer In group of TSS programs

36 Results of promoter search on genes with known mRNAs by different promoter-finding programs. Reproduced from Liu and States (2002) Genome Research 12:

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38 Accuracy of prediction by TSSP on plant genomic sequences
Selected known genomic regions upstream of CDS True positives 92% Total number of False positives for 40 TATA promoters: 22 (1 per 3648 bp) True positives 95% Total number of False positives for 25 TATA –less promoters: 15 (1 per 3300 bp) For every class (TATA and TATA-less) promoters only one predicted TSS with highest score in an interval of 300 bp was taken during the search.

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42 PromH with ortologous sequences

43 Fgenesb_annotator - Bacterial Gene/Operon Prediction and Annotation Pipeline
FGENESB is a new complex package for annotation of bacterial genomes. Its gene prediction algorithm is based on Markov chain models of coding regions and translation and termination sites. Operon models are based on distances between ORFs, frequencies of different genes neighboring each other in known bacterial genomes, predicted promoters and terminators The parameters of gene prediction are self-learning, so the only input necessary for annotation of new genome is a sequence.

44 Fgenesb accuracy on difficult sets

45 rRNA and tRNA annotation
STEP 1. Finds all potential ribosomal RRNA genes using BLAST against bacterial and/or archaeal RRNA databases. and masks detected RRNA genes. STEP 2. Predicts tRNA genes using tRNAscan-SE program. Inside bactg_ann.pl - run tRNAscan-SE and masks detected TRNA genes .

46 Genes and Operon identification
STEP 3. Initial predictions of long, slightly overlapping ORF that are used as a starting point for calculating parameters of predictions. Iterates until stabilizes. Generates parameters such as 5th-order in-frame Markov chains for coding regions, 2nd-order Markov models for region around start codon and upstream RBS site, Stop codon and probability distributions of ORF lengths. Protein coding genes prediction STEP 4. it predicts operons based only on distances between predicted genes.

47 Annotate genes comparing with databases of known proteins
STEP 5. Runs blastp for predicted proteins against COG database- cog.pro and annotate by COGs descriptions STEP 6. Run blastp against NR for proteins having no COGs hits And annotate by NR descriptions.

48 Promoters and Terminators prediction and improvement of operons assignment
STEP 7. Uses information about conservation of neighbor gene pairs in known genomes to improve operon prediction. STEP 8. predicts potential promoters (tssb) and terminators (bterm) in the corresponding 5'-upstream and 3'-downstream regions of predicted genes. Tssb- bacterial promoter prediction (sigma70), using dicriminant function with characteristics of sequence features of promoters (such as conserved motifs, binding sites and etc) Bterm - prediction of pho-independent terminators as hairpins, with energy scoring based on discriminant function of hairpin elements. STEP 9. refines operon predictions using predicted promoters and terminators as additional evidences.

49 Fgenesb_annotator output:
Op / CDS ## COG0593 ATPase involved in DNA + Term + Prom Op / CDS ## COG0592 DNA polymerase + Term + Prom Op / CDS ## COG2501 Uncharacterized ACR Op / CDS ## COG1195 Recombinational DNA 2 Op / CDS ## COG0187 DNA gyrase (topoisomerase II) B subunit + Term + Prom Op CDS ## COG0188 DNA gyrase (topoisomerase II) A subunit + Term + SSU_RRNA # AY [D: ] # 16S ribosomal RNA # Bacillus cereus + TRNA # Ile GAT 0 0 + TRNA # Ala TGC 0 0 + LSU_RRNA # AF [D: ] # 23S ribosomal RNA # Bacillus Op CDS + 5S_RRNA # AE [D: ] # 5S ribosomal RNA # Bacillus Op CDS ## Similar_to_GB Op CDS - Prom

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54 Comparison of 2 bacterial genomes
GenomMatch aligns 2 bacterial genomes 2 MB x 2MB ~ 30 sec

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56 Figure1

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58 Nature (2004) 428 (6978) , p. 37 – 43

59 Oceans/Acid mines/agriculture
Annotation of new bacteria New drugs Annotation of bacterial communities DNA from Specific sources (not growing in Labs) Oceans/Acid mines/agriculture (with mix of 100s species) New ferments

60 Main Collaborators: Asaf Salamov, Igor Seledtsov, Ilham Shahmuradov


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