COmics Tools

If your results are not repeatable they will not be reproducible
If your results are not repeatable they will not be reproducible. Source Patil, Peng, Leek (2016) and ITCN Training Newtork Website (https://jhudatascience.org/Reproducibility_in_Cancer_Informatics/defining-reproducibility.html#what-is-reproducibility)

Our group combines different bioinformatics tools to process and analyse multi-omics data.

You can find the code for our pipelines in GitHub at comicsfct:

We also developed some Docker images. Docker is an open-source project that performs operating-system-level virtualization, automating the deployment of applications inside software containers. Docker really makes it easier to create, run applications and distribute it all out as one package. By doing so, the developer can be assured that the application will run on any other Linux machine regardless of any customized settings that machine might have that could differ from the machine used for writing and testing the code.

Here you have a brief description of the docker images used for analysis of multi-omics data (developed by our group and others):

Docker ImageDescription
argrosso/rbaseR base (CRAN)
argrosso/bioconductorR and BioConductor packages
argrosso/bismarkDNA methylation data analysis 
argrosso/starAlignment for transcriptomic data 
argrosso/htspreprocessingSeveral tools to pre-process HTS data
argrosso/htstoolsSeveral tools to process HTS aligned data
argrosso/rubioseqRubioSeq for variant and CNV from WES
argrosso/pyclone Pyclone for tumor clonal analysis
argrosso/mutsigcvMutSig to identify significant mutations
argrosso/kallistoTranscriptome alignment and quantification

 
You can find more Docker images for Computational Biology and Bioinformatics in: