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Optimizing multiplex SNP-based data analysis for genotyping of Mycobacterium tuberculosis isolates.

Sengstake S, Bablishvili N, Schuitema A, Bzekalava N, Abadia E, de Beer J, Tadumadze N, Akhalaia M, Tuin K, Tukvadze N, Aspindzelashvili R, Bachiyska E, Panaiotov S, Sola C, van Soolingen D, Klatser P, Anthony R, Bergval I - BMC Genomics (2014)

Bottom Line: The data generated were interpreted blindly and then compared to results obtained by reference methods.Based on excellent agreement with the reference methods we conclude that the new data analysis method performed well.All together this will facilitate the implementation of the MLPA assay in different settings.

View Article: PubMed Central - PubMed

Affiliation: KIT Biomedical Research, Royal Tropical Institute, Meibergdreef 39, 1105 AZ Amsterdam, The Netherlands. s.sengstake@kit.nl.

ABSTRACT

Background: Multiplex ligation-dependent probe amplification (MLPA) is a powerful tool to identify genomic polymorphisms. We have previously developed a single nucleotide polymorphism (SNP) and large sequence polymorphisms (LSP)-based MLPA assay using a read out on a liquid bead array to screen for 47 genetic markers in the Mycobacterium tuberculosis genome. In our assay we obtain information regarding the Mycobacterium tuberculosis lineage and drug resistance simultaneously. Previously we called the presence or absence of a genotypic marker based on a threshold signal level. Here we present a more elaborate data analysis method to standardize and streamline the interpretation of data generated by MLPA. The new data analysis method also identifies intermediate signals in addition to classification of signals as positive and negative. Intermediate calls can be informative with respect to identifying the simultaneous presence of sensitive and resistant alleles or infection with multiple different Mycobacterium tuberculosis strains.

Results: To validate our analysis method 100 DNA isolates of Mycobacterium tuberculosis extracted from cultured patient material collected at the National TB Reference Laboratory of the National Center for Tuberculosis and Lung Diseases in Tbilisi, Republic of Georgia were tested by MLPA. The data generated were interpreted blindly and then compared to results obtained by reference methods. MLPA profiles containing intermediate calls are flagged for expert review whereas the majority of profiles, not containing intermediate calls, were called automatically. No intermediate signals were identified in 74/100 isolates and in the remaining 26 isolates at least one genetic marker produced an intermediate signal.

Conclusion: Based on excellent agreement with the reference methods we conclude that the new data analysis method performed well. The streamlined data processing and standardized data interpretation allows the comparison of the Mycobacterium tuberculosis MLPA results between different experiments. All together this will facilitate the implementation of the MLPA assay in different settings.

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Related in: MedlinePlus

Visualization of data generated from 100 Georgian isolates after data normalization and data correction. (A) Dot plot showing normalized and corrected MFI values (black dots) per isolate for all 4300 markers targeted in the 100 Georgian isolates. The grey area highlights 62 (1.4%) unclassifiable markers of which 24 are drug resistance markers. Markers located above this area are classified as positive (971, 22.6%) and below as negative (3267, 76%). (B) Same data as shown in (A) but only the intermediate values are shown and visualized per marker. Each line shows the distribution of normalized and corrected MFI values, sorted from lowest o highest, (black squares), for one marker (individual colors).
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Fig2: Visualization of data generated from 100 Georgian isolates after data normalization and data correction. (A) Dot plot showing normalized and corrected MFI values (black dots) per isolate for all 4300 markers targeted in the 100 Georgian isolates. The grey area highlights 62 (1.4%) unclassifiable markers of which 24 are drug resistance markers. Markers located above this area are classified as positive (971, 22.6%) and below as negative (3267, 76%). (B) Same data as shown in (A) but only the intermediate values are shown and visualized per marker. Each line shows the distribution of normalized and corrected MFI values, sorted from lowest o highest, (black squares), for one marker (individual colors).

Mentions: The principle of the new data analysis method and the calculation of the correction factors is described in detail in the Methods section and in Figure 1. The results obtained with the new data analysis method from DNA of cultured isolates from individual patient samples collected at the National TB Reference Laboratory in Tbilisi, Georgia is illustrated in Figure 2.Figure 1


Optimizing multiplex SNP-based data analysis for genotyping of Mycobacterium tuberculosis isolates.

Sengstake S, Bablishvili N, Schuitema A, Bzekalava N, Abadia E, de Beer J, Tadumadze N, Akhalaia M, Tuin K, Tukvadze N, Aspindzelashvili R, Bachiyska E, Panaiotov S, Sola C, van Soolingen D, Klatser P, Anthony R, Bergval I - BMC Genomics (2014)

Visualization of data generated from 100 Georgian isolates after data normalization and data correction. (A) Dot plot showing normalized and corrected MFI values (black dots) per isolate for all 4300 markers targeted in the 100 Georgian isolates. The grey area highlights 62 (1.4%) unclassifiable markers of which 24 are drug resistance markers. Markers located above this area are classified as positive (971, 22.6%) and below as negative (3267, 76%). (B) Same data as shown in (A) but only the intermediate values are shown and visualized per marker. Each line shows the distribution of normalized and corrected MFI values, sorted from lowest o highest, (black squares), for one marker (individual colors).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4117977&req=5

Fig2: Visualization of data generated from 100 Georgian isolates after data normalization and data correction. (A) Dot plot showing normalized and corrected MFI values (black dots) per isolate for all 4300 markers targeted in the 100 Georgian isolates. The grey area highlights 62 (1.4%) unclassifiable markers of which 24 are drug resistance markers. Markers located above this area are classified as positive (971, 22.6%) and below as negative (3267, 76%). (B) Same data as shown in (A) but only the intermediate values are shown and visualized per marker. Each line shows the distribution of normalized and corrected MFI values, sorted from lowest o highest, (black squares), for one marker (individual colors).
Mentions: The principle of the new data analysis method and the calculation of the correction factors is described in detail in the Methods section and in Figure 1. The results obtained with the new data analysis method from DNA of cultured isolates from individual patient samples collected at the National TB Reference Laboratory in Tbilisi, Georgia is illustrated in Figure 2.Figure 1

Bottom Line: The data generated were interpreted blindly and then compared to results obtained by reference methods.Based on excellent agreement with the reference methods we conclude that the new data analysis method performed well.All together this will facilitate the implementation of the MLPA assay in different settings.

View Article: PubMed Central - PubMed

Affiliation: KIT Biomedical Research, Royal Tropical Institute, Meibergdreef 39, 1105 AZ Amsterdam, The Netherlands. s.sengstake@kit.nl.

ABSTRACT

Background: Multiplex ligation-dependent probe amplification (MLPA) is a powerful tool to identify genomic polymorphisms. We have previously developed a single nucleotide polymorphism (SNP) and large sequence polymorphisms (LSP)-based MLPA assay using a read out on a liquid bead array to screen for 47 genetic markers in the Mycobacterium tuberculosis genome. In our assay we obtain information regarding the Mycobacterium tuberculosis lineage and drug resistance simultaneously. Previously we called the presence or absence of a genotypic marker based on a threshold signal level. Here we present a more elaborate data analysis method to standardize and streamline the interpretation of data generated by MLPA. The new data analysis method also identifies intermediate signals in addition to classification of signals as positive and negative. Intermediate calls can be informative with respect to identifying the simultaneous presence of sensitive and resistant alleles or infection with multiple different Mycobacterium tuberculosis strains.

Results: To validate our analysis method 100 DNA isolates of Mycobacterium tuberculosis extracted from cultured patient material collected at the National TB Reference Laboratory of the National Center for Tuberculosis and Lung Diseases in Tbilisi, Republic of Georgia were tested by MLPA. The data generated were interpreted blindly and then compared to results obtained by reference methods. MLPA profiles containing intermediate calls are flagged for expert review whereas the majority of profiles, not containing intermediate calls, were called automatically. No intermediate signals were identified in 74/100 isolates and in the remaining 26 isolates at least one genetic marker produced an intermediate signal.

Conclusion: Based on excellent agreement with the reference methods we conclude that the new data analysis method performed well. The streamlined data processing and standardized data interpretation allows the comparison of the Mycobacterium tuberculosis MLPA results between different experiments. All together this will facilitate the implementation of the MLPA assay in different settings.

Show MeSH
Related in: MedlinePlus