1. Introduction to the CGE servers

2. Center for Genomic Epidemiology
Aim:
To provide the scientific foundation for future internet-based solutions, where a central database will enable simplification of total genome sequence information and comparison to all other sequenced isolates including spatial-temporal analysis.
To develop algorithms for rapid analyses of whole genome DNA-sequences, tools for analyses and extraction of information from the sequence data and internet/web-interfaces for using the tools in the global scientific and medical community. 

4. Tools for species identification
Name of Service
Description
URL (cge.cbs.dtu.dk/services/)
Status
Publication
SpeciesFinder
Species identification using 16S rRNA
Online
Published Feb 2014 PMID:
KmerFinder
Species identification using overlapping 16mers
Published Jan 2014 PMID:
TaxonomyFinder
Taxonomy identification using functional protein domains
Published in PMID: Oksana's PhD thesis
Reads2Type
Species identification on client computer

5. Benchmarking of Methods for Bacterial Species Identification
PMID:

6. Training data Evaluation data
1,647 completed / almost completed genomes downloaded from NCBI in 2011 (1,009 different species)
Evaluation data
NCBI draft genomes
695 isolates from species that overlap with training set (151 species)
SRA draft genomes
10,407 sets of short reads from Illumina (168 species)
10,407 draft genomes from Illumina data (168 species)

7. 16S rRNA
16S rRNA sequencing has dominated molecular taxonomy of prokaryotes for more than 30 years (Fox et al, Int. J. Syst. Bacteriol., 1977)
Tremendous amounts of 16S rRNA sequence data are available in databases
Concerns:
Low resolution
Some genomes contain several copies of the 16S rRNA gene with inter-gene variation
The 16S rRNA gene represents only about 0.1% of the coding part of a microbial genome

8. CGE implementation of 16S species identification SpeciesFinder
Reference database
16S rRNA genes are isolated from genomes in training data using RNAmmer (Lagesen, NAR, 2007).
Method
Input genomes are BLASTed against 16S rRNA genes in reference database.
Best hit is selected based on a combination of coverage, % identity, bitscore, number of mistmatches and number of gaps in the alignments.
BLAST will not work isolating the 16S RNA gene. RNAmmer is based on a hidden markov model
BLAST hits is based on a combination of coverage length, identity and bit score

9. KmerFinder Genomes in training data is chopped into 16mers: 9mer
A T G A C G T A T G A T T G A T G A C G T A G T A G T C C
9mer
Immune system inspired downsampling
Only 16mers with specific prefix are kept
The human allele HLA-A:02:01 prefers leucine on position 2 and valine on position 9. If all amino acids were equally frequent, restriction by this motif would make it bind 1 out of 400 peptides. The fixation of the 2 anchor positions of a 9mer peptide by MHC still makes the peptide as selective as any other 9mer peptide but only a fraction of the 9mer peptides have the right anchors. As a result of this binding specificity, the immune system does not recognize the entire proteome of a microbe but only subset of it. The “microbe database” that is actually remembered by the immune system can be ~200 times smaller than it would otherwise be. This may be important, since there are only ~10^12 cells in the immune system and without a reduction in the microbe database, the number of cells may be insufficient to save information about all the microbes we encounter throughout our lives.
ATGA is the prefix used in this example.
A database is generated where each 16mer is a key and the value is a list of all the isolates in the trainingset containing this 16mer
MHC-I

10. CP001921 (Acinetobacter baumanii) CP000521 (Acinetobacter baumanii)
16mer database
CP (Acinetobacter baumanii)
CP (Acinetobacter baumanii)
CP (Acinetobacter baumanii)
ATGAATGTGTGAGTGA
CP (Acinetobacter baumanii)
CP (Buchnera aphidicola)
ATGACTGTGCCCCTGA
Unknown isolate
Species
Match
No. of Kmer hits
Acinetobacter baumannii
CP001921 2
CP000521 1
CP002521
Buchnera aphidicola
CP002301
Unique 16 mers:
A database is generated where each 16mer is a key and the value is a list of all the isolates in the trainingset containing this 16mer
Very robust method - it just needs one 16mer to make a prediction.
ATGAATGTGTGAGTGA
ATGACTGTGCCCCTGA
ATGAAAAAAAAAAAA

11. KmerFinder is very robust – it only needs one 16mer!
Desulfovibrio piger GOR1 SRR097356
>NODE 4 length 92 cov
TAGGACGTGGAATATGGCAAGAAAACTGAAAATCATGGAAAATGAGAAACATCCACTTGA
CGACTTGAAAAATGACGAAATCACTAAAAAACGTGAAAAATGAGAAATGC
>NODE 15 length 82 cov
AGCGAAAAATGTCATAACAACGATCACGACCGATAACCATCTTTGGTCCAAACTTACTCA
CGCAGCAGGCGTATAACTCGCGCATACCAGCTTTGGGCAT
N50 = 110
Total no. of bp: 210
For 41 isolates, the method failed to produce an output. The 41 draft genomes typically had an N50 below 200 (average N50 = 155)
and total no. of bp below
Prediction
Species
Match
No. of Kmer hits
Flavobacterium psycrophilum
AM398681 1

12. TaxonomyFinder

13. Reads2Type
Definition: Quick & dirty taxonomy identification of single isolates
50-mer of marker gene DB
16S rRNA: Training data genomes  RNAmmer (other)
ITS: Training data (Mycobacterium)
GyrB: Training data (Enterobacteriaceae)
Resulting database ~5 MB
Read2Type pushes analysis to user, server provides 50-mers database
SuffixTree: efficient data structure for string matching
Narrow Down Approach:
Reads2Type compares 50-mers of combined marker genes against raw reads
Shared Probes vs Unique Probe

14. rMLST
Jolley KA, Bliss CM, Bennett JS, Bratcher HB, Brehony C, Colles FM, Wimalarathna H, Harrison OB, Sheppard SK, Cody AJ, Maiden MC. Ribosomal multilocus sequence typing: universal characterization of bacteria from domain to strain. Microbiology. 2012 Apr;158(Pt 4):
CGE implementation
For each genome in the training data the 53 ribosomal genes were extracted.
Genomes in evaluation sets were aligned using blat to each gene collection (only hits with at least 95% identity and 95% coverage were considered as a potential match).
The closets match of the training genomes was selected based on a combination of coverage, %identity, bitscore, number of mistmatches and number of gaps in the alignments across all genes.
Average N50 of 1329 for failed isolates

15. Results
(16s rRNA)
On the SRA drafts set, rMLST is not able to make a prediction for 3.5% of the isolates, TaxonomyFinder 1.8%, KmerFinder 0.4%, SpeciesFinder 0.2%

16. Overlap in predictions
One of the six isolates that all methods agree are not correctly annotated has actually been re-annotated since we downloaded them

17. Isolates in the NCBIdrafts set for which all four methods predict the species to be different from the annotated one.
* NZAEPO has been re-annotated as S. oralis since we downloaded the data.

18. All four methods agree that 2 of the B. cereus is B. weihenstephanensis

19. Bacillus cereus predicted to be B. thuringiensis is problematic
Bacillus cereus predicted to be B. thuringiensis is problematic. Likewise recently diverged: Y pestis <> Y pseudotuberculosis, M. tuberculosis <> M. bovis,

20. Speed Method Estimated speed (mm:ss) 16S 00:13* KmerFinder 00:09*
TaxonomyFinder
11:33*
rMLST
00:45*
Reads2Type
00:55**
*Estimation based on draft genomes
**Estimation based on short reads

21. Summary of taxonomy benchmark study
KmerFinder had the highest accuracy and was the fastest method.
SpeciesFinder (16S rRNA-based) had the lowest accuracy.
Methods that only sample genomic loci (16S, Reads2Type, rMLST) had difficulties distin- guishing species that only recently diverged, especially when main difference is a plasmid.
Recently diverged: Y pestis <> Y pseudotuberculosis, M. tuberculosis <> M. bovis,

24. Tools for further typing
Name of Service
Description
URL
( )
Publication
MLST
Multilocus sequence typing
Published Apr 2012, PMID:
Plasmid-Finder
Identification of plasmids in Enterobacteriaceae
PlasmidFinder
Published Apr 2014, PMID
pMLST
pMLST of plasmids in Enterobacteriaceae

25. Multilocus Sequence Typing (MLST)
First developed in 1998 for Neisseria meningitis
(Maiden et al. PNAS : )
The nucleotide sequence of internal regions of app. 7 housekeeping genes are determined by PCR followed by Sanger sequencing
Different alleles are each assigned a random number
The unique combination of alleles is the sequence type (ST)

26. Using WGS data for MLST

27. www.cbs.dtu.dk/services/MLST Acinetobacter baumannii #1
Arcobacter
Borrelia burgdorferi
Bacillus cereus
Brachyspira hyodysenteriae
Bifidobacterium
Brachyspiria intermedia
Bordetella
Burkholderia pseudomallei
Brachyspira
Burkholeria cepacia complex
Campylobacter jejuni
Clostridium botulinum
Clostridium difficile #1
Clostridium difficile #2
Campylobacter helveticus
Campylobacter insulaenigrae
Clostridium septicum
C. diphtheriae
Campylobacter fetus
Chlamydiales
Campylobacter lari
Cronobacter
C. upsaliensis
Escherichia coli #1
Escherichia coli #2
Enterococcus faecalis
Enterococcus faecium
F. psychrophilum
Haemophilus influenzae
Haemophilus parasuis
Helicobacter pylori
Klebsiella pneumoniae
Lactobacillus casei
Lactococcus lactis
Leptospira
Listeria
Listeria monocytogenes
Moraxella catarrhalis
Mannheimia haemolytica
Neisseria
P. gingivalis
P. acne
Pseudomonas aeruginosa
Pasteurella multocida
Staphylococcus aureus
Streptococcus agalactiae
Salmonella enterica
Staphylococcus epidermidis
S. maltophilia
Streptococcus pneumoniae
Streptococcus oralis
S. zooepidemicus
Streptococcus pyogenes
Streptococcus suis
Streptococcus thermophilus
Streptomyces
Streptococcus uberis
Vibrio parahaemolyticus
Vibrio vulnificus
Wolbachia
Xylella fastidiosa
Y. pseudotuberculosis
Assembled genome
454 – single end reads
454 – paired end reads
Illumina – single end reads
Illumina – paired end reads
Ion Torrent
SOLiD – single end reads
SOLiD – mate pair reads

29. Extended Output

30. Extended Output
aro: WARNING, Identity: 100%, HSP/Length: 349/498, Gaps: 0, aro_122 is the best match for aro

31. What is the MLST web-service used for?
Our most used service - In the first 9 month of 2014, it was on average used more than 1,500 times per month. From Sep – Oct. 2014, the service was used more than 20,000 times in total.

32. PlasmidFinder and pMLST
The PlasmidFinder database contains replicons, not entire plasmids.

33. (https://cge.cbs.dtu.dk/services/ )
Tools for phenotyping
Name of Service
Description
URL
( )
Publication
ResFinder
Identification of acquired antibiotic resistance genes
Published Nov 2012, PMID:
Virulence-Finder
Identification of virulence genes in E. coli (and S. aureus and Enterococcus)
VirulenceFinder
E. coli published Feb 2014, PMID:
MyDbFinder
Identification of genes from the users own database
Will be published in book chapter
Pathogen-Finder
Prediction of pathogenic potential
PathogenFinder
Published Oct 2013, PMID:

34. Theoretical resistance phenotype
ResFinder
ResFinder
(BLAST)
NGS
Illumina
Ion torrent
454..
Assembly pipeline
Resistance gene profile
List of genes
Accession numbers
Theoretical resistance phenotype
Sanger
Fasta
Fasta
Sanger

36. From S. aureus

37. ResFinder, 98 %ID, 60% length coverage
200 isolates from 4 different species (Salmonella Typhimurium, Escherichia coli, Enterococcus faecalis and Enterococcus faecium)
ResFinder, 98 %ID, 60% length coverage
Phenotypic tests, 3,051 in total
482 Resistant
2569 Susceptible
=> 99,74% of the results were in agreement between ResFinder and the phenotypic tests
23 discrepancies -> 16, typically in relation to spectinomycin in E. coli

38. Alternatives to ResFinder

39. Unpublished or uncategorized
Name of Service
Description
URL
( )
Status
Publication
PanFunPro
Groups homologous proteins based on functional domain content
Online
Published in F1000Research 2013, 2:265
Serotype-Finder
Identification of serotypes
SerotypeFinder-1.0
Not yet published
Restriction-ModificationFinder
Identification of RM system genes
Will only be published in book chapter
HostPhinder
Prediction of the host of a bacteriophage
Online, but under development
MetaVir-Finder
Identification of virus in metegenomic data
MetaVirFinder
MGmapper
Identifies the content of metagenomic samples

40. Tools for phylogeny Name of Service Description Status Publication
URL (cge.cbs.dtu.dk/services)
Status
Publication
SnpTree
Creation of phylogenetic trees based on SNPs
snpTree
Online
Published Dec 2012, PMID:
CSIPhylo-geny
CSIPhylogeny
Planned
NDtree
Creation of phylogenetic trees
Published in Feb 2014, PMID:

41. Web-service usage

42. Type of data uploaded to MLST web-service