RNA-based next-generation sequencing (RNA-Seq) provides a tremendous amount of new information regarding gene and transcript structure, expression and regulation. This is particularly true for non-coding RNAs where whole transcriptome analyses have revealed that the much of the genome is transcribed and that many non-coding transcripts have widespread functionality. However, uniform resources for raw, cleaned and processed RNA-Seq data are sparse for most organisms and this is especially true for non-human primates (NHPs). Here, we describe a large-scale RNA-Seq data and analysis infrastructure, the NHP reference transcriptome resource (http://nhprtr.org); it presently hosts data from12 species of primates, to be expanded to 15 species/subspecies spanning great apes, old world monkeys, new world monkeys and prosimians. Data are collected for each species using pools of RNA from comparable tissues. We provide data access in advance of its deposition at NCBI, as well as browsable tracks of alignments against the human genome using the UCSC genome browser. This resource will continue to host additional RNA-Seq data, alignments and assemblies as they are generated over the coming years and provide a key resource for the annotation of NHP genomes as well as informing primate studies on evolution, reproduction, infection, immunity and pharmacology.

The non-human primate reference transcriptome resource (NHPRTR, available online at http://nhprtr.org/) aims to generate comprehensive RNA-seq data from a wide variety of non-human primates (NHPs), from lemurs to hominids. In the 2012 Phase I of the NHPRTR project, 19 billion fragments or 3.8 terabases of transcriptome sequences were collected from pools of ∼ 20 tissues in 15 species and subspecies. Here we describe a major expansion of NHPRTR by adding 10.1 billion fragments of tissue-specific RNA-seq data. For this effort, we selected 11 of the original 15 NHP species and subspecies and constructed total RNA libraries for the same ∼ 15 tissues in each. The sequence quality is such that 88% of the reads align to human reference sequences, allowing us to compute the full list of expression abundance across all tissues for each species, using the reads mapped to human genes. This update also includes improved transcript annotations derived from RNA-seq data for rhesus and cynomolgus macaques, two of the most commonly used NHP models and additional RNA-seq data compiled from related projects. Together, these comprehensive reference transcriptomes from multiple primates serve as a valuable community resource for genome annotation, gene dynamics and comparative functional analysis.

Location analysis for estrogen receptor-alpha (ERalpha)-bound cis-regulatory elements was determined in MCF7 cells using chromatin immunoprecipitation (ChIP)-on-chip. Here, we present the estrogen response element (ERE) sequences that were identified at ERalpha-bound loci and quantify the incidence of ERE sequences under two stringencies of detection: <10% and 10-20% nucleotide deviation from the canonical ERE sequence. We demonstrate that approximately 50% of all ERalpha-bound loci do not have a discernable ERE and show that most ERalpha-bound EREs are not perfect consensus EREs. Approximately one-third of all ERalpha-bound ERE sequences reside within repetitive DNA sequences, most commonly of the AluS family. In addition, the 3-bp spacer between the inverted ERE half-sites, rather than being random nucleotides, is C(A/T)G-enriched at bona fide receptor targets. Diverse ERalpha-bound loci were validated using electrophoretic mobility shift assay and ChIP-polymerase chain reaction (PCR). The functional significance of receptor-bound loci was demonstrated using luciferase reporter assays which proved that repetitive element ERE sequences contribute to enhancer function. ChIP-PCR demonstrated estrogen-dependent recruitment of the coactivator SRC3 to these loci in vivo. Our data demonstrate that ERalpha binds to widely variant EREs with less sequence specificity than had previously been suspected and that binding at repetitive and nonrepetitive genomic targets is favored by specific trinucleotide spacers.

Cytosines at cytosine-guanine (CG) dinucleotides are the near-exclusive target of DNA methyltransferases in mammalian genomes. Spontaneous deamination of methylcytosine to thymine makes methylated cytosines unusually susceptible to mutation and consequent depletion. The loci where CG dinucleotides remain relatively enriched, presumably due to their unmethylated status during the germ cell cycle, have been referred to as CpG islands. Currently, CpG islands are solely defined by base compositional criteria, allowing annotation of any sequenced genome. Using a novel bioinformatic approach, we show that CG clusters can be identified as an inherent property of genomic sequence without imposing a base compositional a priori assumption. We also show that the CG clusters co-localize in the human genome with hypomethylated loci and annotated transcription start sites to a greater extent than annotations produced by prior CpG island definitions. Moreover, this new approach allows CG clusters to be identified in a species-specific manner, revealing a degree of orthologous conservation that is not revealed by current base compositional approaches. Finally, our approach is able to identify methylating genomes (such as Takifugu rubripes) that lack CG clustering entirely, in which it is inappropriate to annotate CpG islands or CG clusters.