MicroRNAs are approximately 22-nucleotide (nt) RNAs processed from foldback segments of endogenous transcripts. Some are known to play important gene regulatory roles during animal and plant development by pairing to the messages of protein-coding genes to direct the post-transcriptional repression of these messages. Previously, we developed a computational method called MiRscan, which scores features related to the foldbacks, and used this algorithm to identify new miRNA genes in the nematode Caenorhabditis elegans. In the present study, to identify sequences that might be involved in processing or transcriptional regulation of miRNAs, we aligned sequences upstream and downstream of orthologous nematode miRNA foldbacks. These alignments showed a pronounced peak in sequence conservation about 200 bp upstream of the miRNA foldback and revealed a highly significant sequence motif, with consensus CTCCGCCC, that is present upstream of almost all independently transcribed nematode miRNA genes. Scoring the pattern of upstream/downstream conservation, the occurrence of this sequence motif, and orthology of host genes for intronic miRNA candidates, yielded substantial improvements in the accuracy of MiRscan. Nine new C. elegans miRNA gene candidates were validated using a PCR-sequencing protocol. As previously seen for bacterial RNA genes, sequence features outside of the RNA secondary structure can therefore be very useful for the computational identification of eukaryotic noncoding RNA genes. The total number of confidently identified nematode miRNAs now approaches 100. The improved analysis supports our previous assertion that miRNA gene identification is nearing completion in C. elegans with apparently no more than 20 miRNA genes now remaining to be identified.

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs? 5? end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target?s 3? UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method?s specificity. The initial seeding step determines an algorithm?s sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost?s weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).

Cells are controlled by the complex and dynamic actions of thousands of genes. With the sequencing of many genomes, the key problem has shifted from identifying genes to knowing what the genes do; we need a framework for expressing that knowledge. Even the most rigorous attempts to construct ontological frameworks describing gene function (e.g., the Gene Ontology project) ultimately rely on manual curation and are thus labor-intensive and subjective. But an alternative exists: the field of functional genomics is piecing together networks of gene interactions, and although these data are currently incomplete and error-prone, they provide a glimpse of a new, probabilistic view of gene function. We outline such a framework, which revolves around a statistical description of gene interactions derived from large, systematically compiled data sets. In this probabilistic view, pleiotropy is implicit, all data have errors and the definition of gene function is an iterative process that ultimately converges on the correct functions. The relationships between the genes are defined by the data, not by hand. Even this comprehensive view fails to capture key aspects of gene function, not least their dynamics in time and space, showing that there are limitations to the model that must ultimately be addressed.

Proteins function mainly through interactions, especially with DNA and other proteins. While some large-scale interaction networks are now available for a number of model organisms, their experimental generation remains difficult. Consequently, interolog mapping--the transfer of interaction annotation from one organism to another using comparative genomics--is of significant value. Here we quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins. Using interaction information from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a joint E-value <10(-70). (These "joint" quantities are the geometric means of the identities or E-values for the two pairs of interacting proteins.) We generalize our interolog analysis to protein-DNA binding, finding such interactions are conserved at specific thresholds between 30% and 60% sequence identity depending on the protein family. Furthermore, we introduce the concept of a "regulog"--a conserved regulatory relationship between proteins across different species. We map interologs and regulogs from yeast to a number of genomes with limited experimental annotation (e.g., Arabidopsis thaliana) and make these available through an online database at http://interolog.gersteinlab.org. Specifically, we are able to transfer approximately 90,000 potential protein-protein interactions to the worm. We test a number of these in two-hybrid experiments and are able to verify 45 overlaps, which we show to be statistically significant.

To obtain a global view of functional interactions among genes in a metazoan genome, we computationally integrated interactome data, gene expression data, phenotype data, and functional annotation data from three model organisms-Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster-and predicted genome-wide genetic interactions in C. elegans. The resulting genetic interaction network (consisting of 18,183 interactions) provides a framework for system-level understanding of gene functions. We experimentally tested the predicted interactions for two human disease-related genes and identified 14 new modifiers.