Biology subjects

Porto, F.

Publications and source records attributed to Porto, F..

3 recordsLinked to original sources

bioRxiv2023

HIHISIV: a database of gene expression in HIV and SIV host immune response

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV). Both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories. Database URLhttps://hihisiv.github.io

bioinformatics↗

bioRxiv2022

RecGraph: adding recombinations to sequence-to-graph alignments

The transition towards graph pangenomes is posing several new challenging questions, most notably how to extend the classical notion of read alignment from a sequence-to-sequence to a sequence-to-graph setting. Especially on variation graphs, where paths corresponding to individual genomes are labeled, notions of alignments that are strongly inspired by the classical ones are usually able to capture only variations that can be expressed by mismatches or gaps, such as SNPs or short insertions and deletions. On the other hand the recent investigation of pangenomes at bacterial scale (Colquhoun et al, 2021) shows that most tools are tailored for human pangenomes and are not suited to bacteria which exhibit, among other characteristics, a larger variability. Such variability leads to the need for incorporating a greater flexibility when computing an alignment. In this paper, we extend the usual notion of sequence-to-graph alignment by including recombinations among the variations that explicitly represented and evaluated in an alignment. From a computational modeling point of view, a recombination corresponds to identifying a new path of the variation graph which is a mosaic of two different paths, possibly joined by a new arc. We provide a dynamic programming algorithm for computing an optimal alignment that allows recombinations with an affine penalty. We have implemented our approach with the tool RecGraph and we have analyzed its accuracy over some over some bacterial pangenome graphs.

bioinformatics↗

bioRxiv2016

GeNNet: An Integrated Platform for Unifying Scientific Workflow Management and Graph Databases for Transcriptome Data Analysis

BackgroundThere are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced may additionally be integrated with other biological databases, such as Protein-Protein Interactions and annotations. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managingthe resulting data and its respective metadata are challenging tasks. Running in-silico experiments to structure and compose the information as needed for analysis is a daunting task. Different programsmay need to be applied and different files are produced during the experiment cycle. In this context,the availability of a platform supporting experiment execution is paramount.\n\nResultsWe present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. GeNNet includes pre-loaded biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and geneset enrichment analysis. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships.The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene regulatory networks.\n\nConclusionsGeNNet is the first platform to integrate the analytical process of transcriptome data with graph database. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers as well can add new functionality to each component of GeNNet. The resulting data allows for testing previous hypotheses about an experiment as well as exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms.

bioinformatics↗