Bioconductor: Open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. We detail some of the design decisions, software paradigms and operational strategies that have allowed a small number of researchers to provide a wide variety of innovative, extensible, software solutions in a relatively short time. The use of an object oriented programming paradigm, the adoption and development of a software package system, designing by contract, distributed development and collaboration with other projects are elements of this project's success. Individually, each of these concepts are useful and important but when combined they have provided a strong basis for rapid development and deployment of innovative and flexible research software for scientific computation. A primary objective of this initiative is achievement of total remote reproducibility of novel algorithmic research results.

Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

THE LEGACY OF GN RAMACHANDRAN AND THE DEVELOPMENT OF STRUCTURAL BIOLOGY IN INDIA

G.N. Ramachandran is among the founding fathers of structural molecular biology. He made pioneering contributions in computational biology, modelling and what we now call bioinformatics. The triple helical coiled coil structure of collagen proposed by him forms the basis of much of collagen research at the molecular level. The Ramachandran map remains the simplest descriptor and tool for validation of protein structures. He has left his imprint on almost all aspects of biomolecular conformation. His contributions in the area of theoretical crystallography have been outstanding. His legacy has provided inspiration for the further development of structural biology in India. After a pause, computational biology and bioinformatics are in a resurgent phase. One of the two schools established by Ramachandran pioneered the development of macromolecular crystallography, which has now grown into an important component of modern biological research in India. Macromolecular NMR studies in the country are presently gathering momentum. Structural biology in India is now poised to again approach heights of the kind that Ramachandran conquered more than a generation ago.

The legacy of G. N. Ramachandran and the development of structural biology in India

G. N. Ramachandran is among the founding fathers of structural molecular biology. He made pioneering contributions in computational biology, modelling and what we now call bioinformatics. The triple helical coiled coil structure of collagen proposed by him forms the basis of much of collagen research at the molecular level. The Ramachandran map remains the simplest descriptor and tool for validation of protein structures. He has left his imprint on almost all aspects of biomolecular conformation. His contributions in the area of theoretical crystallography have been outstanding. His legacy has provided inspiration for the further development of structural biology in India. After a pause, computational biology and bioinformatics are in a resurgent phase. One of the two schools established by Ramachandran pioneered the development of macromolecular crystallography, which has now grown into an important component of modern biological research in India. Macromolecular NMR studies in the country are presently gathering momentum. Structural biology in India is now poised to again approach heights of the kind that Ramachandran conquered more than a generation ago.

Agent-Oriented Data Curation in Bioinformatics

The practitioners of bioinformatics require increasing sophistication from their software tools to take into account the particular characteristics that make their domain complex. For example, there is a great variation of experience of researchers, from novices who would like guidance from experts in the best resources to use to experts that wish to take greater management control of the tools used in their experiments. Also, the range of available, and conflicting, data formats is growing and there is a desire to automate the many trivial manual stages of in-silico experiments. Agent-oriented software development is one approach to tackling the design of complex applications. In this paper, we argue that, in fact, agent-oriented development is a particularly well-suited approach to developing bioinformatics tools that take into account the wider domain characteristics. To illustrate this, we design a data curation tool, which manages the format of experimental data, extend it to better account for the extra requirements placed by the domain characteristics, and show how the characteristics lead to a system well suited to an agent-oriented view.

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

New insights into functional regulation in MS-based drug profiling

We present a novel data analysis strategy which combined with subcellular fractionation and liquid chromatography-mass spectrometry (LC-MS) based proteomics provides a simple and effective workflow for global drug profiling. Five subcellular fractions were obtained by differential centrifugation followed by high resolution LC-MS and complete functional regulation analysis. The methodology combines functional regulation and enrichment analysis into a single visual summary. The workflow enables improved insight into perturbations caused by drugs. We provide a statistical argument to demonstrate that even crude subcellular fractions leads to improved functional characterization. We demonstrate this data analysis strategy on data obtained in a MS-based global drug profiling study. However, this strategy can also be performed on other types of large scale biological data.

Inferring Contagion in Regulatory Networks

Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e., asymmetries in gene expression dependences of regulatory networks. Moreover, we present a bootstrap algorithm in order to test the contagion concept. This technique was applied in simulated data and, also, in an actual large sample of biological data. Literature review has confirmed some genes identified by contagion as actually belonging to the TP53 pathway.

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)