Assessment and improvement of Indian-origin rhesus macaque and Mauritian-origin cynomolgus macaque genome annotations using deep transcriptome sequencing data.

BACKGROUND: The genome annotations of rhesus (Macaca mulatta) and cynomolgus (Macaca fascicularis) macaques, two of the most common non-human primate animal models, are limited. METHODS: We analyzed large-scale macaque RNA-based next-generation sequencing (RNAseq) data to identify un-annotated macaque transcripts. RESULTS: For both macaque species, we uncovered thousands of novel isoforms for annotated genes and thousands of un-annotated intergenic transcripts enriched with non-coding RNAs. We also identified thousands of transcript sequences which are partially or completely 'missing' from current macaque genome assemblies. We showed that many newly identified transcripts were differentially expressed during SIV infection of rhesus macaques or during Ebola virus infection of cynomolgus macaques. CONCLUSIONS: For two important macaque species, we uncovered thousands of novel isoforms and un-annotated intergenic transcripts including coding and non-coding RNAs, polyadenylated and non-polyadenylated transcripts. This resource will greatly improve future macaque studies, as demonstrated by their applications in infectious disease studies.

Comprehensive cross production system assessment of the impact of in vitro microenvironment on the expression of messengers and long non-coding RNAs in the bovine blastocyst.

In vitro production (IVP) of cattle embryos over the past two decades has revealed several negative impacts that have been attributed to the artificial microenvironment. Studies on embryos produced in vitro clearly point to aberrant gene expression levels. So far, the causal association between phenotype and measured gene expression has not led to substantial improvement of IVP systems. The aim of this study was to generate a unique dataset composed of microarray-derived relative transcript abundance values for blastocysts produced in ten in vitro systems differing primarily in culture medium formulation. Between-group comparisons determine the level of overall similarity among systems relative to in vivo reference embryos. The use of the dataset to contrast all in vitro treatments with the in vivo blastocysts pointed to a single common gene network. The 'boutique' array contained a panel of novel uncharacterized transcripts that were variably expressed depending on the medium in which the blastocysts were produced. These novel transcripts were differentially expressed in blastocysts even as carryover from conditions encountered 7 days earlier during oocyte maturation. All of the selected novel candidates thus expressed were from intergenic regions. The function of this long non-coding RNA remains unknown but clearly points to an additional level of complexity in early embryo development.

Whole transcriptome analysis: what are we still missing?

New technologies such as tag-based sequencing and tiling arrays have provided unique insights into the transcriptional output of cells. Many new RNA classes have been uncovered in the past decade, despite limitations in current technologies. Even as the repertoire of known functional elements of the transcriptome increases and contemporary technologies become mainstream, inadequacies in conventional protocols for library preparation, sequencing and mapping continue to hamper revelation of the entire transcriptome of cells. In this article, we review current protocols and outline their deficiencies. We also provide our view on what we may be overlooking in the transcriptome, despite exhaustive investigations, and indicate future areas of technological development and research.

De novo search for non-coding RNA genes in the AT-rich genome of Dictyostelium discoideum: performance of Markov-dependent genome feature scoring.

Genome data are increasingly important in the computational identification of novel regulatory non-coding RNAs (ncRNAs). However, most ncRNA gene-finders are either specialized to well-characterized ncRNA gene families or require comparisons of closely related genomes. We developed a method for de novo screening for ncRNA genes with a nucleotide composition that stands out against the background genome based on a partial sum process. We compared the performance when assuming independent and first-order Markov-dependent nucleotides, respectively, and used Karlin-Altschul and Karlin-Dembo statistics to evaluate the significance of hits. We hypothesized that a first-order Markov-dependent process might have better power to detect ncRNA genes since nearest-neighbor models have been shown to be successful in predicting RNA structures. A model based on a first-order partial sum process (analyzing overlapping dinucleotides) had better sensitivity and specificity than a zeroth-order model when applied to the AT-rich genome of the amoeba Dictyostelium discoideum. In this genome, we detected 94% of previously known ncRNA genes (at this sensitivity, the false positive rate was estimated to be 25% in a simulated background). The predictions were further refined by clustering candidate genes according to sequence similarity and/or searching for an ncRNA-associated upstream element. We experimentally verified six out of 10 tested ncRNA gene predictions. We conclude that higher-order models, in combination with other information, are useful for identification of novel ncRNA gene families in single-genome analysis of D. discoideum. Our generalizable approach extends the range of genomic data that can be searched for novel ncRNA genes using well-grounded statistical methods.