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What are the best ways to share bioinformatics data with your collaborators?

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Bioinformatics is the application of computational methods to analyze biological data, such as DNA sequences, gene expression, protein structures, and metabolic pathways. Bioinformatics data can be large, complex, and diverse, requiring specialized tools and formats to store, process, and visualize. If you are working on a bioinformatics project with collaborators, you need to share your data efficiently and effectively, while ensuring its quality, reproducibility, and security. In this article, you will learn about some of the best ways to share bioinformatics data with your collaborators, such as:

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Geert van Geest

Bioinformatician/Trainer at Universit?t Bern and SIB Swiss Institute of Bioinformatics
Prof Denis C. Bauer, GAICD

Tackling the thorniest problems in MedTech | Award-winning scientist | International speaker/commentator | AWS Data Hero
Brendan Hayward

Senior Software Engineer at Orica

1 Choosing a data format

The first step to share bioinformatics data is to choose a suitable data format that can capture the information and structure of your data, as well as facilitate its analysis and interpretation. Depending on the type and source of your data, you may use different formats, such as FASTA, FASTQ, SAM, BAM, VCF, GFF, BED, or CSV. You should also consider the compatibility and interoperability of your data format with the tools and platforms that you and your collaborators use. For example, if you are using R or Python for data analysis, you may want to use CSV or HDF5 formats that can be easily imported and manipulated. Alternatively, if you are using web-based applications or databases, you may want to use JSON or XML formats that can be easily exchanged and parsed.

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Fedor Lipskerov

PhD Student in Molecular Biology at Technion | Science Communicator
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In my opinion, the key to effective bioinformatics data sharing lies in the integration of comprehensive metadata standards. Using MIAME for microarray data or MINSEQE for sequencing data ensures that datasets are not only compatible with various tools but also interpretable and reusable by other researchers.

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2 Using a data repository

The second step to share bioinformatics data is to use a data repository that can store, manage, and distribute your data securely and reliably. A data repository is an online platform that allows you to upload, download, and access your data files, as well as provide metadata, documentation, and version control. There are many data repositories available for bioinformatics data, such as NCBI, EMBL-EBI, DDBJ, Bioconductor, Zenodo, Figshare, or Dryad. You should choose a data repository that meets your needs and preferences, such as the size and type of your data, the level of access and privacy, the licensing and citation policies, and the integration and support features.

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3 Adopting a data standard

The third step to share bioinformatics data is to adopt a data standard that can ensure the quality, consistency, and reproducibility of your data. A data standard is a set of rules and guidelines that define how your data should be collected, organized, documented, and reported. By following a data standard, you can make your data more understandable, comparable, and reusable by yourself and your collaborators. There are many data standards available for bioinformatics data, such as MIAME, MINSEQE, MIAPE, MIxS, FAIR, or BioCompute. You should choose a data standard that matches the scope and purpose of your data, as well as the expectations and requirements of your collaborators and stakeholders.

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4 Developing a data pipeline

The fourth step to share bioinformatics data is to develop a data pipeline that can automate and streamline your data processing and analysis. A data pipeline is a sequence of steps that perform various tasks on your data, such as filtering, trimming, aligning, mapping, annotating, quantifying, or visualizing. By developing a data pipeline, you can save time and effort, reduce errors and inconsistencies, and enhance the reproducibility and transparency of your data. There are many tools and frameworks available for developing data pipelines, such as Snakemake, Nextflow, Galaxy, Taverna, or CWL. You should choose a tool or framework that suits your skills and preferences, as well as the complexity and diversity of your data.

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Vamshidhar Gangu

Senior Data Engineer
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nextflow is my favorite among these data pipelines. You can reuse a lot of open source community developed pipelines, especially nf-core pipelines. The DSL2 make pipeline structure more organized and suitable for reusability. It support many channels for application management, backend configurations. My typical setup is using nextflow with docker and AWS for scalability. Also use gitlab for version control and setup some CICD for building and pushing containers as you do pipeline releases.

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5 Creating a data dashboard

The fifth step to share bioinformatics data is to create a data dashboard that can display and communicate your data results and insights. A data dashboard is a graphical interface that shows various indicators and metrics of your data, such as summary statistics, tables, charts, maps, or networks. By creating a data dashboard, you can monitor and evaluate your data performance, identify and explore trends and patterns, and communicate and disseminate your data findings. There are many tools and platforms available for creating data dashboards, such as R Shiny, Plotly Dash, Bokeh, Grafana, or Tableau. You should choose a tool or platform that enables you to customize and interact with your data dashboard, as well as share and collaborate with your audience.

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Geert van Geest

Bioinformatician/Trainer at Universit?t Bern and SIB Swiss Institute of Bioinformatics
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Dashboards are great, but they can take a lot of time to build and maintain. Consider building a single dashboard/app for one data type you frequently deliver. Also, there are lots of pre-built dashboards/applications available, like asap and shinycell for single cell data.

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Brendan Hayward

Senior Software Engineer at Orica
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Whatever tools you make a dashboard with, try to answer a question or enable a decision, don't just show as many plots and data as possible. About 5 visuals +/- 1 is a good rule of thumb so you don't overload yourself or your user. I absolutely must recommend the work of Tamara Munzner, Hadley Wickham and of course Edward Tufte in this area. Munzner has a lot of great practical stuff backed by research.

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6 Soliciting a data feedback

The sixth step to share bioinformatics data is to solicit a data feedback that can improve and enhance your data quality and utility. A data feedback is a process of collecting and analyzing the opinions and suggestions of your collaborators and users on your data. By soliciting a data feedback, you can identify and address the strengths and weaknesses of your data, as well as the needs and expectations of your collaborators and users. There are many methods and tools available for soliciting data feedback, such as surveys, interviews, focus groups, reviews, ratings, or comments. You should choose a method or tool that allows you to reach and engage with your target audience, as well as collect and analyze their data feedback.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Prof Denis C. Bauer, GAICD

Tackling the thorniest problems in MedTech | Award-winning scientist | International speaker/commentator | AWS Data Hero
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True repeatability and reproducibility is hard to achieve. I like to use public cloud provider's Marketplaces for that. Here you can spin up the exact environment along with the data and the analysis workflows, and it runs on the same hardware as the original analysis. All of this can be done at the press of a button and it is in a scalable environment so you can point it to your data and do real experiments.

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What are the best ways to share bioinformatics data with your collaborators?

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1 Choosing a data format

2 Using a data repository

3 Adopting a data standard

4 Developing a data pipeline

5 Creating a data dashboard

6 Soliciting a data feedback

7 Here’s what else to consider

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