Bioinformatics: alive and kicking

Bioinformatics has become too central to biology to be left to specialist bioinformaticians. Biologists are all bioinformaticians now.     

Meeting review: plant bioinformatics at the NSF and NPGI (PAMGX satellite) meetings

This brief meeting review summarizes the recommendations of NSF and NPGI funded bioinformaticians concerning the future requirements for plant bioinformatics systems and databases.     

BioMoby extensions to the Taverna workflow management and enactment software

Background  

As biology becomes an increasingly computational science, it is critical that we develop software tools that support not only bioinformaticians, but also bench biologists in their exploration of the vast and complex data-sets that continue to build from international genomic, proteomic, and systems-biology projects. The BioMoby interoperability system was created with the goal of facilitating the movement of data from one Web-based resource to another to fulfill the requirements of non-expert bioinformaticians. In parallel with the development of BioMoby, the European myGrid project was designing Taverna, a bioinformatics workflow design and enactment tool. Here we describe the marriage of these two projects in the form of a Taverna plug-in that provides access to many of BioMoby's features through the Taverna interface.     

R/parallel – speeding up bioinformatics analysis with R

Background  

R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.     

Generations of interdisciplinarity in bioinformatics

Bioinformatics, a specialism propelled into relevance by the Human Genome Project and the subsequent -omic turn in the life science, is an interdisciplinary field of research. Qualitative work on the disciplinary identities of bioinformaticians has revealed the tensions involved in work in this “borderland.” As part of our ongoing work on the emergence of bioinformatics, between 2010 and 2011, we conducted a survey of United Kingdom-based academic bioinformaticians. Building on insights drawn from our fieldwork over the past decade, we present results from this survey relevant to a discussion of disciplinary generation and stabilization. Not only is there evidence of an attitudinal divide between the different disciplinary cultures that make up bioinformatics, but there are distinctions between the forerunners, founders and the followers; as inter/disciplines mature, they face challenges that are both inter-disciplinary and inter-generational in nature.     

Biggest challenges in bioinformatics

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the ‘Biggest Challenges in Bioinformatics’ in a ‘World Café’ style event.     

Large-scale production of mouse phenotypes: the search for animal models for inherited diseases in humans

This paper is aimed principally at bioinformaticians and biologists as an introduction to recent advances in mouse mutagenesis, concentrating on genome-wide screens utilising the powerful mutagen N-ethyl-N-nitroso-urea (ENU). It contains a brief background to the underlying genetics as well as details of the practical aspects of organisation and data capture for such projects.     

充分利用Bioperl加速生物信息学的研究

Bioperl是Perl语言专门用于生物信息的工具与函数模块集,是世界各地的Perl开发人员在生物信息学、基因组学以及其他生命科学领域的智能结晶,服务于研究生物学问题的生物学家或计算机专家。通过对Bioperl进行了详细的介绍,并利用几个研究中的应用实例充分说明Bioperl在生物信息学研究中的重要地位。     

Bioinformatics: A Practical Guide to the Analysis of Genes & Proteins, (third editon). * Edited by Andreas D. Baxevanis and B.F. Francis Ouellette * New York: John Wiley & Sons; * ISBN: 0471 478784; 540pp.; 2004; $79.95.

In the post-genomics era of contemporary biological research,it is imperative for molecular biologists to survey and utilizebioinformatic algorithms, software tools and databases. Theprosperity of bioinformatic resources in the public domain isevidently an answer to the needs of molecular biologists, whichin return post new challenges and point out to new researchdirections for bioinformaticians,     

Bioinformatic strategies for better understanding of immune function

Novartis Foundation sponsored a Symposium which brought together a group of experimental immunologists, theoretical immunologists, and bioinformaticians to discuss the new field of immunoinformatics. The discussion focused on immunological databases, antigen processing and presentation, immunogenomics, host-pathogen interactions, and mathematical modelling of the immune system. A main conclusion of the meeting is the critical role played by immunoinformatics in current immunology research. In particular, immunoinformatics provides a foundation for the emerging fields of systems immunology and immunogenomics.     

Shopping in the genome market with EnsMart

Life scientists who work with the supermarket of genome data will find the EnsMart database and software package offers a valuable door to a wealth of genes and genome features. Not only available to lab biologists on the web, this popular multi-organism genome database can be installed and used on your own Unix computer with relative ease. It offers a flexible, fast and practical data-mining framework for computer-savvy biologists and bioinformaticians.     

Clustering cancer gene expression data: a comparative study

Background  

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context.     

OptFlux: an open-source software platform for <Emphasis Type="Italic">in silico</Emphasis> metabolic engineering

Background  

Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications.     

Membrane proteins: from bench to bits

Membrane proteins currently receive a lot of attention, in large part thanks to a steady stream of high-resolution X-ray structures. Although the first few structures showed proteins composed of tightly packed bundles of very hydrophobic more or less straight transmembrane α-helices, we now know that helix-bundle membrane proteins can be both highly flexible and contain transmembrane segments that are neither very hydrophobic nor necessarily helical throughout their lengths. This raises questions regarding how membrane proteins are inserted into the membrane and fold in vivo, and also complicates life for bioinformaticians trying to predict membrane protein topology and structure.     

Beyond hairballs: The use of quantitative mass spectrometry data to understand protein-protein interactions

The past 10years have witnessed a dramatic proliferation in the availability of protein interaction data. However, for interaction mapping based on affinity purification coupled with mass spectrometry (AP-MS), there is a wealth of information present in the datasets that often goes unrecorded in public repositories, and as such remains largely unexplored. Further, how this type of data is represented and used by bioinformaticians has not been well established. Here, we point out some common mistakes in how AP-MS data are handled, and describe how protein complex organization and interaction dynamics can be inferred using quantitative AP-MS approaches.     

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.