Biology subjects

Pablo Pareja-Tobes

Publications and source records attributed to Pablo Pareja-Tobes.

3 recordsLinked to original sources

bioRxiv2016

Bacterial sequences detected in 99 out of 99 serum samples from Ebola patients

Evolution and clinical manifestations of Ebola virus (EBOV) infection overlap with the pathologic processes that occur in sepsis1. Some viruses certainly compromise the immune system, leading to a breach in the integrity of the mucosal epithelial barrier, thus allowing bacterial translocation2, 3. Guided by these facts, we wondered if bacteria could be involved in the pathogenesis of some of the septic shock-like symptoms typical of EBOV infected patients, something that could have a dramatic impact on the design of new treatment approaches. We decided to search for bacteria in available EBOV patient sequence datasets. Given that EBOV is an RNA virus and that, hence, some NGS sequencing experiments carried out to sequence the EBOV genomes were RNA-Seq experiments, we thought that, if there were any bacteria in patient serum, at least some bacterial RNA might probably be detected in the sequenced material from Ebola patients. Thus, we searched for bacteria in a RNA-Seq public dataset from 99 Ebola samples from the last outbreak4, and surprisingly, in spite of the certainly suboptimal experimental conditions for bacterial RNA sequencing, we found bacteria in all of the 99 samples

Bioinformatics↗

bioRxiv2015

MG7: Configurable and scalable 16S metagenomics data analysis

As part of the Cambrian explosion of omics data, metagenomics brings to the table a specific, defining trait: its social essence. The meta prefix exerts its influence, with multitudes manifesting themselves everywhere; from samples to data analysis, from actors involved to (present and future) applications. Of these dimensions, data analysis is where needs lay further from what current tools provide. Key features are, among others, scalability, reproducibility, data provenance and distribution, process identity and versioning. These are the goals guiding our work in MG7, a 16S metagenomics data analysis system. The basic principle is a new approach to data analysis, where configuration, processes, or data locations are static, type-checked and subject to the standard evolution of a well-maintained software project. Cloud computing, in its Amazon Web Services incarnation, when coupled with these ideas, produces a robust, safely configurable, scalable tool. Processes, data, machine behaviors and their dependencies are expressed using a set of libraries which bring as much as possible checking and validation to the type level, without sacrificing expressiveness. Together they form a toolkit for defining scalable cloud-based workflows composed of stateless computations, with a static reproducible specification of dependencies, behavior and wiring of all steps. The modeling of taxonomy data is done using Bio4j, where the new paradigm of graph databases allows for both a simple expression of taxonomic assignment tasks and the calculation of taxa abundance values considering the hierarchic structure of the taxonomy tree. MG7 includes a new 16S reference database, 16S-DB7, built with a flexible and sustainable update system, and the possibility of project-driven personalization.

Bioinformatics↗

bioRxiv2015

Bio4j: a high-performance cloud-enabled graph-based data platform

BackgroundNext Generation Sequencing and other high-throughput technologies have brought a revolution to the bioinformatics landscape, by offering sheer amounts of data about previously unaccessible domains in a cheap and scalable way. However, fast, reproducible, and cost-effective data analysis at such scale remains elusive. A key need for achieving it is being able to access and query the vast amount of publicly available data, specially so in the case of knowledge-intensive, semantically rich data: incredibly valuable information about proteins and their functions, genes, pathways, or all sort of biological knowledge encoded in ontologies remains scattered, semantically and physically fragmented.\n\nMethods and ResultsGuided by this, we have designed and developed Bio4j. It aims to offer a platform for the integration of semantically rich biological data using typed graph models. We have modeled and integrated most publicly available data linked with proteins into a set of interdependent graphs. Data querying is possible through a data model aware Domain Specific Language implemented in Java, letting the user write typed graph traversals over the integrated data. A ready to use cloud-based data distribution, based on the Titan graph database engine is provided; generic data import code can also be used for in-house deployment.\n\nConclusionBio4j represents a unique resource for the current Bioinformatician, providing at once a solution for several key problems: data integration; expressive, high performance data access; and a cost-effective scalable cloud deployment model.

Bioinformatics↗