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Article ; Online: Facilitating bioinformatics reproducibility with QIIME 2 Provenance Replay.

Keefe, Christopher R / Dillon, Matthew R / Gehret, Elizabeth / Herman, Chloe / Jewell, Mary / Wood, Colin V / Bolyen, Evan / Caporaso, J Gregory

PLoS computational biology

2023 Volume 19, Issue 11, Page(s) e1011676

Abstract: Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different ... ...

Abstract	Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally, many biologists are not trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software.
MeSH term(s)	Reproducibility of Results ; Computational Biology/methods ; Software ; Workflow
Language	English
Publishing date	2023-11-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011676
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Facilitating bioinformatics reproducibility with QIIME 2 provenance Replay.

Christopher R Keefe / Matthew R Dillon / Elizabeth Gehret / Chloe Herman / Mary Jewell / Colin V Wood / Evan Bolyen / J Gregory Caporaso

PLoS Computational Biology, Vol 19, Iss 11, p e

2023 Volume 1011676

Abstract	Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally, many biologists are not trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting Neurodegenerative Disease Using Prepathology Gut Microbiota Composition: a Longitudinal Study in Mice Modeling Alzheimer's Disease Pathologies.

Borsom, Emily M / Conn, Kathryn / Keefe, Christopher R / Herman, Chloe / Orsini, Gabrielle M / Hirsch, Allyson H / Palma Avila, Melanie / Testo, George / Jaramillo, Sierra A / Bolyen, Evan / Lee, Keehoon / Caporaso, J Gregory / Cope, Emily K

Microbiology spectrum

2023 , Page(s) e0345822

Abstract: The gut microbiota-brain axis is suspected to contribute to the development of Alzheimer's disease (AD), a neurodegenerative disease characterized by amyloid-β plaque deposition, neurofibrillary tangles, and neuroinflammation. To evaluate the role of the ...

Abstract	The gut microbiota-brain axis is suspected to contribute to the development of Alzheimer's disease (AD), a neurodegenerative disease characterized by amyloid-β plaque deposition, neurofibrillary tangles, and neuroinflammation. To evaluate the role of the gut microbiota-brain axis in AD, we characterized the gut microbiota of female 3xTg-AD mice modeling amyloidosis and tauopathy and wild-type (WT) genetic controls. Fecal samples were collected fortnightly from 4 to 52 weeks, and the V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq. RNA was extracted from the colon and hippocampus, converted to cDNA, and used to measure immune gene expression using reverse transcriptase quantitative PCR (RT-qPCR). Diversity metrics were calculated using QIIME2, and a random forest classifier was applied to predict bacterial features that are important in predicting mouse genotype. Gene expression of glial fibrillary acidic protein (GFAP; indicating astrocytosis) was elevated in the colon at 24 weeks. Markers of Th1 inflammation (il6) and microgliosis (mrc1) were elevated in the hippocampus. Gut microbiota were compositionally distinct early in life between 3xTg-AD mice and WT mice (permutational multivariate analysis of variance [PERMANOVA], 8 weeks,
Language	English
Publishing date	2023-03-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2807133-5
ISSN	2165-0497 ; 2165-0497
ISSN (online)	2165-0497
ISSN	2165-0497
DOI	10.1128/spectrum.03458-22
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Book ; Online: Facilitating Bioinformatics Reproducibility

Keefe, Christopher R. / Dillon, Matthew R. / Herman, Chloe / Jewell, Mary / Wood, Colin V. / Bolyen, Evan / Caporaso, J. Gregory

2023

Abstract	Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally many biologists aren't trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software. Comment: 5 pages, 2 figures
Keywords	Quantitative Biology - Quantitative Methods
Subject code	020
Publishing date	2023-05-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: PepSIRF + QIIME 2

Brown, Annabelle M. / Bolyen, Evan / Raspet, Isaiah / Altin, John A. / Ladner, Jason T.

software tools for automated, reproducible analysis of highly-multiplexed serology data

2022

Abstract: PepSIRF is a command-line, module-based open-source software package that facilitates the analysis of data from highly-multiplexed serology assays (e.g., PepSeq or PhIP-Seq). It has nine separate modules in its current release (v1.5.0): demux, info, ... ...

Abstract	PepSIRF is a command-line, module-based open-source software package that facilitates the analysis of data from highly-multiplexed serology assays (e.g., PepSeq or PhIP-Seq). It has nine separate modules in its current release (v1.5.0): demux, info, subjoin, norm, bin, zscore, enrich, link, and deconv. These modules can be used together to conduct analyses ranging from demultiplexing raw high-throughput sequencing data to the identification of enriched peptides. QIIME 2 is an open-source, community-developed and plugin-based bioinformatics platform that focuses on data and analytical transparency. QIIME 2's features include integrated and automatic tracking of data provenance, a semantic type system, and built-in support for many types of user interfaces. Here, we describe three new QIIME 2 plugins that allow users to conduct PepSIRF analyses within the QIIME 2 environment and extend the core functionality of PepSIRF in two key ways: 1) enabling generation of interactive visualizations and 2) enabling automation of analysis pipelines that include multiple PepSIRF modules. Comment: 9 pages, 6 figures, software announcement
Keywords	Quantitative Biology - Quantitative Methods
Subject code	004
Publishing date	2022-07-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Introduction to Applied Bioinformatics: a free, open, and interactive text.

Bolyen, Evan / Rideout, Jai Ram / Chase, John / Pitman, T Anders / Shiffer, Arron / Mercurio, Willow / Dillon, Matthew R / Caporaso, J Gregory

The Journal of open source education

2018 Volume 1, Issue 5

Language	English
Publishing date	2018-10-02
Publishing country	United States
Document type	Journal Article
ISSN	2577-3569
ISSN (online)	2577-3569
DOI	10.21105/jose.00027
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: cual-id: Globally Unique, Correctable, and Human-Friendly Sample Identifiers for Comparative Omics Studies.

Chase, John H / Bolyen, Evan / Rideout, Jai Ram / Caporaso, J Gregory

mSystems

2015 Volume 1, Issue 1

Abstract: The number of samples in high-throughput comparative "omics" studies is increasing rapidly due to declining experimental costs. To keep sample data and metadata manageable and to ensure the integrity of scientific results as the scale of these projects ... ...

Abstract	The number of samples in high-throughput comparative "omics" studies is increasing rapidly due to declining experimental costs. To keep sample data and metadata manageable and to ensure the integrity of scientific results as the scale of these projects continues to increase, it is essential that we transition to better-designed sample identifiers. Ideally, sample identifiers should be globally unique across projects, project teams, and institutions; short (to facilitate manual transcription); correctable with respect to common types of transcription errors; opaque, meaning that they do not contain information about the samples; and compatible with existing standards. We present cual-id, a lightweight command line tool that creates, or mints, sample identifiers that meet these criteria without reliance on centralized infrastructure. cual-id allows users to assign universally unique identifiers, or UUIDs, that are globally unique to their samples. UUIDs are too long to be conveniently written on sampling materials, such as swabs or microcentrifuge tubes, however, so cual-id additionally generates human-friendly 4- to 12-character identifiers that map to their UUIDs and are unique within a project. By convention, we use "cual-id" to refer to the software, "CualID" to refer to the short, human-friendly identifiers, and "UUID" to refer to the globally unique identifiers. CualIDs are used by humans when they manually write or enter identifiers, while the longer UUIDs are used by computers to unambiguously reference a sample. Finally, cual-id optionally generates printable label sticker sheets containing Code 128 bar codes and CualIDs for labeling of sample collection and processing materials.
Language	English
Publishing date	2015-12-22
Publishing country	United States
Document type	Journal Article
ISSN	2379-5077
ISSN	2379-5077
DOI	10.1128/mSystems.00010-15
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Article: q2-sample-classifier: machine-learning tools for microbiome classification and regression.

Bokulich, Nicholas A / Dillon, Matthew R / Bolyen, Evan / Kaehler, Benjamin D / Huttley, Gavin A / Caporaso, J Gregory

Journal of open research software

2018 Volume 3, Issue 30

Abstract: q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience of non-bioinformatics specialists. ...

Abstract	q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience of non-bioinformatics specialists.
Language	English
Publishing date	2018-10-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2740435-3
ISSN	2049-9647
ISSN	2049-9647
DOI	10.21105/joss.00934
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Article: q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data.

Bokulich, Nicholas A / Dillon, Matthew R / Zhang, Yilong / Rideout, Jai Ram / Bolyen, Evan / Li, Huilin / Albert, Paul S / Caporaso, J Gregory

mSystems

2018 Volume 3, Issue 6

Abstract: Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population ... ...

Abstract	Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use.
Language	English
Publishing date	2018-11-20
Publishing country	United States
Document type	Journal Article
ISSN	2379-5077
ISSN	2379-5077
DOI	10.1128/mSystems.00219-18
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Article ; Online: Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

Bokulich, Nicholas A / Kaehler, Benjamin D / Rideout, Jai Ram / Dillon, Matthew / Bolyen, Evan / Knight, Rob / Huttley, Gavin A / Gregory Caporaso, J

Microbiome

2018 Volume 6, Issue 1, Page(s) 90

Abstract: Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis.: Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel ... ...

Abstract	Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.
MeSH term(s)	Algorithms ; Bacteria/genetics ; Base Sequence/genetics ; Computer Simulation ; DNA, Intergenic/genetics ; Fungi/genetics ; Machine Learning ; Microbiota/genetics ; RNA, Ribosomal, 16S/genetics ; Sequence Alignment/methods ; Software
Chemical Substances	DNA, Intergenic ; RNA, Ribosomal, 16S
Language	English
Publishing date	2018-05-17
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2697425-3
ISSN	2049-2618 ; 2049-2618
ISSN (online)	2049-2618
ISSN	2049-2618
DOI	10.1186/s40168-018-0470-z
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