Oswaldo Trelles

Integrated analysis of gene expression by association rules discovery

BackgroundMicroarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.ResultsIn this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.ConclusionThe integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Enge ne software package.

Big data, but are we ready?

, 647–657 (2010)) , which presents cloud and heterogeneous computing as solutions for tackling large-scale and high-dimensional data sets. These technologies have been around for years, raising the question: why are they not used more often in bioinformatics? The answer is that, apart from introducing complexity, they quickly break down when a large amount of data is communicated between computing nodes.In their Review, Schadt and colleagues state that computational analysis in biology is high-dimensional, and predict that peta-bytes, even exabytes, of data will be soon stored and analysed. We agree with this predicted scenario and illustrate, through a simple calculation, how suitable current computational technologies really are for such large volumes of data.Currently, it takes minimally 9 hours for each of 1,000 cloud nodes to process 500 GB, at a cost of US

Intelligent client for integrating bioinformatics services

3,000 (500 GB to 500 TB of total data). The bottleneck in this process is the input/output (IO) hardware that links data storage to the calculation node

PreP: gene expression data pre-processing

MOTIVATION In addition to existing bioinformatics software, a lot of new tools are being developed world wide to supply services for an ever growing, widely dispersed and heterogeneous collection of biological data. The integration of these resources under a common platform is a challenging task. To this end, several groups are developing integration technologies, in which services are usually registered in some sort of catalogue to allow novel discovering and accessing mechanisms to be implemented. However, each service demands specific interfaces to accommodate their parameters and it is a complicated task linking the different service inputs and outputs to solve a biological problem. RESULTS In this work we address the design and implementation of a versatile web client to access BioMOBY compatible services (a system by which a client can interact with multiple sources of biological data regardless of the underlying format or schema) using the service description stored in the BioMOBY catalogue. The automatic interface generator significantly reduces developing time and produces uniform service access mechanisms. The design and proof of concept (for such a client) including the generic interface generator have been developed and implemented in the National Institute for Bioinformatics in Spain. AVAILABILITY The INB (National Institute for Bioinformatics, Spain) platform is available at www.inab.org/MOWServ

Engene: the processing and exploratory analysis of gene expression data

UNLABELLED PreP is a versatile, powerful, standalone application that aims at pre-processing gene expression data. AVAILABILITY Documentation and executable file for MS-Windows are available at http://chirimoyo.ac.uma.es/bitlab/services/index.htm

Sma3s: A Three-Step Modular Annotator for Large Sequence Datasets

Engene is a versatile, and platform-independent web tool for exploratory analysis of gene expression data that aims at storing, visualizing and processing large sets of gene expression patterns.

Early and delayed long-term transcriptional changes and short-term transient responses during cold acclimation in olive leaves

Automatic sequence annotation is an essential component of modern ‘omics’ studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ∼85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes.

Parallel implementation of DNAm1 program on message-passing architectures

Low temperature severely affects plant growth and development. To overcome this constraint, several plant species from regions having a cool season have evolved an adaptive response, called cold acclimation. We have studied this response in olive tree (Olea europaea L.) cv. Picual. Biochemical stress markers and cold-stress symptoms were detected after the first 24 h as sagging leaves. After 5 days, the plants were found to have completely recovered. Control and cold-stressed plants were sequenced by Illumina HiSeq 1000 paired-end technique. We also assembled a new olive transcriptome comprising 157,799 unigenes and found 6,309 unigenes differentially expressed in response to cold. Three types of response that led to cold acclimation were found: short-term transient response, early long-term response, and late long-term response. These subsets of unigenes were related to different biological processes. Early responses involved many cold-stress-responsive genes coding for, among many other things, C-repeat binding factor transcription factors, fatty acid desaturases, wax synthesis, and oligosaccharide metabolism. After long-term exposure to cold, a large proportion of gene down-regulation was found, including photosynthesis and plant growth genes. Up-regulated genes after long-term cold exposure were related to organelle fusion, nucleus organization, and DNA integration, including retrotransposons.

jORCA: easily integrating bioinformatics Web Services

Abstract We present a new computing approach for the parallelization on message-passing computer architectures of the DNAml algorithm, one of the most powerful tools available for constructing phylogenetic trees from DNA sequences. An analysis of the data dependencies of the method gave little chances to develop an efficient parallel approach. However, a careful run-time analysis of the behaviour of the algorithm allowed us to propose a very efficient parallel implementation based on the combination of advanced dynamic scheduling strategies, speculative running-time execution decisions and I/O buffering. In this work, we discuss specific Parallel Virtual Machine (PVM)-based implementations for a cluster of workstations and for Distributed Memory multiprocessors, with high performance results. The code can be obtained from our public-domain sites.

Mining association rules from biological databases

MOTIVATION Web services technology is becoming the option of choice to deploy bioinformatics tools that are universally available. One of the major strengths of this approach is that it supports machine-to-machine interoperability over a network. However, a weakness of this approach is that various Web Services differ in their definition and invocation protocols, as well as their communication and data formats-and this presents a barrier to service interoperability. RESULTS jORCA is a desktop client aimed at facilitating seamless integration of Web Services. It does so by making a uniform representation of the different web resources, supporting scalable service discovery, and automatic composition of workflows. Usability is at the top of the jORCA agenda; thus it is a highly customizable and extensible application that accommodates a broad range of user skills featuring double-click invocation of services in conjunction with advanced execution-control, on the fly data standardization, extensibility of viewer plug-ins, drag-and-drop editing capabilities, plus a file-based browsing style and organization of favourite tools. The integration of bioinformatics Web Services is made easier to support a wider range of users. .