Briefings in Bioinformatics

GPU computing for systems biology.

Publication Date: 2010 Mar 7 PMID: 20211843
Authors: Dematte, L. - Prandi, D.
Journal: Brief Bioinform

The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by general-purpose scientific computing on graphics processing units (GPGPU), which offers the power of a small computer cluster at a cost of approximately $400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.

post to: CiteULike

Ensemble learning algorithms for classification of mtDNA into haplogroups.

Publication Date: 2010 Mar 4 PMID: 20203074
Authors: Wong, C. - Li, Y. - Lee, C. - Huang, C. H.
Journal: Brief Bioinform

Classification of mitochondrial DNA (mtDNA) into their respective haplogroups allows the addressing of various anthropologic and forensic issues. Unique to mtDNA is its abundance and non-recombining uni-parental mode of inheritance; consequently, mutations are the only changes observed in the genetic material. These individual mutations are classified into their cladistic haplogroups allowing the tracing of different genetic branch points in human (and other organisms) evolution. Due to the large number of samples, it becomes necessary to automate the classification process. Using 5-fold cross-validation, we investigated two classification techniques on the consented database of 21 141 samples published by the Genographic project. The support vector machines (SVM) algorithm achieved a macro-accuracy of 88.06% and micro-accuracy of 96.59%, while the random forest (RF) algorithm achieved a macro-accuracy of 87.35% and micro-accuracy of 96.19%. In addition to being faster and more memory-economic in making predictions, SVM and RF are better than or comparable to the nearest-neighbor method employed by the Genographic project in terms of prediction accuracy.

post to: CiteULike

Preprocessing and downstream analysis of microarray DNA copy number profiles.

Publication Date: 2010 Feb 19 PMID: 20172948
Authors: van de Wiel, M. A. - Picard, F. - van Wieringen, W. N. - Ylstra, B.
Journal: Brief Bioinform

Analysis of DNA copy number profiles requires methods tailored to the specific nature of these data. The number of available data analysis methods has grown enormously in the last 5 years. We discuss the typical characteristics of DNA copy number data, as measured by microarray technology and review the extensive literature on preprocessing methods such as segmentation and calling. Subsequently, the focus narrows to applications of DNA copy number in cancer, in particular, several downstream analyses of multi-sample data sets such as testing, clustering and classification. Finally, we look ahead: what should we prepare for and which methodology-related topics may deserve attention in the near future?

post to: CiteULike

DNA barcoding: a six-question tour to improve users' awareness about the method.

Publication Date: 2010 Feb 15 PMID: 20156987
Authors: Casiraghi, M. - Labra, M. - Ferri, E. - Galimberti, A. - De Mattia, F.
Journal: Brief Bioinform

DNA barcoding is a recent and widely used molecular-based identification system that aims to identify biological specimens, and to assign them to a given species. However, DNA barcoding is even more than this, and besides many practical uses, it can be considered the core of an integrated taxonomic system, where bioinformatics plays a key role. DNA barcoding data could be interpreted in different ways depending on the examined taxa but the technique relies on standardized approaches, methods and analyses. The existing reference towards a common way to treat DNA barcoding data, analyses and results is the Barcode of Life Data Systems. However, the scientific community has produced in the recent years a number of alternative methods to manage barcoding data. The present work starts from this point, because users should be aware of the consequences their choices produce on the results. Despite the fact that a strict standardization is the essence of DNA barcoding, we propose a tour of six questions to improve the users' awareness about the method, the correct use of concepts and alternative tools provided by scientific community.

post to: CiteULike

Fast and efficient searching of biological data resources--using EB-eye.

Publication Date: 2010 Feb 11 PMID: 20150321
Authors: Valentin, F. - Squizzato, S. - Goujon, M. - McWilliam, H. - Paern, J. - Lopez, R.
Journal: Brief Bioinform

The EB-eye is a fast and efficient search engine that provides easy and uniform access to the biological data resources hosted at the EMBL-EBI. Currently, users can access information from more than 62 distinct datasets covering some 400 million entries. The data resources represented in the EB-eye include: nucleotide and protein sequences at both the genomic and proteomic levels, structures ranging from chemicals to macro-molecular complexes, gene-expression experiments, binary level molecular interactions as well as reaction maps and pathway models, functional classifications, biological ontologies, and comprehensive literature libraries covering the biomedical sciences and related intellectual property. The EB-eye can be accessed over the web or programmatically using a SOAP Web Services interface. This allows its search and retrieval capabilities to be exploited in workflows and analytical pipe-lines. The EB-eye is a novel alternative to existing biological search and retrieval engines. In this article we describe in detail how to exploit its powerful capabilities.

post to: CiteULike