Biology subjects

Bassaganyas, L.

Publications and source records attributed to Bassaganyas, L..

5 recordsLinked to original sources

bioRxiv2023

GEGVIC: A workflow to analyze Gene Expression, Genetic Variations and Immune cell Composition of tumor samples using Next Generation Sequencing data

BackgroundThe application of next-generation sequencing techniques for genome and transcriptome profiling is to build the main source of data for cancer research. Hundreds of bioinformatic pipelines have been developed to handle the data generated by these technologies, but their use often requires specialized expertise in data wrangling and analysis that limit many biomedical researchers. Providing easy-to-use, yet comprehensive and integrative open-source tools is essential to help wet-lab and clinical scientists feel more autonomous in performing common omics data analysis in cancer research. ResultsHere, we present GEGVIC, an R tool to easily perform a set of frequently used analyses in cancer research, including differential gene expression, genomic mutations exploration and immune cell deconvolution using minimally processed human/mouse genomic and transcriptomic sequencing data. GEGVIC is designed as a modular pipeline that combines a variety of widely used available methods distributed in three principal modules (Gene Expression, Genomic Variation and Immune Composition), which run independently and include several visualization tools. This open-source software is also presented as a graphical user interface (GUI) using the Shiny framework (GEGVICShine) to eliminate the coding barrier for non-R users and enable comprehensive analyses of tumor samples via one-click features. ConclusionsIn summary, GEGVIC provides a simple, powerful and highly flexible workflow for researchers to process and interpret tumor transcriptomic and genomic data while decreasing or eliminating coding burden and facilitating efficiency for inexperienced bioinformatics users. GEGVIC R package instructions and source code are published on Github (https://github.com/oriolarques/GEGVIC), whereas GEGVICShine is hosted at https://gegvic.vhio.net/.

bioinformatics↗

bioRxiv2022

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different bulk datasets, including new Visium liver metastasis ST data, with experimental validation of selected interactions. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

bioinformatics↗

bioRxiv2019

New factors for protein transport identified by a genome-wide CRISPRi screen in mammalian cells

Protein and membrane trafficking pathways are critical for cell and tissue homeostasis. Traditional genetic and biochemical approaches have shed light on basic principles underlying these processes. However, the list of factors required for secretory pathways function remains incomplete, and mechanisms involved in their adaptation poorly understood. Here, we present a powerful strategy based on a pooled genome-wide CRISPRi screen that allowed the identification of new factors involved in protein transport. Two newly identified factors, TTC17 and CCDC157, localized along the secretory pathway and were found to interact with resident proteins of ER-Golgi membranes. In addition, we uncovered that upon TTC17 knockdown, the polarized organization of Golgi cisternae was altered, creating glycosylation defects, and that CCDC157 is an important factor for the fusion of transport carriers to the Golgi complex. In conclusion, our work identified and characterized new actors in the mechanisms of protein transport and secretion, and opens stimulating perspectives for the use of our platform in physiological and pathological contexts.

cell biology↗

bioRxiv2018

CNApp: a web-based tool for integrative analysis of genomic copy number alterations in cancer

Somatic copy number alterations (CNAs) are a hallmark of cancer. Although CNA profiles have been established for most human tumor types, their precise role in tumorigenesis as well as their clinical and therapeutic relevance remain largely unclear. Thus, computational and statistical approaches are required to thoroughly define the interplay between CNAs and tumor phenotypes. Here we developed CNApp, a user-friendly web tool that offers sample- and cohort-level computational analyses, allowing a comprehensive and integrative exploration of CNAs with clinical and molecular variables. By using purity-corrected segmented data from multiple genomic platforms, CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples. We applied CNApp to a pan-cancer dataset of 10,635 genomes from TCGA showing that CNA patterns classify cancer types according to their tissue-of-origin, and that broad and focal CNA scores positively correlate in samples with low amounts of whole-chromosome and chromosomal arm-level imbalances. Moreover, using the hepatocellular carcinoma cohort from the TCGA repository, we demonstrate the reliability of the tool in identifying recurrent CNAs, confirming previous results. Finally, we establish machine learning-based models to predict colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and specific genomic imbalances. In summary, CNApp facilitates data-driven research and provides a unique framework for the first time to comprehensively assess CNAs and perform integrative analyses that enable the identification of relevant clinical implications. CNApp is hosted at http://cnapp.bsc.es.

genomics↗

bioRxiv2018

Association study of long-term kidney transplant rejection using whole-exome sequencing

Long-term renal allograft rejection is the most common outcome in kidney transplantation. Continuing the crusade to extend allograft function after the first year post-transplantation, we attempted to associate genetic factors that might contribute to long-term allograft outcomes by sequencing the exomes of patients diagnosed with chronic allograft nephropathy/interstitial fibrosis and tubular atrophy. A variety of association analyses were employed, but these analyses failed to identify statistically significant associations. The study was underpowered to detect the association of rare genomic variants with small effect sizes. However, it confirmed previous reports of the absence of large effects from common variants. We have made both the study data and analysis workflow available for public use, and we hope that these resources will help to power future meta-analyses that may detect smaller effects.

genetics↗