Facilitating bioinformatics reproducibility with QIIME 2 Provenance Replay.

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.

RESCRIPt: Reproducible sequence taxonomy reference database management.

Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt.

Experiences and lessons learned from two virtual, hands-on microbiome bioinformatics workshops.

In October of 2020, in response to the Coronavirus Disease 2019 (COVID-19) pandemic, our team hosted our first fully online workshop teaching the QIIME 2 microbiome bioinformatics platform. We had 75 enrolled participants who joined from at least 25 different countries on 6 continents, and we had 22 instructors on 4 continents. In the 5-day workshop, participants worked hands-on with a cloud-based shared compute cluster that we deployed for this course. The event was well received, and participants provided feedback and suggestions in a postworkshop questionnaire. In January of 2021, we followed this workshop with a second fully online workshop, incorporating lessons from the first. Here, we present details on the technology and protocols that we used to run these workshops, focusing on the first workshop and then introducing changes made for the second workshop. We discuss what worked well, what didn't work well, and what we plan to do differently in future workshops.

QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data.

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses-e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data Support Protocol: Further microbiome analyses.

Microbiotyping the Sinonasal Microbiome.

This study offers a novel description of the sinonasal microbiome, through an unsupervised machine learning approach combining dimensionality reduction and clustering. We apply our method to the International Sinonasal Microbiome Study (ISMS) dataset of 410 sinus swab samples. We propose three main sinonasal "microbiotypes" or "states": the first is Corynebacterium-dominated, the second is Staphylococcus-dominated, and the third dominated by the other core genera of the sinonasal microbiome (Streptococcus, Haemophilus, Moraxella, and Pseudomonas). The prevalence of the three microbiotypes studied did not differ between healthy and diseased sinuses, but differences in their distribution were evident based on geography. We also describe a potential reciprocal relationship between Corynebacterium species and Staphylococcus aureus, suggesting that a certain microbial equilibrium between various players is reached in the sinuses. We validate our approach by applying it to a separate 16S rRNA gene sequence dataset of 97 sinus swabs from a different patient cohort. Sinonasal microbiotyping may prove useful in reducing the complexity of describing sinonasal microbiota. It may drive future studies aimed at modeling microbial interactions in the sinuses and in doing so may facilitate the development of a tailored patient-specific approach to the treatment of sinus disease in the future.

The international sinonasal microbiome study: A multicentre, multinational characterization of sinonasal bacterial ecology.

The sinonasal microbiome remains poorly defined, with our current knowledge based on a few cohort studies whose findings are inconsistent. Furthermore, the variability of the sinus microbiome across geographical divides remains unexplored. We characterize the sinonasal microbiome and its geographical variations in both health and disease using 16S rRNA gene sequencing of 410 individuals from across the world. Although the sinus microbial ecology is highly variable between individuals, we identify a core microbiome comprised of Corynebacterium, Staphylococcus, Streptococcus, Haemophilus and Moraxella species in both healthy and chronic rhinosinusitis (CRS) cohorts. Corynebacterium (mean relative abundance = 44.02%) and Staphylococcus (mean relative abundance = 27.34%) appear particularly dominant in the majority of patients sampled. Amongst patients suffering from CRS with nasal polyps, a statistically significant reduction in relative abundance of Corynebacterium (40.29% vs 50.43%; P = .02) was identified. Despite some measured differences in microbiome composition and diversity between some of the participating centres in our cohort, these differences would not alter the general pattern of core organisms described. Nevertheless, atypical or unusual organisms reported in short-read amplicon sequencing studies and that are not part of the core microbiome should be interpreted with caution. The delineation of the sinonasal microbiome and standardized methodology described within our study will enable further characterization and translational application of the sinus microbiota.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity.

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2's unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The effect of an online lecture on psychosocial cancer care providers' attitudes about hypnosis.

Hypnosis has been shown to be efficacious in the control of the symptoms and side effects of cancer and its treatment across all stages of the cancer continuum. Yet, hypnosis has generally failed to widely disseminate to clinical cancer settings, potentially due in part to provider attitudes about hypnosis. In a sample of 340 trainees (psychosocial cancer care providers), we tested the effects of a 12-minute online video hypnosis lecture on provider attitudes (using the Attitudes Toward Hypnosis Questionnaire). We hypothesized that viewing the online video would improve attitudes about hypnosis. Using a repeated measures design, total attitudes toward hypnosis improved following the lecture [F(1,339) = 321.97, p < .0001], as did all hypnosis attitude subscales. Older age and ethnicity (Latino/a) were associated with more positive attitudes across assessment points (ps < .05). Those trainees without prior hypnosis experience had the most attitude improvement (p < .05). The results support the use of a brief, online hypnosis lecture to improve cancer care provider attitudes about hypnosis, and suggest a path forward to facilitate more widespread dissemination of hypnosis to cancer care.

q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data.

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. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.

Qiita: rapid, web-enabled microbiome meta-analysis.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

Interest and Attitudes about Hypnosis in a Large Community Sample.

Despite the available empirical evidence supporting the efficacy of hypnosis for alleviating symptoms and side effects across a variety of clinical contexts, hypnosis has failed to disseminate widely. One way to try to better understand the lack of hypnosis dissemination is to apply a marketing theory approach, focusing on attitudes and beliefs about a product (hypnosis) held by consumers. Better understanding of such factors can lead to strategies to promote the product among consumers, and in this case, encourage dissemination. The goal of the study was to investigate relationships between interest in hypnosis use and: 1) attitudes about hypnosis; 2) beliefs about the effectiveness of hypnosis (i.e., hypnosis credibility, and hypnosis effectiveness expectancies); 3) past experience with hypnosis; and 4) the perceived hedonic value and utility of hypnosis. The study also explored participants' preferences for hypnosis delivery method (i.e., live or recorded), as well as preferences for hypnosis labeling (i.e., how hypnosis is defined). Participants (N = 509) were recruited through Amazon Mechanical Turk and completed an anonymous online survey. The results revealed that participants' attitudes about hypnosis, their expectancies for the effectiveness of hypnosis, and the perceived hedonic value of hypnosis accounted for unique variance in participants' interest in hypnosis, ps < .05. Together, these variables accounted for 73% of the variance in participants' interest in hypnosis use. Based on these findings, we recommend that these key variables should be considered when planning for greater dissemination and uptake of empirically supported hypnosis interventions.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

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.

Cancer treatment experiences among survivors of childhood sexual abuse: A qualitative investigation of triggers and reactions to cumulative trauma.

ABSTRACTObjective:Some 25% of women and 8% of men in the United States have experienced childhood sexual abuse (CSA) before the age of 18. For these individuals, healthcare visits and interactions can be retraumatizing due to perceived similarities to past abuse (e.g., pain, undressing, lack of control). However, no prior studies have provided formal qualitative analyses regarding CSA survivors' reactions to cancer treatment. Therefore, our study's objective was to identify key themes pertaining to CSA survivors' cancer treatment experiences. METHOD: Male and female members of the Amazon Mechanical Turk (N = 159, mean age = 44.27 years, SD = 10.02) participated in an anonymous online survey study. The inclusion criteria included reporting: history of CSA; a diagnosis of colorectal, gynecological, breast, or skin cancer; and experience of triggers and/or difficulties during cancer treatment. Participants' responses to open-ended questions were analyzed using inductive thematic analysis. RESULTS: We identified two primary themes describing CSA survivors' experiences: Theme 1: treatment-related triggers (key subthemes: procedure-related, provider-related, and emotional triggers); and Theme 2: questioning the meaning of cumulative trauma (e.g., "Why me again?"). SIGNIFICANCE OF RESULTS: For CSA survivors, cancer and its treatment can trigger thoughts and emotions associated with the original abuse as well as negative evaluations of themselves, the world, and their future. Our findings are consistent with past research on CSA survivors' experiences in non-cancer healthcare settings and add to the literature by highlighting their struggles during cancer treatment. The present results can inform further research on trauma survivors' reactions to cancer treatment and give cancer care providers the context they need to understand and sensitively serve a substantial yet often overlooked patient group.

q2-sample-classifier: machine-learning tools for microbiome classification and regression.

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.

Cognitive-Behavioral Therapy Plus Hypnosis for Distress During Breast Radiotherapy: A Randomized Trial.

Radiotherapy is a common and effective treatment for women with breast cancer. However, radiotherapy has also been shown to adversely affect patients' emotional well-being. Currently, few mind-body interventions are designed to improve patients' quality of life during radiotherapy. One intervention which has demonstrated clinical efficacy in the breast cancer radiotherapy setting is Cognitive-Behavioral Therapy plus Hypnosis. The goal of this study was to investigate the impact of Cognitive-Behavioral Therapy plus Hypnosis on emotional distress in women with breast cancer undergoing radiotherapy. One hundred patients were randomly assigned to either the Cognitive-Behavioral Therapy plus Hypnosis (n = 50) or Attention Control (n = 50) group. Results revealed significant benefits of Cognitive-Behavioral Therapy plus Hypnosis on emotional distress at the mid-point (d = 0.54), the conclusion (d = 0.64), and 4 weeks following the conclusion (d = 0.65) of radiotherapy (all ps < 0.05). In summary, results support further study of Cognitive-Behavioral Therapy plus Hypnosis as an evidence-based intervention to reduce emotional distress in women with breast cancer. Cognitive-Behavioral Therapy plus Hypnosis has the benefits of being brief, noninvasive, lacking side-effects, and producing beneficial effects which last beyond the conclusion of radiotherapy. Given these strengths, we propose that Cognitive-Behavioral Therapy plus Hypnosis is a strong candidate for greater dissemination and implementation in cancer populations.

Allocation of food allergy responsibilities and its correlates for children and adolescents.

This study examined the degree to which children and adolescents with food allergy accept responsibility for their own care, and the extent to which greater self-management is associated with past history of a life-threatening allergic reaction or anxiety. For children (n = 190), caregiver and patient report of self-management was consistent, but agreement was poor for adolescent dyads (n = 59). History of a life-threatening allergic reaction was associated with greater self-management for children only, while among adolescents, it was associated with greater anxiety. Given that shifting to self-management may be challenging, discussion and preparation about this process is warranted.

The Florida Department of Health and the Florida Association of County Health Department Business Administrators: a model of successful collaboration to sustain operational excellence.

The Florida Association of County Health Department Business Administrators, Inc., is an organization of public health business professionals working collaboratively with the state's department of health administrative offices in promoting best practices throughout local county health departments. This article discusses a number of joint initiatives, past and present, that are benefiting public health services in Florida. In addition, mentoring and training, contract management, cost allocation, and financial reporting, among others, are "hot button" topics of interest to all states in promoting improved administration of their public health programs.