1.
GMI proficiency testing-
Progress report 2016
9th GMI meeting
23th - 25th May 2016
Rome, Italy
Presented by
James Pettengill (US FDA)
Rene Hendriksen (DTU-Food)

2.
Layout of the full roll out
The PT consist of three components
“Wet-lab”
• 1a) DNA extraction, purification, library-preparation, and whole-
genome-sequencing of six bacterial cultures;
– two Salmonella strains
– two Escherichia coli strains (only one was included)
– two Staphylococcus aureus strains
Upload reads to an ftp-site
Optionally, identify MLST and resistance genes present in the
strains
• 1b) Whole-genome-sequencing of pre-prepared DNA of the same six
bacterial strains mentioned in component 1a for comparison of DNA
and library prep from component 1a
“Dry-lab”
• 2) Variant detection and phylogenetic/clustering analysis of three
datasets fastq datasets from app. 20 genomes of S. Typhimurium,
E. coli and S. aureus
2

3.
3
Updated draft Action Plan and Milestones
for 2014/15 ”Full PT roll-out phase”
Preparation of
reference material
Dispatch of
reference
materials
Adjust documentation,
prenotification, invitation
letter, instructions (SOP)
and guidelines for PT
Final
analysis
(report) of
the pilot PT
Invitation for
the full PT
roll out
Final
analysis of
the full roll
PT
Invitation for
the full PT
roll out
Adjust documentation,
prenotification, invitation
letter, instructions (SOP)
and guidelines for PT
Preparation of
reference material
Dispatch of
reference
materials

4.
Participation in the PT
49 participating labs in total

5.
5 1 June 2016
Survey to capture technical and background
information - component 2, wet-lab

6.
Measured QC parameters –
component 2 - wet-lab
• Number of reads
• Average read length
• Number of reads mapped to
– reference DNA sequence
– reference chromosome
– reference plasmid #1
– reference plasmid #2
– reference plasmid #3
• Proportion of reads mapped to the above
• Depth of coverage, of the above
• Size of assembled genome
• Size of assembled genome per total size of DNA sequence (%)
• Total number of contigs
• Number of contigs > 200 bp
• N50
• NG50
6 1 June 2016

7.
Size of assembled genome per total size of
DNA sequence (%)
7 1 June 2016
The proportion of contigs, which map directly to the closed genome
(same strain). This cannot exceed 100%.
100%

8.
N50
8 1 June 2016
The N50 length is defined as the length for which the collection of all
contigs of that length or longer contains at least half of the sum of the
lengths of all contigs, and for which the collection of all contigs of that
length or shorter also contains at least half of the sum of the lengths of
all contigs. A N50 more than 15000 normally indicate good quality.
300.000
15.000
700.000

9.
Total number of contigs
9 1 June 2016
The total number of contigs assembled. A number of contigs less
than 1000 normally indicate good quality.
1000

10.
Proportion of reads mapped to reference
DNA sequence (%)
10 1 June 2016
The proportion of reads produced which map directly to the closed
genome (same strain). This cannot exceed more than 100%.
100%

11.
SNP analysis of the different QC strains
11 1 June 2016

12.
Participant feedback - component 2, wet-lab
12 1 June 2016

13.
Analysis of the - component 2, wet-lab
• All outliers will be removed from the final analysis to suggest
tentative QC thresholds
• To suggest tentative QC thresholds, QC data will be related to
MLST and AMR “ref.” data analysis, SNP analysis, as well as
technical / background data
• Provided MLST and AMR data will be analyzed in relation to “ref.”
data to evaluate the performance of bioinformatic tool utilized for
the PT
13 1 June 2016

14.
Dry-lab
14
The objective:
• Assess the differences that exists in the detection of
variants (e.g., single nucleotide polymorphisms (SNPs))
from the analysis of whole genome sequence data.
• Participants were provided 3 datasets to analyze with the
current protocol implemented in their lab
Submitted by participants:
1. Answer an online survey
2. Fasta formatted matrix of variants
3. Newick formatted tree (phylogeny) file.
Formatting
issues

15.
Dry-lab
participation/result
files
15

16.
Dry-lab
Survey results: a
diversity of methods
and decisions
16

17.
Dry-lab
17
Figure3. Violin plotsof thecorrelationsin thepairwisedistancebetween samplesamong thedifferent SNP
matrices.
0.00
0.25
0.50
0.75
1.00
ST SA EC
Taxon
CorrelationinNumberofSNPdifferences
Results of the analysis of fasta matrices
Labs congruent
Labs incongruent
1 June 2016

18.
Dry-lab
18
Results of the analysis of newick tree files – trees are pretty
different
1 June 2016

19.
19 1 June 2016
Dry-lab
Summary and key findings:
• A total of 190 results files were submitted with a relatively
even distribution across the three taxonomic groups and
file type (fasta or newick tree)
• Not surprisingly, there are a diversity of algorithms being
employed (e.g., map reads and infer a phylogeny)
• Participants also differed in the choices they made with
respect to quality filtering and contamination checking
• The number of positions within the different fasta
matrices differed greatly but they seem to carry similar
information content in terms of the relative magnitude of
differences between samples
• The trees differed greatly
• What does this mean for traceback?
• Should we capture different information to compare
results (e.g., positions within the reference)?

20.
Acknowledgement and Supported by
20 01-06-2016
Susanne Karlsmose (DTU Food)
Oksana Lukjancenko (DTU Food)
Charlotte Ingvorsen (DTU Food)
Pimlapas Leekitcharoenphon (DTU Food)
Rolf Sommer Kaas (DTU Food)
Inge Marianne Hansen (DTU Food)
Jose Luis Bellod Cisneros (DTU Systems Biology)
Anthony Underwood (PHE)
Division of Microbiology (CFSAN/FDA)
Brian Beck (Microbiologics)
Isabel Cuesta de la plaza (ISCIII)
Angel Zaballos (ISCIII)
Jorge De La Barrera Martinez (ISCIII)
…..and the rest of WG 4 (“advisory group”)

21.
DTU Food, Technical University of Denmark
Thank you for your attention