Literature DB >> 30649166

HMP16SData: Efficient Access to the Human Microbiome Project Through Bioconductor.

Lucas Schiffer1,2, Rimsha Azhar1,2, Lori Shepherd3, Marcel Ramos1,2,3, Ludwig Geistlinger1,2, Curtis Huttenhower4,5, Jennifer B Dowd1,6, Nicola Segata7, Levi Waldron1,2.   

Abstract

Phase 1 of the Human Microbiome Project (HMP) investigated 18 body subsites of 242 healthy American adults to produce the first comprehensive reference for the composition and variation of the "healthy" human microbiome. Publicly available data sets from amplicon sequencing of two 16S ribosomal RNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S ribosomal RNA variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Bioconductor; Human Microbiome Project; bioinformatics; databases; metagenomics; microbiome; statistical software

Mesh:

Substances:

Year:  2019        PMID: 30649166      PMCID: PMC6545282          DOI: 10.1093/aje/kwz006

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   5.363


  14 in total

1.  UniFrac: an effective distance metric for microbial community comparison.

Authors:  Catherine Lozupone; Manuel E Lladser; Dan Knights; Jesse Stombaugh; Rob Knight
Journal:  ISME J       Date:  2010-09-09       Impact factor: 10.302

2.  Accessible, curated metagenomic data through ExperimentHub.

Authors:  Edoardo Pasolli; Lucas Schiffer; Paolo Manghi; Audrey Renson; Valerie Obenchain; Duy Tin Truong; Francesco Beghini; Faizan Malik; Marcel Ramos; Jennifer B Dowd; Curtis Huttenhower; Martin Morgan; Nicola Segata; Levi Waldron
Journal:  Nat Methods       Date:  2017-10-31       Impact factor: 28.547

3.  Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

Authors:  Patrick D Schloss; Sarah L Westcott; Thomas Ryabin; Justine R Hall; Martin Hartmann; Emily B Hollister; Ryan A Lesniewski; Brian B Oakley; Donovan H Parks; Courtney J Robinson; Jason W Sahl; Blaz Stres; Gerhard G Thallinger; David J Van Horn; Carolyn F Weber
Journal:  Appl Environ Microbiol       Date:  2009-10-02       Impact factor: 4.792

Review 4.  Orchestrating high-throughput genomic analysis with Bioconductor.

Authors:  Wolfgang Huber; Vincent J Carey; Robert Gentleman; Simon Anders; Marc Carlson; Benilton S Carvalho; Hector Corrada Bravo; Sean Davis; Laurent Gatto; Thomas Girke; Raphael Gottardo; Florian Hahne; Kasper D Hansen; Rafael A Irizarry; Michael Lawrence; Michael I Love; James MacDonald; Valerie Obenchain; Andrzej K Oleś; Hervé Pagès; Alejandro Reyes; Paul Shannon; Gordon K Smyth; Dan Tenenbaum; Levi Waldron; Martin Morgan
Journal:  Nat Methods       Date:  2015-02       Impact factor: 28.547

5.  A framework for human microbiome research.

Authors: 
Journal:  Nature       Date:  2012-06-13       Impact factor: 49.962

6.  Structure, function and diversity of the healthy human microbiome.

Authors: 
Journal:  Nature       Date:  2012-06-13       Impact factor: 49.962

7.  Evaluation of 16S rDNA-based community profiling for human microbiome research.

Authors: 
Journal:  PLoS One       Date:  2012-06-13       Impact factor: 3.240

8.  phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

Authors:  Paul J McMurdie; Susan Holmes
Journal:  PLoS One       Date:  2013-04-22       Impact factor: 3.240

9.  Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis.

Authors:  Bo Yang; Yong Wang; Pei-Yuan Qian
Journal:  BMC Bioinformatics       Date:  2016-03-22       Impact factor: 3.169

10.  Bioconductor workflow for microbiome data analysis: from raw reads to community analyses.

Authors:  Ben J Callahan; Kris Sankaran; Julia A Fukuyama; Paul J McMurdie; Susan P Holmes
Journal:  F1000Res       Date:  2016-06-24
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  12 in total

Review 1.  Microbiome data science.

Authors:  Sudarshan A Shetty; Leo Lahti
Journal:  J Biosci       Date:  2019-10       Impact factor: 1.826

2.  TreeSummarizedExperiment: a S4 class for data with hierarchical structure.

Authors:  Ruizhu Huang; Charlotte Soneson; Felix G M Ernst; Kevin C Rue-Albrecht; Guangchuang Yu; Stephanie C Hicks; Mark D Robinson
Journal:  F1000Res       Date:  2020-10-15

3.  Naturalization of the microbiota developmental trajectory of Cesarean-born neonates after vaginal seeding.

Authors:  Se Jin Song; Jincheng Wang; Cameron Martino; Lingjing Jiang; Wesley K Thompson; Liat Shenhav; Daniel McDonald; Clarisse Marotz; Paul R Harris; Caroll D Hernandez; Nora Henderson; Elizabeth Ackley; Deanna Nardella; Charles Gillihan; Valentina Montacuti; William Schweizer; Melanie Jay; Joan Combellick; Haipeng Sun; Izaskun Garcia-Mantrana; Fernando Gil Raga; Maria Carmen Collado; Juana I Rivera-Viñas; Maribel Campos-Rivera; Jean F Ruiz-Calderon; Rob Knight; Maria Gloria Dominguez-Bello
Journal:  Med (N Y)       Date:  2021-06-17

4.  treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses.

Authors:  Ruizhu Huang; Charlotte Soneson; Pierre-Luc Germain; Thomas S B Schmidt; Christian Von Mering; Mark D Robinson
Journal:  Genome Biol       Date:  2021-05-17       Impact factor: 13.583

5.  Waldron et al. Reply to "Commentary on the HMP16SData Bioconductor Package".

Authors:  Levi Waldron; Lucas Schiffer; Rimsha Azhar; Marcel Ramos; Ludwig Geistlinger; Nicola Segata
Journal:  Am J Epidemiol       Date:  2019-06-01       Impact factor: 5.363

Review 6.  The food-gut axis: lactic acid bacteria and their link to food, the gut microbiome and human health.

Authors:  Francesca De Filippis; Edoardo Pasolli; Danilo Ercolini
Journal:  FEMS Microbiol Rev       Date:  2020-07-01       Impact factor: 16.408

7.  Comparison of lung microbiota between antineutrophil cytoplasmic antibody-associated vasculitis and sarcoidosis.

Authors:  Shoichi Fukui; Shimpei Morimoto; Kunihiro Ichinose; Shota Nakashima; Hiroshi Ishimoto; Atsuko Hara; Tomoyuki Kakugawa; Noriho Sakamoto; Yoshika Tsuji; Toshiyuki Aramaki; Tomohiro Koga; Shin-Ya Kawashiri; Naoki Iwamoto; Mami Tamai; Hideki Nakamura; Tomoki Origuchi; Yukitaka Ueki; Shino Suzuki; Hiroshi Mukae; Atsushi Kawakami
Journal:  Sci Rep       Date:  2020-06-11       Impact factor: 4.379

8.  Statistical evaluation of diet-microbe associations.

Authors:  Xiang Zhang; Max Nieuwdorp; Albert K Groen; Aeiko H Zwinderman
Journal:  BMC Microbiol       Date:  2019-05-09       Impact factor: 3.605

9.  mbImpute: an accurate and robust imputation method for microbiome data.

Authors:  Ruochen Jiang; Wei Vivian Li; Jingyi Jessica Li
Journal:  Genome Biol       Date:  2021-06-28       Impact factor: 13.583

10.  Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data.

Authors:  Matteo Calgaro; Chiara Romualdi; Levi Waldron; Davide Risso; Nicola Vitulo
Journal:  Genome Biol       Date:  2020-08-03       Impact factor: 13.583

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