MIxS:

A specification for the Minimum Information about any (X) Sequence standard

Repository Contents

This repo contains computable artifacts that define the MIxS standard, versions 6 and later. Older versions of the MIxS standard are available at https://github.com/GenomicsStandardsConsortium/mixs-legacy

Beginning with version 6, MIxS is being managed using the LinkML toolkit. LinkML uses YAML files to describe schemas and provides code to convert YAML to multiple other formats.

How to access MIxS standards

The standards can be browsed at https://genomicsstandardsconsortium.github.io/mixs/. The latests stable version of the MIxS standard in various formatsdownloaded in machine readable formats from the release folder. MIxS is currently available in GraphQL, JSON-LD, JSON schema, OWL, prefixmap, protobuf, SCHACL, SHEX, SQL schema and Python. Java will be addde in a future release, as will Excel templates for data collection. There is currently only one version of MIxS released in this repository (MIxS 6). When newer versions are released, links to older versions will be provided here

To support existing implementers who have been using previous Excel-based versions of MIxS, we have provide a manually created Excel workbook with the same format as MIxS 5. However, we highly recommend using one of the machine readable artifacts available in the release folder.

MIxS is divided into checklists and packages.

Checklists include the require, recommended, and optional metadata fields for a specific type sequence: genome, metagenome, marker gene, or xxx. For genomic sequences (MIGS), there are checklists for specific taxa (Eukaryotes, Bacteria, Viruses, and Plants as hosts).

Packages supplement checklists by providing additional terms to describe specific environements in which a sample was collected. For example, the Agriculture package has a number of terms to describe agricultural environments.

Packages can be used in conjunction with any checklist, so, for example, you if you sequence metagenome with in an agricultural environment, you would use MIMS + the Agriculture packages. All possible combinations of checklists and packages are available on the web browser.

To request changes to the MIxS standards, please use the issue tracker in this repo.

Other repository contents

• generated contains a working version of the MIxS artifacts. It is not stable and should not be used!
• doc_templates contains jinja templates for the LinkML auto-generated documentation
• changelogs contains documents describing the changes with each release
• MAINTAINERS.md provides more technical details on the editing and release process

Guidelines for the reuse and citation of content on this repository

The MIxS standards and the content of this repo are freely available under the Creative Commons 0 (open source) agreement.

Cite this repo

If you reuse code from this repository, please site the repository URL

Cite the standard

If you use any of the MIxS standards, please site this paper:

DOI: https://doi.org/10.1038/nbt.1823

Or, in RIS format

General Background

Without specific guidelines, most genomic, metagenomic and marker gene sequences in databases are sparsely annotated with the information required to guide data integration, comparative studies and knowledge generation. Even with complex keyword searches, it is currently impossible to reliably retrieve sequences that have originated from certain environments or particular locations on Earth—for example, all sequences from ‘soil’ or ‘freshwater lakes’ in a certain region of the world. Because public databases of the International Nucleotide Sequence Database Collaboration (INSDC; comprising DNA Data Bank of Japan (DDBJ), the European Nucleotide Archive (EBI-ENA) and GenBank depend on author-submitted information to enrich the value of sequence data sets, we argue that the only way to change the current practice is to establish a standard of reporting that requires contextual data to be deposited at the time of sequence submission. The adoption of such a standard would elevate the quality, accessibility and utility of information that can be collected from INSDC or any other data repository.

The GSC has defined a set of core descriptors for genomes and metagenomes in the form of a MIGS/MIMS specification. MIGS/MIMS extends the minimum information already captured by the INSDC. The recently introduced MIMARKS now captures information about marker genes. Additionally, we also introduced ‘environmental packages’ that standardize sets of measurements and observations describing particular habitats that are applicable across all GSC checklists and beyond. We define ‘environment’ as any location in which a sample or organism is found, e.g., soil, air, water, human-associated, plant-associated or laboratory. The original MIGS/MIMS checklists included contextual data about the location from which a sample was isolated and how the sequence data were produced. However, standard descriptions for a more comprehensive range of environmental parameters, which would help to better contextualize a sample, were not included. The environmental packages presented here are relevant to any genome sequence of known origin and are designed to be used in combination with MIGS, MIMS and MIMARKS checklists.

To create a single entry point to all minimum information checklists from the GSC and to the environmental packages, we created an overarching framework, the MIxS standard . MIxS includes the technology-specific checklists from the previous MIGS and MIMS standards, provides a way of introducing additional checklists such as MIMARKS, and also allows annotation of sample data using environmental packages.

The adoption of the GSC standards by major data providers and organizations, as well as the INSDC, supports efforts to contextually enrich sequence data and complements recent efforts to enrich other (meta) ‘omics data. The MIxS standards have been developed to the point that it is ready for use in the publication of sequences. A defined procedure for requesting new features and stable release cycles will facilitate implementation of the standard across the community. Compliance among authors, adoption by journals and use by informatics resources will vastly improve our collective ability to mine and integrate invaluable sequence data collections for knowledge- and application-driven research.