1
The “Minimum Information about a MARKer gene Sequence” (MIMARKS)
2
specification
3
4
Pelin Yilmaz1,2, Renzo Kottmann1, Dawn Field3, Rob Knight4,5, James R. Cole6,7, Linda
5
Amaral-Zettler8, Jack A. Gilbert9,10,11, Ilene Karsch-Mizrachi12, Anjanette Johnston12,
6
Guy Cochrane13, Robert Vaughan13, Christopher Hunter13, Joonhong Park14, Norman
7
Morrison3,15, Philippe Rocca-Serra16, Peter Sterk3, Manimozhiyan Arumugam17, Mark
8
Bailey3, Laura Baumgartner18, Bruce W. Birren19, Martin J. Blaser20, Vivien Bonazzi21,
9
Tim Booth3, Peer Bork17, Frederic D. Bushman22, Pier Luigi Buttigieg1,2, Patrick S. G.
10
Chain7,23,24, Emily Charlson22, Elizabeth K. Costello4, Heather Huot-Creasy25, Peter
11
Dawyndt26, Todd DeSantis27, Noah Fierer28, Jed A. Fuhrman30, Rachel E. Gallery31, Dirk
12
Gevers19, Richard A. Gibbs32,33, Michelle Gwinn Giglio25, Inigo San Gil34, Antonio
13
Gonzalez35, Jeffrey I. Gordon36, Robert Guralnick28,29, Wolfgang Hankeln1,2, Sarah
14
Highlander32,37, Philip Hugenholtz38, Janet Jansson39, Scott T. Kelley40, Jerry Kennedy4,
15
Dan Knights35, Omry Koren41, Justin Kuczynski18, Nikos Kyrpides23, Robert Larsen4,
16
Christian L. Lauber42, Teresa Legg28, Ruth E. Ley41, Catherine A. Lozupone4, Wolfgang
17
Ludwig43, Donna Lyons42, Eamonn Maguire16, Barbara A. Methé44, Folker Meyer10, Sara
18
Nakielny4, Karen E. Nelson44, Diana Nemergut45, Josh D. Neufeld46, Lindsay K.
19
Newbold3, Anna E. Oliver3, Norman R. Pace18, Giriprakash Palanisamy47, Jörg Peplies48,
20
Jane Peterson21, Joseph Petrosino32,37, Lita Proctor21, Elmar Pruesse1,2, Christian Quast1,
21
Jeroen Raes49, Sujeevan Ratnasingham50, Jacques Ravel25, David A. Relman51,52, Susanna
22
Assunta-Sansone16, Patrick D. Schloss53, Lynn Schriml25, Rohini Sinha22, Erica
23
Sodergren54, Aymé Spor41, Jesse Stombaugh4, James M. Tiedje7, Doyle V. Ward19,
1
24
George M. Weinstock54, Doug Wendel4, Owen White25, Andrew Whiteley3, Andreas
25
Wilke10, Jennifer R. Wortman25, Frank Oliver Glöckner1,2
26
27
28
1 Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine
29
Microbiology, D-28359 Bremen, Germany
30
2 Jacobs University Bremen gGmbH, D-28759 Bremen, Germany
31
3 Natural Environment Research Council Environmental Bioinformatics Centre,
32
Wallington CEH, Oxford OX10 8BB, UK
33
4 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado
34
80309, USA
35
5 Howard Hughes Medical Institute, San Francisco, California 94143-2208, USA
36
6 Ribosomal Database Project, Michigan State University, East Lansing, Michigan
37
48824-4320, USA
38
7 Center for Microbial Ecology, Michigan State University, East Lansing, Michigan
39
48824-1325, USA
40
8 The Josephine Bay Paul Center for Comparative Molecular Biology and Evolution,
41
Marine Biological Laboratory, Woods Hole, Massachusetts, USA
42
9 Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
43
10 Mathematics and Computer Science Division, Argonne National Laboratory,
44
Argonne, Illinois 60439, USA
45
11 Department of Ecology and Evolution, University of Chicago, Chicago, Illinois
46
60637, USA
47
12 National Center for Biotechnology Information (NCBI), National Library of
2
48
Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
49
13
50
Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge
51
CB10 1SD, UK
52
14 School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749,
53
Republic of Korea
54
15 School of Computer Science, University of Manchester, Manchester M13 9PL, UK
55
16 Oxford e-Research Centre, University of Oxford, Oxford OX1 3QG, UK
56
17 Structural and Computational Biology Unit, European Molecular Biology Laboratory,
57
D-69117 Heidelberg, Germany
58
18 Department of Molecular, Cellular and Developmental Biology, University of
59
Colorado, Boulder, Colorado 80309, USA
60
19 Broad Institute of Massachusetts Institute of Technology and Harvard University,
61
Cambridge, Massachusetts 02142, USA
62
20 Department of Medicine and the Department of Microbiology, New York University
63
Langone Medical Center, New York 10017, USA
64
21 National Human Genome Research Institute, National Institutes of Health, Bethesda,
65
Maryland 20892, USA
66
22 Department of Microbiology, University of Pennsylvania School of Medicine,
67
Philadelphia 19104, USA
68
23 DOE Joint Genome Institute, Walnut Creek, California 94598, USA
69
24 Los Alamos National Laboratory, Bioscience Division, Los Alamos, New Mexico,
70
USA
European
Molecular
Biology
Laboratory
3
(EMBL)
Outstation,
European
71
25 Institute for Genome Sciences, University of Maryland School of Medicine,
72
Baltimore, Maryland 21201, USA
73
26 Department of Applied Mathematics and Computer Science, Ghent University, 9000
74
Ghent, Belgium
75
27 Center for Environmental Biotechnology, Lawrence Berkeley National Laboratory,
76
Berkeley, California, USA
77
28 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder,
78
Colorado 80309, USA
79
29 University of Colorado Museum of Natural History, University of Colorado, Boulder,
80
Colorado 80309, USA
81
30 Department of Biological Sciences, University of Southern California, Los Angeles,
82
California 90089, USA
83
31 National Ecological Observatory Network (NEON), Boulder, Colorado 80301, USA
84
32 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
85
77030, USA
86
33 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston,
87
Texas 77030, USA
88
34 Department of Biology, University of New Mexico, LTER Network Office,
89
Albuquerque, New Mexico 87131, USA
90
35 Department of Computer Science, University of Colorado, Boulder, Colorado 80309,
91
USA
92
36 Center for Genome Sciences and Systems Biology, Washington University School of
93
Medicine, St. Louis, Missouri 63108, USA
4
94
37 Department of Molecular Virology and Microbiology, Baylor College of Medicine,
95
Houston, Texas 77030, USA
96
38 Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences,
97
The University of Queensland, Brisbane QLD 4072, Australia
98
39 Earth Science Division, Lawrence Berkeley National Laboratory, Berkeley,
99
California, USA
100
40 Department of Biology, San Diego State University, San Diego, California 92182-
101
4614, USA
102
41 Department of Microbiology, Cornell University, Ithaca, New York 14853, USA
103
42 Cooperative Institute for Research in Environmental Sciences, University of Colorado,
104
Boulder, Colorado 80302, USA
105
43 Lehrstuhl für Mikrobiologie, Technische Universität München, D-853530 Freising,
106
Germany
107
44 J. Craig Venter Institute, Rockville, Maryland 20850-3213, USA
108
45 Department of Environmental Sciences, University of Colorado, Boulder, Colorado
109
80309, USA
110
46 Department of Biology, University of Waterloo, Ontario, N2L 3G1, Canada
111
47 Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge,
112
Tennessee 37830-8020, USA
113
48 Ribocon GmbH, D-28359 Bremen, Germany
114
49 VIB - Vrije Universiteit Brussel, 1050 Brussels, Belgium
115
50 Canadian Centre for DNA Barcoding, Biodiversity Institute of Ontario, University of
116
Guelph, Guelph, Ontario N1G 2W1, Canada
5
117
51 Departments of Microbiology and Immunology and of Medicine, Stanford University
118
School of Medicine, Stanford, California 94305, USA
119
52 Veterans Affairs Palo Alto Health Care System, Palo Alto, California 94304, USA
120
53 Department of Microbiology and Immunology, Ann Arbor, Michigan 48109-5620,
121
USA
122
54 The Genome Center, Department of Genetics, Washington University in St. Louis
123
School of Medicine, St. Louis, Missouri 63108, USA
124
125
6
126
Summary
127
We present the Genomic Standards Consortium’s (GSC) “Minimum Information
128
about a MARKer Gene Sequence” (MIMARKS) standard for describing marker
129
genes. Adoption of MIMARKS will enhance our ability to analyze natural genetic
130
diversity across the Tree of Life as it is currently being documented by massive
131
DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.
7
132
Acronyms
133
amoA: ammonia monooxygenase-alpha subunit
134
BOLI: Barcode of Life Initiative
135
CBOL: Consortium for the Barcode of Life
136
COI: cytochrome c oxidase I
137
DDBJ: DNA DataBank of Japan
138
DOE-JGI: Department of Energy Joint Genome Institute
139
DOI: Digital Object Identifier
140
DRA: DDBJ Sequence Read Archive
141
dsrAB: dissimilatory sulfite reductase, subunits A and B
142
ENA: European Nucleotide Archive
143
EnvO: Environment Ontology
144
GAZ: Gazetteer
145
GCDML: Genomic Contextual Data Markup Language
146
GSC: Genomic Standards Consortium
147
gyrA: DNA gyrase (type II topoisomerase), subunit A
148
HSP70: 70 kilodalton heat shock protein
149
ICoMM: International Census of Marine Microbes
150
INSDC: International Nucleotide Sequence Database Collaboration
151
ISA: Investigation/Study/Assay Infrastructure
152
ISO: International Organization for Standardization
153
ITS: internal transcribed spacer region
154
LSU: large subunit
155
MICROBIS: The Microbial Oceanic Biogeographic Information System
156
MIMARKS: Minimum Information about a MARKer Gene Sequence
157
MIGS/MIMS: Minimum Information about a Genome/Metagenome Sequence
8
158
MIRADA-LTERS: Microbial Inventory Research Across Diverse Aquatic Long Term
159
Ecological Research Sites
160
MLST: multi-locus sequence typing
161
NGS: next generation sequencing
162
nifH: dinitrogenase reductase subunit
163
ntcA: nitrogen regulator gene subunit
164
OBO: Open Biological and Biomedical Ontologies
165
phnA: phosphonoacetate hydrolase subunit
166
phnJ: carbon-phosphorous lyase complex subunit
167
PMID: Pubmed ID
168
pMMO: particulate methane monooxygenase
169
RDP: Ribosomal Database Project
170
recA: recombinase A subunit
171
rpoB: beta subunit of the bacterial RNA polymerase
172
rRNA: ribosomal RNA
173
SI: International System of Units
174
SRA: Sequence Read Archive
175
SSU: small subunit
176
URL: Uniform Resource Locator
177
WGS84: World Geodetic System 84
178
XML Schema: Extensible Markup Language Schema
9
179
Big Data need Standards
180
The term Big Data is increasingly used to describe the vast capacity of high-throughput
181
experimental methodologies, especially next-generation sequencing, to generate data1, 2.
182
Sharing and re-use of such data, and translating such data into knowledge, requires
183
widely-adopted standards that are best developed within the auspices of international
184
working groups3. Here we describe a new standard, developed by a large and diverse
185
community of researchers, to describe marker gene data sets – one of the most abundant
186
and useful types of sequence data.
187
The wealth of marker gene data sets
188
The adoption of phylogenetic marker genes as molecular proxies for tracking and
189
cataloguing microbial diversity has revolutionized the way we view the biological world,
190
and provided us with insights into how organisms are genetically interrelated. In the
191
1970s, studies of small subunit (SSU) ribosomal RNA (rRNA) genes from environmental
192
samples led to the discovery of the domain Archaea and the proposal for a three-domain
193
classification of life4. SSU rRNA gene surveys allow organisms from any community, no
194
matter how diverse, to be compared using the same universal phylogenetic tree. This
195
molecular approach in characterizing natural communities of organisms provided
196
unprecedented culture-independent access to the diversity and distribution of
197
microorganisms in situ. As a result, we are now aware that the vast majority (90-99%) of
198
microorganisms have evaded isolation by existing cultivation methods5, 6.
199
Over the past three decades, the 16S rRNA, 18S rRNA and internal transcribed spacer
200
gene sequences (ITS) from Bacteria, Archaea, and microbial Eukaryotes have provided
201
deep insights into the topology of the tree of life7, 8, and the composition of ecological
10
202
communities from diverse environments, ranging from deep sea hydrothermal vents to
203
ice sheets in the Arctic9-20.
204
Numerous other phylogenetic marker genes have also proven useful21: Currently, around
205
40 such phylogenetic marker genes are in wide use, representing well-conserved
206
housekeeping genes including initiation factors, such as RNA polymerase subunits
207
(rpoB), DNA gyrases (gyrB), DNA recombination and repair proteins (recA) and heat
208
shock proteins (HSP70)8. Combinations of these genes can also be used in multi-locus
209
sequence typing (MLST) approaches, in order to increase phylogenetic resolution and
210
differentiate between closely related species within the same genus22. Marker genes can
211
also reveal key metabolic functions; examples include carbon cycling (pMMO), nitrogen
212
cycling (amoA, nifH, ntcA)23, 24, sulfate reduction (dsrAB)25 or phosphorus metabolism
213
(phnA, phnJ) 26.
214
The molecular approach has been extended to the phylogeny and systematics of higher
215
Eukaryotes. The Barcode of Life Initiative (BOLI) adapted the molecular approach with
216
the standardized use of a specific gene sequence: the 680 base-pair region of
217
mitochondrial cytochrome c oxidase I (COI), as a means of rapid species identification
218
and discrimination27.
219
In this paper we collectively define all of these phylogenetic and functional genes (or
220
gene fragments) as “marker genes” as they are used to profile genetic diversity across the
221
Tree of Life, and argue that a small amount of additional effort invested in describing
222
them with specific guidelines in public databases will revolutionize the study types that
223
can be performed with these large data. This effort is timely, given the need to determine
224
how climate change and various other anthropogenic perturbations of our biosphere are
11
225
affecting biodiversity, and how marked changes in our cultural traditions and lifestyles
226
are affecting human microbial ecology, and, ultimately, human health.
227
The collective value of marker gene sequences
228
The quality and quantity of marker gene sequence data used to make phylogenetic
229
assignments, infer metabolic traits, unravel succession as a function of the environment,
230
and assess biogeographic distributions continues to increase rapidly due to the availability
231
of next generation sequencing (NGS) technologies. Specific associations of microbial
232
dynamics with the environment and geography were achieved for cultured
233
microorganisms long before the advent of metagenomics and NGS technologies28-31.
234
However, with these powerful technologies at our service, it is possible to unravel the
235
diversity and function of the uncultured majority, as well as to study increasingly
236
complex and/or divergent ecosystems. For example, a clear correlation between
237
phylogenetic similarity and similar living conditions was observed using data available in
238
SSU sequence repositories and culture collections32. Furthermore, it was shown that
239
temporally driven environmental factors, such as temperature and nutrients, correlate
240
with local seasonal succession of marine microbial communities33. A cross-habitat study
241
has suggested that salinity and pH influence bacterial and archaeal community
242
compositions, respectively34, 35. In the human body, it has been suggested that microbial
243
community composition varies systematically across body habitats, individuals and
244
time36. A recent study combined habitat type and 16S rRNA based operational taxonomic
245
units (OTUs) in a graph-theoretic approach to demonstrate that different habitats harbor
246
unique assemblages of co-occurring microorganisms37. For multicellular organisms,
247
modeling approaches to predict global distributions of marine species have been applied
12
248
in projects such as AquaMaps (http://www.aquamaps.org/). Such efforts, combined with
249
the potential of COI in conjunction with other markers to unveil historical processes, may
250
be applied in determining factors responsible for the contemporary geographic
251
distributions
252
(http://www.marinebarcoding.org/assets/files/MarBOLWorkshopsReport06jul09.pdf).
253
Unfortunately, only a few of these large-scale environmental surveys of biodiversity and
254
biogeography have relied on existing marker gene sequence datasets found in the public
255
databases. Without specific guidelines, most marker gene sequences in databases are
256
sparsely annotated with information required to guide data integration, comparative
257
studies, and knowledge generation. Even with complex keyword searches, it is currently
258
impossible to reliably retrieve marker gene sequences that have originated from certain
259
environments or particular locations on Earth; for example, all sequences from “soil” or
260
“freshwater lakes” in a certain region of the world. In human health and the study of
261
epidemiology, it would be desirable to have additional contextual data to help monitor the
262
origins and spread of pandemics38. Combining clinical and environmental datasets could
263
provide new insight into where the trillions of bacteria that inhabit the human body come
264
from, and could help predict new outbreaks of disease or assist in understanding the
265
ecology of opportunistic pathogens. Known correlations of some microbial taxa with
266
different environmental conditions, such as depth in the marine environment39, 40, or pH
267
in the soil environment41, can be extended. Careful integration of bacterial, archaeal and
268
eukaryotic SSU and large subunit (LSU) rRNA sequence data with their geographical and
269
environmental context can shed light on new mechanisms by which these three domains
270
interact.
of
these
13
organisms
271
272
The MIMARKS Specification
273
Public databases of the International Nucleotide Sequence Database Collaboration
274
(INSDC; comprised of DDBJ (DNA Data Bank of Japan), ENA (European Nucleotide
275
Archive), and GenBank; http://www.insdc.org) depend on author-submitted information
276
to enrich the value of marker gene sequence datasets, but few of these datasets contain
277
contextual information. We argue that the only way to change the current practice is to
278
establish a standard of reporting that requires contextual data to be deposited at the time
279
of sequence submission3. The adoption of such a standard would elevate the quality,
280
accessibility, and utility of information that can be collected from INSDC.
281
Here we present a reporting guideline for marker genes, MIMARKS (Minimum
282
Information about a MARKer gene Sequence), which is based on the framework of the
283
“Minimum Information about a (Meta) Genome Sequence” (MIGS/MIMS) specification
284
issued by the Genomic Standards Consortium (GSC)42. The proposed MIMARKS
285
standard (Table 1) complements the MIGS/MIMS specification for genomes and
286
metagenomes by adding two new report types, a “MIMARKS-survey” and a
287
“MIMARKS-specimen”, the former being the checklist of choice for uncultured diversity
288
marker gene surveys, the latter designed for marker gene sequences obtained from any
289
material identifiable via specimens. A specific focus of the extended requirements is the
290
sets of measurements and observations describing particular habitats, termed
291
“environmental packages”. A schematic overview of MIGS/MIMS and MIMARKS
292
(MIxS) checklists and an example of how to combine them with the environmental
293
packages is given in figure 1.
14
294
The MIMARKS checklist adopts and incorporates the standards being developed by the
295
Consortium
296
(http://www.barcodeoflife.org/sites/default/files/legacy/pdf/DWG_data_standards-
297
Final.pdf). Therefore, the specification can be universally applied to any marker gene,
298
from SSU rRNA to COI, to all taxa, and to studies ranging from single individuals to
299
complex communities.
300
The MIMARKS checklist was developed by collating information from several sources
301
and evaluating it in the framework of the existing MIGS/MIMS specification. These
302
include four independent community-led surveys, examination of the parameters reported
303
in published studies, and examination of compliance with optional features in INSDC
304
documents. The overall goal of these activities was to design the backbone of the
305
MIMARKS specification that describes the most important aspects of marker gene
306
contextual data.
307
Results of community-led surveys
308
To date, four online surveys concerning descriptors for marker genes have been
309
conducted to determine researcher preferences. Two of these (Department of Energy
310
Joint Genome Institute and SILVA44) surveys focused on general descriptor contextual
311
data for marker gene, while the Ribosomal Database Project (RDP)43 focused on
312
prevalent habitats for rRNA gene surveys, and the Terragenome Consortium45 focused on
313
soil metagenome project contextual data (supplementary information 1). Additionally, an
314
extensive set of contextual data items that were voted during a special session of the 2005
315
International Census of Marine Microbes (ICoMM) meeting, and were analyzed along
316
with survey results.
for
the
Barcode
15
of
Life
(CBOL)
317
The results of these user surveys provided valuable insights into community requests for
318
contextual data items to be included in the MIMARKS specification and the main
319
habitats constituting the environmental packages.
320
Survey of published parameters
321
We reviewed published rRNA gene studies, retrieved via SILVA and the ICoMM
322
database MICROBIS (The Microbial Oceanic Biogeographic Information System)
323
(http://icomm.mbl.edu/microbis) to further supplement contextual data items that are
324
included in the respective environmental packages. In total, thirty-nine publications from
325
SILVA and over 40 ICoMM projects were scanned for contextual data items to constitute
326
the core of the environmental package sub-tables (supplementary information 1).
327
Survey of INSDC source feature qualifiers
328
As a final analysis step, we surveyed usage statistics of INSDC source feature key
329
qualifier values of rRNA gene sequences contained in SILVA (supplementary
330
information 1). Strikingly, less than 10% of the 1.2 million 16S rRNA gene sequences
331
(SILVA release 100) were associated with even basic information such as
332
latitude/longitude, collection date, or PCR primers.
333
The MIMARKS checklist in full
334
The MIMARKS specification provides users with an “electronic laboratory notebook”
335
containing core contextual data items required for consistent reporting of marker gene
336
investigations. Project details are hosted in the “Investigation” section of MIMARKS,
337
facilitating access to the outline of contextual data of a marker gene survey. The
338
“Environment” section provides the geospatial, temporal and environmental context.
339
Fourteen
“environmental-packages”
provide
16
a
wealth
of
environmental
and
340
epidemiological contextual data fields for a complete description of sampling
341
environments. Furthermore, the environmental packages can be combined with any of the
342
GSC checklists as an extension section (figure 1 and supplementary information 2).
343
Researchers within The Human Microbiome Project
344
and all human packages. The Terragenome Consortium contributed sediment and soil
345
packages. Finally, ICoMM, Microbial Inventory Research Across Diverse Aquatic Long
346
Term Ecological Research Sites (MIRADA-LTERS), and the Max Planck Institute for
347
Marine Microbiology contributed the water package. The MIMARKS working group
348
developed the remaining packages (air, microbial mat/biofilm, miscellaneous natural or
349
artificial environment, plant-associated, and wastewater/sludge). The package names
350
describe high-level habitat terms in order to be exhaustive. The miscellaneous natural or
351
artificial environment package contains a generic set of parameters, and is included for
352
any other habitat that does not fall into the other thirteen categories. Whenever needed,
353
multiple packages may be used for the description of the environment.
354
MIMARKS uses the MIGS/MIMS specifications with respect to the nucleic acid
355
sequence source and sequencing contextual data, but extends them with further
356
experimental contextual data such as PCR primers and conditions, or target gene name.
357
For clarity and ease of use, all items within the MIMARKS specification are presented
358
with a value syntax description, as well as a clear definition of the item. Whenever terms
359
from a specific ontology are required as the value of an item, these terms can be readily
360
found in the respective ontology browsers, which are linked by URLs in the item
361
definition. Although this version of the MIMARKS specification does not contain unit
362
specifications, we recommend all units to be chosen from and follow the International
17
46
contributed the host associated
363
System of Units (SI) recommendations. In addition, we strongly urge the community to
364
provide feedback regarding the best unit recommendations for given parameters. To
365
facilitate comparative studies, unit standardization across data sets will be vital in future
366
versions of MIMARKS. An Excel® version of the MIMARKS checklist is provided to the
367
community on the GSC web site at: http://gensc.org/gc_wiki/index.php/MIMARKS.
368
Adoption by Major Database and Informatics Resources
369
A variety of efforts are under way to aid sequence submitters in compliance. In the past,
370
the INSDC has issued a reserved “BARCODE” keyword for the CBOL47. Following this
371
model, the INSDC has recently recognized the GSC as an authority for the MIGS/MIMS
372
and MIMARKS standard and issued it with official keywords within INSDC nucleotide
373
sequence records48. This greatly facilitates automatic validation of the submitted
374
contextual data and provides support for datasets compliant with previous versions by
375
including the checklist version in the keyword.
376
GenBank accepts MIMARKS metadata in tabular format using the sequin and tbl2asn
377
submission tools, validates MIMARKS compliance, and reports MIMARKS fields in the
378
structured comment block. The ENA Webin submission system provides prepared web
379
forms for the submission of MIMARKS compliant data; it presents all of the appropriate
380
fields with descriptions, explanations, and examples, and validates the data entered. One
381
tool that can aid submitting contextual data via a suitable submission tool is MetaBar49, a
382
spreadsheet and web-based software designed to assist users in the consistent acquisition,
383
electronic storage, and submission of contextual data associated with their samples in
384
compliance with the MIGS/MIMS/MIMARKS specifications. The online tool
18
385
CDinFusion (http://www.megx.net/cdinfusion) was created to facilitate the combination
386
of contextual data with sequence data, and. generation of submission ready files.
387
The next-generation Sequence Read Archive (SRA) collects and displays MIMARKS
388
compliant metadata in sample and experiment objects. There are several tools that are
389
already available or under development to assist users in SRA submissions. The myRDP
390
SRA PrepKit allows users to prepare and edit their submissions of reads generated from
391
ultra-high-throughput sequencing technologies. A set of suggested attributes in the data
392
forms assist researchers in providing metadata conforming to MIMARKS specifications.
393
The Quantitative Insight Into Microbial Ecology ("QIIME") web application
394
(http://www.microbio.me/qiime) allows users to generate and validate MIMARKS-
395
compliant templates. These templates can be viewed and completed in the users'
396
spreadsheet editor of choice (e.g. Microsoft Excel®). The QIIME web-platform also
397
offers an ontology lookup and georeferencing tool to aid users when completing the
398
MIMARKS templates. The Investigation/Study/Assay (ISA) is a software suite that
399
assists in the curation, reporting, and local management of experimental metadata.
400
Specific ISA configurations (available from http://isa-tools.org/tools.html) have been
401
developed to ensure MIMARKS compliance by providing templates and validation
402
capability while another tool, ISAconverter, produces SRA.xml documents, facilitating
403
submission to the SRA repository 50.
404
Further detailed guidance for submission processes can be found under the respective
405
wiki pages (http://gensc.org/gc_wiki/index.php/MIMARKS) of the MIMARKS standard.
406
Examples of MIMARKS compliant datasets
407
Several MIMARKS compliant reports are included in Supplementary Information 3.
19
408
These include a 16S rRNA gene survey from samples obtained in the North Atlantic, an
409
18S pyrosequencing tag study of anaerobic protists in a permanently anoxic North Sea
410
basin , a pmoA survey from Negev Desert soils , Israel, a dsrAB survey of Gulf of Mexico
411
marine sediments , and finally a 16S pyrosequencing tag study of bacterial diversity in
412
the Western English Channel (accessible via SRA study accession number SRP001108).
413
“Living Standards”
414
MIMARKS, as well as MIGS/MIMS, are “living checklists” and not final specifications.
415
Therefore, further developments, extensions, and enhancements will be recognized, and
416
improved versions of the checklists, if necessitated, will be released annually, while
417
preserving the validity of former versions. A public issue tracking system, which can be
418
reached via http://mixs.gensc.org/, is set up to track changes and accomplish feature
419
requests.
420
Maintenance of the MIMARKS checklist
421
The GSC checklists, including MIMARKS, are maintained in a relational database
422
system at the Max Planck Institute for Marine Microbiology, Bremen on behalf of the
423
GSC. This provides a secure and stable mechanism for updating the checklist suite and
424
versioning. In future, we plan to develop programmatic access to this database in order to
425
allow automatic retrieval of the latest version of each checklist for INSDC databases and
426
for GSC community resources. Moreover, the Genomic Contextual Data Markup
427
Language (GCDML) is a reference implementation of the GSC checklists by the GSC
428
and now implements MIMARKS. It is based on the XML Schema technology and thus
429
serves as an interoperable data exchange format for Web Service based infrastructures 51.
430
20
431
Conclusions and Call for Action
432
The GSC is an international working body with a stated mission of working towards
433
richer descriptions of the complete collection of genomes and metagenomes. With the
434
development of the MIMARKS specification, this mission has been extended to marker
435
gene sequences as well. The GSC is an open initiative that welcomes the participation of
436
the wider community. This includes an open call to contribute to refinements of the
437
MIMARKS specification or its implementations.
438
The adoption of the GSC standards by major data providers and organizations as well as
439
the three primary public sequence data repositories (INSDC) with an active poll for
440
standards compliant data underlines the efforts to contextually enrich our marker gene
441
collection, and complements the recent efforts to enrich other (meta)omics data. The
442
MIMARKS checklist has been developed to the point that it is ready to be used in the
443
publication of sequences. A defined procedure for requesting new features and the stable
444
release cycles will facilitate implementation of the standard across the community.
445
Compliance among authors, adoption by journals and use by informatics resources will
446
vastly improve our collective ability to mine and integrate invaluable sequence data
447
collections for knowledge and application driven research. In particular, the ability to
448
combine microbial community samples collected from any source, using the universal
449
Tree of Life as a measure to compare even the most diverse communities, should provide
450
new insights into the dynamic spatiotemporal distribution of microbial life on our planet
451
and in our own bodies.
452
21
453
Figure Legend
454
Figure 1: Schematic overview about the GSC MIxS (MIGS, MIMS and MIMARKS)
455
checklists (brown) and their combination with specific “environmental packages” (blue).
456
Brown checklist items are shared between all MIxS checklists (EU: Eukaryotes, BA:
457
Bacteria/Archaea, PL: Plasmid, VI: Virus, ORG: Organelle, ME: metagenome, SU:
458
Survey, SP: Specimen), but the requirements are different for each different checklist (M:
459
Mandatory, C: Conditional Mandatory, X: Optional, -: not applicable). Here, the
460
MIMARKS-survey checklist is selected, and complemented with soil “environmental
461
package”. The final product is illustrated in the combined table (brown-blue).
462
22
464
References
465
1.
Community cleverness required. Nature 455, 1-1 (2008).
466
2.
Field, D. et al. 'Omics Data Sharing. Science 326, 234-236 (2009).
467
3.
Taylor, C.F. et al. Promoting coherent minimum reporting guidelines for
468
biological and biomedical investigations: the MIBBI project. Nat. Biotechnol. 26,
469
889-896 (2008).
470
4.
471
472
Woese, C.R. & Fox, E. Phylogenetic structure of the prokaryotic domain: the
primary kingdoms. Proc. Nat. Acad. Sci. USA 74, 5088-5090 (1977).
5.
Amann, R.I., Ludwig, W. & Schleifer, K.H. Phylogenetic identification and in-
473
situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59,
474
143-169 (1995).
475
6.
476
477
its limits. Proc. Nat. Acad. Sci. USA 99, 10494-10499 (2002).
7.
478
479
Curtis, T.P., Sloan, W.T. & Scannell, J.W. Estimating prokaryotic diversity and
Ludwig, W. et al. Bacterial phylogeny based on comparative sequence analysis.
Electrophoresis 19, 554-568 (1998).
8.
Ludwig, W. & Schleifer, K.H. in Microbial phylogeny and evolution, concepts
480
and controversies. (ed. J. Sapp) 70-98 (Oxford university press, New York, USA,
481
2005).
482
9.
483
484
RNA sequences. Science 224, 409-411 (1984).
10.
485
486
Stahl, D.A. Analysis of hydrothermal vent associated symbionts by ribosomal
Giovannoni, S.J., Britschgi, T.B., Moyer, C.L. & Field, K.G. Genetic diversity in
Sargasso Sea bacterioplankton. Nature 345, 60-63 (1990).
11.
Ward, D.M., Weller, R. & Bateson, M.M. 16S rRNA sequences reveal numerous
23
487
488
uncultured microorganisms in a natural community. Nature 345, 63-65 (1990).
12.
489
490
89, 5685-5689 (1992).
13.
491
492
Fuhrman, J.A., McCallum, K. & Davis, A.A. Novel major archaebacterial group
from marine plankton. Nature 356, 148-149 (1992).
14.
493
494
DeLong, E.F. Archaea in coastal marine environments. Proc. Nat. Acad. Sci. USA
Pace, N.R. A molecular view of microbial diversity and the biosphere. Science
276, 734-740 (1997).
15.
Diez, B., Pedros-Alio, C. & Massana, R. Study of Genetic Diversity of Eukaryotic
495
Picoplankton in Different Oceanic Regions by Small-Subunit rRNA Gene
496
Cloning and Sequencing. Appl. Environ. Microbiol. 67, 2932-2941 (2001).
497
16.
Lopez-Garcia, P., Rodriguez-Valera, F., Pedros-Alio, C. & Moreira, D.
498
Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature
499
409, 603-607 (2001).
500
17.
Moon-van der Staay, S.Y., De Wachter, R. & Vaulot, D. Oceanic 18S rDNA
501
sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature
502
409, 607-610 (2001).
503
18.
Huber, J.A., Butterfield, D.A. & Baross, J.A. Temporal changes in archaeal
504
diversity and chemistry in a mid-ocean ridge subseafloor habitat. Appl. Environ.
505
Microbiol. 68, 1585-1594 (2002).
506
19.
507
508
509
Rappe, M.S. & Giovannoni, S.J. The uncultured microbial majority. Annu. Rev.
Microbiol. 57, 369-394 (2003).
20.
Hewson, I. & Fuhrman, J.A. Richness and diversity of bacterioplankton species
along an estuarine gradient in Moreton Bay, Australia. Appl. Environ. Microbiol.
24
510
511
70, 3425-3433 (2004).
21.
512
513
Doolittle, W.F. Fun With Genealogy. Proc. Nat. Acad. Sci. USA 94, 12751-12753
(1997).
22.
Cole, J.R., Konstantinidis, K., Farris, R.J. & Tiedje, J.M. in Environmental
514
Molecular Microbiology. (eds. W.-T. Liu & J.K. Jansson) 1-19 (Caister Academic
515
Press, Norfolk, UK, 2010).
516
23.
Zehr, J.P., Mellon, M.T. & Zani, S. New nitrogen-fixing microorganisms detected
517
in oligotrophic oceans by amplification of nitrogenase (nifH) genes. Appl.
518
Environ. Microbiol. 64, 3444-3450 (1998).
519
24.
Francis, C.A., Beman, J.M. & Kuypers, M.M.M. New processes and players in
520
the nitrogen cycle: the microbial ecology of anaerobic and archaeal ammonia
521
oxidation. Isme J. 1, 19-27 (2007).
522
25.
Minz, D. et al. Diversity of sulfate-reducing bacteria in oxic and anoxic regions of
523
a microbial mat characterized by comparative analysis of dissimilatory sulfite
524
reductase genes. Appl. Environ. Microbiol. 65, 4666-4671 (1999).
525
26.
Martinez, A., W. Tyson, G. & DeLong, E., F. Widespread known and novel
526
phosphonate utilization pathways in marine bacteria revealed by functional
527
screening and metagenomic analyses. Environ. Microbiol. 12, 222-238 (2009).
528
27.
Hebert, P.D.N., Cywinska, A., Ball, S.L. & Dewaard, J.R. Biological
529
identifications through DNA barcodes. Proc. R. Soc. Lond. B. Biol. Sci. 270, 313-
530
321 (2003).
531
532
28.
ZoBell, C.E. & Johnson, F.H. The influence of hydrostatic pressure on the growth
and viability of terrestrial and marine bacteria. Journal of Bacteriology 57, 179
25
533
534
(1949).
29.
Brock, T.D. & Brock, M.L. Relationship between Environmental Temperature
535
and Optimum Temperature of Bacteria along a Hot Spring Thermal Gradient.
536
Journal of Applied Microbiology 31, 54-58 (1968).
537
30.
538
539
Cho, J.-C. & Tiedje, J.M. Biogeography and Degree of Endemicity of Fluorescent
Pseudomonas Strains in Soil. Appl. Environ. Microbiol. 66, 5448-5456 (2000).
31.
Pomeroy, L.R. & Wiebe, W.J. Temperature and substrates as interactive limiting
540
factors for marine heterotrophic bacteria. Aquat. Microb. Ecol. 23, 187-204
541
(2001).
542
32.
543
544
communities in diverse environments. Science 315, 1126-1130 (2007).
33.
545
546
34.
35.
Auguet, J.-C., Barberan, A. & Casamayor, E.O. Global ecological patterns in
uncultured Archaea. Isme J. 4, 182-190 (2010).
36.
551
552
Lozupone, C.A. & Knight, R. Global patterns in bacterial diversity. Proc. Nat.
Acad. Sci. USA 104, 11436-11440 (2007).
549
550
Gilbert, J., A. et al. The seasonal structure of microbial communities in the
Western English Channel. Environ. Microbiol. 11, 3132-3139 (2009).
547
548
von Mering, C. et al. Quantitative phylogenetic assessment of microbial
Costello, E.K. et al. Bacterial community variation in human body habitats across
space and time. Science 326, 1694-1697 (2009).
37.
Chaffron, S., Rehrauer, H., Pernthaler, J. & von Mering, C. A global network of
553
coexisting microbes from environmental and whole-genome sequence data.
554
Genome Res. 20, 947-959 (2010).
555
38.
Schriml, L.M. et al. GeMInA, Genomic Metadata for Infectious Agents, a
26
556
557
geospatial surveillance pathogen database. Nucl Acids Res 38, D754-D764 (2010).
39.
558
559
DeLong, E.F. et al. Community genomics among stratified microbial assemblages
in the ocean's interior. Science 311, 496-503 (2006).
40.
Moreira, D., Rodriguez-Valera, F. & Lopez-Garcia, P. Metagenomic analysis of
560
mesopelagic Antarctic plankton reveals a novel deltaproteobacterial group.
561
Microbiology 152, 505-517 (2006).
562
41.
Lauber, C.L., Hamady, M., Knight, R. & Fierer, N. Soil pH as a predictor of soil
563
bacterial community structure at the continental scale: a pyrosequencing-based
564
assessment. Appl. Environ. Microbiol. 75, 5111-5120 (2009).
565
42.
566
567
specification. Nat. Biotechnol. 26, 541-547 (2008).
43.
568
569
Field, D. et al. The minimum information about a genome sequence (MIGS)
Cole, J.R. et al. The Ribosomal Database Project: improved alignments and new
tools for rRNA analysis. Nucleic Acids Res. 37, D141-145 (2009).
44.
Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked
570
and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids
571
Res. 35, 7188-7196 (2007).
572
45.
573
574
Vogel, T.M. et al. TerraGenome: a consortium for the sequencing of a soil
metagenome. Nat. Rev. Microbiol. 7, 252-252 (2009).
46.
575
Turnbaugh, P.J. et al. The Human Microbiome Project. Nature 449, 804-810
(2007).
576
47.
Benson, D.A. et al. GenBank. Nucl. Acids Res. 36, D25-30 (2008).
577
48.
Hirschman, L. et al. Meeting report: Metagenomics, Metadata and Meta-analysis”
578
(M3) Workshop at the Pacific Symposium on Biocomputing 2010. SIGS 2, 357-
27
579
580
360 (2010).
49.
581
582
Hankeln, W. et al. MetaBar - a tool for consistent contextual data acquisition and
standards compliant submission. BMC Bioinformatics 11, 358 (2010).
50.
Rocca-Serra, P. et al. ISA infrastructure: supporting standards-compliant
583
experimental reporting and enabling curation at the community level.
584
Bioinformatics 26, 2354-2356 (2010).
585
51.
Kottmann, R. et al. A standard MIGS/MIMS compliant XML schema: Toward
586
the development of the Genomic Contextual Data Markup Language (GCDML).
587
OMICS 12, 115-121 (2008).
588
28
589
29