BIG DATA paper

ForPeerReview
Only/NotforDistribution
Big Data
Towards more transparent and reproducible omics studies
through a common metadata checklist and data publications
Journal: Big Data
Manuscript ID: BIG-2013-0039
Manuscript Type: Original Article
Date Submitted by the Author: 05-Nov-2013
Complete List of Authors: Kolker, Eugene; Seattle Children's Research Institute, Bioinformatics and
High-Throughput Analysis Laboratory; Seattle Children's, Predictive
Analytics; Data-Enabled Life Sciences Alliance,
Ozdemir, Vural; Gaziantep University, Office of the President; Data-
Enabled Life Sciences Alliance,
Martens, Lennart; VIB, Department of Medical Protein Research; Ghent
University, Department of Biochemistry; Data-Enabled Life Sciences
Alliance,
Hancock, William; Northeastern University, Barnett Institute; Data-Enabled
Life Sciences Alliance,
Anderson, Gordon; Pacific Northwest National Laboratory, Fundamental &
Computational Sciences Directorate; Data-Enabled Life Sciences Alliance,
Anderson, Nathaniel; Seattle Children's Research Institute, Bioinformatics
and High-Throughput Analysis Laboratory; Data-Enabled Life Sciences
Alliance,
Aynacioglu, Sukru; Gaziantep University, Department of Pharmacology;
Data-Enabled Life Sciences Alliance,
Baranova, Ancha; George Mason University, School of Systems Biology;
Campagna, Shawn; University of Tennesee, Knoxville, Department of
Chemistry; Data-Enabled Life Sciences Alliance,
Chen, Rui; Stanford University, Department of Genetics; Data-Enabled Life
Sciences Alliance,
Choiniere, John; Seattle Children's Research Institute, Bioinformatics and
High-Throughput Analysis Laboratory; Data-Enabled Life Sciences Alliance,
Dearth, Stephen; University of Tennesee, Knoxville, Department of
Feng, Wu-Chun; Virginia Tech, SyNeRGy Laboratory; Virginia Tech,
Department of Computer Science; Virginia Tech, Department of Electrical
and Computer Engineering; Data-Enabled Life Sciences Alliance,
Ferguson, Lynnette; University of Auckland, Department of Nutrition; Data-
Fox, Geoffrey; Indiana University, School of Informatics and Computing;
Frishman, Dmitrij; Technische Universitat Muchen, ; Data-Enabled Life
Sciences Alliance,
Grossman, Robert; University of Chicago, Institute for Genomics and
ScholarOne Support phone: 434-964-4100 email: ts.mcsupport@thomson.com
Mary Ann Liebert, Inc.

ForPeerReview
Systems Biology; University of Chicago, Department of Medicine; Data-
Heath, Allison; University of Chicago, Institute for Genomics and Systems
Biology; Data-Enabled Life Sciences Alliance,
Higdon, Roger; Seattle Children's Research Institute, Bioinformatics and
High-Throughput Analysis Laboratory; Seattle Children's, Predictive
Analytics; Data-Enabled Life Sciences Alliance,
Hutz, Mara; Federal University of Rio Grande do Sul, Departamento de
Genetica; Data-Enabled Life Sciences Alliance,
Janko, Imre; Seattle Children's Research Institute, High-Throughput
Analysis Core; Data-Enabled Life Sciences Alliance,
Jiang, Lihua; Stanford University, Department of Genetics; Data-Enabled
Joshi, Sanjay; EMC, Life Sciences; Data-Enabled Life Sciences Alliance,
Kel, Alexander; GeneXplain GmbH, ; Data-Enabled Life Sciences Alliance,
Kemnitz, Joseph; University of Wisconsin-Madison, Department of Cell and
Regenerative Biology; University of Wisconsin-Madison, Wisconsin National
Primate Research Center; Data-Enabled Life Sciences Alliance,
Kohane, Isaac; Harvard Medical School, Department of Pediatrics; Harvard
Medical School, Health Sciences and Technology; Harvard Medical School,
Center for Biomedical Informatics; Data-Enabled Life Sciences Alliance,
Kolker, Natali; Seattle Children’s Research Institute, High-throughput
Analysis Core; Seattle Children’s Hospital, Predictive Analytics; Data-
Lancet, Doron; Weizmann Institute of Science, Department of Molecular
Genetics; Data-Enabled Life Sciences Alliance,
Lee, Elaine; Data-Enabled Life Sciences Alliance, ; Seattle Children's
Research Institute, High-Throughput Analysis Core
Li, Weizhong; University of California, San Diego, Center for Research in
Biological Systems; Data-Enabled Life Sciences Alliance,
Lisitsa, Andrey; Russian Human Proteome Organization, ; Institute of
Biomedical Chemistry, ; Data-Enabled Life Sciences Alliance,
Llerena, Adrian; Extremadura University Hospital and Medical School,
Clinical Research Center; Data-Enabled Life Sciences Alliance,
MacNealy-Koch, Courtney; Seattle Children's Research Institute,
Bioinformatics and High-throughput Analysis Laboratory; Data-Enabled Life
Sciences Alliance (DELSA Global),
Marshall, Jean-Claude; Catholic Health Initiatives, Center for Translational
Research; Data-Enabled Life Sciences Alliance,
Masuzzo, Paola; VIB, Department of Medical Protein Research; Ghent
Alliance,
May, Amanda; University of Tennesee, Knoxville, Department of
Mias, George; Stanford University, Department of Genetics; Data-Enabled
Monroe, Matthew; Pacific Northwest National Laboratory, Biological
Sciences Division; Data-Enabled Life Sciences Alliance,
Montague, Elizabeth; Seattle Children's Research Institute, Bioinformatics
and High-Throughput Analysis Laboratory; Data-Enabled Life Sciences
Alliance,
Mooney, Sean; The Buck Institute for Research on Aging, ; Data-Enabled
Nesvizhskii, Alexey; University of Michigan, Department of Pathology;
University of Michigan, Department of Computational Medicine and
Bioinformatics; Data-Enabled Life Sciences Alliance,
Noronha, Santosh; Indian Institute of Technology Bombay, Department of
Chemical Engineering; Data-Enabled Life Sciences Alliance,
Omenn, Gilbert; University of Michigan, Department of Computational
Medicine and Bioinformatics; University of Michigan, Department of
Molecular Medicine and Genetics; University of Michigan, School of Public
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ForPeerReview
Health ; Data-Enabled Life Sciences Alliance,
Rajasimha, Harsha; Jeeva Informatics Solutions LLC, ; Data-Enabled Life
Sciences Alliance,
Ramamoorthy, Preveen; National Jewish Health, Department of Medicine;
Sheehan, Jerry; University of California, San Diego, California Institute of
Telecommunications and Information Technology; Data-Enabled Life
Sciences Alliance,
Smarr, Larry; University of California, San Diego, California Institute of
Telecommunications and Information Technology; Data-Enabled Life
Sciences Alliance,
Smith, Charles; Seattle Children's Research Institute, Center for
Developmental Therapeutics; Data-Enabled Life Sciences Alliance,
Smith, Todd; Digital World Biology, ; Data-Enabled Life Sciences Alliance,
Snyder, Michael; Stanford University, Department of Genetics; Stanford
University, Center for Genomics and Personalize Medicine; Data-Enabled
Rapole, Srikanth; National Centre for Cell Science, Proteomics Laboratory;
Srivastava, Sanjeeva; Indian Institute of Technology Bombay, Proteomics
Laboratory; Data-Enabled Life Sciences Alliance,
Stanberry, Larissa; Seattle Children's Research Institute, Bioinformatics
and High-throughput Analysis Laboratory; Seattle Children’s Hospital,
Predictive Analytics; Data-Enabled Life Sciences Alliance,
Stewart, Elizabeth; Seattle Children's, Bioinformatics and High-Throughput
Data Analysis Laboratory; Data Enabled Life Sciences Alliance ,
Toppo, Stefano; University of Padova, Department of Molecular Medicine;
Uetz, Peter; J Craig Venter Institute, ; Data-Enabled Life Sciences Alliance,
Verheggen, Kenneth; VIB, Department of Medical Protein Research; Ghent
Alliance,
Voy, Brynn; University of Tennessee, Knoxville, Department of Animal
Science; Data-Enabled Life Sciences Alliance,
Warnich, Louise; University of Stellenbosch, Department of Genetics; Data-
Wilhelm, Steven; University of Tennessee, Knoxville, Department of
Microbiology; Data-Enabled Life Sciences Alliance,
Yandl, Gregory; Seattle Children's Research Institute, Bioinformatics and
High-throughput Analysis Laboratory; Data-Enabled Life Sciences Alliance,
Keywords: genomics, computational biology, data acquisition and cleaning
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Title: Towards more transparent and reproducible omics studies
through a common metadata checklist and data publications
Running Title:
Metadata checklist for omics studies and data publications
Keywords:
Multi-disciplinary, multi-omics, metadata, checklist, guidelines, transparency, reproducibility,
integrated analysis, data, reuse
Authors:
Eugene Kolker*
Bioinformatics and High-Throughput Analysis Laboratory,
Seattle Children’s Research Institute,
Predictive Analytics, Seattle Children’s
1900 Ninth Avenue
Seattle, WA 98101
Email: eugene.kolker@seattlechildrens.org
Phone: 206-884-7170
Vural Özdemir
Office of the President, Gaziantep University,
International Affairs and Global Development Strategy,
and Faculty of Communications
Üniversite Bulvarı P.K. 27310 Şehitkamil
Gaziantep, Kilis Yolu, Turkey
Email: vural.ozdemir@gantep.edu.tr
Phone: 90-545-550-88-80
Lennart Martens
Department of Medical Protein Research,
VIB, Ghent, Belgium
Department of Biochemistry,
Ghent University, Ghent, Belgium
Albert Baertsoenkaai 3
9000 GENT
Email: lennart.martens@vib-ugent.be
Phone: 32-9-264-93-58
William Hancock
Barnett Institute, Department of Chemistry
Northeastern University
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341 Mugar Building
Boston MA 02155
Email: wi.hancock@neu.edu
Phone: 617-373-4881
Gordon Anderson
Fundamental & Computational Sciences Directorate
Pacific Northwest National Laboratory
902 Battelle Boulevard
P.O. Box 999, MSIN K8-98
Richland, WA 99352
Email: Gordon@pnl.gov
Phone: 509-371-6582
Nathaniel Anderson
Seattle Children’s Research Institute
1900 Ninth Avenue
Seattle, WA 98101
Email: nate.anderson@seattlechildrens.org
Phone: 206-884-1230
Şükrü Aynacioglu
Department of Pharmacology
Gaziantep University
University Boulevard, 27310 Sehitkamil
Gaziantep, Turkey
Email: aynacioglu@gantep.edu.tr
Phone: 90-342-360-1023
Ancha Baranova
School of Systems Biology
George Mason University
10900 University Blvd, MSN 5B3
Manassas, VA 20110
Email: abaranov@gmu.edu
Phone: 703-993-4293
Shawn R. Campagna
Department of Chemistry
University of Tennessee, Knoxville
618 Buehler Hall
Knoxville, TN 37996-1600
Email: campagna@utk.edu
Phone: 865-974-7337
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ForPeerReview
Rui Chen
Department of Genetics
Stanford University
300 Pasteur Drive, Alway M-308
Stanford, CA 94305-5120
Email: ruichens@stanford.edu
Phone: 650-723-3277
John Choiniere
Bioinformatics and High-Throughput Analysis Laboratory
1900 Ninth Avenue
Seattle, WA 98101
Email: john.choiniere@seattlechildrens.org
Phone: 206-884-1251
Stephen P. Dearth
1420 Circle Dr.
Knoxville, TN 37996
sdearth@ion.chem.utk.edu
Phone: 865-974-3141
Wu-Chun Feng
SyNeRGy Laboratory
Departments of Computer Science & Electrical and Computer Engineering
Virginia Tech
2202 Kraft Dr
Blacksburg, VA 24060
Email: feng@cs.vt.edu
Phone: 540-231-1192
Lynnette Ferguson
Auckland Cancer Society Research Centre
Department of Nutrition
University of Auckland
Private Bag 92019, Auckland Mail Centre
Auckland 1142
New Zealand
Email: l.ferguson@auckland.ac.nz
Phone: 64-9-373-7599, ext. 86372
Geoffrey Fox
School of Informatics and Computing
Indiana University
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815 E 10th Street, Room 105
Bloomington, IN 47408
Email: gcf@indiana.edu
Phone: 812-856-7977
Dmitrij Frishman
Technische Universität München,
Wissenshaftzentrum Weihenstephan ,A
m Forum 1, 85354 Freising, Germany
Email: d.frishman@wzw.tum.de
Phone: 49-8161-712134
Robert Grossman
Institute for Genomics and Systems Biology
and Department of Medicine,
University of Chicago
900 East 57th Street, Room 10142
Chicago, IL 60637
Email: robert.grossman@uchicago.edu
Phone: 773-834-4669
Allison Heath
Institute for Genomics and Systems Biology,
University of Chicago
Knapp Center for Biomedical Discovery
900 East 57th Street
Chicago, IL 60637
Email: aheath@uchicago.edu
Phone: 773 573-9560
Roger Higdon
1900 Ninth Avenue
Seattle, WA 98101
Email: roger.higdon@seattlechildrens.org
Phone: 206-884-7172
Mara H. Hutz
Departamento de Genética,
Instituto de Biociências,
Federal University of Rio Grande do Sul,
Caixa Postal 15053, 91501-970
Porto Alegre, RS, Brazil.
E-mail: mara.hutz@ufrgs.br
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Imre Janko
High-Throughput Analysis Core,
1900 Ninth Avenue
Seattle, WA 98101
Email: imre.janko@seattlechildrens.org
Phone: 206-884-1064
Lihua Jiang
Stanford University
300 Pasteur Drive, M-344A
Stanford, California 94305
Email: lihuaj@stanford.edu
Phone: 650-723-9914
Sanjay Joshi
Life Sciences
EMC2
Email: sanjay.joshi@emc.com
Phone: 425-429-4727
Alexander Kel
GeneXplain GmbH
Am Exer 10 b
D-38302 Wolfenbüttel
Germany
Email: alexander.kel@genexplain.com
Phone: +49-05331-992200-13
Joseph W. Kemnitz
Department of Cell and Regenerative Biology
Wisconsin National Primate Research Center
University of Wisconsin-Madison
1220 Capitol Court
Madison, WI 53715-1299
Email: kemnitz@primate.wisc.edu
Phone: 608-263-3588
Isaac S. Kohane
Pediatrics and Health Sciences and Technology
Children's Hospital and Harvard Medical School
HMS Center for Biomedical Informatics
Countway Library of Medicine
10 Shattuck Street
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ForPeerReview
Boston, MA 02115
Email: isaac_kohane@harvard.edu
Phone: 617-432-2144
Natali Kolker
High-Throughput Analysis Core,
1900 Ninth Avenue
Seattle, WA 98101
Email: natali.kolker@seattlechildrens.org
Phone: 206-884-7171
Doron Lancet
Crown Human Genome Center
Department of Molecular Genetics
Weizmann Institute of Science
Rehovot 76100, Israel
Email: doron.lancet@weizmann.ac.il
Phone: 972-8-934-3683
Elaine Lee
High-throughput Analysis Core,
1900 Ninth Avenue
Seattle, WA 98101
Email: Elaine.lee@seattlechildrens.org
Phone: 206-884-1250
Weizhong Li
Center for Research in Biological Systems
University of California, San Diego
9500 Gilman Drive MC 0446
Atkinson Hall, Room 3113
La Jolla, CA 92093-0446
Email: liwz@sdsc.edu
Phone: 858-534 4143
Andrey Lisitsa
Russian Human Proteome Organization (RHUPO)
Institute of Biomedical Chemistry
Moscow, 199121 Russia
Pogodinskaya str., 10
Email: Lisitsa063@gmail.com
Phone: 7-499-246-3731
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Adrián Llerena
Clinical Research Center
Extremadura University Hospital and Medical School
Hospital Infanta Cristina Av de Elvas s/n
06080 Badajoz, Spain
Email: allerena@unex.es
Phone: +34924218040
Courtney MacNealy-Koch
DELSA Global,
1900 Ninth Avenue
Seattle, WA 98101
Email: courtney.macnealykoch@seattlechildrens.org
Phone: 206-884-1171
Jean-Claude Marshall
Center for Translational Research
Catholic Health Initiatives
7601 Osler Drive,
Towson, MD 21204
Email: Jean-ClaudeMarshall@catholichealth.net
Phone: 410.427.2587
Paola Masuzzo
Department of Medical Protein Research
VIB, Ghent, Belgium
Department of Biochemistry
9000 GENT
Email: paola.masuzzo@vib-ugent.be
Phone: 32-9-264-93-33
Amanda L. May
UT Knoxville
1420 Circle Dr.
Knoxville, TN 37996-1600
Email: amay@ion.chem.utk.edu
Phone: 865-974-3141
George Mias
Stanford University
300 Pasteur Drive, Alway Building M308
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Stanford, CA 94305-5120
Email: george.mias@stanford.edu
Phone: 650-723-3277
Matthew Monroe
Biological Sciences Division
Pacific Northwest National Laboratory
902 Batelle Boulevard
P.O. Box 999 MSIN K8-98
Richland, WA 99352
Email: matthew.monroe@pnnl.gov
Phone: 509-371-6580
Elizabeth Montague
Bioinformatics and High-Throughput Analysis Lab,
1900 Ninth Avenue
Seattle, WA 98101
Email: elizabeth.montague@seattlechildrens.org
Phone: 206-884-1137
Sean Mooney
The Buck Institute for Research on Aging
8001 Redwood Blvd.
Novato, CA 94945
Email: smooney@buckinstitute.org
Phone: 415-209-2038
Alexey Nesvizhskii
Department of Pathology,
Department of Computational Medicine and Bioinformatics
University of Michigan
4237 Med Sci I, 1301 Catherine Road
Ann Arbor, MI 48109-0602
Email: nesvi@med.umich.edu
Phone: 734)-764-3516
Santosh Noronha
Department of Chemical Engineering
Indian Institute of Technology Bombay
Powai, Mumbai
400076, INDIA
Email: noronha@iitb.ac.in
Phone: 91-22-2576 7238
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Gilbert Omenn
Center for Computational Medicine and Bioinformatics
Departments of Molecular Medicine & Genetics and Human Genetics
Department of Computational Medicine and Bioinformatics
School of Public Health
100 Washtenaw Avenue
2017 Palmer Commons
Ann Arbor, MI 48109-2218
Email: gomenn@umich.edu
Phone: 734-763-7583
Harsha K. Rajasimha
Jeeva Informatics Solutions LLC
7706 Majestic Way
Derwood, MD USA
Email: harsha@jeevadx.com
Phone: 540-239-0465
Preveen Ramamoorthy
Advanced Diagnostic Laboratories,
Department of Medicine National Jewish Health
1400 Jackson St
Denver, CO 80206
Email: RamamoorthyP@NJHealth.org
Phone: 303-398-1501
Jerry Sheehan
California Institute for Telecommunications and Information Technology
9500 Gilman Drive #0436
La Jolla, CA 92093-0436
Email: jsheehan@ucsd.edu
Phone: 858-534-1723
Larry Smarr
California Institute for Telecommunications and Information Technology
9500 Gilman Drive
La Jolla, CA 92093-0436
Email: lsmarr@ucsd.edu
Phone: 858-822-4284
Charles V. Smith
Center for Developmental Therapeutics
1900 Ninth Avenue
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Seattle, WA 98101
Email: charlesskip.smith@seattlechildrens.org
Phone: 206-884-7453
Todd Smith
Digital World Biology
2442 NW Market St. PMB 160
Seattle WA, 98107
Email: todd@digitalworldbiology.com
Phone: 206-375-6843
Michael Snyder
Stanford Center for Genomics and Personalized Medicine
Stanford University
300 Pasteur Drive, M-344A
Stanford, California 94305
Email: mpsnyder@Stanford.edu
Phone: 650-736-8099
Srikanth Rapole
Proteomics Laboratory
National Centre for Cell Science
NCCS Complex, Pune University Campus
Ganeshkhind, Maharashtra
Pune- 411007, India
Email: rsrikanth@nccs.res.in
Phone: +91-20-25708075
Sanjeeva Srivastava
Proteomics Laboratory
Indian Institute of Technology Bombay
Powai, Mumbai
400 076, India
Email: sanjeeva@iitb.ac.in
Phone: 022-2576-7779
Larissa Stanberry
1900 Ninth Avenue
Seattle, WA 98101
Email: larissa.stanberry@seattlechildrens.org
Phone: 206-884-1059
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ForPeerReview
Elizabeth Stewart
Bioinformatics and High-Throughput Analysis Laboratory
1900 Ninth Avenue
Seattle, WA 98101
Email: elizabeth.stewart@seattlechildrens.org
Phone: 206-884-7176
Stefano Toppo
Department of Molecular Medicine
University of Padova
Via U. Bassi 58/b
I-35131 Padova, Italy
Email: stefano.toppo@unipd.it
Phone: 39-049-8276958
Peter Uetz
J Craig Venter Institute (JCVI)
9704 Medical Center Drive
Rockville, MD 20850
Email: uetz@jcvi.org
Phone: 301-795-7589
Kenneth Verheggen
Department of Medical Protein Research
VIB, Ghent, Belgium
Department of Biochemistry
9000 GENT
Email: kenneth.verheggen@ugent.be
Phone: 32-9-264-93-58
Brynn H. Voy
Department of Animal Science
UT Institute of Agriculture
2506 River Drive
Knoxville, Tennessee 37996
Email: bhvoy@utk.edu
Phone: 865-974-4729
Louise Warnich
Faculty of AgriSciences
University of Stellenbosch
Private Bag X1,
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Matieland, 7602,
Stellenbosch, South Africa
E-mail: lw@sun.ac.za
Phone: 27-21-808-5888
Steven W. Wilhelm
Department of Microbiology
M409 Walters Life Sciences
Knoxville, Tennessee 37996-0845
Email: Wilhelm@utk.edu
Phone: 865-974-0665
Gregory Yandl
1900 Ninth Avenue
Seattle, WA 98101
Email: gregory.yandl@seattlechildrens.org
Phone: 206-884-3009
*Signifies corresponding author
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Abstract
Biological processes are fundamentally driven by complex interactions between
biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells,
organisms, or their communities. With the advent of new post-genomics technologies omics
studies are becoming increasingly prevalent yet the full impact of these studies can only be
realized through data harmonization, sharing, meta-analysis, and integrated research,. These
three essential steps require consistent generation, capture, and distribution of the metadata. To
ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of
life sciences studies, we propose a simple common omics metadata checklist. The proposed
checklist is built on the rich ontologies and standards already in use by the life sciences
community. The checklist will serve as a common denominator to guide experimental design,
capture important parameters, and be used as a standard format for stand-alone data publications.
This omics metadata checklist and data publications will create efficient linkages between omics
data and knowledge-based life sciences innovation and importantly, allow for appropriate
attribution to data generators and infrastructure science builders in the post-genomics era. We
ask that the life sciences community test the proposed omics metadata checklist and data
publications and provide feedback for their use and improvement.
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A COMMON OMICS METADATA CHECKLIST PROPOSAL
Modern life science technologies enable rapid and efficient acquisition of omics data.
These data comprehensively measure multi-layered molecular networks and provide a snapshot
of biological processes in a cell, organism, or their communities. Collected on the same sample
at the same time, omics data provide information on the functioning of biomolecules and their
interactions. Omics studies are essential for the systemic investigation of biological systems--an
endeavor that is crucial to improve our ability to manage and cure diseases, identify drug targets,
understand regulatory cascades, and predict ecosystem responses to environmental changes.
Through the pioneering efforts of Drs. Smarr and Snyder 1-4
, two powerful multi-omics
human datasets were recently made available. Smarr's dataset includes a wide variety of
molecular measures and clinical parameters meticulously collected and cataloged for years,
while Snyder's integrative personal multi-omics study presents his personal genomics,
transcriptomics, proteomics, metabolomics, and autoantibody profiles collected over a 14-month
period. Both studies yielded unique physiological insights not previously possible, including
early indications of vulnerabilities to specific diseases.
In the near future these kinds of personal omics studies will become routine and will
inevitably result in vast and diverse volumes of omics data. Therefore, the scientific community
must commit to a common format for publishing the design and analysis of these studies that will
ensure the compatibility, reproducibility, and reuse of the resulting data.
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The use, integration, and reuse of data require accurate and comprehensive capture of the
associated metadata including details describing experimental design, sample acquisition and
preparation, instrument protocols, and processing steps. The data and metadata must be captured
together in a rigorous and consistent manner to allow the integration of data across omics
experiments. The use of ontologies, naming conventions and standards can increase the
compatibility and usability of these diverse data. Fortunately, life sciences data have certain core
similarities. However, combined with these similarities come the different nuances among
various technology platforms such as transcriptomics, proteomics and metabolomics, as well as
application contexts such as neuroscience and hematology. The differences are compounded by
the multiplicity of standards within a field--transcriptomics alone has at least 15 standards
potentially applicable to the data 5,6
. Such complexities not only make reproducible, integrative,
accurate, and comprehensive capture of data and metadata an intricate challenge that must be
overcome but also place an excessive burden on researchers trying to convey metadata 5,7
.
Pioneering attempts in this area were made in 2007, when the “Minimum Information
about a Biomedical or Biological Investigation" project brought many of these efforts for the life
sciences together into an umbrella organization: MIBBI 8,9
. In MIBBI, each set of guidelines is
developed by a working group concentrated in a specific field, (for example, fMRI, or QTL and
association studies). Through this approach, MIBBI aspires to capture all essential metadata and
data that are necessary to replicate any given experiment within a field. Also the framework
known as Minimal Information about any Sequence (MIxS) expands the breadth of information
available by integrating the individual genomics checklists developed by the Genomics
Standards Consortium with environmental information 10
. In addition, the NIH’s National Center
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for Biotechnology Information developed a format for cataloging information about samples
enabling further metadata availability 11
. While these frameworks are critical to the reuse of data,
they do not fully take into account the interlocking aspects needed for harmonization of diverse
omics data types.
Recently, the Nature Publishing Group implemented a publication checklist that provides
another example of an approach to improve the transparency and reproducibility of life sciences
publications 12,13
. The checklist requires the researcher and/or corresponding author to enter
specific information on experimental design, statistical analysis, and reagents. This checklist is
endorsed by the Data-Enabled Life Sciences Alliance (DELSA Global, 14
).
The unveiling of the Nature publication initiative brought into focus the need for a
complementary omics checklist that allows the capture and publication of critical metadata
associated with omics data sets. To this end, life sciences researchers from DELSA Global 15-19
propose a single common omics metadata checklist as described below. By integrating DELSA
researchers’ collective experiences with omics guidelines and publication requirements, one
simplified, yet informative and flexible checklist was created to capture the essential aspects of
omics studies 20
.
Publication of a completed checklist will serve to inform the life sciences community of
the details needed to properly utilize the given data set. This type of “resource publication” has
long been done by Nucleic Acid Research in its annual Database issue. Nanopubs and
Micropubs are two newer publication avenues that could serve to quickly and accurately share
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information 21,22
. There are also other forms of data publications including, for example, ISA
Tools and the Scientific Data journal 23,24
.
It is worth noting that multi-omics data from a longitudinal study of a single individual
(e.g., the Smarr and Snyder datasets) in their entirety constitute essentially a whole new data
type. Supplied with detailed metadata, these data could become a part of a greater, well-
documented collage of data within a specific domain. Due to the large amount of data and the
complexity in the data acquisition it is exceedingly difficult to capture, disseminate, and interpret
the metadata. Generally, minimal reporting requirements are aimed at enabling replication of an
experiment, a concept that is not easily applied to the longitudinal personal omics studies.
Reuse of data can be enabled with more succinct and concise reporting.
The checklist we propose therefore has a simple structure covering four concise sections:
experiment information, experimental design, experimental methods, and data processing. The
experiment information section includes details of the lab, funding sources, data identification,
and a brief abstract to address why the experiment was done. The experimental design section is
meant to capture the high level data about the experiment and its statistical design, including
sample selection, replication, and randomization. The experimental methods section contains
details about instrumentation and sample preparation. The data processing section captures
information regarding methods and tools used in experimental data processing and data analysis.
Table 1.
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The metadata captured by this checklist will serve as interlocking bridges for data
harmonization and therefore they focus on details of the experimental design and subsequent
data analyses. In multi-omics studies, the researcher would fill a checklist for each omics data-
type measured. As test cases, two datasets of the integrative personal multi-omics study were
used 25
. The proposed checklist integrates existing ontologies and standards in order to
standardize terminology and simplify data input. In its short structured form, the checklist
captures important experimental parameters and strikes a balance between comprehensiveness
and ease of use. As such, the checklist can serve as a guide to the design of omics studies.
Implementation of this checklist will enable efficient portability and meta-analysis of the
data, as well as transparent communication and greater reproducibility of omics studies. Yet the
checklist is just the first step towards full utilization of the data. Traditional publication avenues
and new data publications, for example, OMICS Journal of Integrative Biology, Journal of
Proteome Research, Big Data, eLife, and Scientific Data could test and adopt the format to
ensure that the crucial information needed to allow data to be harmonized for broader usage is
published 26-28
. The assessment of the metadata quality and the data they accompany could be
done through community resources like PubMed Commons 29,30
.
Data submissions to single omics databases such as, for example, ArrayExpress and Geo
for transcriptomics, or PRIDE and Proteome Exchange for proteomics, would benefit from both
additional omics metadata within the given database and robust harmonization with other data-
types in other databases 31-34
. The checklist could also aid submissions to multi-omics databases,
data repositories or data clouds. Examples include: data clouds, such as The Open Science Data
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Cloud, and data repositories, such as Dryad for raw data, and MOPED for processed data 35-37
.
When compatibility and sharing of data and metadata cease to be an issue, a deeper
understanding of cells, organisms, and their communities will ensue.
CONCLUSION
The proposed metadata checklist offers a much-needed and balanced approach to bring
about data harmonization across omics studies. This is accomplished while also maintaining the
flexibility needed to adapt to complex and ever evolving study designs and omics application
contexts in the post-genomics era of the life sciences.
ACKNOWLEDGEMENTS
All authors of this publication are members of the Data-Enabled Life Sciences Alliance (DELSA
Global). This manuscript has been reviewed and endorsed by the Alliance. Research reported in
this publication was supported by Seattle Children’s Research Institute (SCRI), EMC2
, and Intel.
This support is very much appreciated. The content is solely the responsibility of the authors and
does not necessarily represent the official views of the Moore Foundation, SCRI, EMC2
, or Intel.
AUTHOR DISCLOSURE STATEMENT
No competing financial interests exist.
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Table 1. Multi-omics metadata checklist
Checklist Version Version 1.0 (2013)
Experiment Information Description
Lab Name Lab conducting the experiment
Date Checklist submission date
Author Information Name, organization, contacts
Title of Experiment One-sentence description of the particular experiment
Project Project name, ID, organization
Funding Funding sources for the project
Digital ID Multiple Digital IDs may be listed, such as those to GEO, MOPED,
PRIDE, DOIs, etc.
Abstract A short description of the experiment briefly stating the goals of the
research and principal outcomes if any (100 words or less)
Experimental Design Description
Organism e.g., Human, Mouse
OMICS Type(s) Utilized e.g., Proteomics, Metabolomics
Reference Published paper that utilize these data, their PMID, or other relevant
IDs or links
Experimental Design Design Specifications, Type of Replication (Biological, Technical, Time
points), Grouping of subjects, samples or replicates, Randomization,
Comparisons, other salient design attributes
Experiment Focus Description of experiment goals and objectives
Sample Description Description of samples
Tissue/Cell Type ID* e.g., BRENDA
Localization ID e.g., GO
Condition ID DOID, Text
Experimental Methods Description
Sample Prep Description Description of steps taken, kits used
Platform Type e.g., Microarray, LC-MS/MS, GC-MS, sequencing platform
Instrument Name e.g., LTQ-Orbitrap, psi-MS ontology, HiSeq, IonTorrent, Chip name
(Microarray)
Instrument Details e.g. Ion source, mass analyzer
Instrument Protocol e.g., Fragmentation method (CID, HCD, ETD), MS/MS scans per MS
scan, sequencing cycles, paired ends, single reads, hybridization
methods (microarray)
Data Processing Description
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Instrument Software Name List of software used, including version
Processing/Normalization
Methods/Software
Description of processing and normalization methods & software
Sequence/Annotation
Database
Source, version or date
ID Method/Software Name of search engine used + post processing to ID molecules
ID/Expression measures e.g., thresholds and cutoffs for ID, spectral counts, peak area, reads,
log2 expression
Data Analysis
Method/Software
Methods and software used for expression analysis, error estimation
I/O Data File Formats List of files formats for raw and processed data (e.g., txt, xml, etc.),
specifications and software tools to ensure readability
Reserved/Extension Any additional information related to the experiment
*ID – Identification
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