Biomashups: the new world of exploratory bioinformatics?

Bioinformatics is dominated by online databases and sophisticated web-accessible tools. As such, it is ideally placed to benefit from the rapid, purpose specific combination of services achievable via web mashups. The recent introduction of a number of sophisticated frameworks has greatly simplified the mashup creation process, making them accessible to scientists with limited programming expertise. In this paper we investigate the feasibility of mashups as a new approach to bioinformatic experimentation, focusing on an exploratory niche between interactive web usage and robust workflows, and attempting to identify the range of computations for which mashups may be employed. While we treat each of the major frameworks, we illustrate the ideas with a series of examples developed under the Popfly framework

Proteomic and bioinformatics tools to understand virulence mechanisms in Staphylococcus aureus

Staphylococcus aureus, one of the major pathogenic bacteria, is associated with substantial morbidity and mortality. The disease burden of staphylococcal infections is significant, which is primarily attributed to its adaptability and resistance to environmental stresses. S. aureus has the ability to develop multiple resistances to antimicrobial agents. These high resistances make pathogenicity of S. aureus one of the most complex mechanisms to understand and manage. Proteomic and bioinformatics approaches show great potential in exploring microbial adaptation strategies, ability to cause disease by pathogenic bacteria and the development of diagnostic tools. A summary of the latest developments in the application of ‘omics’ technologies to understand resistance mechanisms in S. aureus and their future role in antistaphylococcal vaccine and/or drug discovery is given here.

Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

Optimizing I/O cost and managing memory for composition vector method based on correlation matrix calculation in bioinformatics

The generation of a correlation matrix for set of genomic sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. Each sequence may be millions of bases long and there may be thousands of such sequences which we wish to compare, so not all sequences may fit into main memory at the same time. Each sequence needs to be compared with every other sequence, so we will generally need to page some sequences in and out more than once. In order to minimize execution time we need to minimize this I/O. This paper develops an approach for faster and scalable computing of large-size correlation matrices through the maximal exploitation of available memory and reducing the number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different bioinformatics problems with different correlation matrix sizes. The significant performance improvement of the approach over previous work is demonstrated through benchmark examples.

Beyond blue gene: Intellectual property and bioinformatics

This article considers the challenges posed to intellectual property law by the emerging field of bioinformatics. It examines the intellectual property strategies of established biotechnology companies, such as Celera Genomics, and information technology firms entering into the marketplace, such as IBM. First this paper argues that copyright law is not irrelevant to biotechnology, as some commentators would suggest. It claims that the use of copyright law and contract law is fundamental to the protection of biomedical and genomic databases. Second this article questions whether biotechnology companies are exclusively interested in patenting genes and genetics sequences. Recent evidence suggests that biotechnology companies and IT firms are patenting bioinformatics software and Internet business methods, as well as underlying instrumentation such as microarrays and genechips. Finally, this paper evaluates what impact the privatisation of bioinformatics will have on public research and scientific communication. It raises important questions about integration, interoperability, and the risks of monopoly. It finally considers whether open source software such as the Ensembl Project and peer to peer technology like DSAS will be able to counter this trend of privatisation.

CattleTickBase: An integrated Internet-based bioinformatics resource for Rhipicephalus (Boophilus) microplus

The Rhipicephalus micro plus genome is large and complex in structure, making it difficult to assemble a genome sequence and costly to resource the required bioinformatics. In light of this, a consortium of international collaborators was formed to pool resources to begin sequencing this genome. We have acquired and assembled genomic DNA into contigs that represent over 1.8 Gigabase pairs of DNA from gene-enriched regions of the R. micro plus genome. We also have several datasets containing transcript sequences from a number of gene expression experiments conducted by the consortium. A web-based resource was developed to enable the scientific community to access our datasets and conduct analysis through a web-based bioinformatics environment called YABI. The collective bioinformatics resource is termed CattleTickBase. Our consortium has acquired genomic and transcriptomic sequence data at approximately 0.9X coverage of the gene-coding regions of the R. microplus genome. The YABI tool will facilitate access and manipulation of cattle tick genome sequence data as the genome sequencing of R. microplus proceeds. During this process the CattleTickBase resource will continue to be updated. Published by Elsevier Ltd. on behalf of Australian Society for Parasitology Inc.

A comparative structural bioinformatics analysis of the insulin receptor family ectodomain based on phylogenetic information

The insulin receptor (IR), the insulin-like growth factor 1 receptor (IGF1R) and the insulin receptor-related receptor (IRR) are covalently-linked homodimers made up of several structural domains. The molecular mechanism of ligand binding to the ectodomain of these receptors and the resulting activation of their tyrosine kinase domain is still not well understood. We have carried out an amino acid residue conservation analysis in order to reconstruct the phylogeny of the IR Family. We have confirmed the location of ligand binding site 1 of the IGF1R and IR. Importantly, we have also predicted the likely location of the insulin binding site 2 on the surface of the fibronectin type III domains of the IR. An evolutionary conserved surface on the second leucine-rich domain that may interact with the ligand could not be detected. We suggest a possible mechanical trigger of the activation of the IR that involves a slight ‘twist’ rotation of the last two fibronectin type III domains in order to face the likely location of insulin. Finally, a strong selective pressure was found amongst the IRR orthologous sequences, suggesting that this orphan receptor has a yet unknown physiological role which may be conserved from amphibians to mammals.

Differential Activities of the Two Closely Related Withanolides, Withaferin A and Withanone: Bioinformatics and Experimental Evidences

Background and Purpose: Withanolides are naturally occurring chemical compounds. They are secondary metabolites produced via oxidation of steroids and structurally consist of a steroid-backbone bound to a lactone or its derivatives. They are known to protect plants against herbivores and have medicinal value including anti-inflammation, anti-cancer, adaptogenic and anti-oxidant effects. Withaferin A (Wi-A) and Withanone (Wi-N) are two structurally similar withanolides isolated from Withania somnifera, also known as Ashwagandha in Indian Ayurvedic medicine. Ashwagandha alcoholic leaf extract (i-Extract), rich in Wi-N, was shown to kill cancer cells selectively. Furthermore, the two closely related purified phytochemicals, Wi-A and Wi-N, showed differential activity in normal and cancer human cells in vitro and in vivo. We had earlier identified several genes involved in cytotoxicity of i-Extract in human cancer cells by loss-of-function assays using either siRNA or randomized ribozyme library. Methodology/Principal Findings: In the present study, we have employed bioinformatics tools on four genes, i.e., mortalin, p53, p21 and Nrf2, identified by loss-of-function screenings. We examined the docking efficacy of Wi-N and Wi-A to each of the four targets and found that the two closely related phytochemicals have differential binding properties to the selected cellular targets that can potentially instigate differential molecular effects. We validated these findings by undertaking parallel experiments on specific gene responses to either Wi-N or Wi-A in human normal and cancer cells. We demonstrate that Wi-A that binds strongly to the selected targets acts as a strong cytotoxic agent both for normal and cancer cells. Wi-N, on the other hand, has a weak binding to the targets; it showed milder cytotoxicity towards cancer cells and was safe for normal cells. The present molecular docking analyses and experimental evidence revealed important insights to the use of Wi-A and Wi-N for cancer treatment and development of new anti-cancer phytochemical cocktails.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial `driver' mutations in oncogenesis. They also provide the foundation toward the design of protein protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview. (C) 2014 Elsevier Ltd. All rights reserved.

Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat

During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.