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iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis

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dc.contributor.author Buza, Teresia M.
dc.contributor.author Tonui, Triza
dc.contributor.author Stomeo, Francesca
dc.contributor.author Tiambo, Christian
dc.contributor.author Katani, Robab
dc.contributor.author Schilling, Megan
dc.contributor.author Lyimo, Beatus
dc.contributor.author Gwakisa, Paul
dc.contributor.author Cattadori, Isabella M.
dc.contributor.author Buza, Joram
dc.contributor.author Kapur, Vivek
dc.date.accessioned 2019-10-17T12:15:42Z
dc.date.available 2019-10-17T12:15:42Z
dc.date.issued 2019
dc.identifier.uri https://doi.org/10.1186/s12859-019-2965-4
dc.identifier.uri http://dspace.nm-aist.ac.tz/handle/123456789/499
dc.description Research Article published BMC Bioinformatics en_US
dc.description.abstract Background: One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking. Results: We have developed a wrapper named iMAP (Integrated Microbiome Analysis Pipeline) to provide the microbiome research community with a user-friendly and portable tool that integrates bioinformatics analysis and data visualization. The iMAP tool wraps functionalities for metadata profiling, quality control of reads, sequence processing and classification, and diversity analysis of operational taxonomic units. This pipeline is also capable of generating web-based progress reports for enhancing an approach referred to as review-as-you-go (RAYG). For the most part, the profiling of microbial community is done using functionalities implemented in Mothur or QIIME2 platform. Also, it uses different R packages for graphics and R-markdown for generating progress reports. We have used a case study to demonstrate the application of the iMAP pipeline. Conclusions: The iMAP pipeline integrates several functionalities for better identification of microbial communities present in a given sample. The pipeline performs in-depth quality control that guarantees high-quality results and accurate conclusions. The vibrant visuals produced by the pipeline facilitate a better understanding of the complex and multidimensional microbiome data. The integrated RAYG approach enables the generation of web-based reports, which provides the investigators with the intermediate output that can be reviewed progressively. The intensively analyzed case study set a model for microbiome data analysis. en_US
dc.language.iso en en_US
dc.publisher BMC Bioinformatics en_US
dc.subject Microbiome bioinformatics en_US
dc.subject Microbiome data analysis en_US
dc.subject Microbiome data visualization en_US
dc.subject Bioinformatics pipeline en_US
dc.subject Phylogenetic analysis en_US
dc.subject Phylogenetic annotation en_US
dc.title iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis en_US
dc.type Article en_US


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