miR-217 is an oncogene that enhances the germinal center reaction.

microRNAs are a class of regulators of gene expression that have been shown critical for a great number of biological processes; however, little is known of their role in germinal center (GC) B cells. Although the GC reaction is crucial to ensure a competent immune response, GC B cells are also the origin of most human lymphomas, presumably due to bystander effects of the immunoglobulin gene remodeling that takes place at these sites. Here we report that miR-217 is specifically upregulated in GC B cells. Gain- and loss-of-function mouse models reveal that miR-217 is a positive modulator of the GC response that increases the generation of class-switched antibodies and the frequency of somatic hypermutation. We find that miR-217 down-regulates the expression of a DNA damage response and repair gene network and in turn stabilizes Bcl-6 expression in GC B cells. Importantly, miR-217 overexpression also promotes mature B-cell lymphomagenesis; this is physiologically relevant as we find that miR-217 is overexpressed in aggressive human B-cell lymphomas. Therefore, miR-217 provides a novel molecular link between the normal GC response and B-cell transformation.S

Quantification of miRNA-mRNA Interactions

miRNAs are small RNA molecules (′ 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO)

Extracción automática de tópicos en biología a partir de la literatura científica

Los recientes avances en Biología Molecular y en Informática son responsables de la acumulación de muchos y cada vez más complejos tipos de datos. Este incremento se ha visto también reflejado en el elevado número de publicaciones relacionadas. Todo esto se debe a los experimentos a gran escala que ahora se pueden llevar a cabo en este tipo de investigación. Genomas completos pueden ser secuenciados en meses o semanas, métodos computacionales permiten la identificación de miles de genes en el DNA secuenciado y se han desarrollado herramientas que analizan automáticamente las propiedades de los genes y las proteínas. No obstante, no sólo los resultados de los distintos experimentos sirven para encontrar información biológica, actualmente es posible explorar la literatura biomédica en busca de evidencias biológicas. Sin embargo, ese proceso de extracción de información a partir de las publicaciones es, en su gran mayoría, manual. Un grupo de anotadores se encarga de leer todos los artículos científicos, extraer evidencias biológicas y almacenarlas en las bases de datos y ontologías biológicas públicas accesibles a través de internet. Debido a la gran acumulación de documentos científicos, se necesita desarrollar métodos y herramientas que automaticen el proceso de extracción de información. En este contexto se propone un método de extracción de información biológica a partir de la literatura biomédica basado en la extracción de anotaciones enriquecidas en términos encontrados en publicaciones y bases de datos. Un posterior análisis estadístico, utilizando varios test como el de χ2 o el de la distribución hipergeométrica y corrigiendo el problema de la hipoótesis múltiple, nos permitirá evaluar el nivel de relevancia de las anotaciones recuperadas. Esta metodología permite integrar datos obtenidos de la literatura con otras fuentes de información como anotaciones funcionales o reguladores transcripcionales y es de gran utilidad para el descubrimiento de asociaciones entre información biológica de los genes y proteínas y documentos o conjuntos de palabras

Simulador de sistema de memoria de caches adaptativas con PIN

El objetivo de nuestro proyecto es implementar un simulador dinámico de caches adaptativas. Con él podemos comprobar la eficacia de las técnicas de hardware adaptativo sobre diversos benchmarks. En primer lugar, se ha implementado un sistema de caches de datos e instrucciones con varios niveles, permitiendo configurar completamente las características de cada nivel. Tras esto, añadimos la posibilidad de adaptar dinámicamente el número de vías en función de la tasa de fallos. Para instrumentar dinámicamente el código hemos utilizado la herramienta Pin desarrollada por Intel. Como era necesario validar los resultados obtenidos, los hemos comparado con los obtenidos por otro simulador de caches, Dinero IV. Las pruebas demuestran que el error entre unos y otros es muy reducido. Por último se llevaron a cabo pruebas para estudiar la eficacia de los mecanismos adaptativos en cache. Los resultados obtenidos demuestran que, sin incrementar significativamente la tasa de fallos, sí se consigue reducir el número de vías de cada nivel. Esto supone una mejora en el consumo energético de la jerarquía de memoria. [ABSTRACT] The goal of our project is to develop an adaptative cache dynamic simulator. We can use it to test the e®ectiveness of adaptative hardware techniques in several benchmarks. First, we have built a shared multilevel cache. It is possible to configure completely each level modifying its configuration file. After that, we add the possibility of dynamically adapt the number of ways according to miss rate. To dynamically instrument code we have used Pin, property of Intel. In the way to validate results, we have compared our memory system with Dinero IV, another cache simulator. The comparative demonstrate results are very close. Finally, tests were carried out to study the effectiveness of adaptative methods applied in caches. Results demonstrate that, without increasing miss rate significantly, it is possible to reduce the average number of ways each level has. This involves an improvement in the power consumption of the memory hierarchy

Proteopathogen, una base datos de proteínas para el estudio de la interacción Candida albicans-Hospedador

Comunicaciones a congreso

Quantification of miRNA-mRNA interactions

miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es/

Maximum enrichment values on experimentally-validated targets for MCC dataset.

<p>The table shows the maximum enrichment values (point of minimum p-value) for the union of TaRBase and miRecords, for MCC dataset. N_E: is the number of experimentally-validated targets rescued in the point of minimum p-value and N_T: is the total number of predicted targets in that minimum. N_E⁵⁰⁰: is the amount of experimentally-validated targets in the first 500 predicted interactions.</p

Maximum enrichment values on experimentally-validated targets for LDS dataset.

<p>The table shows the maximum enrichment values (point of minimum p-value) for the union of TaRBase and miRecords, for MCC dataset. N_E: is the number of experimentally-validated targets rescued in the point of minimum p-value and N_T: is the total number of predicted targets in that minimum. N_E⁵⁰⁰: is the amount of experimentally-validated targets in the first 500 predicted interactions.</p