Current progress in bioinformatics 2007

Briefings inBioinformatics is pleased to present our third annual ‘Current Progress in Bioinformatics’ special issue. As in previous years, we have attempted to identify exciting or emerging fields of bioinformatics, and have asked leaders in these fields to present a brief summary of progress over the last 18–24 months and an annotated biography drawing attention to papers of particular significance. Each year, we have a logistical task of setting the order of the articles to appear in this volume. Typically, we organize them based on the linear logic of biology’s central dogma: from DNA to RNA to protein to function and phenotype. The central dogma has undergone a transformation in the last 10 years, however. Biologists have demonstrated that the simple, linear model must be augmented with multiple feedback loops. For example, RNA feeds back to affect gene regulation, and feeds forward to modulate protein function. RNA itself is modified by proteins that can alter the message. The linear central dogma has become the networked central dogma! Our field of bioinformatics is also starting to become a network. The relationship between different subdisciplines is getting increasingly complex, and promises to keep us all busy for some time. So what is the appropriate order of seven reviews on (i) metabolomics, (ii) structured RNA, (iii) proteomics, (iv) gene product networks, (v) proteins and disease, (vi) biodiversity and (vii) text mining? Well, we chose that order. Hopefully it makes sense, but probably it doesn’t matter! In the first review, David Wishart provides a summary on ‘Current Progress in Computational Metabolomics.’ He first provides a useful introduction to metabolomics—the study of the small molecules that interact with biological macromolecules as one of the major ‘omics’ pillars. As expected, many of the challenges to metabolomics are analogous to similar challenges in other branches of bioinformatics—the need to catalog small molecules in databases, to search, compare and classify them. In addition, there are important vocabulary and standards issues. There are particular challenges relating to the experimental reality of proteomics, where analytic measurements require special purpose software for laboratory information management and interpretation. Metabolomics is a field where chemoinformatics touches genomics. Thus, we get a glimpse of a future where informatics tools from neighboring disciplines are interoperable and create a potent infrastructure for discovery and engineering. Alain Laederach next reports on ‘Informatics challenges in Structured RNA.’ Understanding the protean (!) functions of RNA is a new challenge for computational biology and bioinformatics. In particular, the field is approaching challenges associated with understanding the physical properties of 3D RNA molecules, which (like proteins) fold into precise 3D shapes, catalyze many important reactions, and participate in the control of gene expression. Unlike proteins, however, they are made of four subunits (not 20), are dominated by electrostatics (RNA itself is extremely electronegative), and form their secondary structure almost entirely before assembling into their final 3D structure. Special purpose methods, therefore, are required to analyze the experimental data about their folding kinetics, the nature of their thermodynamic stability, and to understand how to use RNA 3D structure to understand function when the 1D sequence alone is insufficient. Bobbie-Jo Webb-Robertson writes about ‘Current Trends in Computational Inference from Mass Spectrometry-based Proteomics.’ She focuses, in particular, on recent achievements in massspectrometry (MS) based proteomics. The power of MS for interrogating cellular protein populations is immense—it can be used to identify proteins, characterize new proteins by de novo sequencing, characterize posttranslationally modified proteins, quantify proteins in the cellular milieu and assess protein–protein interactions. As high-throughput biology extends from genome to transcriptome to proteome, the richness of information with direct relevance to phenotype is exciting. Of course, systems biological models should benefit greatly from proteomic technologies—which provides parameters for the models and is useful for validation. BRIEFINGS IN BIOINFORMATICS. VOL 8. NO 5. 277^278 doi:10.1093/bib/bbm041 Advance Access publication August 27, 2007