PhyloCloud: an online platform for making sense of phylogenomic data.

Phylogenomics data have grown exponentially over the last decades. It is currently common for genome-wide projects to generate hundreds or even thousands of phylogenetic trees and multiple sequence alignments, which may also be very large in size. However, the analysis and interpretation of such data still depends on custom bioinformatic and visualisation workflows that are largely unattainable for non-expert users. Here, we present PhyloCloud, an online platform aimed at hosting, indexing and exploring large phylogenetic tree collections, providing also seamless access to common analyses and operations, such as node annotation, searching, topology editing, automatic tree rooting, orthology detection and more. In addition, PhyloCloud provides quick access to tools that allow users to build their own phylogenies using fast predefined workflows, graphically compare tree topologies, or query taxonomic databases such as NBCI or GTDB. Finally, PhyloCloud offers a novel tree visualisation system based on ETE Toolkit v4.0, which can be used to explore very large trees and enhance them with custom annotations and multiple sequence alignments. The platform allows for sharing tree collections and specific tree views via private links, or make them fully public, serving also as a repository of phylogenomic data. PhyloCloud is available at https://phylocloud.cgmlab.org.

Cost-constrained optimal sampling for system identification in pharmacokinetics applications with population priors and nuisance parameters.

Pharmacokinetics (PK) applications can be seen as a special case of nonlinear, causal systems with memory. There are cases in which prior knowledge exists about the distribution of the system parameters in a population. However, for a specific patient in a clinical setting, we need to determine her system parameters so that the therapy can be personalized. This system identification is performed many times by measuring drug concentrations in plasma. The objective of this work is to provide an irregular sampling strategy that minimizes the uncertainty about the system parameters with a fixed amount of samples (cost constrained). We use Monte Carlo simulations to estimate the average Fisher's information matrix associated to the PK problem, and then estimate the sampling points that minimize the maximum uncertainty associated to system parameters (a minimax criterion). The minimization is performed employing a genetic algorithm. We show that such a sampling scheme can be designed in a way that is adapted to a particular patient and that it can accommodate any dosing regimen as well as it allows flexible therapeutic strategies.

Functional profiling and gene expression analysis of chromosomal copy number alterations.

Contrarily to the traditional view in which only one or a few key genes were supposed to be the causative factors of diseases, we discuss the importance of considering groups of functionally related genes in the study of pathologies characterised by chromosomal copy number alterations. Recent observations have reported the existence of regions in higher eukaryotic chromosomes (including humans) containing genes of related function that show a high degree of coregulation. Copy number alterations will consequently affect to clusters of functionally related genes, which will be the final causative agents of the diseased phenotype, in many cases. Therefore, we propose that the functional profiling of the regions affected by copy number alterations must be an important aspect to take into account in the understanding of this type of pathologies. To illustrate this, we present an integrated study of DNA copy number variations, gene expression along with the functional profiling of chromosomal regions in a case of multiple myeloma.

ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.

We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.