typing staphylococcus aureus using the protein a gene phaedra agius – january, 2008, completed at...
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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
• 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.
•Staph can cause a multitude of infections, from skin infections to more deadly infections such as pneumonia and meningitis
•It can spread rapidly
•Some strains are resistant to antibiotics (MRSA)
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 GAAGACAACAACAAGCCTGGTAAA GAAGATGGCAACAAGCCTGGT AAA GAAGACAACAAAAAACCTGGCAAA GAAGATGGCAACAAACCTGGT AAA GAAGACGGCAACAAGCCTGGT AAA GAAGATGGCAACAAGCCTGGT
X1K1A1O1M1Q1
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
• The MLST allelic profile is provided for each sequence
• 194 sequences labeled with their MLST type
DukeId SpaMotif 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 O U D Y D A Y
G * O * * A W A 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 AffineT1 J1 * * * * B1 B1 B1 D1
0 3 1 1 1 0 0 1 Distance = 7 Similarity =
3
BCGS-Best Common Gap-weighted Subsequence
P A R T Y H A R D
P A N T * * * R Y
Common subsequences are:
S1=A,T,R, S2=AT, S3=TR, S4=ATR
Gap weighted scores: Choose a weight 0< 1>=ג
S1 = 1̧ 0 = 1, S2 = 2̧ ,S3 = 2̧ 3, S4 = 3̧ 4
If 1=ג , then S4 is the optimal choice.
If 0.9=ג , the scores are 1, 1.8, 1.46 and 1.97 respectively
If 0.8=ג , the scores are 1, 1.6, 1.02 and 1.23 respectively
S1=A,T,R, S2=AT, S3=TR, S4=ATR
S1 = 1̧ 0 = 1, S2 = 2̧ ,S3 = 2̧ 3, S4 = 3̧ 4
Normalizing the similarity scores
• The similarity scores M are normalized as follows:
where n1 and n2 are the sequence lengths
Example: C L O U D Y D A Y
G * O * * A W A Y
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
B
E
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:
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.
D(s1;s2) = 1¡ sim(s1;s2)
Hierarchical Clustering
Uses a distance matrix
It iteratively ‘merges’ the two nearest items/clusters
1 2 3 4 5 6 7 8
1 0 9 4 7 8 4 5 9
2 0 6 9 6 8 5 8
3 0 6 7 1 2 9
4 0 5 4 5 3
5 0 7 5 4
6 0 2 6
7 0 5
8 0
---Cutoff c … this determines the number of clusters to be formed
Training and Testing
• 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
Train
Test
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.
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.
>t
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 measuredpairwise between the spa clusterings obtained at each iteration
Spa clustering 1
Spa clustering 3
Spa clustering 2J1
J2J3
Stability = mean(J1,J2,J3)
Iterations 1, 2, 3 ….
Accuracy
Spa group
MLST group
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.Accuracy = 8/11
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’
-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
MLST 1
MLST 5MLST 8
MLST 15
MLST 30
MLST 45
MLST 59MLST 109
MLST 188
MDS with our distances
MDS – a closer look
-0.22 -0.2 -0.18 -0.16 -0.14 -0.12 -0.1 -0.08 -0.06
0
0.05
0.1
0.15
0.2
0.25
0.3
MLST 20T1-G2-M1-F1-B1-B1-B1T1-G2-M1-F1-F1-B1-B1-B1U1-G2-M1-F1-B1-L1-B1U1-G2-M1-F1-B1-B1-L1-B1
MLST 59Z1-D1-M1-D1-M1-N1-K1-B1Z1-D1-M1-D1-M1-N1-K1-E1Z1-D1-M1-N1-K1-B1
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• Spa Typing method for Discriminating among Staphylococcus
aureus Isolates: Implications for Use of a Single marker to Detect Genetic Micro and MacrovariationLarry 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 StrainsB. 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 BiologyJoao Setubal and Joao Meidanis
• Kernel Methods for Pattern AnalysisJohn Shawe-Taylor and Nello Cristianini
• Framework for kernel regularization with application to protein clusteringFan Lu, Sunduz Keles, Stephen J. Wright and Grace Wahba
This work is published in IEEE/ACM Transactions on Computational Biology and BioinformaticsVolume 4, Issue 4, Oct.-Dec. 2007 Page(s):693 - 704
Thanks!Questions?