OPINION/HYPOTHESIS
Forming Consensus To Advance Urobiome Research
Linda Brubaker,a Jean-Philippe F. Gourdine,b Nazema Y. Siddiqui,c Amanda Holland,d Thomas Halverson,e Roberto Limeria,f
David Pride,g,h Lenore Ackerman,i Catherine S. Forster,j Kristin M. Jacobs,k Krystal J. Thomas-White,l Catherine Putonti,e,m
Qunfeng Dong,n,o Michael Weinstein,p,q Emily S. Lukacz,a Lisa Karstens,r Alan J. Wolfee,f
Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Female Pelvic Medicine and Reconstructive Surgery, University of California, San Diego,
San Diego, California, USA
a
Oregon Clinical & Translational Research Institute (OCTRI), Oregon Health & Science University, Portland, Oregon, USA
b
c
Department of Obstetrics and Gynecology, Division of Female Pelvic Medicine and Reconstructive Surgery, Duke University, Durham, North Carolina, USA
Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA
d
Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
e
Loyola Genomics Facility, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
f
Department of Pathology, University of California, San Diego, San Diego, California, USA
g
h
i
Department of Medicine, University of California, San Diego, San Diego, California, USA
Department of Urology, Division of Female Pelvic Medicine and Reconstructive Surgery, University of California, Los Angeles, Los Angeles, California, USA
Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
j
k
l
Department of Obstetrics and Gynecology, Division of Female Pelvic Medicine and Reconstructive Surgery, Rush University, Chicago, Illinois, USA
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA
Department of Biology, Bioinformatics Program, Loyola University Chicago, Chicago, Illinois, USA
m
n
Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
Center for Biomedical Informatics, Loyola University Chicago, Chicago, Illinois, USA
o
Molecular, Cell, and Developmental Biology, University of California, Los Angeles, Los Angeles, California, USA
p
Zymo Research, Irvine, California, USA
q
Department of Clinical Epidemiology and Medical Informatics, Oregon Health & Science University, Portland, Oregon, USA
Urobiome research has the potential to advance the understanding of a
wide range of diseases, including lower urinary tract symptoms and kidney disease.
Many scientific areas have benefited from early research method consensus to facilitate the greater, common good. This consensus document, developed by a group of
expert investigators currently engaged in urobiome research (UROBIOME 2020 conference participants), aims to promote standardization and advances in this field by
the adoption of common core research practices. We propose a standardized nomenclature as well as considerations for specimen collection, preservation, storage,
and processing. Best practices for urobiome study design include our proposal for
standard metadata elements as part of core metadata collection. Although it is
impractical to follow fixed analytical procedures when analyzing urobiome data, we
propose guidelines to document and report data originating from urobiome studies.
We offer this first consensus document with every expectation of subsequent revision as our field progresses.
ABSTRACT
KEYWORDS consensus, guideline, human microbiome, research, statement, urinary
microbiome, urobiome
Citation Brubaker L, Gourdine J-PF, Siddiqui
NY, Holland A, Halverson T, Limeria R, Pride D,
Ackerman L, Forster CS, Jacobs KM, ThomasWhite KJ, Putonti C, Dong Q, Weinstein M,
Lukacz ES, Karstens L, Wolfe AJ. 2021. Forming
consensus to advance urobiome research.
mSystems 6:e01371-20. https://doi.org/10
.1128/mSystems.01371-20.
Editor Jian Xu, Qingdao Institute of BioEnergy
and Bioprocess Technology, Chinese Academy
of Sciences
Copyright © 2021 Brubaker et al. This is an
open-access article distributed under the terms
of the Creative Commons Attribution 4.0
International license.
Address correspondence to Linda Brubaker,
[email protected].
ince the discovery of the human urinary microbiome (urobiome), urobiome
research has been impacted by inconsistent sampling conditions, technical conditions, and participant-related factors (1). The number of investigators currently working
in urobiome research is still relatively small; however, the rapid growth of the field and
the variety of approaches used to date have highlighted an urgent need for consensus
on optimal strategies for the scientific investigation of the urobiome. A group of expert
S
July/August 2021 Volume 6 Issue 4 e01371-20
Faster advances with shared approaches see the consensus statement of the UROBIOME
2020 conference. Much to learn if we all pull
together. #urobiome #urinary microbiome
@brubaker1030
Published 20 July 2021
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r
Opinion/Hypothesis
FIG 1 Key recommendations for urobiome research.
TERMINOLOGY
Standard terminology for urine specimens is necessary (Fig. 2). Many descriptors,
including “bladder,” “urinary,” “urogenital,” and “genitourinary,” have been used, and
these terms are often conflated. We propose a standardized nomenclature to explicitly
describe the specimen as it relates to the collection method. The preferred, recommended terminology for a voided urine sample is “urogenital sample.” The preferred,
recommended terminology for a catheterized urine sample (either transurethral or
suprapubic) is “urinary bladder.” Samples obtained by urethral swabs, by urothelial/tissue biopsy, or from the kidney pelvis should be so named.
URINE SPECIMEN COLLECTION
The urine specimen collection method must guide analysis and data interpretation,
appropriately recognizing anatomical differences between sexes. Although the microbial biomass increases as the urine moves from the kidney to the bladder, urethra, and
external genitalia, the urobiome has a low microbial biomass compared to other
human microbial niches. Several studies have provided convincing evidence that the
female urobiome includes vulvovaginal microbes (2, 3) when conventional “clean
catch” midstream voided urine is used; thus, this type of sample should be referred to
as a urogenital sample. A catheterized urine sample minimizes the inclusion of vulvovaginal microbes. When catheterization is not feasible or not desired (due to the
potential disturbance of the urobiome itself) or when researchers wish to answer questions concerning the lower urinary tract microbiota, voided urine samples can be collected with a urinal device (i.e., Peezy midstream [Forte Medical]) that decreases microbial abundance and diversity, apparently by decreasing posturethral contamination (4).
When multiple samples are collected from the same research participant, the order of
collection should be specified. An alternative is to include a periurethral swab to allow
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investigators currently engaged in urobiome research gathered to share research progress and exchange ideas at the National Institutes of Health (NIH)-sponsored international UROBIOME conferences in 2019 and 2020. This consensus document, developed
by UROBIOME 2020 conference participants and their collaborators, aims to promote
standardization and advances in this field by the adoption of common core research
practices (Fig. 1).
Opinion/Hypothesis
FIG 2 Recommended terminology for urobiome samples.
SPECIMEN PRESERVATION AND STORAGE
Immediately upon procurement, specimens should be cooled on ice or in a 4°C refrigerator and should be received by research personnel within 4 h. To avoid inappropriate microbial growth or degradation of nucleic acids, specimens should be appropriately preserved.
For all culture-based techniques, we recommend the use of BD Vacutainer Plus C&S
boric acid sodium borate/formate (“gray top”) tubes (Becton, Dickinson and Company,
Franklin Lakes, NJ). These are commonly used for clinical culture and antimicrobial sensitivity testing because they maintain microbial viability for at least 24 h under ambient
conditions while inhibiting growth. This 24-h period gives research personnel some
flexibility and permits overnight shipping.
For culture-independent analyses, we recommend the addition of AssayAssure
(Sierra Molecular Corporation, Princeton, NJ) directly to the sample in a 1:10 ratio. This
reagent is designed to inhibit 31 enzyme families known to degrade nucleic acids and
thus stabilizes nucleic acids (DNA and RNA) over extended time periods without freezing or refrigeration. Importantly, it does not inhibit the amplification fundamental to
PCR-based analyses such as 16S rRNA gene sequencing. It is recommended that the
specimen be frozen at 280°C upon receipt. However, a benchmarking study showed
that AssayAssure in combination with immediate cooling to 4°C or freezing at 220°C
allowed storage for up to 4 days with a minimal impact on alpha diversity (8).
Although the AssayAssure product guide states that samples can be maintained for up
to 4 days at room temperature, we recommend caution when interpreting data from
specimens held in this fashion compared to those immediately cooled in the presence
of AssayAssure as different taxa may be recovered under different temperatures (8).
We recommend rapid shipment (overnight if possible) on dry ice; however, the 4-day
window allows flexibility as long as the samples remain cool. Other nucleic acid preservatives exist (e.g., DNA/RNA Shield [Zymo Research Corporation, Irvine, CA]) and can
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the separate detection of genital microbes (5). Separate urethral swabs should be used
for studies of the urethral microbiota. There is much less research informing urinary
sample collection techniques in men; however, the currently available evidence supports the following conclusions: (i) the microbiome of voided urine most closely resembles that of urethral swabs, and (ii) catheterized urine does not tend to resemble
voided urine (6, 7). Therefore, for males, we recommend using the term “urogenital”
for voided urine and “urinary bladder” for catheterized urine or suprapubic aspirates.
Opinion/Hypothesis
SAMPLE PROCESSING
Traditional and enhanced culture techniques, as well as culture-independent methods, can be used for microbial detection. Culture techniques facilitate microbial detection and demonstrate that the microbe is alive, allowing subsequent experiments with
the microbe itself. Lists of known urinary microbes and their growth conditions have
been published (9, 10). Enhanced culture techniques, also known as metaculturomics,
move beyond the traditional method described by Kass (11), allowing detection of
microbes similar to that achieved with sequencing techniques (9, 10). Several
enhanced culture methods have been reported (12, 13), including the expanded quantitative urine culture (EQUC) protocol, which has been used extensively for urobiome
studies (9, 10, 13). To account for the very low biomass of catheterized urine specimens, we recommend plating 100 m l, which allows the detection of 10 CFU/ml.
However, smaller volumes (1 or 10 m l) are recommended to achieve accurate counts of
CFU per milliliter for voided urine samples or swabs (e.g., urethral or vaginal).
Compared to the standard method, EQUC uses additional growth media (9, 10). The
selection of media will depend on the research question, the cohort under study, the
sample type, and resource constraints. The use of Columbia CNA (colistin naladixic
acid) blood agar plates is critical to detect underlying Gram-positive bacteria that are
often overwhelmed by more numerous and faster-growing Gram-negative bacteria
such as Escherichia coli (9, 10). EQUC uses more atmospheric conditions than the standard method; 5% CO2 allows the growth of most urinary species, which prefer less oxygen. Anaerobic conditions are used for obligate anaerobes; when possible, we recommend an anaerobic chamber. If a chamber is not available, anaerobic jars can suffice
for many but not all anaerobes. Finally, an extended incubation period (48 instead of
24 h) allows for the growth of slow-growing microbes and for the morphological differences between species to develop (8).
For sequencing, investigators should have a complete and detailed workflow
(including nucleic acid isolation, library preparation, and sequencing) that aligns with
the study hypotheses and bioinformatic analysis. Currently, marker gene (amplicon)
sequencing is most commonly used for urobiome investigations. Studies of the bacterial communities rely on a hypervariable region of the 16S rRNA gene, while fungal
community surveys target the internal transcribed spacer (ITS) region (14). Whereas
amplicon sequencing can be used for taxonomic assignment and to determine relative
quantities, shotgun metagenomic sequencing can provide insight into urobiome functionality and can also detect the viral fraction, which lacks a conserved marker gene
(15).
Nucleic acid isolation techniques affect sequencing results, with some nucleic acid
isolation kits showing biases that could specifically affect urobiome information (15).
Enzymatic lysis is generally more reproducible among a range of laboratory environments (16). When establishing an enzymatic lysis protocol within a new laboratory,
testing must be performed to determine whether the lysing enzymes contain nucleic
acids from their manufacturing process (contaminants known as “the kitome”).
Lysozyme and mutanolysin have been shown to contain a minimum amount of kitome
contamination while having the best lysis efficiency (16).
Purification methodologies can be done with either silica column or magnetic bead
protocols. Silica columns are easy to use; however, as they tend to shear DNA during
extraction, they should be used only for short-read sequencing. Magnetic beads are
easier to automate and can provide similar yields and purities (17, 18). We recommend
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be used if AssayAssure is unavailable. As there is no current evidence that either pelleting/freezing bacteria or boric acid will adequately preserve nucleic acid, we recommend that this preservative/storage method should be included as a study limitation
when nucleic acid preservatives are not utilized (due to affordability or other reasons).
Studies reporting on urobiome findings should explicitly describe the use of preservative and storage conditions.
Opinion/Hypothesis
CORE METADATA AND UROBIOME STUDY DESIGN
In clinical research, standardized guidelines for reporting randomized trials and
observational studies have led to increased reporting quality and transparency for
readers (20–22). In microbiome research, metadata guidelines function in a similar
capacity to improve transparency, enhance interpretation, and facilitate integration
and comparison of results among studies (23–25). Readers should be able to understand the design, conduct, and analysis of a microbiome study in order to comprehend
and interpret results. Detailed and thorough reporting of metadata, the information
that describes a sampling event and subsequent data generation efforts, facilitates a
shared understanding of the relevance of research findings. In addition, collection and
reporting of a common, minimal set of metadata across different projects will foster
data comparisons and analysis; they will facilitate comparisons across studies and combining of studies to allow more powerful meta-analyses.
Following a review of other consensus-based guidelines and based on iterative discussions within the urobiome research community, we propose standard metadata
elements for urobiome studies. These include the minimum required metadata elements as well as those that are optional but highly desired for publication (Table 1).
Since urobiome studies commonly involve human subject research, protected health
information must not be included in the sequencing data or metadata.
Within the proposed metadata elements, “required” elements refer to the absolute
minimum information needed to make data interpretable. The “desired” elements include
characteristics that enhance the reader’s ability to interpret findings within specific
cohorts. These elements have been associated with differences in microbiota in previous
studies and thus are considered potentially confounding elements. We suggest that study
teams aiming for a high level of rigor should collect information pertaining to the desired
elements and either include this information when disseminating their research or explain
the lack of inclusion. Researchers are highly encouraged to consider additional items relevant to their study design or specific research question. The recommended metadata elements in Table 1 are organized based on important biological, environmental, and technical factors that could introduce variability or confound results.
For studies that include marker gene sequencing (e.g., 16S rRNA gene sequencing),
we have complied with the Genome Standards Consortium (GSC) recommendations
for minimum information standards (MixS) for describing and publicly sharing these
data (26). In collaborating with the GSC, we have created an environmental package
(MixS-Urobiome) consisting of a checklist for describing minimum and desired
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that, whenever possible, all samples from entire projects be sequenced at once on the
same machine to minimize technical variations. When that is not possible, we recommend that machines with the most similar chemistries/flow cells be used and that
available reagent lot numbers be recorded so that these metadata can be considered
during analysis. We recommend running positive-control samples with each batch to
identify any differences due to the batch.
For 16S amplicon sequencing and Illumina’s paired-end 250-bp chemistry, one must
choose between longer sequences that span multiple variable regions of the 16S rRNA
gene (e.g., V1-V3) or shorter regions (e.g., V4). Longer regions possess more sequence information for downstream taxonomic assignment. However, sequence read quality
diminishes at the ends. For shorter regions, this problem is reduced because the reads in
both directions overlap, and sequencing errors can be eliminated by comparing complementary reads. For longer regions, poor-quality sequence overlap in the middle region
can yield artifacts, which artificially increase sample diversity.
The choice of sequencing chemistries for whole-genome sequencing of purified isolates is important. Short-read chemistries (e.g., Illumina and Ion Torrent) are recommended if draft assemblies are sufficient. If complete genome assemblies are required,
then long-read sequencing chemistries (e.g., PacBio or Nanopore) can be used to provide scaffolding to assemble data from the short-read chemistries (19).
Opinion/Hypothesis
Element(s)
Biological elements
Age
Sex
Antibiotic usage
Required/desired
Descriptiond
Required
Required
Desired
Hormone status
Desired
Contraception
Body mass index
Race, ethnicity
Surgery
Desired
Desired
Desired
Desired
Birth detailsb
Desired
Medical history
Desired
Urine characteristics
Desired
Age in years or months/days if appropriate for infant/young child populationb
Biological sex; gender if relevant for the study
There is a lack of knowledge about postantibiotic microbiome recovery; when possible,
we recommend recording of use in the prior 3 months or length of time between last
antibiotic exposure and sample collection
Pubertal stagea
Pregnant/postpartum
Menopausal status: perimenopausal, postmenopausal
Also specify if taking supplemental hormones (estrogen) and route (oral, transdermal, or
vaginal, etc.)
Last menstrual period (if menstruating)
Use of oral contraceptives, other hormonal or nonhormonal/barrier, or none
Body mass index at the time of the study visit, calculated from height and weight
If possible, use standard terminology from sources such as the U.S. census and SNOMED CT
Performed in the prior 3 months
Prior GU surgeries
Prior implanted GU materials
Gestational age
Mode of delivery
NICU stay
Method of feeding
Diabetes/prediabetes
Other relevant medical comorbidities
Use of steroids or immunosuppressant medications
GU anatomical abnormalities
Recurrent GU infections
Recent GU instrumentation
pH, specific gravity, leukocyte esterase, blood
Environmental variables
Method of collection
Required
Geographic locationc
Seasonal
Dietary
Sexual activity
Required
Desired
Desired
Desired
Technical variables
Date and time of collectionc
with conditions
Required
Date and time of freezing
Required
Preservative
DNA extraction
Sequencing methodc
Required
Required
Required
Processing details
Desired
Void, collection device (Peezy)
Catheter (use of Mitrofanoffa)
Suprapubic aspirate
Can be discrete, including geographic coordinates, or broad, such as region or country
Month of collection
Consumption of a special diet, use of fiber supplementation, yogurt consumption
Time interval between last sexual activity and sample collection, if sexually active
Used to ensure that samples stored at room temp for long periods are highlighted as
such, potentially impacting the validity of results
Ensure that the date is generic enough to be included or use a date range
Time interval between sample collection and freezing
Omit if samples undergo immediate DNA extraction
If used, name
Method/kit used
e.g., Illumina, Ion Torrent, Nanopore, PacBio, Sanger, pyrosequencing; include amplicon/
variable region(s) used
Including, but not limited to, details of sample transfer method and extraction protocol
(sterile hood or technique), etc.
aAdditional
recommendation for pediatric populations.
recommendation for infant populations.
cRequired when uploading sequence data to the Sequence Read Archive (SRA) (27) or the European Nucleotide Archive (ENA) (36) public data repository.
dOCP, oral contraceptive pill; GU, genitourinary; NICU, neonatal intensive care unit.
bAdditional
information about marker gene analyses (26). Table S1 in the supplemental material
displays a checklist structured to facilitate the uploading of information to public databases such as the Sequence Read Archive (SRA), where raw sequencing data are often
shared (27). A Research Electronic Data Capture (REDCap) database template encompasses required and desired metadata elements should study teams wish to use a
standard template for prospective studies (28).
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TABLE 1 Proposed elements to be included in the minimum metadata standards for reporting of urobiome research
Opinion/Hypothesis
TABLE 2 Guidelines for processing sequencing data for urobiome researcha
Assigning taxonomy
Data cleaning
Whole-genome sequencing
Data cleaning
Read processing
Annotation
Software pipelines for data analysis
Marker genes
WGS
Viral
aOTUs,
Description (reference[s])
Sequencing reads can be grouped into OTUs or ASVs; ASVs offer several advantages over OTUs, such as better
accuracy and resolution, and hence are preferred (37); current ASV algorithms include DADA2 (38) and
Deblur (39); significantly outdated OTU clustering algorithms (such as uclust [40]) should be avoided
Algorithm: taxonomy can be assigned with taxonomic classifiers such as naive Bayes or BLCA classifiers (41, 42);
species-level assignment needs to be performed with algorithms designed for species-level assignments,
such as BLCA or the exact matching approach implemented in DADA2 (43)
Database: the Silva (42) and NCBI 16S (44) databases are preferred, as they are more representative of
microbiota in the urobiome than the currently available version of the Greengenes database (v13_8) (43)
Chimeras: chimeras arise from PCR and should be removed using an algorithm such as ChimeraSlayer (45) or
UCHIME (37, 46)
Contaminants: since catheter-collected specimens are typically low-biomass specimens, computational
strategies for bacterial contaminants, identification, and removal should be used; Decontam is currently the
preferred approach in conjunction with an exptl design that includes negative controls and/or a mock
microbial dilution series to evaluate performance (47)
Host DNA needs to be removed using tools such as Bowtie2 with the current human reference genome (48)
Sequencing reads can be processed using metagenomic de novo sequence assembly using tools such as
metaSPAdes (49) or binned, where reads are clustered by sequence similarity, using tools such as MaxBin (50)
Taxonomic annotation: marker genes such as 16S rRNA and well-characterized functional genes can be used
for genus- and species-level annotations using tools such as Metaphlan (51)
Gene annotation: identifying relevant features of bacterial genomes can be performed using tools such as
Prokka (52)
Metabolic pathway analysis: the metabolic functional potential of a microbial community can be modeled and
explored using tools such as CarveMe (53); as with marker gene sequencing, annotation is highly dependent
on the reference databases used and how well the urobiome microbiota are represented
QIIME2 (54), mothur (55), and DADA2 (38)
MG-RAST (56), EBI MetaGenomics (57), and IMG/M (58)
Classification of eukaryotic viruses and bacteriophage: Virmine (59)
Classification of bacteriophage: VirSorter (60)
operational taxonomic units; ASVs, amplicon sequence variants; WGS, whole genome sequencing.
BIOINFORMATIC APPROACHES AND DATA ANALYSIS
Analyzing urobiome data is often tailored to the specific research questions
addressed in a particular project, making it impractical to follow fixed analytical procedures. Table 2 displays guidance for documenting and reporting urobiome study data
(29, 30). To ensure that urobiome data are appropriately handled and interpreted, it is
essential to collaborate with bioinformaticians or computational biologists; consultation in the early stages of study design is recommended.
Several manipulations are needed to distill sequencing reads into biologically
meaningful data for statistical analysis. Standard steps include quality filtering and
denoising, grouping sequences by similarity for marker gene studies or binning
approaches for whole-genome sequencing (WGS) studies, assembly for WGS studies,
removing technical artifacts and noise, and assigning taxonomy (31). While the
approach for a specific study depends on the data generated, the steps can be completed using freely available sequence processing platforms. Table 2 displays current
guidelines and recommendations.
Urobiome studies are typically limited by a small sample size yet a large number of
measured variables (taxa or genes). Thus, ecological community analyses such as alpha
diversity (e.g., the Chao1, Simpson, Shannon, and Pielou indices) and beta diversity
(e.g., Bray-Curtis and UniFrac) using nonmetric multidimensional scaling (NMDS) and
principal-coordinate analysis (PCoA) are applied for multivariate analyses of microbiomes (32). These measures can identify overall differences between study groups.
Drilling down to the level of taxa or genes is often desired, but the process is complex.
Although standard statistical methods are often applied, it is important to realize that
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Data processing step
Marker gene sequencing
Grouping reads
Opinion/Hypothesis
TABLE 3 Minimum information for reporting bioinformatics methods in urobiome studies
Information to be
included
Software
Databases
Code
Data
aSRA,
Description (reference)a
Include software package and version; if using a package such as QIIME (61), reference key algorithms for OTU/ASV generation,
taxonomy assignment, chimera removal, and contaminant detection
Include databases used and version
Include essential custom-written code for analysis or data processing as supplemental material or link to code repository such
as GitHub
Raw sequencing data: stored in a public repository such as SRA (27), ENA (36), or dbGaP (62)
WGS assemblies: stored in a public repository such as GenBank
Metadata: follow MIMARKS (26) or MixS guidelines; upload with raw data
Sequence Read Archive; ENA, European Nucleotide Archive; dbGaP, Database of Genotypes and Phenotypes.
CONCLUDING COMMENTS
Urobiome research has the potential to advance our understanding of human
health and a wide range of diseases, including lower urinary tract symptoms and kidney disease. Many scientific areas have benefited from early consensus on research
methods by allowing investigators to more appropriately compare their findings with
those of their colleagues, optimizing transparency and communication and facilitating
research for the greater, common good. We offer this first consensus document with
every expectation of subsequent revision as our field progresses.
SUPPLEMENTAL MATERIAL
Supplemental material is available online only.
TABLE S1, XLSX file, 0.04 MB.
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these methods are often not suitable because urobiome data are compositional, multivariate, nonnormal, highly skewed, and zero inflated. Therefore, we encourage the use
of statistical methods tailored to microbiome data (33). Multiple-test correction is important for controlling for false positives in statistical analyses; however, these efforts
may diminish real scientific findings. Thus, we recommend that investigators report
raw and corrected P values and provide scientific justification for results that should be
subject to further investigation and validation. Furthermore, it is important to realize
that the exploratory nature of most urobiome projects (at least at the initial phase)
makes defining a meaningful “effect size” a priori required for sample size calculation
challenging.
To ensure the reproducibility of an analysis, documentation of computational steps,
software, and data used is essential (34). For example, analysis performed in the R statistical programming language can be documented in RMarkdown (35). This documentation can be shared as supplemental material or stored on a code repository such as
GitHub. Both raw data and the associated metadata should be deposited in public
repositories for reanalysis (26). In the manuscript methods, software details should be
appropriately mentioned and referenced (Table 3).
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Knight R, Wolfe AJ, Pride DT. 2019. Benchmarking urine storage and collection conditions for evaluating the female urinary microbiome. Sci Rep
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