Biology subjects

Loucera, C.

Publications and source records attributed to Loucera, C..

6 recordsLinked to original sources

bioRxiv2021

From partial to whole genome imputation of SARS-CoV-2 for epidemiological surveillance

Backgroundthe current SARS-CoV-2 pandemic has emphasized the utility of viral whole genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is increasingly producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and therefore useless, sequences. However, viral sequences evolve in the context of a complex phylogeny and therefore different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data. ResultsWe developed impuSARS, an application that includes Minimac, the most widely used strategy for genomic data imputation and, taking advantage of the enormous amount of SARS-CoV-2 whole genome sequences available, a reference panel containing 239,301 sequences was built. The impuSARS application was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing) showing great fidelity when reconstructing the original sequences. The impuSARS application is also able to impute whole genomes from commercial kits covering less than 20% of the genome or only from the Spike protein with a precision of 0.96. It also recovers the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (< 20%) Conclusionsimputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. impuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole genome sequencing.

genomics↗

bioRxiv2020

Mechanistic modeling of the SARS-CoV-2 disease map

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map in which the detailed activity of the human signaling circuits related to the viral infection and the different antiviral responses, including immune and inflammatory activities, can be inferred from gene expression experiments. Moreover, given to the mechanistic properties of the model, the effect of potential interventions, such as knock-downs, over-expression or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied in specific conditions. By providing a holistic, systems biology approach to the understanding of the complexities of the viral infection process, this tool will become an important asset in the search for efficient antiviral treatments. The tool is freely available at: http://hipathia.babelomics.org/covid19/

systems biology↗

bioRxiv2020

SMN1 copy-number and sequence variant analysis from next generation sequencing data

Spinal Muscular Atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next generation sequencing (NGS) very difficult Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available on Github at: www.github.com/babelomics/SMAca

bioinformatics↗

bioRxiv2019

Mechanistic models of signaling pathways deconvolute the functional landscape of glioblastoma at single cell resolution

The rapid development of single cell RNA-sequencing (scRNA-seq) technologies is revealing an unexpectedly large degree of heterogeneity in gene expression levels across the different cells that compose the same tissue sample. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell-fate decisions to the collective behavior of the tissues these cells are part of. Mechanistic models of signaling pathways have already proven to be useful tools for understanding relevant aspects of cell functionality. Here we propose to use this mechanistic modeling strategy to deconvolute the complexity of the functional behavior of a tissue by dissecting it into the individual functional landscapes of its component cells by using a single-cell RNA-seq experiment of glioblastoma cells. This mechanistic modeling analysis revealed a high degree of heterogeneity at the scale of signaling circuits, suggesting the existence of a complex functional landscape at single cell level. Different clusters of neoplastic glioblastoma cells have been characterized according to their differences in signaling circuit activity profiles, which only partly overlap with the conventional glioblastoma subtype classification. The activity of signaling circuits that trigger cell functionalities which can easily be assimilated to cancer hallmarks reveals different functional strategies with different degrees of aggressiveness followed by any of the clusters. In addition, mechanistic modeling allows simulating the effect of interventions on the components of the signaling circuits, such as drug inhibitions. Thus, effects of drug inhibitions at single cell level can be dissected, revealing for the first time the mechanisms that individual cells use to avoid the effect of a targeted therapy which explain why and how a small proportion of cells display, in fact, different degrees of resistance to the treatment. The results presented here strongly suggest that mechanistic modeling at single cell level not only allows uncovering the molecular mechanisms of the tumor progression but also can predict the success of a treatment and can contribute to a better definition of therapeutic targets in the future.

systems biology↗

bioRxiv2019

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

BackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.\n\nResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.\n\nConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

bioinformatics↗

bioRxiv2018

Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples

BackgroundThe availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.\n\nResultsHere we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.\n\nConclusionsWe demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.

genomics↗