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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

Stepwise approach of the data analysis method. Dot blots illustrate MFI values for 43 genetic markers targeted in 88 clinical isolates and laboratory strains [10] obtained from (A) the MAGPIX csv file, (B) after normalization and (C) after normalization and correction. (A) Raw MFI values obtained for every targeted marker per strain. The dashed line indicates the threshold of MFI 150 which was initially chosen for the classification of targeted makers. Red dots show the MFI values obtained which are located in the intermediate range after normalization and correction in panel C. (B) MFI values after intra-strain normalization of raw MFI values. (C) MFI signals after normalization and inter-strain correction using marker-specific correction factors. The grey area defines the intermediate range calculated as the area between one and two standard deviations from the average MFINORM = 860.
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Fig1: Stepwise approach of the data analysis method. Dot blots illustrate MFI values for 43 genetic markers targeted in 88 clinical isolates and laboratory strains [10] obtained from (A) the MAGPIX csv file, (B) after normalization and (C) after normalization and correction. (A) Raw MFI values obtained for every targeted marker per strain. The dashed line indicates the threshold of MFI 150 which was initially chosen for the classification of targeted makers. Red dots show the MFI values obtained which are located in the intermediate range after normalization and correction in panel C. (B) MFI values after intra-strain normalization of raw MFI values. (C) MFI signals after normalization and inter-strain correction using marker-specific correction factors. The grey area defines the intermediate range calculated as the area between one and two standard deviations from the average MFINORM = 860.

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)

Stepwise approach of the data analysis method. Dot blots illustrate MFI values for 43 genetic markers targeted in 88 clinical isolates and laboratory strains [10] obtained from (A) the MAGPIX csv file, (B) after normalization and (C) after normalization and correction. (A) Raw MFI values obtained for every targeted marker per strain. The dashed line indicates the threshold of MFI 150 which was initially chosen for the classification of targeted makers. Red dots show the MFI values obtained which are located in the intermediate range after normalization and correction in panel C. (B) MFI values after intra-strain normalization of raw MFI values. (C) MFI signals after normalization and inter-strain correction using marker-specific correction factors. The grey area defines the intermediate range calculated as the area between one and two standard deviations from the average MFINORM = 860.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: Stepwise approach of the data analysis method. Dot blots illustrate MFI values for 43 genetic markers targeted in 88 clinical isolates and laboratory strains [10] obtained from (A) the MAGPIX csv file, (B) after normalization and (C) after normalization and correction. (A) Raw MFI values obtained for every targeted marker per strain. The dashed line indicates the threshold of MFI 150 which was initially chosen for the classification of targeted makers. Red dots show the MFI values obtained which are located in the intermediate range after normalization and correction in panel C. (B) MFI values after intra-strain normalization of raw MFI values. (C) MFI signals after normalization and inter-strain correction using marker-specific correction factors. The grey area defines the intermediate range calculated as the area between one and two standard deviations from the average MFINORM = 860.
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