Visentin, L. | Search bioRxiv

bioRxiv2023

Profiling the Expression of Transportome Genes in cancer: A systematic approach

The transportome, the -omic layer encompassing all Ion Channels and Transporters (ICTs), is crucial for cell physiology. It is therefore reasonable to hypothesize a role of the transportome in disease, and in particular in cancer. Here, we present the Membrane Transport Protein DataBase (MTP-DB), a database collecting information on ICTs, and a pipeline that takes expression data and the MTP-DB as input to produce a broad overview of transportome dysregulation in cancer. The MTP-DB may prove useful for the study of the transportome in general, and the pipeline may be used to study the transportome in other diseases. Both tools are open source and can be found on GitHub at TCP-Lab/mtp-db and TCP-Lab/transportome_profiler, under permissive licenses. We detect that the transportome is dysregulated in cancer, and that dysregulation patterns are shared among different cancer types. It is still unclear how these patterns are linked to cancer patho-physiology.

bioinformatics↗

bioRxiv2022

BioTEA: containerized methods of analysis for microarray-based transcriptomics data

Tens of thousands of gene expression data sets describing a variety of model organisms in a many different pathophysiological conditions are currently stored in publicly available databases such as Gene Expression Omnibus (GEO) and ArrayExpress. As microarray technology is giving way to RNA-Seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring at the same time the reproducibility of the results. To meet this need, here we present bioTEA, a novel software tool that combines the ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options--including gene filtering, batch effect handling, sample pairing, statistical test type--to adapt the algorithm flow to the structure of the particular data set. All these options are saved in single text file which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of analysis. Overall, these features make bioTEA an invaluable tool for both bioiformaticians and wet-lab biologists interested in transcriptomics.

bioinformatics↗