Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

DRUG DISCOVERY: AN APPRAISAL

The process of drug discovery and development has undergone radical changes over the years. Introduction of several novel technologies in genomics, proteomics and other omics areas have enabled drug target identification and validation more specific. In silico virtual screening and other computational chemistry methods like QSAR (Quantitative Structure-Activity Relationship) and QSPR (Quantitative Structure-Property Relationship) have enabled the emergence of new drugs with minimal toxicity and higher efficacy in this post-genomics era. Moreover, initiative like Open Source Drug Discovery (OSDD) is playing a promising role in accelerating the pace of drug discovery process. Better understanding of these methods and initiatives by researchers will kindle interest towards adopting it. Hence, by this review, we intend to present a comprehensive view of overall transition and modernization of the drug discovery process and it's impacts on the scientific community.Â

Discovering bipartite substructure in directed networks

Bipartivity is an important network concept that can be applied to nodes, edges and communities. Here we focus on directed networks and look for subnetworks made up of two distinct groups of nodes, connected by “one-way” links. We show that a spectral approach can be used to find hidden substructure of this form. Theoretical support is given for the idealised case where there is limited overlap between subnetworks. Numerical experiments show that the approach is robust to spurious and missing edges. A key application of this work is in the analysis of high-throughput gene expression data, and we give an example where a biologically meaningful directed bipartite subnetwork is found from a cancer microarray dataset