Ingo Braasch, John Postlethwait and colleagues report the genome of the spotted gar (Lepisosteus oculatus), whose lineage diverged from teleosts before genome duplication. Their data provide insights into the evolution of genes involved in immunity, mineralization and development and facilitate the comparison of cis-regulatory elements between teleosts and humans. To connect human biology to fish biomedical models, we sequenced the genome of spotted gar (Lepisosteus oculatus), whose lineage diverged from teleosts before teleost genome duplication (TGD). The slowly evolving gar genome has conserved in content and size many entire chromosomes from bony vertebrate ancestors. Gar bridges teleosts to tetrapods by illuminating the evolution of immunity, mineralization and development (mediated, for example, by Hox, ParaHox and microRNA genes). Numerous conserved noncoding elements (CNEs; often cis regulatory) undetectable in direct human-teleost comparisons become apparent using gar: functional studies uncovered conserved roles for such cryptic CNEs, facilitating annotation of sequences identified in human genome-wide association studies. Transcriptomic analyses showed that the sums of expression domains and expression levels for duplicated teleost genes often approximate the patterns and levels of expression for gar genes, consistent with subfunctionalization. The gar genome provides a resource for understanding evolution after genome duplication, the origin of vertebrate genomes and the function of human regulatory sequences.

Ning Li and colleagues report the whole-genome sequence of the duck, Anas platyrhynchos, a natural host of avian influenza viruses. They examine host response to infection by comparing the lung transcriptomes of ducks that were infected with influenza A viruses. The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A viruses. We present the duck genome sequence and perform deep transcriptome analyses to investigate immune-related genes. Our data indicate that the duck possesses a contractive immune gene repertoire, as in chicken and zebra finch, and this repertoire has been shaped through lineage-specific duplications. We identify genes that are responsive to influenza A viruses using the lung transcriptomes of control ducks and ones that were infected with either a highly pathogenic (A/duck/Hubei/49/05) or a weakly pathogenic (A/goose/Hubei/65/05) H5N1 virus. Further, we show how the duck's defense mechanisms against influenza infection have been optimized through the diversification of its β-defensin and butyrophilin-like repertoires. These analyses, in combination with the genomic and transcriptomic data, provide a resource for characterizing the interaction between host and influenza viruses.

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that guide the post-transcriptional processing of other non-coding RNAs (mostly ribosomal RNAs), but have also been implicated in processes ranging from microRNA-dependent gene silencing to alternative splicing. In order to construct an up-to-date catalog of human snoRNAs we have combined data from various databases, de novo prediction and extensive literature review. In total, we list more than 750 curated genomic loci that give rise to snoRNA and snoRNA-like genes. Utilizing small RNA-seq data from the ENCODE project, our study characterizes the plasticity of snoRNA expression identifying both constitutively as well as cell type specific expressed snoRNAs. Especially, the comparison of malignant to non-malignant tissues and cell types shows a dramatic perturbation of the snoRNA expression profile. Finally, we developed a high-throughput variant of the reverse-transcriptase-based method for identifying 2′-O-methyl modifications in RNAs termed RimSeq. Using the data from this and other high-throughput protocols together with previously reported modification sites and state-of-the-art target prediction methods we re-estimate the snoRNA target RNA interaction network. Our current results assign a reliable modification site to 83% of the canonical snoRNAs, leaving only 76 snoRNA sequences as orphan.

Motivation: Small nucleolar RNAs (snoRNAs) are an abundant class of non-coding RNAs with a wide variety of cellular functions including chemical modification of RNA, telomere maintanance, pre-rRNA processing and regulatory activities in alternative splicing. The main role of box C/D snoRNAs is to determine the targets for 2^′-O-ribose methylation, which is important for rRNA maturation and splicing regulation of some mRNAs. The targets are still unknown, however, for many ‘orphan’ snoRNAs. While a fast and efficient target predictor for box H/ACA snoRNAs is available, no comparable tool exists for box C/D snoRNAs, even though they bind to their targets in a much less complex manner. Results:PLEXY is a dynamic programming algorithm that computes thermodynamically optimal interactions of a box C/D snoRNA with a putative target RNA. Implemented as scanner for large input sequences and equipped with filters on the duplex structure, PLEXY is an efficient and reliable tool for the prediction of box C/D snoRNA target sites. Availability: The perl script PLEXY is freely available at http://www.bioinf.uni-leipzig.de/Software/PLEXY. Contact:steffi@bioinf.uni-leipzig.de Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many ‘orphan’ snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms. Results:RNAsnoop implements a dynamic programming algorithm that computes thermodynamically optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an support vector machine (SVM)-based machine learning approach to distinguish true binding sites from spurious solutions and a system to evaluate comparative information, it presents an efficient and reliable tool for the prediction of H/ACA snoRNA target sites. We apply RNAsnoop to identify the snoRNAs that are responsible for several of the remaining ‘orphan’ pseudouridine modifications in human rRNAs, and we assign a target to one of the five orphan H/ACA snoRNAs in Drosophila. Availability: The C source code of RNAsnoop is freely available at http://www.tbi.univie.ac.at/∼htafer/RNAsnoop Contact:htafer@tbi.univie.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.