Computational Analysis of Genome Sequences Steven Salzberg The Institute for Genomic Research (TIGR) and The Johns Hopkins University

1995: 1st genome (H. influenzae, TIGR) 1996: 1st eukaryote (S. cerevisiae) 2000: 29 complete microbial genomes 22 in progress at TIGR 50+ in progress worldwide 3 complete eukaryotes yeast, nematode, fruit fly 2 major projects in 2000: Human (3.3 billion bp) Arabidopsis thaliana (125 million bp) The Genomics Revolution

Organism (genome size)Reference Haemophilus influenzae (1.83 Mb)Fleischmann et al., Science 269, (1995). Mycoplasma genitalium (0.58 Mb)Fraser et al., Science 270, (1995). Methanococcus jannaschii(1.7 Mb)Bult et al., Science 273, (1996). Helicobacter pylori(1.6 Mb)Tomb et al., Nature 388, (1997). Archeoglobus fulgidus (2.1 Mb)Klenk et al., Nature 390, (1997). Borrelia burgdorferi(1.5 Mb)Fraser et al., Nature 390, (1997). Treponema pallidum(1.1 Mb)Fraser et al., Science 281, (1998). Plasmodium falciparum chr2 (1 Mb)Gardner et al., Science 282, (1998). Thermotoga maritima (1.8 Mb)Nelson et al., Nature 399, (1999). Deinococcus radiodurans(3.3 Mb)White et al., Science 286, (1999). Arabidopsis thaliana chr2 (19 Mb)Lin et al., Nature 402, (1999). Neisseria meningitidis (2.3 Mb)Tettelin et al., Science 287, (2000). Chlamydia pneumoniae (1.2 Mb)Read et al., Nucleic Acids Res 28, (2000). Chlamydia trachomatis (1.0 Mb)Read et al., Nucleic Acids Res 28, (2000). Vibrio cholerae (4.0 Mb)Heidelberg et al., Nature, in press. Mycobacterium tuberculosis(4.4 Mb)Fleischmann et al., manuscript in preparation Streptococcus pneumoniae(2.2 Mb)Tettelin et al., manuscript in preparation Caulobacter crescentus (4.0 Mb)Nierman et al., manuscript in preparation Chlorobium tepidum (2.1 Mb)Eisen et al., manuscript in preparation Porphyromonas gingivalis (2.2 Mb)Fleishmann et al., manuscript in preparation Genomes Completed at TIGR

Organism (genome size)Funding source Plasmodium falciparum chr 14 (3.4 Mb)BWF/DoD Plasmodium falciparum chr 10,11 (4 Mb)NIAID/DoD Trypanosoma brucei chr 2 (1 Mb)NIAID Enterococcus faecalis (3.0 Mb)NIAID Mycobacterium avium (4.4 Mb)NIAID Pseudomonas putida (6.2 Mb)DOE Schewanella putrefaciens (4.5 Mb)DOE Staphylococcus aureus (2.8 Mb)NIAID, MGRI Dehalococcoides ethenogenes (1.5Mb)DOE Desulfovibrio vulgaris (3.2Mb)DOE Thiobacillus ferrooxidans (2.9 Mb)DOE Chlamydia psittaci GPIC (1.2Mb)NIAID Bacillus anthracis (5.0Mb)ONR/DOE/NIAID Treponema denticola (3.0 Mb)NIDR C. hydrogenoformans (2.0 Mb)DOE Methylococcus capsulatus (4.6 Mb)DOE Geobacter sulfurreducens (4.0 Mb)DOE Wolbachia sp (Drosophila) (1.4 Mb)NIH Colwellia sp (1.0 Mb)DOE Mycobacterium smegmatis (4.0Mb)NIAID Staphylococcus epidermidis (2.5 Mb)NIAID Theileria parva (10Mb)ILRI/TIGR Genomes in progress at TIGR

A Microbial Genome Sequencing Project Random sequencingGenome AssemblyAnnotationData Release Library construction Colony picking Template preparation Sequencing reactions Base calling Sequence files TIGR Assembler Genome scaffold Ordered contig set Gap closure sequence editing Re-assembly ONE ASSEMBLY! Combinatorial PCR POMP Gene finding Homology searches Initial role assignments Metabolic pathways Gene families Comparative genomics Transcriptional/ translational regularory elements Repetitive sequences Publication Sample tracking

Gene Finding  Gene finding plays an ever-larger role in high-speed DNA sequencing projects  There’s no time for much else!  1000’s of genes generated each month at a high-throughput sequencing facility  Separate gene finders are needed for every organism  Training on organism X, finding genes on Y, generates inferior results  Bootstrapping problem: training data is hard to find

Open Reading Frames: 6 possibilities TCG TAC GTA GCT AGC TAG CTA AGC ATG CAT CGA TCG ATC GAT T CGT ACG TAG CTA GCT AGC TA A GCA TGC ATC GAT CGA TCG AT TC GTA CGT AGC TAG CTA GCT A AG CAT GCA TCG ATC GAT CGA T identical sequence

G LIMMER : A Microbial Gene Finder  G LIMMER 2.0: released late 1999  > 200 site licenses worldwide  Works on bacteria, archaea, viruses too  Malaria (eukaryotic) version: G LIMMER M  Refs: Salzberg et al., NAR, 1998, Genomics 1999; Delcher et al., NAR, 1999  Web site and code:

Uniform Markov Models  Use conditional probability of a sequence position given previous k positions in the sequence.  Fixed, k th -order model: bigger k ‘s yield better models (as long as data is sufficient).  Probability (score) of sequence s 1 s 2 s 3 … s n is:

 Advantages:  Easy to train. Count frequencies of (k+1)mers in training data.  Easy to assign a score to a sequence.  Disadvantages:  (k+1)mers can be undersampled; i.e., occur too infrequently in training data.  Models sequence as fixed-length chunks, which may not be the best model of biology. Uniform Markov Models

Interpolated Markov Models  Use a linear combination of 8 different Markov chains; for example:  c 8 P (g|atcagtta) + c 7 P (g|tcagtta) + …  + c 1 P (g|a) + c 0 P (g)  where c 0 + c 1 + c 2 + c 3 + c 4 = 1  Equivalent to interpolating the results of multiple Markov chains  Score of a sequence is the product of interpolated probabilities of bases in the sequence

IMM’s vs. Fixed-Order Models  Performance:  IMM should always do at least as well as fixed-order.  E.g., even if k th -order model is correct, it can be simulated by (k+1) st -order  Our results support this.  IMM result can be used as fixed-order model.  IMM slightly harder to train and uses more memory.

IMM Training  Problem: How to determine the weights of all the thousands of k-mers?  Traditionally done with E-M algorithm using cross-validation (deleted estimation).  Slow.  Overtraining can be a problem.

G LIMMER IMM Training  Our approach assumes:  Longer context is always better  Only reason not to use it is undersampling in training data.  If sequence occurs frequently enough in training data, use it, i.e., = 1  Otherwise, use frequency and  2 significance to set.

How G LIMMER Works  Three separate programs:  long-orfs: automatically extract long open reading frames that do not overlap other long orfs.  IMM model builder. Takes any kind of sequence data.  Gene predictor. Takes genome sequence and finds all the genes.

Gene Predictor  Finds & scores entire ORF’s.  Uses 7 competing models: 6 reading frames plus “random” model.  Score for an ORF is the probability that the “right” model generated it.  3-periodic Markov model  High-scoring ORF’s are then checked for overlaps.

Glimmer 2.0 IMM design ATGCATGATCGAG 12bp Pos -1 a c t Pos -3 Pos -2 g Pos -3 Pos -4 8 levels deep Context

Better Overlap Resolution

G LIMMER 2.0 ’s Performance Organism Genes Genes Additional Annotated Found Genes H. influenzae (99.0%)250(14%) M. genitalium483480(99.4%)81(17%) M. jannaschii (99.7%)221(13%) H. pylori (97.5%)293(18%) E. coli (97.4%)824(19%) B. subtilis (98.3%)586(14%) A. fulgidis (98.6%)274(11%) B. burgdorferi853843(99.3%)62(7%) T. pallidum (97.6%)180(17%) T. maritima (98.8%)190(10%)

G LIMMER 2.0 on known genes Organism Genes Known Correct Annotated Genes Predictions H. influenzae (99.7%) M. genitalium (99.6%) M. jannaschii (99.8%) H. pylori (99.3%) E. coli (99.1%) B. subtilis (98.6%) A. fulgidis (99.3%) B. burgdorferi (99.8%) T. pallidum (98.9%) T. maritima (99.3%) Average(99.3%)

 Speed  Training for 2 Megabase genome: < 1 minute (on a Pentium-450)  Find all genes in 2Mb genome: < 1 minute  Impact: G LIMMER was used for:  B. burgdorferi (Lyme disease), T. pallidum (syphilis) (TIGR)  C. trachomatis (blindness,std) (Berkeley/Stanford)  C. pneumoniae (pneumonia) (Berkeley/Stanford/UCSF)  T. maritima, D. radiodurans, M. tuberculosis, V. cholerae, S. pneumoniae, C. trachomatis, C. pneumoniae, N. meningitidis (TIGR)  X. fastidiosa (Brazilian consortium)  Plasmodium falciparum (malaria) [GlimmerM]  Arabidopsis thaliana (model plant) [GlimmerM]  Others: viruses, simple eukaryotes, more bacteria

Self-Similarity Scans Idea: analyze a whole genome by counting 3-mers in all 6 frames Analyze small windows (2000 bp, 10000bp) using the same statistic Algorithm: Build model of entire sequence Build model of entire sequence Apply to compare windows to the genome itself Apply the  2 statistic to compare windows to the genome itself

Haemophilus influenzae (meningitis) GC% 22

Thermotoga maritima (hyperthermophile)

Vibrio cholerae (cholera)

On the other side of CTX  prophage is a region encoding an RTX toxin (rtxA) and its activator (rtxC) and transporters (rtxBD). A third transporter gene has been identified that is a paralog of rtxB, and is transcribed in the same direction as rtxBD. Downstream of this gene are two genes encoding a sensor histidine kinase and response regulator. Trinucleotide composition analysis suggests that the RTX region was horizontally acquired along with the sensor histidine kinase/response regulator, suggesting these regulators effect expression of the closely linked RTX transcriptional units. --Heidelberg et al., Nature, in press.

28  Aligns 2 complete genomes  Maximal Unique Matches  Suffix trees  Very fast alignment of very long DNA sequences  Ref: Delcher et al., Nucl. Acids Res., 1999  Software at: MUMmer

Efficiently compute alignments between long sequences to identify biologically interesting features. E.g., two strains of M. tuberculosis, each ~4.4MB E.g., two versions of a genome at different stages of closure Compute alignment in less than 2 minutes The Problem

Sequences in genomes A and B that: Occur exactly once in A and in B Are not contained in any larger such sequence Maximal Unique Sequences

Select the longest consistent set of MUMs Occur in the same order in A and B

 A tree with edges labelled by strings  Labels of child edges of a node begin with distinct letters  Each leaf L represents a sequence—the labels on the path to L from the root  Holds all suffixes of a set of sequences  A suffix is a subsequence that extends to the end of its sequence  The suffix tree for sequences A and B :  Contains less than 2(|A | + |B |) nodes.  Can be constructed in O (|A | + |B |) time!  Still need lots of RAM  All the analyses here were run on a desktop PC Suffix Trees

 A nalyze the gaps between adjacent MUMs  Small gaps can be aligned with Smith-Waterman algorithm  Large gaps can be aligned recursively  Large inserts can be searched for separately. Many will be inconsistent MUMs  Overlapping MUMs indicate variation in copy number of small repeats

M. tuberculosis CSU93 vs. H37Rv ACGT A C G T a MUM

M genitalium vs. M. pneumoniae

H. pylori vs. J99

V. cholera (forward) vs. E. coli Origin

V. cholera (reverse) vs. E. coli

V. cholera (both strands) vs. E. coli: a puzzle?

V. cholera vs. itself

S. pyogenes vs. S. pneumoniae

S. pyogenes vs. itself

M. leprae vs M. tuberculosis M. leprae M. tuberculosis

X-alignments: how? Ori

Chr 2 vs. Chr 4 of Arabidopsis thaliana: discovery of a 4 Mb duplication 1100 genes 430 (39%) duplicated

46 Acknowledgements G LIMMER, G LIMMER M Arthur Delcher, Simon Kasif, Owen White, Mihaela Pertea MUMmer Arthur Delcher, Simon Kasif, Jeremy Peterson, Rob Fleischmann, Owen White Analyses Numerous TIGR faculty and staff, including: Jonathan Eisen, Owen White, Rob Fleischmann, Hervé Tettelin, Tim Read, Maria Ermolaeva, John Heidelberg, Ian Paulsen, Malcolm Gardner, Claire Fraser, Clyde Hutchison,... Supported by: National Institutes of Health (NHGRI, NLM) National Science Foundation (CISE, BIO)