Accelerating Multiple Sequence Alignment with the Cell BE Processor

The Cell Broadband Engine (BE) Architecture is a new heterogeneous multi-core architecture targeted at compute-intensive workloads. The architecture of the Cell BE has several features that are unique in high-performance general-purpose processors, most notably the extensive support for vectorization, scratch pad memories and explicit programming of direct memory accesses (DMAs) and mailbox communication. While these features strongly increase programming complexity, it is generally claimed that significant speedups can be obtained by using Cell BE processors. This paper presents our experiences with using the Cell BE architecture to accelerate Clustal W, a bio-informatics program for multiple sequence alignment. We report on how we apply the unique features of the Cell BE to Clustal W and how important each is in obtaining high performance. By making extensive use of vectorization and by parallelizing the application across all cores, we demonstrate a speedup of 24.4 times when using 16 synergistic processor units on a QS21 Cell Blade compared to single-thread execution on the power processing unit. As the Cell BE exploits a large number of slim cores, our highly optimized implementation is just 3.8 times faster than a 3-thread version running on an Intel Core2 Duo, as the latter processor exploits a small number of fat cores.

Cystic Fibrosis from Laboratory to Bedside: The Role of A20 in NF-κB-Mediated Inflammation

Cystic fibrosis (CF) is a lifelong, inflammatory multi-organ disease and the most common lethal, genetic condition in Caucasian populations, with a median survival rate of 41.5 years. Pulmonary disease, characterized by infective exacerbations, bronchiectasis and increasing airway insufficiency is the most serious manifestation of this disease process, currently responsible for over 80% of CF deaths. Chronic dysregulation of the innate immune and host inflammatory response has been proposed as a mechanism central to this genetic condition, primarily driven by the nuclear factor κB (NF-κB) pathway. Chronic activation of this transcription factor complex leads to the production of pro-inflammatory cytokines and mediators such as IL-6, IL-8 and TNF-α. A20 has been described as a central and inducible negative regulator of NF-κB. This intracellular molecule negatively regulates NF-κB-driven pro-inflammatory signalling upon toll-like receptor activation at the level of TRAF6 activation. Silencing of A20 increases cellular levels of p65 and induces a pro-inflammatory state. We have previously shown that A20 expression positively correlates with lung function (FEV1%) in CF. Despite improvement in survival rates in recent years, advancements in available therapies have been incremental. We demonstrate that the experimental use of naturally occurring plant diterpenes such as gibberellin on lipopolysaccharide-stimulated cell lines reduces IL-8 release in an A20-dependent manner. We discuss how the use of a novel bio-informatics gene expression connectivity-mapping technique to identify small molecule compounds that similarly mimic the action of A20 may lead to the development of new therapeutic approaches capable of reducing chronic airway inflammation in CF. 

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

Populational Genetic Databases Is a Specific Ethical and Legal Framework Necessary?

Recent scientific advances and new technological developments, most notably the advent of bio-informatics, have led to the emergence of genetic databases with particular characteristics and structures. Paralleling these developments, there has been a proliferation of ethical and legal texts aimed at the regulation of this new form of genetic database.

Évolution de familles de gènes par duplications et pertes : algorithmes pour la correction d’arbres bruités

Les gènes sont les parties du génome qui codent pour les protéines. Les gènes d’une ou plusieurs espèces peuvent être regroupés en "familles", en fonction de leur similarité de séquence. Cependant, pour connaître les relations fonctionnelles entre ces copies de gènes, la similarité de séquence ne suffit pas. Pour cela, il est important d’étudier l’évolution d’une famille par duplications et pertes afin de pouvoir distinguer entre gènes orthologues, des copies ayant évolué par spéciation et susceptibles d’avoir conservé une fonction commune, et gènes paralogues, des copies ayant évolué par duplication qui ont probablement développé des nouvelles fonctions. Étant donnée une famille de gènes présents dans n espèces différentes, un arbre de gènes (obtenu par une méthode phylogénétique classique), et un arbre phylogénétique pour les n espèces, la "réconciliation" est l’approche la plus courante permettant d’inférer une histoire d’évolution de cette famille par duplications, spéciations et pertes. Le degré de confiance accordé à l’histoire inférée est directement relié au degré de confiance accordé à l’arbre de gènes lui-même. Il est donc important de disposer d’une méthode préliminaire de correction d’arbres de gènes. Ce travail introduit une méthodologie permettant de "corriger" un arbre de gènes : supprimer le minimum de feuilles "mal placées" afin d’obtenir un arbre dont les sommets de duplications (inférés par la réconciliation) sont tous des sommets de "duplications apparentes" et obtenir ainsi un arbre de gènes en "accord" avec la phylogénie des espèces. J’introduis un algorithme exact pour des arbres d’une certaine classe, et une heuristique pour le cas général.

Polimorfismos del gen Butirilcolinesterasa responsables de reacciones adversas en pacientes consumidores de “cocaína”

La butirilcolinesterasa humana (BChE; EC 3.1.1.8) es una enzima polimórfica sintetizada en el hígado y en el tejido adiposo, ampliamente distribuida en el organismo y encargada de hidrolizar algunos ésteres de colina como la procaína, ésteres alifáticos como el ácido acetilsalicílico, fármacos como la metilprednisolona, el mivacurium y la succinilcolina y drogas de uso y/o abuso como la heroína y la cocaína. Es codificada por el gen BCHE (OMIM 177400), habiéndose identificado más de 100 variantes, algunas no estudiadas plenamente, además de la forma más frecuente, llamada usual o silvestre. Diferentes polimorfismos del gen BCHE se han relacionado con la síntesis de enzimas con niveles variados de actividad catalítica. Las bases moleculares de algunas de esas variantes genéticas han sido reportadas, entre las que se encuentra las variantes Atípica (A), fluoruro-resistente del tipo 1 y 2 (F-1 y F-2), silente (S), Kalow (K), James (J) y Hammersmith (H). En este estudio, en un grupo de pacientes se aplicó el instrumento validado Lifetime Severity Index for Cocaine Use Disorder (LSI-C) para evaluar la gravedad del consumo de “cocaína” a lo largo de la vida. Además, se determinaron Polimorfismos de Nucleótido Simple (SNPs) en el gen BCHE conocidos como responsables de reacciones adversas en pacientes consumidores de “cocaína” mediante secuenciación del gen y se predijo el efecto delos SNPs sobre la función y la estructura de la proteína, mediante el uso de herramientas bio-informáticas. El instrumento LSI-C ofreció resultados en cuatro dimensiones: consumo a lo largo de la vida, consumo reciente, dependencia psicológica e intento de abandono del consumo. Los estudios de análisis molecular permitieron observar dos SNPs codificantes (cSNPs) no sinónimos en el 27.3% de la muestra, c.293A>G (p.Asp98Gly) y c.1699G>A (p.Ala567Thr), localizados en los exones 2 y 4, que corresponden, desde el punto de vista funcional, a la variante Atípica (A) [dbSNP: rs1799807] y a la variante Kalow (K) [dbSNP: rs1803274] de la enzima BChE, respectivamente. Los estudios de predicción In silico establecieron para el SNP p.Asp98Gly un carácter patogénico, mientras que para el SNP p.Ala567Thr, mostraron un comportamiento neutro. El análisis de los resultados permite proponer la existencia de una relación entre polimorfismos o variantes genéticas responsables de una baja actividad catalítica y/o baja concentración plasmática de la enzima BChE y algunas de las reacciones adversas ocurridas en pacientes consumidores de cocaína.

IaaS clouds vs. clusters for HPC: a performance study

The increasing amount of data collected in the fields of physics and bio-informatics allows researchers to build realistic, and therefore accurate, models/simulations and gain a deeper understanding of complex systems. This analysis is often at the cost of greatly increased processing requirements. Cloud computing, which provides on demand resources, can offset increased analysis requirements. While beneficial to researchers, adaption of clouds has been slow due to network and performance uncertainties. We compare the performance of cloud computers to clusters to make clear the advantages and limitations of clouds. Focus has been put on understanding how virtualization and the underlying network effects performance of High Performance Computing (HPC) applications. Collected results indicate that performance comparable to high performance clusters is achievable on cloud computers depending on the type of application run.

A survey of approaches and frameworks to carry out genomic data analysis on the cloud

High Performance Computing (HPC) clouds have started to change the way how research in science, in particular medicine and genomics (bioinformatics) is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. However, most HPC clouds are provided at an Infrastructure as a Service (IaaS) level, users are presented with a set of virtual servers which need to be put together to form HPC environments via time consuming resource management and software configuration tasks, which make them practically unusable by discipline, non-computing specialists. In response, there is a new trend to expose cloud applications as services to simplify access and execution on clouds. This paper firstly examines commonly used cloud-based genomic analysis services (Tuxedo Suite, Galaxy and Cloud Bio Linux). As a follow up, we propose two new solutions (HPCaaS and Uncinus), which aim to automate aspects of the service development and deployment process. By comparing and contrasting these five solutions, we identify key mechanisms of service creation, execution and access that are required to support genomic research on the SaaS cloud, in particular by discipline specialists. © 2014 IEEE.

Exposing HPC and sequential applications as services through the development and deployment of a SaaS cloud

Cloud and service computing has started to change the way research in science, in particular biology and medicine, is being carried out. Researchers that have taken advantage of this technology (making use of public and private cloud compute resources) can process large amounts of data (big data) and speed up discovery. However, this requires researchers to acquire a solid knowledge and skills in the development of sequential and high performance computing (HPC), and cloud development and deployment background. In response a technology exposing HPC applications as services through the development and deployment of a SaaS cloud, and its proof of concept in the form of implementation of a cloud environment, Uncinus, has been developed and implemented to allow researchers easy access to cloud computing resources. The new technology offers and Uncinus supports the development of applications as services and the sharing of compute resources to speed up applications' execution. Users access these cloud resources and services through web interfaces. Using the Uncinus platform, a bio-informatics workflow was executed on a private (HPC) cloud, server and public cloud (Amazon EC2) resources, performance results showing a 3 fold improvement compared to local resources' performance. Biology and medicine specialists with no programming and application deployment on clouds background could run the case study applications with ease.

Selected approaches and frameworks to carry out genomic data provision and analysis on the cloud

While High Performance Computing clouds allow researchers to process large amounts of genomic data, complex resource and software configuration tasks must be carried out beforehand. The current trend exposes applications and data as services, simplifying access to clouds. This paper examines commonly used cloud-based genomic analysis services, introduces the approach of exposing data as services and proposes two new solutions (HPCaaS and Uncinus) which aim to automate service development, deployment process and data provision. By comparing and contrasting these solutions, we identify key mechanisms of service creation, execution and data access required to support non-computing specialists employing clouds.

Identification of unannotated exons of low abundance transcripts in Drosophila melanogaster and cloning of a new serine protease gene upregulated upon injury

Abstract Background The sequencing of the D.melanogaster genome revealed an unexpected small number of genes (~ 14,000) indicating that mechanisms acting on generation of transcript diversity must have played a major role in the evolution of complex metazoans. Among the most extensively used mechanisms that accounts for this diversity is alternative splicing. It is estimated that over 40% of Drosophila protein-coding genes contain one or more alternative exons. A recent transcription map of the Drosophila embryogenesis indicates that 30% of the transcribed regions are unannotated, and that 1/3 of this is estimated as missed or alternative exons of previously characterized protein-coding genes. Therefore, the identification of the variety of expressed transcripts depends on experimental data for its final validation and is continuously being performed using different approaches. We applied the Open Reading Frame Expressed Sequence Tags (ORESTES) methodology, which is capable of generating cDNA data from the central portion of rare transcripts, in order to investigate the presence of hitherto unnanotated regions of Drosophila transcriptome. Results Bioinformatic analysis of 1,303 Drosophila ORESTES clusters identified 68 sequences derived from unannotated regions in the current Drosophila genome version (4.3). Of these, a set of 38 was analysed by polyA+ northern blot hybridization, validating 17 (50%) new exons of low abundance transcripts. For one of these ESTs, we obtained the cDNA encompassing the complete coding sequence of a new serine protease, named SP212. The SP212 gene is part of a serine protease gene cluster located in the chromosome region 88A12-B1. This cluster includes the predicted genes CG9631, CG9649 and CG31326, which were previously identified as up-regulated after immune challenges in genomic-scale microarray analysis. In agreement with the proposal that this locus is co-regulated in response to microorganisms infection, we show here that SP212 is also up-regulated upon injury. Conclusion Using the ORESTES methodology we identified 17 novel exons from low abundance Drosophila transcripts, and through a PCR approach the complete CDS of one of these transcripts was defined. Our results show that the computational identification and manual inspection are not sufficient to annotate a genome in the absence of experimentally derived data.

Modelling and simulating in systems biology: an approach based on multi-agent systems

Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts. This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems. In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments.

Changes in miRNA Expression in a Model of Microcephaly

miRNAs function to regulate gene expression through post-transcriptional mechanisms to potentially regulate multiple aspects of physiology and development. Whole transcriptome analysis has been conducted on the citron kinase mutant rat, a mutant that shows decreases in brain growth and development. The resulting differences in RNA between mutant and wild-type controls can be used to identify genetic pathways that may be regulated differentially in normal compared to abnormal neurogenesis. The goal of this thesis was to verify, with quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), changes in miRNA expression in Cit-k mutants and wild types. In addition to confirming miRNA expression changes, bio-informatics software TargetScan 5.1 was used to identify potential mRNA targets of the differentially expressed miRNAs. The miRNAs that were confirmed to change include: rno-miR-466c, mmu-miR-493, mmu-miR-297a, hsa-miR-765, and hsa-miR-1270. The TargetScan analysis revealed 347 potential targets which have known roles in development. A subset of these potential targets include genes involved in the Wnt signaling pathway which is known to be an important regulator of stem cell development.

Essentials in Ontology Engineering: Methodologies, Languages, and Tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative.