Binpairs: Utilization of Illumina Paired-End Information for Improving Efficiency of Taxonomic Binning of Metagenomic Sequences

Dutta, Anirban; Tandon, Disha; Mh, Mohammed; Bose, T. K.; Mande, Sharmila S.

doi:10.1371/journal.pone.0114814

Cited by 4 publications

(2 citation statements)

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“…Paired-end sequencing protocols available with some of the NGS platforms allow sequencing of a stretch of DNA from both its ends (Rodrigue et al, 2010; Dutta et al, 2014). For example, Illumina HiSeq sequencing platforms can be used for paired-end sequencing to generate up to 2x250bp reads.…”

Section: Resultsmentioning

confidence: 99%

Can Targeting Non-Contiguous V-Regions With Paired-End Sequencing Improve 16S rRNA-Based Taxonomic Resolution of Microbiomes?: An In Silico Evaluation

et al. 2019

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Background: Next-generation sequencing (NGS) technologies have enabled probing of microbial diversity in different environmental niches with unprecedented sequencing depth. However, due to read-length limitations of popular NGS technologies, 16S amplicon sequencing-based microbiome studies rely on targeting short stretches of the 16S rRNA gene encompassing a selection of variable (V) regions. In most cases, such a short stretch constitutes a single V-region or a couple of V-regions placed adjacent to each other on the 16S rRNA gene. Given that different V-regions have different resolving ability with respect to various taxonomic groups, selecting the optimal V-region (or a combination thereof) remains a challenge. Methods: The accuracy of taxonomic profiles generated from sequences encompassing 1) individual V-regions, 2) adjacent V-regions, and 3) pairs of non-contiguous V-regions were assessed and compared. Subsequently, the discriminating capability of different V-regions with respect to different taxonomic lineages was assessed. The possibility of using paired-end sequencing protocols to target combinations of non-adjacent V-regions was finally evaluated with respect to the utility of such an experimental design in providing improved taxonomic resolution. Results: Extensive validation with simulated microbiome datasets mimicking different environmental and host-associated microbiome samples suggest that targeting certain combinations of non-contiguously placed V-regions might yield better taxonomic classification accuracy compared to conventional 16S amplicon sequencing targets. This work also puts forward a novel in silico combinatorial strategy that enables creation of consensus taxonomic profiles from experiments targeting multiple pair-wise combinations of V-regions to improve accuracy in taxonomic classification. Conclusion: The study suggests that targeting non-contiguous V-regions with paired-end sequencing can improve 16S rRNA–based taxonomic resolution of microbiomes. Furthermore, employing the novel in silico combinatorial strategy can improve taxonomic classification without any significant additional experimental costs and/or efforts. The empirical observations obtained can potentially serve as a guideline for future 16S microbiome studies, and facilitate researchers in choosing the optimal combination of V-regions for a specific experiment/sampled environment.

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Section: Resultsmentioning

confidence: 99%

Can Targeting Non-Contiguous V-Regions With Paired-End Sequencing Improve 16S rRNA-Based Taxonomic Resolution of Microbiomes?: An In Silico Evaluation

et al. 2019

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“…The last decade has witnessed the development of several specialised tools/algorithms catering to various stages of microbiome data analysis viz. host-sequence decontamination [ 11 – 13 ], contig assembly [ 14 – 16 ], taxonomic binning [ 17 – 26 ], functional characterization [ 27 – 30 ], and comparative analysis of microbial communities [ 31 – 33 ]. Beyond elucidating (and comparing) microbial diversity in taxonomic and functional terms, it is important to obtain insights about intra-community microbial interaction patterns and understand the dynamics of these interaction patterns as a function of external environmental changes.…”

Section: Discussionmentioning

confidence: 99%

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

2016

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The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

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Fruit ripening and chitosan coating impacts on the expression profile of Banana Lectin (BanLec) genes

Dwivany,

Utami,

Yuswindia

et al. 2024

Scientia Horticulturae

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Binpairs: Utilization of Illumina Paired-End Information for Improving Efficiency of Taxonomic Binning of Metagenomic Sequences

Cited by 4 publications

References 11 publications

Can Targeting Non-Contiguous V-Regions With Paired-End Sequencing Improve 16S rRNA-Based Taxonomic Resolution of Microbiomes?: An In Silico Evaluation

Can Targeting Non-Contiguous V-Regions With Paired-End Sequencing Improve 16S rRNA-Based Taxonomic Resolution of Microbiomes?: An In Silico Evaluation

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

Fruit ripening and chitosan coating impacts on the expression profile of Banana Lectin (BanLec) genes

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