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A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets.

Leimena MM, Ramiro-Garcia J, Davids M, van den Bogert B, Smidt H, Smid EJ, Boekhorst J, Zoetendal EG, Schaap PJ, Kleerebezem M - BMC Genomics (2013)

Bottom Line: Reproducibility of the metatranscriptome sequencing approach was established by independent duplicate experiments.In addition, comparison of metatranscriptome analysis employing single- or paired-end sequencing methods indicated that the latter approach does not provide improved functional or phylogenetic insights.The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

View Article: PubMed Central - HTML - PubMed

Affiliation: TI Food and Nutrition (TIFN), P,O, Box 557, 6700 AN, Wageningen, The Netherlands.

ABSTRACT

Background: Next generation sequencing (NGS) technologies can be applied in complex microbial ecosystems for metatranscriptome analysis by employing direct cDNA sequencing, which is known as RNA sequencing (RNA-seq). RNA-seq generates large datasets of great complexity, the comprehensive interpretation of which requires a reliable bioinformatic pipeline. In this study, we focus on the development of such a metatranscriptome pipeline, which we validate using Illumina RNA-seq datasets derived from the small intestine microbiota of two individuals with an ileostomy.

Results: The metatranscriptome pipeline developed here enabled effective removal of rRNA derived sequences, followed by confident assignment of the predicted function and taxonomic origin of the mRNA reads. Phylogenetic analysis of the small intestine metatranscriptome datasets revealed a strong similarity with the community composition profiles obtained from 16S rDNA and rRNA pyrosequencing, indicating considerable congruency between community composition (rDNA), and the taxonomic distribution of overall (rRNA) and specific (mRNA) activity among its microbial members. Reproducibility of the metatranscriptome sequencing approach was established by independent duplicate experiments. In addition, comparison of metatranscriptome analysis employing single- or paired-end sequencing methods indicated that the latter approach does not provide improved functional or phylogenetic insights. Metatranscriptome functional-mapping allowed the analysis of global, and genus specific activity of the microbiota, and illustrated the potential of these approaches to unravel syntrophic interactions in microbial ecosystems.

Conclusions: A reliable pipeline for metatransciptome data analysis was developed and evaluated using RNA-seq datasets obtained for the human small intestine microbiota. The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

Show MeSH
Distribution of COG functional categories for datasets A and B. Total COG distribution profiles were analyzed using reads with a minimum alignment bit score of 74. Genus specific COG distributions of the two most dominant genera were obtained using a minimum alignment bit score of 148. The COG distribution of the genes annotated in the complete genomes of representative (intestinal and non-intestinal) genomes of strains belonging to the three genera displayed here were included for comparison purposes.
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Figure 4: Distribution of COG functional categories for datasets A and B. Total COG distribution profiles were analyzed using reads with a minimum alignment bit score of 74. Genus specific COG distributions of the two most dominant genera were obtained using a minimum alignment bit score of 148. The COG distribution of the genes annotated in the complete genomes of representative (intestinal and non-intestinal) genomes of strains belonging to the three genera displayed here were included for comparison purposes.

Mentions: The majority of the COG function-assigned transcripts that were detected belong to functional categories “information storage and processing” (predominantly sub-category “translation, ribosomal structure and biogenesis”) and “metabolism”. (predominantly subcategory “carbohydrate transport and metabolism”) (Figure 4). These findings suggest that the microbial communities are growing and metabolically active. Notably, in a metatranscriptome study that targeted the fecal microbiota [16], a predominance of the same functional categories was observed.


A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets.

Leimena MM, Ramiro-Garcia J, Davids M, van den Bogert B, Smidt H, Smid EJ, Boekhorst J, Zoetendal EG, Schaap PJ, Kleerebezem M - BMC Genomics (2013)

Distribution of COG functional categories for datasets A and B. Total COG distribution profiles were analyzed using reads with a minimum alignment bit score of 74. Genus specific COG distributions of the two most dominant genera were obtained using a minimum alignment bit score of 148. The COG distribution of the genes annotated in the complete genomes of representative (intestinal and non-intestinal) genomes of strains belonging to the three genera displayed here were included for comparison purposes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750648&req=5

Figure 4: Distribution of COG functional categories for datasets A and B. Total COG distribution profiles were analyzed using reads with a minimum alignment bit score of 74. Genus specific COG distributions of the two most dominant genera were obtained using a minimum alignment bit score of 148. The COG distribution of the genes annotated in the complete genomes of representative (intestinal and non-intestinal) genomes of strains belonging to the three genera displayed here were included for comparison purposes.
Mentions: The majority of the COG function-assigned transcripts that were detected belong to functional categories “information storage and processing” (predominantly sub-category “translation, ribosomal structure and biogenesis”) and “metabolism”. (predominantly subcategory “carbohydrate transport and metabolism”) (Figure 4). These findings suggest that the microbial communities are growing and metabolically active. Notably, in a metatranscriptome study that targeted the fecal microbiota [16], a predominance of the same functional categories was observed.

Bottom Line: Reproducibility of the metatranscriptome sequencing approach was established by independent duplicate experiments.In addition, comparison of metatranscriptome analysis employing single- or paired-end sequencing methods indicated that the latter approach does not provide improved functional or phylogenetic insights.The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

View Article: PubMed Central - HTML - PubMed

Affiliation: TI Food and Nutrition (TIFN), P,O, Box 557, 6700 AN, Wageningen, The Netherlands.

ABSTRACT

Background: Next generation sequencing (NGS) technologies can be applied in complex microbial ecosystems for metatranscriptome analysis by employing direct cDNA sequencing, which is known as RNA sequencing (RNA-seq). RNA-seq generates large datasets of great complexity, the comprehensive interpretation of which requires a reliable bioinformatic pipeline. In this study, we focus on the development of such a metatranscriptome pipeline, which we validate using Illumina RNA-seq datasets derived from the small intestine microbiota of two individuals with an ileostomy.

Results: The metatranscriptome pipeline developed here enabled effective removal of rRNA derived sequences, followed by confident assignment of the predicted function and taxonomic origin of the mRNA reads. Phylogenetic analysis of the small intestine metatranscriptome datasets revealed a strong similarity with the community composition profiles obtained from 16S rDNA and rRNA pyrosequencing, indicating considerable congruency between community composition (rDNA), and the taxonomic distribution of overall (rRNA) and specific (mRNA) activity among its microbial members. Reproducibility of the metatranscriptome sequencing approach was established by independent duplicate experiments. In addition, comparison of metatranscriptome analysis employing single- or paired-end sequencing methods indicated that the latter approach does not provide improved functional or phylogenetic insights. Metatranscriptome functional-mapping allowed the analysis of global, and genus specific activity of the microbiota, and illustrated the potential of these approaches to unravel syntrophic interactions in microbial ecosystems.

Conclusions: A reliable pipeline for metatransciptome data analysis was developed and evaluated using RNA-seq datasets obtained for the human small intestine microbiota. The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

Show MeSH