Typing Staphylococcus aureus
using the protein A gene
Phaedra Agius – January, 2008,
completed at RPI in New York
in collaboration with Barry Kreiswirth, Steve Naidich, Kristin Bennett
Introduction
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What is staph?
Typing methods and the spA gene
The data
Comparing Sequences
Similarities and differences
Hierarchical clustering
Evaluating the results
Multidimensional Scaling
Conclusion
•Staphylococcus aureus is a bacteria
often living on the skin or in the nose of a
healthy person.
•It can spread rapidly
•Some strains are resistant to antibiotics
(MRSA)
•Staph can cause a multitude of infections,
from skin infections to more deadly infections
such as pneumonia and meningitis
Typing Methods
• Multi Locus Sequence Typing (MLST)
is a well established typing method
that looks at 7 house-keeping genes
in staph. These are genes that are
always turned on.
• Our method looks at just ONE gene –
the spA gene.
The spA gene
• The spA gene contains information for
making Protein A.
• The protein A in staph is a virulence factor.
It inhibits white blood cells from ingesting
and destroying the bacteria by acting as
an immunological disguise.
Preprocessed DNA sequences of
the spA gene
AAA GAG GAAGACAACAACAAGCCTGGT
AAA
GAAGATGGCAACAAGCCTGGT
AAA
GAAGACAACAAAAAACCTGGC
AAA
GAAGATGGCAACAAACCTGGT
AAA
GAAGACGGCAACAAGCCTGGT
AAA
GAAGATGGCAACAAGCCTGGT
X1
K1
A1
O1
M1
Q1
The spA DNA sequences can be preprocessed into a sequence of
repeats, or cassettes.
Instead of dealing with the long DNA sequences, we use these
shorter preprocessed spa sequences
X1-K1-A1-O1-M1-Q1
Note, first cassette has 27bp, the others have 24bp
Labeled data
• 194 sequences labeled with their MLST type
• The MLST allelic profile is provided for each sequence
SpaMotif
DukeId
spa MLST arcc aroe glpf gmk pta tpi yqil
1075014
X1-K1-A1-M1-B3
538
395
10
47
8
26
26
32
2
584
X1-K1-B1-B3
541
?
10
?
8
26
26
32
2
1771
X1-K1-B1
93
47
10
11
8
6
10
3
2
40
X1-K1-A1-K1-A1-O1-M1-Q1-Q1
468
30
2
2
2
2
6
3
2
1073088
X1-K1-A1-K1-A1-O1-M1-Q1-Q1-Q1
536
30
2
2
2
2
6
3
2
349
X1-K1-A1-O1-M1-Q1
390
30
2
2
2
2
6
3
2
Spa sequences
MLST labels
Comparing spa sequences
• T1-J1-M1-G1-M1-K1
• T1-K1-B1-M1-D1-M1-G1-M1-K1
• T1-M1-B1-M1-D1-M1-G1-M1-K1
• T1-M1-D1-M1-G1-M1-M1-K1
• U1-J1-F1-K1-P1-E1
• T1-J1-F1-K1-B1-P1-E1
• U1-J1-G1-F1-M1-B1
These ‘preprocessed’ sequences are highly conserved.
How can we generate numbers from sequences that reflect
the subtle differences and/or similarities between them?
Comparing spa sequences
– Global alignment
– Affine alignment
– BCGS - Best common gap-weighted
subsequence
• Weighting the sequence ends (B and E)
Using these methods each spa sequence can be
represented as a vector of similarity scores
between itself and all the other sequences
Global alignment
• Costs: Gap =1, Mismatch = 1
C L OU D Y D A Y
G * O * * A WA Y
1 0 1 1 1 1 0
• Distance: d = 5
Similarity: s = 2
Affine gap alignment
• Costs: Gap Initialization = 2, Gap =1, Mismatch = 1
U1 J1 G1 F1 B1 B1 B1 B1 P1 B1
Global
T1 J1 * * B1 B1 B1 * * D1
0 3 1 0 0 0 3 1
Distance = 8
Similarity = 4
U1 J1 G1 F1 B1 B1 B1 B1 P1 B1
Affine
T1 J1 * * * * B1 B1 B1 D1
0 3 1 1 1 0 0 1
Distance = 7
Similarity = 3
BCGS-Best Common
Gap-weighted Subsequence
P ARTYHAR D
P ANT * * *R Y
Common subsequences are:
S1 = A ,T ,R, S2 = AT ,
S3 = T R, S4 = AT R
Gap weighted scores: Choose a weight 0< ‫=<ג‬1
S1 = 1¸ 0 = 1, S2 = 2¸ ,
S3 = 2¸ 3 , S4 = 3¸ 4
S1 = A,T ,R, S2 = AT , S3 = T R, S4 = AT R
S1 = 1¸ 0 = 1, S2 = 2¸ ,
S3 = 2¸ 3 , S4 = 3¸ 4
If ‫=ג‬1, then S4 is the optimal choice.
If ‫=ג‬0.9, the scores are 1, 1.8, 1.46 and 1.97 respectively
If ‫=ג‬0.8, the scores are 1, 1.6, 1.02 and 1.23 respectively
Normalizing the similarity scores
• The similarity scores
follows:
M
are normalized as
where n1 and n2 are
the sequence lengths
Example:
L OU D Y D A Y
G * O * * A WA Y
C
Similarity = 3, Normalized similarity = 3/√(7*4)=0.57
B and E
The cassettes at the beginning (B) and end
(E) of a sequence are highly conserved
within spa families
These cassettes shall be compared
separately, scored as a match (1) or
mismatch (0) and weighted
E
B
M=middle
Let B and E have a weight of 20%
in the overall score
Sim score = 0.2*B + 0.6*M + 0.2*E
Similarities  Distances
Normalized similarity scores can be transformed
to distances as follows:
D (s1 ; s2 ) = 1 ¡ si m(s1 ; s2 )
Spa sequence  vector of distances between that
sequence and every other sequence in the dataset.
The set of spa sequences is now represented by a
(normalized) distance matrix.
Hierarchical Clustering
Uses a distance matrix
It iteratively ‘merges’ the
two nearest
items/clusters
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
0
9
4
7
8
4
5
9
0
6
9
6
8
5
8
0
6
7
1
2
9
0
5
4
5
3
0
7
5
4
0
2
6
0
5
0
---Cutoff c … this
determines the number of
clusters to be formed
Training and Testing
Test
Train
• Split the data into two –
a TRAINING set and a TEST set
• Build a model on the Training set by
choosing optimal B, E and c
parameters
• Assign the Test data to the nearest
clusters
• Evaluate the results
• Repeat multiple times for validation
Assigning Test sequences to the
Training clusters
•We define the distance between a
point and a cluster to be the mean of
the distances between that point and
the members of the cluster.
>t
IF the distance between a test point
and the nearest cluster exceeds an
outlier threshold t , the test point is
defined to be an outlier (a novel strain
of the bacteria)
ELSE the test point is assigned to the
nearest cluster.
Evaluation
• Compare our clusters to the groups defined by
the MLST labels via the Jaccard coefficient
• Split our data into a Training and Testing set
multiple times and measure the consistency of
the clusters formed via a Stability score
• Measure the Accuracy of our spa groups by
comparing them to the MLST groups
Jaccard coefficient
Clustering S
Clustering M
Stability
The stability is measured over the n Training
and Testing iterations.
It is defined to be the mean of the Jaccard scores measured
pairwise between the spa clusterings obtained at each iteration
Iterations 1, 2, 3 ….
J1
Spa clustering 1
Spa clustering 2
J3
J2
Spa clustering 3
Stability = mean(J1,J2,J3)
Accuracy
Spa group
MLST group
Accuracy = 8/11
The MLST label assigned to a
spa group is the label of the MLST
group with which the spa group
has the largest intersection.
The accuracy for that spa group is
defined to be the percentage of
correctly labeled points.
The overall accuracy of a spa
clustering is defined to be the
percentage of correctly labeled points.
Results: Jaccard scores
(40 iters, outlier threshold = 1.5 sd)
Results: Stability scores
(40 iters, outlier threshold = 1.5 sd)
Results: Accuracy scores
(40 iters, outlier threshold = 1.5 sd)
Results: Outlier detection
(40 iters, outlier threshold = 1.5 sd)
Results: Varying the Outlier threshold
(10 iters, test set size = 30%)
Multidimensional Scaling (MDS)
• MDS translates a distances matrix to a set of
coordinates such that the distances between
the points are approximately equal to the
dissimilarities.
Picture taken from Forrest W. Young’s paper ‘Multidimensional Scaling’
0.4
MDS with our distances
0.3
0.2
0.1
0
MLST 1
MLST 5
MLST 8
MLST 15
MLST 30
MLST 45
MLST 59
MLST 109
MLST 188
-0.1
-0.2
-0.3
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
MDS – a closer look
0.3
0.25
0.2
0.15
MLST 20
T1-G2-M1-F1-B1-B1-B1
T1-G2-M1-F1-F1-B1-B1-B1
U1-G2-M1-F1-B1-L1-B1
U1-G2-M1-F1-B1-B1-L1-B1
0.1
0.05
MLST 59
Z1-D1-M1-D1-M1-N1-K1-B1
Z1-D1-M1-D1-M1-N1-K1-E1
Z1-D1-M1-N1-K1-B1
0
-0.22
-0.2
-0.18
-0.16
-0.14
-0.12
-0.1
-0.08
-0.06
Conclusion and future work
• The Spa clustering method can refine groups in ways that
MLST cannot
• BCGS worked best
• MDS on our spa distances clearly draws out the clusters
Future research
• More data, compare to other typing methods
• Use BCGS on other data types
• Different distance measures
• Different ways of assigning test points to clusters
• Better ways for finding the optimal parameters other than a
grid search
References
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Spa Typing method for Discriminating among Staphylococcus aureus
Isolates: Implications for Use of a Single marker to Detect Genetic
Micro and Macrovariation
Larry koreen, Srinivas Ramaswamy, Edward Graviss, Steven Naidich,
James Musser and Barry Kreiswirth
Evaluation of protein A Gene Polymorphic Region DNA Sequencing for
Typing of Staphylococcus aureus Strains
B. Shopsin, M. Gomes, S.O. Montgomery, D.H. Smith, M. Waddington, D.E.
Dodge, D.A.Bost, M. Riehman, S. Naidich and B. Kreiswirth
Introduction to Computational molecular Biology
Joao Setubal and Joao Meidanis
Kernel Methods for Pattern Analysis
John Shawe-Taylor and Nello Cristianini
Framework for kernel regularization with application to protein
clustering
Fan Lu, Sunduz Keles, Stephen J. Wright and Grace Wahba
Thanks!
Questions?
This work is published in
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume 4, Issue 4, Oct.-Dec. 2007 Page(s):693 - 704