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Impact of T-RFLP data analysis choices on assessments of microbial community structure and dynamics.

Fredriksson NJ, Hermansson M, Wilén BM - BMC Bioinformatics (2014)

Bottom Line: Variations in the estimation of T-RF sizes were observed and these variations were found to affect the alignment of the T-RFs.Large differences in the outcome of assessments of bacterial community structure and dynamics were observed between different alignment and normalization methods.The results of this study can therefore be of value when considering what methods to use in the analysis of T-RFLP data.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.

ABSTRACT

Background: Terminal restriction fragment length polymorphism (T-RFLP) analysis is a common DNA-fingerprinting technique used for comparisons of complex microbial communities. Although the technique is well established there is no consensus on how to treat T-RFLP data to achieve the highest possible accuracy and reproducibility. This study focused on two critical steps in the T-RFLP data treatment: the alignment of the terminal restriction fragments (T-RFs), which enables comparisons of samples, and the normalization of T-RF profiles, which adjusts for differences in signal strength, total fluorescence, between samples.

Results: Variations in the estimation of T-RF sizes were observed and these variations were found to affect the alignment of the T-RFs. A novel method was developed which improved the alignment by adjusting for systematic shifts in the T-RF size estimations between the T-RF profiles. Differences in total fluorescence were shown to be caused by differences in sample concentration and by the gel loading. Five normalization methods were evaluated and the total fluorescence normalization procedure based on peak height data was found to increase the similarity between replicate profiles the most. A high peak detection threshold, alignment correction, normalization and the use of consensus profiles instead of single profiles increased the similarity of replicate T-RF profiles, i.e. lead to an increased reproducibility. The impact of different treatment methods on the outcome of subsequent analyses of T-RFLP data was evaluated using a dataset from a longitudinal study of the bacterial community in an activated sludge wastewater treatment plant. Whether the alignment was corrected or not and if and how the T-RF profiles were normalized had a substantial impact on ordination analyses, assessments of bacterial dynamics and analyses of correlations with environmental parameters.

Conclusions: A novel method for the evaluation and correction of the alignment of T-RF profiles was shown to reduce the uncertainty and ambiguity in alignments of T-RF profiles. Large differences in the outcome of assessments of bacterial community structure and dynamics were observed between different alignment and normalization methods. The results of this study can therefore be of value when considering what methods to use in the analysis of T-RFLP data.

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Community stability: Bray-Curtis similarity between all profiles and the profile of the first sample in the time series. The data was treated in nine different ways before calculation of Bray-Curtis similarities (BC). Panel A: PDT50 TFN-A, B: PDT50 TFN-H, C: PDT 50 NoNorm, D: PDT50 NoNorm, NoAlCorr, E: PDT100 TFN-H, F: PDT100 TFN-H RepNorm, G: TRex-A, H: TRex-H, I: TRex-H Round-up. The treatments are described in Additional file 1: Table S5.
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Fig9: Community stability: Bray-Curtis similarity between all profiles and the profile of the first sample in the time series. The data was treated in nine different ways before calculation of Bray-Curtis similarities (BC). Panel A: PDT50 TFN-A, B: PDT50 TFN-H, C: PDT 50 NoNorm, D: PDT50 NoNorm, NoAlCorr, E: PDT100 TFN-H, F: PDT100 TFN-H RepNorm, G: TRex-A, H: TRex-H, I: TRex-H Round-up. The treatments are described in Additional file 1: Table S5.

Mentions: The time series dataset of 38 activated sludge samples was analyzed in nine different ways to evaluate the effect of different data treatments on the outcome of analyses of the dynamics of the community. The resulting T-RF profiles were then analyzed using Jaccard and Bray-Curtis similarities to assess the stability (Figures 9 and 10) and the rate of change (Additional file 2: Figures S4 and S5) of the bacterial community.Figure 9


Impact of T-RFLP data analysis choices on assessments of microbial community structure and dynamics.

Fredriksson NJ, Hermansson M, Wilén BM - BMC Bioinformatics (2014)

Community stability: Bray-Curtis similarity between all profiles and the profile of the first sample in the time series. The data was treated in nine different ways before calculation of Bray-Curtis similarities (BC). Panel A: PDT50 TFN-A, B: PDT50 TFN-H, C: PDT 50 NoNorm, D: PDT50 NoNorm, NoAlCorr, E: PDT100 TFN-H, F: PDT100 TFN-H RepNorm, G: TRex-A, H: TRex-H, I: TRex-H Round-up. The treatments are described in Additional file 1: Table S5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4232699&req=5

Fig9: Community stability: Bray-Curtis similarity between all profiles and the profile of the first sample in the time series. The data was treated in nine different ways before calculation of Bray-Curtis similarities (BC). Panel A: PDT50 TFN-A, B: PDT50 TFN-H, C: PDT 50 NoNorm, D: PDT50 NoNorm, NoAlCorr, E: PDT100 TFN-H, F: PDT100 TFN-H RepNorm, G: TRex-A, H: TRex-H, I: TRex-H Round-up. The treatments are described in Additional file 1: Table S5.
Mentions: The time series dataset of 38 activated sludge samples was analyzed in nine different ways to evaluate the effect of different data treatments on the outcome of analyses of the dynamics of the community. The resulting T-RF profiles were then analyzed using Jaccard and Bray-Curtis similarities to assess the stability (Figures 9 and 10) and the rate of change (Additional file 2: Figures S4 and S5) of the bacterial community.Figure 9

Bottom Line: Variations in the estimation of T-RF sizes were observed and these variations were found to affect the alignment of the T-RFs.Large differences in the outcome of assessments of bacterial community structure and dynamics were observed between different alignment and normalization methods.The results of this study can therefore be of value when considering what methods to use in the analysis of T-RFLP data.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. johan.fredriksson@gu.se.

ABSTRACT

Background: Terminal restriction fragment length polymorphism (T-RFLP) analysis is a common DNA-fingerprinting technique used for comparisons of complex microbial communities. Although the technique is well established there is no consensus on how to treat T-RFLP data to achieve the highest possible accuracy and reproducibility. This study focused on two critical steps in the T-RFLP data treatment: the alignment of the terminal restriction fragments (T-RFs), which enables comparisons of samples, and the normalization of T-RF profiles, which adjusts for differences in signal strength, total fluorescence, between samples.

Results: Variations in the estimation of T-RF sizes were observed and these variations were found to affect the alignment of the T-RFs. A novel method was developed which improved the alignment by adjusting for systematic shifts in the T-RF size estimations between the T-RF profiles. Differences in total fluorescence were shown to be caused by differences in sample concentration and by the gel loading. Five normalization methods were evaluated and the total fluorescence normalization procedure based on peak height data was found to increase the similarity between replicate profiles the most. A high peak detection threshold, alignment correction, normalization and the use of consensus profiles instead of single profiles increased the similarity of replicate T-RF profiles, i.e. lead to an increased reproducibility. The impact of different treatment methods on the outcome of subsequent analyses of T-RFLP data was evaluated using a dataset from a longitudinal study of the bacterial community in an activated sludge wastewater treatment plant. Whether the alignment was corrected or not and if and how the T-RF profiles were normalized had a substantial impact on ordination analyses, assessments of bacterial dynamics and analyses of correlations with environmental parameters.

Conclusions: A novel method for the evaluation and correction of the alignment of T-RF profiles was shown to reduce the uncertainty and ambiguity in alignments of T-RF profiles. Large differences in the outcome of assessments of bacterial community structure and dynamics were observed between different alignment and normalization methods. The results of this study can therefore be of value when considering what methods to use in the analysis of T-RFLP data.

Show MeSH