This work presents a new approach to the analysis of aperiodic pulsatile heteroscedastic time-series data, specifically hormone pulsatility. We have utilized growth hormone (GH) concentration time-series data as an example for the utilization of this new algorithm. While many previously published approaches used for the analysis of GH pulsatility are both subjective and cumbersome to use, AutoDecon is a nonsubjective, standardized, and completely automated algorithm. We have employed computer simulations to evaluate the true-positive, the false-positive, the false-negative, and the sensitivity percentages of several of the routinely employed algorithms when applied to GH concentration time-series data. Based on these simulations, it was concluded that this new algorithm provides a substantial improvement over the previous methods. This novel method has many direct applications in addition to hormone pulsatility, for example, to time-domain fluorescence lifetime measurements, as the mathematical forms that describe these experimental systems are both convolution integrals.

In contrast to infections with human immunodeficiency virus (HIV) in humans and simian immunodeficiency virus (SIV) in macaques, SIV infection of a natural host, sooty mangabeys (Cercocebus atys), is non-pathogenic despite high viraemia. Here we sequenced and assembled the genome of a captive sooty mangabey. We conducted genome-wide comparative analyses of transcript assemblies from C. atys and AIDS-susceptible species, such as humans and macaques, to identify candidates for host genetic factors that influence susceptibility. We identified several immune-related genes in the genome of C. atys that show substantial sequence divergence from macaques or humans. One of these sequence divergences, a C-terminal frameshift in the toll-like receptor-4 (TLR4) gene of C. atys, is associated with a blunted in vitro response to TLR-4 ligands. In addition, we found a major structural change in exons 3-4 of the immune-regulatory protein intercellular adhesion molecule 2 (ICAM-2); expression of this variant leads to reduced cell surface expression of ICAM-2. These data provide a resource for comparative genomic studies of HIV and/or SIV pathogenesis and may help to elucidate the mechanisms by which SIV-infected sooty mangabeys avoid AIDS.

Wastewater surveillance has become essential for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The quantification of SARS-CoV-2 RNA in wastewater correlates with the coronavirus disease 2019 (COVID-19) caseload in a community. However, estimating the proportions of different SARS-CoV-2 haplotypes has remained technically difficult. We present a phylogenetic imputation method for improving the SARS-CoV-2 reference database and a method for estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples. The phylogenetic imputation method uses the global SARS-CoV-2 phylogeny and imputes based on the maximum of the posterior probability of each nucleotide. We show that the imputation method has error rates comparable to, or lower than, typical sequencing error rates, which substantially improves the reference database and allows for accurate inferences of haplotype composition. Our method for estimating relative proportions of haplotypes uses an initial step to remove unlikely haplotypes and an expectation maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different haplotypes in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions reflect the true proportions given sufficiently high sequencing depth.

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 human brain and human cognitive abilities are strikingly different from those of other great apes despite relatively modest genome sequence divergence. However, little is presently known about the interspecies divergence in gene structure and transcription that might contribute to these phenotypic differences. To date, most comparative studies of gene structure in the brain have examined humans, chimpanzees, and macaque monkeys. To add to this body of knowledge, we analyze here the brain transcriptome of the western lowland gorilla (Gorilla gorilla gorilla), an African great ape species that is phylogenetically closely related to humans, but with a brain that is approximately one-third the size. Manual transcriptome curation from a sample of the planum temporale region of the neocortex revealed 12 protein-coding genes and one noncoding-RNA gene with exons in the gorilla unmatched by public transcriptome data from the orthologous human loci. These interspecies gene structure differences accounted for a total of 134 amino acids in proteins found in the gorilla that were absent from protein products of the orthologous human genes. Proteins varying in structure between human and gorilla were involved in immunity and energy metabolism, suggesting their relevance to phenotypic differences. This gorilla neocortical transcriptome comprises an empirical, not homology- or prediction-driven, resource for orthologous gene comparisons between human and gorilla. These findings provide a unique repository of the sequences and structures of thousands of genes transcribed in the gorilla brain, pointing to candidate genes that may contribute to the traits distinguishing humans from other closely related great apes.

The genome annotations of rhesus (Macaca mulatta) and cynomolgus (Macaca fascicularis) macaques, two of the most common non-human primate animal models, are limited.

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.

Human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most closely related, by average genetic distance, to two coronaviruses isolated from bats, RaTG13 and RmYN02. However, there is a segment of high amino acid similarity between human SARS-CoV-2 and a pangolin-isolated strain, GD410721, in the receptor-binding domain (RBD) of the spike protein, a pattern that can be caused by either recombination or by convergent amino acid evolution driven by natural selection. We perform a detailed analysis of the synonymous divergence, which is less likely to be affected by selection than amino acid divergence, between human SARS-CoV-2 and related strains. We show that the synonymous divergence between the bat-derived viruses and SARS-CoV-2 is larger than between GD410721 and SARS-CoV-2 in the RBD, providing strong additional support for the recombination hypothesis. However, the synonymous divergence between pangolin strain and SARS-CoV-2 is also relatively high, which is not consistent with a recent recombination between them, instead, it suggests a recombination into RaTG13. We also find a 14-fold increase in the dN /dS ratio from the lineage leading to SARS-CoV-2 to the strains of the current pandemic, suggesting that the vast majority of nonsynonymous mutations currently segregating within the human strains have a negative impact on viral fitness. Finally, we estimate that the time to the most recent common ancestor of SARS-CoV-2 and RaTG13 or RmYN02 based on synonymous divergence is 51.71 years (95% CI, 28.11-75.31) and 37.02 years (95% CI, 18.19-55.85), respectively.