Share your best tips with protocol repositories


Research protocols are like good old recipes, they contain time-tested knowledge, and their secrets are not shared with anybody. However, due to the complexity of today’s experiments, it has become more important than ever for scientists to find and use the best protocols available. New “omics” technologies require precise and controlled sample preparation, and the smallest variation can lead to huge differences in the resulting output.

The need for protocol repositories

The “big data” has fostered the development of a number of repositories, for bioinformatics tools (OMICtools), software codes (GitHub), and peer-reviewed journals dedicated to protocols (Nature Protocols, JOVE).

Following this trend, a number of initiative propose to store and reference research protocols on so-called protocol repositories. These repositories share common features: they follow a collaborative and community model, and they are open-access. Here are some protocol repositories that might help you find the best protocol for your experiments. is an online repository for scientific protocols that can be used at any point in the research cycle, before publication to be shared and run easily and in total confidentiality and/or during the manuscript submission process, as an alternative to the online Material & Methods section. is runnable as an iOS and Android application, and as already partnered with the journals Genetics and GigaScience to share protocols via the service instead of supplementary files.

To learn more, here is a video presentation of gather a lot of protocols from users and can be run on smartphones.

Protocol Exchange:

The Protocol Exchange is an open resource from the journal Nature Protocols, where the community of scientists pool their experimental know-how. Users can discover new protocols, share a protocol, join a lab group, comment on protocols, and organize their favorite and personalize their experience.

Protocols added to protocol exchange are standardized and usually include the following section: Introduction, reagents and equipment, procedure and timing, troubleshooting, and anticipated results/figures.

The database contains protocols from any branch of science with a focus on protocols being used to answer outstanding biological and biomedical science research questions.

Protocol Exchange homepage.

OpenWetWare (OWW):

OpenWetWare is an effort to promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering. This website is a wiki that proposes to browse labs and groups around the world, to link to biology courses, protocols, and blogs to share all things related to biological sciences.

OWW also developed an online Lab Notebook where users can create dynamic calendars, create projects, etc.

OpenWetWare homepage.

Of interest, other protocol repositories have been developed for specific research areas such as SDOP-DB for mouse phenotyping protocols or myExperiment for bioinformatics workflows.

So if you come up with a new protocol and you feel it could benefit the scientific community, don’t hesitate to use one of these useful repositories to make science go forward!

How documentation can improve your tools

Bioinformatics tools documentation and guidelines can come in handy when using a complicated piece of software. Yet, tools developers most often overlook the benefit of releasing documentation with their creations.

The main goal of tool documentation is to provide the basics of the software’s functionalities and guidelines on how to use it. Having such documentation will ensure great coverage and impact of your tool, as well as save you countless hours answering basic questions.

Different types of research software documentation

Software documentation can take various formats:

  • Manuscript, usually the original publication describing the tool.
  • Readme, which contains basic instructions for installation and use of the software.
  • Quickstart, a step-by-step protocol for installation and use of the software.
  • Reference manual, a comprehensive documentation of every configurable setting of the software.
  • FAQ, answers to most asked or anticipated questions.

Documentation-generating tools

Writing a comprehensive and easy-to-understand guideline can be a difficult task. For this, software have been developed to generate documentation directly from source codes. Here are some useful software that might help you create your documentation:

  • Doxygen: Generates documentation from source code. Supports most coding langages, including C++, C, Objective-C, C#, PHP, Java, Python, IDL and more. Doxygen can generate online documentation in HTML, or offline reference manuals in various formats. It can also extract code structure from undocumented source files.
  • Javadoc: Generates HTMP pages of application programming interfaces (APIs) documentation from Java source files.
  • Sphinx: Originally created for the Python documentation, this tool uses reStructuredText as its markup language and proposes various output formats (HTML, Latex, ePub, etc.), extensive cross-references, hierarchical structure, automatic indices, code handling, and more.
Example of documentation generated with Sphinx.

To learn more on software documentation and general recommendations, read this useful paper by Karimzadeh and Hoffman: Top considerations for creating bioinformatics software documentation.

No excuses not to write documentation next time!

Map functional networks of ncRNAs with circlncRNAnet


Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) lack protein-coding potential but have nonetheless emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation.

To fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest, Dr. Bertrand Chin-MingTan and his team have implemented an integrative bioinformatics approach to examine in silico the functional networks of non-coding RNAs. Here, they present their web server tool “circlncRNAnet” and discuss its main features.

In-depth analyses of non-coding RNA biology

The main purpose for implementing this web server is to provide biologists with a user-friendly, “one-stop” web tool to study from a network perspective the biology of lncRNAs or circRNAs of interest.

Despite their lack of protein-coding potential, lncRNAs and circRNAs have emerged as key determinant in gene regulation, acting to fine-tune transcriptional and signaling output. Given the widespread regulatory roles and target spectrum of non-coding RNAs, complete understanding of their biological relevance depends on integrative analyses of systems data at multiple levels. However, only a handful of available databases have been reported in this field, and they are limited in the scope of reference data and analytic modules. Through an integrated and streamlined design, circlncRNAnet is aimed to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions on the basis of large-scale RNA-seq data.

Overall design and analytic workflow of circlncRNAnet.

Main functionalities 

  • This web server is implemented with several features representing advances in the bioinformatics of ncRNAs:
    circlncRNAnet is designed with the flexibility of accepting private or public data. To further support efficient analyses and presentation, we have extensively curated public data into reference annotations for the circlncRNAnet workflow.
  • Multi-layer modules and algorithms then provide outputs on expression profiles, co-expression networks & pathways, and molecular interactomes (i.e. microRNAs, RNA-binding proteins, and transcription factors), which are dynamically and interactively displayed according to user-defined criteria.
  • Users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on the circRNAs/lncRNAs of their interest. The overall workflow takes only a few minutes, as compared to hours of manual efforts of independent database searches and analyses.
Schematic showing example outputs of circlncRNAnet analyses of lncRNA-based networks in colorectal cancer. After dataset upload, the server executes differential expression and expression correlation analyses. The web server allows the user to select query genes and correlation criteria. From Shao-Min Wu et al.

CiclncRNAnet is freely available at:

Reference :

(Wu et al., 2017). circlncRNAnet: An integrated web-based resource for mapping functional networks of long or circular forms of non-coding RNAs. Gigascience.