Welcome to the Reproducible Bioinformatics project

Welcome to the Reproducible Bioinformatics project - Reproducible Bioinformatics

The aim of Reproducible Bioinformatics project is the creation of easy to use Bioinformatics workflows that fullfill the following roles (Sandve et al. PLoS Comp Biol. 2013):

  1. For Every Result, Keep Track of How It Was Produced
  2. Avoid Manual Data Manipulation Steps
  3. Archive the Exact Versions of All External Programs Used
  4. Version Control All Custom Scripts
  5. Record All Intermediate Results, When Possible in Standardized Formats
  6. For Analyses That Include Randomness, Note Underlying Random Seeds
  7. Always Store Raw Data behind Plots
  8. Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected
  9. Connect Textual Statements to Underlying Results
  10. Provide Public Access to Scripts, Runs, and Results


The paper on the SeqBox project is on Bioinformatics (Beccuti et al. 2018).

The paper on the Reproducible Bioinformatics project is on BMC Bioinformatics (Kulkarni et al. 2018).

The paper on rCASC: reproducible classification analysis of single-cell sequencing data is on GigaScience (Alessandri et al. 2019)

The registration page for 7th Edition of RNAseq and Single Cell RNseq workshop is now available also on Elixir training platform



The Reproducible Bioinformatics project

Reproducible Bioinformatics is a non-profit and open-source project.

We are a group of Bioinformaticians interested to simplify the use of bioinformatics tools to Biologists w/wo scripting ability. At the same time we are interested in providing robust and reproducible workflows.

For this reason we have developed the docker4seq and the rCASC packages.

At the present time a total of five workflows are available:

Under development are:

  • PDX workflow: variants calling in patient derived xenograft (PDX) from RNAseq and EXOMEseq data
  • Metagenomics workflow

All workflows are controlled by a set of R fuctions, part of docker4seq package, and the algorithms used are all encapsulated into Docker images and stored at docker.io/repbioinfo repository. 

4SeqGUI is the GUI that can be used to control  docker4seq functionalities.

Video tutorials for 4SeqGUI:

HowTo run a full RNAseq analysis

HowTo run a full miRNAseq analysis





Registration for the 6th edition of the RNA-seq and Single-cell-RNAseq workshop, 9-13 March 2020, is open:

The workshop will be held at Molecular Biotechnology Center, via Nizza 52, Torino (Italy).

A description of the course is available in this booklet.

The program of the course is available here. 

To register please fill the present registration form. (REGISTRATION DEADLINE FEBRUARY 28TH 2020)

For any further information please contact raffaele.calogero@unito.it (+39 0116706454)


The SeqBox Project

Short reads sequencing technology has been used for more than a decade now. However, the analysis of RNAseq and ChIPseq data is still computational demanding and the simple access to raw data does not guarantee results reproducibility between laboratories. To address these two aspects, we developed SeqBox, a cheap, efficient and reproducible RNAseq/ChIPseq hardware/software solution based on NUC6I7KYK mini-PC (an Intel consumer game computer with a fast processor and a high performance SSD disk), and Docker container platform. In SeqBox the analysis of RNAseq and ChIPseq data is supported by a friendly GUI. This allows access to fast and reproducible analyses also to scientists with/without scripting experience.

More info on SeqBox characteristics and cost are available at www.seqbox.com

How to be part of the Reproducible Bioinformatics project

Any bioinformatician interested to embed specific applications in the available workflows or interested to develop a new workflow is requested to embed the application(s) in a docker image, save it in a public repository and configure one or more R functions that can be used to interact with the docker image.

Steps required to submit a new application/workflow:

  • Edit the skeleton.R function and the ubuntu docker image (docker.io/repbioinfo/ubuntu) to create the new application.
  • Create a public docker repository for the docker image, e.g. at docker.com.
  • Create a workflow.Rmd vignette using RStudio and publish it via RStudio. As example of a vignette see docker4seq vignette.
  • Once the docker image, the function(s) and vignette are ready please fill this submission form. 
    • We will test and incorporate the code in docker4seq package. 
    • Mantainers will be responsable of the maintainance of their application(s).

If you are interested to participate to the project or if you need more information please contact info@reproducible-bioinformatics.org

Reproducible Bioinformatics

Bx2MVia Nizza 5210126 c/o B&Gu@MBC TorinoTel: +39 0116706454info@reproducibile-bioinformatics.org