PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19.

With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression.

OmicSDK-Transcriptomics: A Web Platform for Transcriptomics Data Analysis.

Omics sciences, especially transcriptomics, have grown exponentially since the first human genome was sequenced in 2003. Different tools have been developed in the past years for the analysis of this kind of data, but many of them require specific programming knowledge to be used. In this paper, we present omicSDK-transcriptomics, the transcriptomics module of OmicSDK, a comprehensive tool for omics data analysis that combines pre-processing, annotation and visualization tools to be used with omics data.

COVID-BIOCHIP: A Web Tool to Analyse COVID-19 Antigen Microarrays.

During the COVID-19 pandemic, there was a growing need to characterise the disease. A very important aspect is the ability to measure the immunisation extent, which can be achieved using antigen microarrays that quantitively measure the presence of COVID-related antibodies. A significant limitation for these tests was the complexity of manually analysing the results, and the limited availability of software for its analysis.

Development of an Expert Knowledge-Based Genomic Variant Prioritisation Platform.

Considering the growing interest towards next generation sequencing (NGS) and data analysis, and the substantial challenges associated to fully exploiting these technologies and data without the proper experience, an expert knowledge-based user-friendly analytical tool was developed to allow non-bioinformatics experts to process NGS genomic data, automatically prioritise genomic variants and make their own annotations. This tool was developed using a user-centred methodology, where an iterative process was followed until a useful product was developed. This tool allows the users to set-up the pre-processing pipeline, filter the obtained data, annotate it using external and local databases (DBs) and help on deciding which variants are more relevant for each study, taking advantage of its customised expert-based scoring system.

DONOR-FINDER: A Web Tool for the Automatic Comparison of Genomic Profiles in ART.

The use of genetic tests in assisted reproduction is growing fast. Among potential usages, the selection of the most suitable donor profile stands out. Being able to consider the genomic compatibility between donor and recipient is very important in order to prevent certain diseases.

MiTPeptideDB: a proteogenomic resource for the discovery of novel peptides.

Motivation: The principal lines of research in MS/MS based Proteomics have been directed toward the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics.

In-Depth Proteomic Characterization of Classical and Non-Classical Monocyte Subsets.

Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players in the inflammation process underlying the mechanism of many diseases.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach.

The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments.

Progress and pitfalls in finding the 'missing proteins' from the human proteome map.

The Human Proteome Project was launched with two main goals: the comprehensive and systematic definition of the human proteome map and the development of ready to use analytical tools to measure relevant proteins in their biological context in health and disease. Despite the great progress in this endeavour, there is still a group of reluctant proteins with no, or scarce, experimental evidence supporting their existence. These are called the 'missing proteins' and represent one of the biggest challenges to complete the human proteome map.

Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence.

The current catalogue of the human proteome is not yet complete, as experimental proteomics evidence is still elusive for a group of proteins known as the missing proteins. The Human Proteome Project (HPP) has been successfully using technology and bioinformatic resources to improve the characterization of such challenging proteins. In this manuscript, we propose a pipeline starting with the mining of the PRIDE database to select a group of data sets potentially enriched in missing proteins that are subsequently analyzed for protein identification with a method based on the statistical analysis of proteotypic peptides.

Proteogenomic Analysis of Single Amino Acid Polymorphisms in Cancer Research.

The integration of genomics and proteomics has led to the emergence of proteogenomics, a field of research successfully applied to the characterization of cancer samples. The diagnosis, prognosis and response to therapy of cancer patients will largely benefit from the identification of mutations present in their genome. The current state of the art of high throughput experiments for genome-wide detection of somatic mutations in cancer samples has allowed the development of projects such as the TCGA, in which hundreds of cancer genomes have been sequenced.

Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project.

A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes.

Review and Challenges of Brain Analysis through DTI Measurements.

Medical images are being studied to analyse the brain in neurological disorders. Measurements extracted from Diffusion tensor image (DTI) such as Fractional Anisotropy (FA) describe the brain changes caused by diseases. However, there is no single best method for the quantitative brain analysis.