microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu

Statistics and Evolution of Functional Genomic Sequence

In this thesis, three separate problems of genomics are addressed, utilizing methods related to the field of statistical mechanics. The goal of the project discussed in the first chapter is the elucidation of post-transcriptional gene regulation imposed by microRNAs, a recently discovered class of tiny non-coding RNAs. A probabilistic algorithm for the computational identification of genes regulated by microRNAs is introduced, which was developed based on experimental data and statistical analysis of whole genome data. In particular, the application of this algorithm to multiple-alignments of groups of related species allows for the specific and sensitive detection of genes targeted by microRNAs on a genome-wide level. Examination of clade-specific predictions and cross-clade comparison yields deeper insights into microRNA biology and first clues about long-term evolution of microRNA regulation, which are discussed in detail. Modeling evolutionary dynamics of microsatellites, an abundant class of repetitive sequence in eukaryotic genomes, was the objective of the second project and is discussed in chapter two. Inspired by the putative functionality of some of these elements and the difficulty of constructing correct sequence alignments that reflect the evolutionary relationships between microsatellites, a neutral model for microsatellite evolution is developed and tested in the fruit fly Drosophila melanogaster by comparing evolutionary rates predicted by the model to independent measurements of these rates from multiple alignments of three closely relates Drosophila species. The model is applied separately to genomic sequence categories of different functional annotations in order to assess the varying influence of selective constraint among these categories. In the last chapter, a general population genetic model is introduced that allows for the determination of transcription factor binding site stability as a function of selection strength, mutation rate and effective population size at arbitrary values of these parameters. The analytical solution of this model indicates the probability of a binding site to be functional. The model is used to compute the population fraction of functional binding sites at fixed selection pressure across a variety of different taxa. The results lead to the conclusion that a decreasing effective population size, such as observed at the evolutionary transition from prokaryotes to eukaryotes, could result in loss of binding site stability. An extension to our model serves us to assess the compensatory effect of the emergence of multiple binding sites for the same transcription factor in order to maintain the existing regulatory relationship

Vive la différence: biogenesis and evolution of microRNAs in plants and animals

MicroRNAs are pervasive in both plants and animals, but many aspects of their biogenesis, function and evolution differ. We reveal how these differences contribute to characteristic features of microRNA evolution in the two kingdoms

Reexamining microRNA Site Accessibility in Drosophila: A Population Genomics Study

Kertesz et al. (Nature Genetics 2008) described PITA, a miRNA target prediction algorithm based on hybridization energy and site accessibility. In this note, we used a population genomics approach to reexamine their data and found that the PITA algorithm had lower specificity than methods based on evolutionary conservation at comparable levels of sensitivity

Identification of candidate regulatory sequences in mammalian 3' UTRs by statistical analysis of oligonucleotide distributions

3' untranslated regions (3' UTRs) contain binding sites for many regulatory elements, and in particular for microRNAs (miRNAs). The importance of miRNA-mediated post-transcriptional regulation has become increasingly clear in the last few years. We propose two complementary approaches to the statistical analysis of oligonucleotide frequencies in mammalian 3' UTRs aimed at the identification of candidate binding sites for regulatory elements. The first method is based on the identification of sets of genes characterized by evolutionarily conserved overrepresentation of an oligonucleotide. The second method is based on the identification of oligonucleotides showing statistically significant strand asymmetry in their distribution in 3' UTRs. Both methods are able to identify many previously known binding sites located in 3'UTRs, and in particular seed regions of known miRNAs. Many new candidates are proposed for experimental verification.Comment: Added two reference

Human MicroRNA targets.

MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 3' untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes