Transforming the health system into an open ended prospective clinical study by using artificial intelligence on clinical big data/ real word data

Because of the continuous decrease of sequencing prices the GA4GH has estimated that by 2023 over 80% of sequencing will be done in a clinical environment. In addition, more than two decades of generalized digitalization of health systems is generating an immense repository of clinical big data. Moreover, in coming years wearable devices will be mainstream for monitoring chronic patients and the elder, producing an enormous amount of health and life style data. On the other hand, the field of artificial intelligence has experienced an enormous activity in the last years, releasing a plethora of new methods or new versions of classical ones, able to find patterns in large datasets, to produce classifications using highly dimensionality data or to derive predictors of unprecedented precision. All this together offer an unprecedented opportunity to analyze this wealth of real world data (RWD) to generate new biomedical knowledge with an enormous translational potential. However, several obstacles preclude the direct exploitation of this data. Firstly, most of this data are highly sensitive and are consequently affected by data protection laws and regulations, which impose severe regulations to its use, especially outside of the health system. Moreover, much of this data are contained in unconnected silos in a non-homogeneous format. Here we will comment some initiatives to integrate genomic and clinical data within the Andalusian Public Health System and to make a systematic exploitation to generate new biomedical knowledge using artificial intelligence

Positive Selection, Relaxation, and Acceleration in the Evolution of the Human and Chimp Genome

For years evolutionary biologists have been interested in searching for the genetic bases underlying humanness. Recent efforts at a large or a complete genomic scale have been conducted to search for positively selected genes in human and in chimp. However, recently developed methods allowing for a more sensitive and controlled approach in the detection of positive selection can be employed. Here, using 13,198 genes, we have deduced the sets of genes involved in rate acceleration, positive selection, and relaxation of selective constraints in human, in chimp, and in their ancestral lineage since the divergence from murids. Significant deviations from the strict molecular clock were observed in 469 human and in 651 chimp genes. The more stringent branch-site test of positive selection detected 108 human and 577 chimp positively selected genes. An important proportion of the positively selected genes did not show a significant acceleration in rates, and similarly, many of the accelerated genes did not show significant signals of positive selection. Functional differentiation of genes under rate acceleration, positive selection, and relaxation was not statistically significant between human and chimp with the exception of terms related to G-protein coupled receptors and sensory perception. Both of these were over-represented under relaxation in human in relation to chimp. Comparing differences between derived and ancestral lineages, a more conspicuous change in trends seems to have favored positive selection in the human lineage. Since most of the positively selected genes are different under the same functional categories between these species, we suggest that the individual roles of the alternative positively selected genes may be an important factor underlying biological differences between these species

Phylemon: a suite of web tools for molecular evolution, phylogenetics and phylogenomics

Phylemon is an online platform for phylogenetic and evolutionary analyses of molecular sequence data. It has been developed as a web server that integrates a suite of different tools selected among the most popular stand-alone programs in phylogenetic and evolutionary analysis. It has been conceived as a natural response to the increasing demand of data analysis of many experimental scientists wishing to add a molecular evolution and phylogenetics insight into their research. Tools included in Phylemon cover a wide yet selected range of programs: from the most basic for multiple sequence alignment to elaborate statistical methods of phylogenetic reconstruction including methods for evolutionary rates analyses and molecular adaptation. Phylemon has several features that differentiates it from other resources: (i) It offers an integrated environment that enables the direct concatenation of evolutionary analyses, the storage of results and handles required data format conversions, (ii) Once an outfile is produced, Phylemon suggests the next possible analyses, thus guiding the user and facilitating the integration of multi-step analyses, and (iii) users can define and save complete pipelines for specific phylogenetic analysis to be automatically used on many genes in subsequent sessions or multiple genes in a single session (phylogenomics). The Phylemon web server is available at http://phylemon.bioinfo.cipf.es

Multidimensional Gene Set Analysis of Genomic Data

Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable

ETE: a python Environment for Tree Exploration

<p>Abstract</p> <p>Background</p> <p>Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.</p> <p>Results</p> <p>Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.</p> <p>Conclusions</p> <p>ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from <url>http://ete.cgenomics.org</url>.</p

Gene set internal coherence in the context of functional profiling

<p>Abstract</p> <p>Background</p> <p>Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.</p> <p>Results</p> <p>Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.</p> <p>This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.</p> <p>Conclusion</p> <p>For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.</p

From genes to functional classes in the study of biological systems

BACKGROUND: With the popularisation of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed. RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics. CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at:

PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes

We have developed a web tool, PupaSuite, for the selection of single nucleotide polymorphisms (SNPs) with potential phenotypic effect, specifically oriented to help in the design of large-scale genotyping projects. PupaSuite uses a collection of data on SNPs from heterogeneous sources and a large number of pre-calculated predictions to offer a flexible and intuitive interface for selecting an optimal set of SNPs. It improves the functionality of PupaSNP and PupasView programs and implements new facilities such as the analysis of user's data to derive haplotypes with functional information. A new estimator of putative effect of polymorphisms has been included that uses evolutionary information. Also SNPeffect database predictions have been included. The PupaSuite web interface is accessible through and through