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Editorial: Current progress in Bioinformatics 2010

In this issue, we provide the next installment of our annual feature ‘Current Progress in Bioinformatics’. Each year, we invite leaders in bioinformatics to summarize the progress and challenges in hot and emerging areas of inquiry. We ask the authors specifically to summarize the past 12–18 months of important literature, and to provide a perspective in the field. The fields are chosen based on many different considerations: we start by studying the newest work in both bioinformatics journals as well as application-area journals in biology and medicine. We also consider the papers presented at bioinformatics conferences throughout the year, and the general ‘buzz’ of scientific areas. This year, the areas really jumped out without us having to work too hard. The field of bioinformatics is now a relatively stable discipline, with journals, conferences and a professional society all going back more than 10 years. Nonetheless, it is amazing to see how certain problems can ‘sneak up’ on us—the emergence of the data handling and analysis challenges associated with next-generation sequencing data is a prime example—many of us would have said that sequence analysis and genome assembly was a relatively dormant field, having solved most of the key problems for analysis and search that confronted biologists. Wow, were we wrong! Within months, many biological lab infrastructures were brought to their knees by the volume of data created by the new sequencing machines. Basic capabilities like storage, raw data triage and certainly search all were stressed by these data and the issues of basic sequence analysis have bounced back as a very busy area of bioinformatics. Similar ‘surprises’ have emerged from translational bioinformatics, synthetic biology and others. So we are pleased to have selected a few key areas where we can present a summary of progress. As usual, we organize these reviews in the rough order of the central dogma from DNA to RNA to proteins to networks to organs to organisms. Dalca and Brudno present a summary of nextgeneration sequencing informatics challenges in ‘Genome variation discovery with next generation sequencing data’. The throughput of these new sequencing technologies is breathtaking, but so are the challenges in terms of noise. After first providing a brief review of the basic sequencing technologies, they review methods for mapping reads on to existing assemblies, SNP and indel discovery, and discovery of structural variation on a larger scale. They also mention software systems that are emerging for providing a platform for the continuing analysis of these important data. The study of biology from a systems perspective has emerged as an important theme of bioinformatics in the last 5 years (often under the rubric of ‘systems biology’ but more generally as network and graphbased views of biological systems). The initial approaches often used static views of interactions in an ‘equilibrium’ view of biological homeostasis. In ‘Towards the dynamic interactome: it’s about time’ Przytycka, Singh and Slonim provide a review of recent methods to look at the evolution of interactions over time. Clearly, in the context of developmental biology (including stem cell biology) and dynamic response to perturbations such as drugs or other environmental inputs, it is absolutely critical to understand the dynamic response modes of cellular networks. In this review, the authors review the importance of time, space and context in understanding biological responses, the data sources that are relevant to these questions and the informatics techniques that are useful. It is exciting to imagine our static pathways ‘coming to life’ as we overlay data sets that shed light on dynamics. They end with the suggestion that our understanding of human genetic variation will necessarily require dynamic models of human biology. A critical technical capability for the analysis of biological systems has become the ability to integrate large data sets. The initial enthusiasm for any particular type of data (e.g. expression data) typically yields to the recognition that biological inferences are most robust when they stem from multiple data sources with different sources of bias and noise. In ‘Knowledge-based data analysis comes of age’, BRIEFINGS IN BIOINFORMATICS. VOL 11. NO 1. 1^2 doi:10.1093/bib/bbq001