"Bacterial Pathogen Genomics at NCBI" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology October 2014 by Dr. Bill Klimke.

1. Bacterial Pathogen Genomics at
NCBI

3. FDA, USDA, CDC State, Local and
Foreign Public Health Agencies
Industry/Academia Additional
DATA ANALYSIS
DATA ASSEMBLY AND
STORAGE and Analysis
DATA ACQUISITION
NCBI, EMBL DDBJ (INDIS)
(Public Access Database)
Our Current Model – Publicly available data
National Network of SequencersIntrenational Network of Sequencers

4. Automated Bacterial Assembly
SRA Reads
sample 1
Trim reads
(Ns, adaptor)
Reference
Distance tree
Find closest reference genome(s)
ArgoCA (Combined Assembly)
De novo assembly panel
Argo (Reference
assisted
assembly)
SOAP denovo
GS-assembler
(newbler)
MaSuRCA
Celera
Assembler
Reads remapped to combined assembly
Contig fasta
Read placements (bam)
Quality profile
SPAdes

5. WGS & Epidemiologically Relevant Distance (ERD)
• WGS allows high resolution genotypic comparison of
pathogen isolates
• What is the epidemiological relevance of genotypic
distance?
• Many methods to compute – we need some common
principles…

6. Since all approaches start with sequence reads, we must
retain for independent confirmation
0
0.2
0.4
0.6
0.8
1
0 500 1000 1500
Millions
FDA-CFSAN: microbial foodborne pathogen
research
SRA format bytes per sequenced base versus
number of bases in MiSeq runs
With Quality Without Qualities
0
0.2
0.4
0.6
0.8
0 200 400 600 800 1000 1200
Millions
OXFORD University: Population Genomics of
Mycobacterium tuberculosis
SRA format bytes per sequenced base versus
number of bases in MiSeq and HiSeq runs
With Quality Without Quality
Storage is manageable…

7. Reliable, transparent, high throughput, high
resolution ERDs?
Major challenge is to distinguish independent
events (SNPs) from single events that generate
multiple nucleotide differences
i.e. collapsed repeats and other artifacts,
alignment errors (reference-based alignments),
sequence quality, & recombination

8. Fairly uniform distribution
of differences along the
two genomes…?
Cumulative count of differences

9. Iterative density filtering
(Richa Agarwala
modification of
Science. 2011 Jan
28;331(6016):430-4.

11. Table: Samples currently processed (as of Sept 5, 2014) in NCBI Pathogen Pipeline
Organisms
Center Listeria Salmonella E. coli Total
CDC 903 903
FDA + State Partners* 858 6129 307 7294
100K 565 34 599
FERA 14 14
Total 1775 6694 341 8810
Processing Status

12. How to measure the system?
need the raw data (sequence reads) in unprocessed form
any read trimming/filtering along with the assembly can be regenerated

13. Assembly metrics
map the reads back to the assembly and generate a profile of each position
(coverage, alleles, qualities)
compare the assembly against other assemblies of the same organism (genus,
species) and check the expected genome size, or similarity to related genomes
annotation metrics such as frameshifted proteins

14. What is the actual measurement for sequence
similarity?
the number of pairwise SNPs between two genomes
What is the threshold?
a pairwise distance (an observationally determined cutoff below which a cluster of 2
or more isolates are considered significantly close enough to warrant further investigation)

15. Sensitivity vs. Specificity
sequence clustering
sensitivity – measure of isolates which belong to the cluster within epidemiologically
relevant distance
(true positives) / true positives + false negatives (not correctly identified)
specificity – measure of isolates which are excluded from a cluster within
epidemiologically relevant distance
(true negatives) / true negatives + false positives

16. Organism
Total
Samples
Not
expected
species1
Mixed
organisms
Less than
5X
coverage Duplicates PacBio
Poor
2nd
read
Failed
assembly
stage
Listeria 1775 20 2 (?) 1 5 1
Salmonella 6694 35 5 9 12
E. coli 341 8 1
1. not L. monocytogenes, S. enterica, or E. coli
Processing Problems

17. PROBLEMS!

18. Reference Materials

20. Streptococcus massiliensis 4401825 - CANO - GCA_000341525.1
Streptococcus massiliensis DSM 18628 - ARCE - GCA_000380065.1
Streptococcus intermedius BA1 - ANFT - GCA_000313655.1
Streptococcus intermedius B196 - - GCA_000463355.1
Streptococcus intermedius C270 - - GCA_000463385.1
Streptococcus intermedius F0413 - AFXO - GCA_000234035.1
Streptococcus intermedius SK54 - AJKN - GCA_000258445.1
Streptococcus intermedius JTH08 - - GCA_000306805.1
Streptococcus intermedius ATCC 27335 - ATFK - GCA_000413475.1
Streptococcus intermedius F0395 - AFXN - GCA_000234015.1
Streptococcus sp. AS20 - JANS - GCA_000524255.1
Streptococcus constellatus subsp. constellatus SK53 - AICQ - GCA_000257785.1
Streptococcus constellatus subsp. constellatus SK53 - BASU - GCA_000474075.1
Streptococcus constellatus subsp. pharyngis C1050 - - GCA_000463425.1
Streptococcus constellatus subsp. pharyngis SK1060 = CCUG 46377 - AFUP - GCA_000223295.2
Streptococcus constellatus subsp. pharyngis SK1060 = CCUG 46377 - BASX - GCA_000474135.1
Streptococcus constellatus subsp. pharyngis C232 - - GCA_000463395.1
Streptococcus constellatus subsp. pharyngis C818 - - GCA_000463445.1
Streptococcus anginosus SK1138 - ALJO - GCA_000287595.1
Streptococcus sp. CM7 - JATP - GCA_000526035.1
Streptococcus sp. OBRC6 - JACR - GCA_000517685.1
Streptococcus anginosus F0211 - AECT - GCA_000184365.2
Streptococcus anginosus 1505 - BASW - GCA_000474115.1
Streptococcus sp. ACC21 - JAQU - GCA_000524375.1
Streptococcus sp. AC15 - JDFJ - GCA_000565055.1
Streptococcus anginosus subsp. whileyi MAS624 - - GCA_000478925.1
Streptococcus anginosus subsp. whileyi CCUG 39159 - AICP - GCA_000257765.1
Streptococcus anginosus C238 - - GCA_000463505.1
Streptococcus anginosus DORA_7 - AZMF - GCA_000508545.1
Streptococcus anginosus 1_2_62CV - ADME - GCA_000186545.1
Streptococcus anginosus C1051 - - GCA_000463465.1
Streptococcus anginosus T5 - BASY - GCA_000474155.1
Streptococcus anginosus SK52 = DSM 20563 - AFIM - GCA_000214555.2
Streptococcus anginosus SK52 = DSM 20563 - AREF - GCA_000373605.1
Streptococcus anginosus SK52 = DSM 20563 - BAST - GCA_000474055.1
Streptococcus intermedius SK54 - BASV - GCA_000474095.1
0.05

22. Escherichia coli KTE179 - ANYQ - GCA_000326485.1
Escherichia coli KTE229 - ANXK - GCA_000353165.1
Escherichia coli H252 - AEFI - GCA_000190895.1
Escherichia coli HVH 180 (4-3051617) - AVYH - GCA_000458685.1
Escherichia coli HVH 73 (4-2393174) - AVUX - GCA_000457025.1
Escherichia coli HVH 104 (4-6977960) - AVVT - GCA_000457455.1
Escherichia coli HVH 19 (4-7154984) - AVTL - GCA_000456265.1
Escherichia coli 908675 - AXTY - GCA_000488755.1
Escherichia coli HVH 127 (4-7303629) - AVWO - GCA_000457855.1
Escherichia coli HVH 12 (4-7653042) - AVTG - GCA_000494955.1
Escherichia coli KOEGE 32 (66a) - AWAD - GCA_000459635.1
Escherichia coli UMEA 3041-1 - AWAW - GCA_000460015.1
Escherichia coli HVH 148 (4-3192490) - AVXH - GCA_000495015.1
Escherichia coli HVH 59 (4-1119338) - AVUQ - GCA_000456885.1
Escherichia coli HVH 222 (4-2977443) - AVZU - GCA_000459455.1
Escherichia coli UMEA 3140-1 - AWBK - GCA_000460295.1
Escherichia coli HVH 178 (4-3189163) - AVYG - GCA_000495055.1
Escherichia coli KTE4 - ANSO - GCA_000350645.1
Escherichia coli KTE3 - ASTO - GCA_000407685.1
Escherichia coli KTE240 - ASUS - GCA_000408305.1
Escherichia coli BIDMC 49b - JAPT - GCA_000522365.1
Escherichia coli BIDMC 49a - JAPU - GCA_000522385.1
Escherichia coli APEC O1 - - GCA_000014845.1
Escherichia coli DSM 30083 = JCM 1649 = ATCC 11775 - BAIM - GCA_000613265.1
Escherichia coli JCM 20135 - BAKV - GCA_000614505.1
Escherichia coli DSM 30083 = JCM 1649 = ATCC 11775 - AGSE - GCA_000690815.1
Escherichia coli DSM 30083 = JCM 1649 = ATCC 11775 - JMST - GCA_000734955.1
Escherichia coli HVH 214 (4-3062198) - AZJN - GCA_000507665.1
Escherichia coli UMEA 3162-1 - AWBU - GCA_000460475.1
Escherichia coli HVH 191 (3-9341900) - AVYR - GCA_000458875.1
Escherichia coli HVH 170 (4-3026949) - AVYA - GCA_000458555.1
Escherichia coli S88 - - GCA_000026285.1
Escherichia coli UMEA 3893-1 - AWEI - GCA_000461775.1
Escherichia coli HVH 217 (4-1022806) - AVZQ - GCA_000459375.1
Escherichia coli KTE5 - ANSP - GCA_000350665.1
Escherichia coli KTE7 - ASTP - GCA_000407705.1
Escherichia coli HVH 32 (4-3773988) - AVTX - GCA_000456505.1
Escherichia coli UMEA 3206-1 - AWCK - GCA_000460795.1
Escherichia coli UMEA 3203-1 - AWCJ - GCA_000460775.1
Escherichia coli KTE62 - ANUK - GCA_000351605.1
Escherichia coli KTE27 - ASTY - GCA_000407885.1
Escherichia coli cloneA_i1 - AEYT - GCA_000233675.2
Escherichia coli 597 - AYQU - GCA_000503475.1
Escherichia coli HVH 203 (4-3126218) - AVZD - GCA_000459115.1
Escherichia coli UMEA 3702-1 - AWDZ - GCA_000461595.1
Escherichia coli UMEA 3662-1 - AWDU - GCA_000461495.1
Escherichia coli HVH 5 (4-7148410) - AVTB - GCA_000456085.1
Escherichia coli HVH 102 (4-6906788) - AVVR - GCA_000465155.1
Escherichia coli HVH 201 (4-4459431) - AVZB - GCA_000459075.1
Escherichia coli HM605 - AJWU - GCA_000264175.1
Escherichia coli HM605 - CADZ - GCA_000285375.1
0.01

25. http://www.ncbi.nlm.nih.gov/assembly/?term=%22anomalous%22[Properties]

27. Contamination (multiple organisms)

29. Assembly for sample SAMN02727350
Type
Number of
contigs
Sum of contig
lengths
Full assembly 667 5251272
contigs with Listeria hits 37 3031650
contigs with Staphylococcus
hits 630 2203573

30. Contamination (carryover contamination)

32. Contamination (multiple strains)

34. Table: Assembly stats for SAMN02693748
measurement result
num_input_reads 4212706
aligned_reads 4040070
assembly_num_bases 3180478
assembly_num_contigs 50
assembly_N50 2817733
poor_quality_support_bases 132321

40. Organism Biosample SRA Run Similarity to:
Listeria monocytogenes IEH-NGS-LIS-00100 SAMN02567873 SRR1207486 Listeria SLCC7179
SRR1220750 Listeria J0161
Salmonella enterica Enteritidis MDH-2014-
00798 SAMN02741943 SRR1553852
Schwarzengrund str.
CVM19633
SRR1272871 Enteritidis str. P125109
Salmonella enterica Fluntern MDH-2013-
00153 SAMN02378158 SRR1067624
Javiana and
Schwarzengrund
SRR1395304 Cubana and Agona

42. Proficiency Testing
• Replicate results (phylogeny, SNPs) from published studies
• Resequencing
 same isolate on multiple platforms
 same isolate in multiple libraries
 same isolate in multiple labs
• Blinded submissions
 already-characterized isolates
 mixed sample isolates
 metagenomic isolates
• Corner cases
 Extreme coverage
 Duplicates
 Sample mixups

47. Acknowledgements
National Center for Biotechnology Information – National Library of Medicine – Bethesda MD 20892 USA
Richa Agarwala
Azat Badretdin
Slava Brover
Joshua Cherry
Vyacheslav Chetvernin
Robert Cohen
Michael DiCuccio
Mike Feldgarden
Dan Haft
William Klimke
Arjun Prasad
Edward Rice
Kirill Rotmistrovskyy
Stephen Sherry
Sergey Shiryev
Martin Shumway
Tatiana Tatusova
Igor Tolstoy
Chunlin Xiao
Leonid Zaslavsky
Alexander Zasypkin
Alejandro A. Schaffer
Lukas Wagner
Aleksandr Morgulis
David Lipman
James Ostell
NCBI
• This research was supported by the Intramural
Research Program of the NIH, National Library of
Medicine. http://www.ncbi.nlm.nih.gov
CDC
FDA/CFSAN
NIHGRI
UC-Davis
USDA
Vendors: PacBio, Illumina, Roche