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.

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual.

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.

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.

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.

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

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 genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.

We report the first genome-wide identification and characterization of alternative splicing in human gene transcripts based on analysis of the full-length cDNAs. Applying both manual and computational analyses for 56,419 completely sequenced and precisely annotated full-length cDNAs selected for the H-Invitational human transcriptome annotation meetings, we identified 6877 alternative splicing genes with 18 297 different alternative splicing variants. A total of 37,670 exons were involved in these alternative splicing events. The encoded protein sequences were affected in 6005 of the 6877 genes. Notably, alternative splicing affected protein motifs in 3015 genes, subcellular localizations in 2982 genes and transmembrane domains in 1348 genes. We also identified interesting patterns of alternative splicing, in which two distinct genes seemed to be bridged, nested or having overlapping protein coding sequences (CDSs) of different reading frames (multiple CDS). In these cases, completely unrelated proteins are encoded by a single locus. Genome-wide annotations of alternative splicing, relying on full-length cDNAs, should lay firm groundwork for exploring in detail the diversification of protein function, which is mediated by the fast expanding universe of alternative splicing variants.

Regions covering one percent of the genome, selected by ENCODE for extensive analysis, were annotated by the HAVANA/Gencode group with high quality transcripts, thus defining a benchmark. The ENCODE Genome Annotation Assessment Project (EGASP) competition aimed at reproducing Gencode and finding new genes. The organizers evaluated the protein predictions in depth. We present a complementary analysis of the mRNAs, including alternative transcript variants.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

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 rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats. We catalogue the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView. We find that organ-enriched, differentially expressed genes reflect the known organ-specific biological activities. A large number of transcripts show organ-specific, age-dependent or sex-specific differential expression patterns. We create a web-based, open-access rat BodyMap database of expression profiles with crosslinks to other widely used databases, anticipating that it will serve as a primary resource for biomedical research using the rat model.

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

Three neuropeptides, gastrin releasing peptide (GRP), natriuritic precursor peptide B (NPPB), and neuromedin B (NMB) have been proposed to play roles in itch sensation. However, the tissues in which these peptides are expressed and their positions in the itch circuit has recently become the subject of debate. Here we used next-gen RNA-Seq to examine the expression of transcripts coding for GRP, NPPB, NMB, and other peptides in DRG, trigeminal ganglion, and the spinal cord as well as expression levels for their cognate receptors in these tissues.

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients' survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

RNA-Seq provides the capability to characterize the entire transcriptome in multiple levels including gene expression, allele specific expression, alternative splicing, fusion gene detection, and etc. The US FDA-led SEQC (i.e., MAQC-III) project conducted a comprehensive study focused on the transcriptome profiling of rat liver samples treated with 27 chemicals to evaluate the utility of RNA-Seq in safety assessment and toxicity mechanism elucidation. The chemicals represented multiple chemogenomic modes of action (MOA) and exhibited varying degrees of transcriptional response. The paired-end 100 bp sequencing data were generated using Illumina HiScanSQ and/or HiSeq 2000. In addition to the core study, six animals (i.e., three aflatoxin B1 treated rats and three vehicle control rats) were sequenced three times, with two separate library preparations on two sequencing machines. This large toxicogenomics dataset can serve as a resource to characterize various aspects of transcriptomic changes (e.g., alternative splicing) that are byproduct of chemical perturbation.

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.

Here we report the new features and improvements in our latest release of the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/), a comprehensive annotation resource for human genes and transcripts. H-InvDB, originally developed as an integrated database of the human transcriptome based on extensive annotation of large sets of full-length cDNA (FLcDNA) clones, now provides annotation for 120 558 human mRNAs extracted from the International Nucleotide Sequence Databases (INSD), in addition to 54 978 human FLcDNAs, in the latest release H-InvDB_4.6. We mapped those human transcripts onto the human genome sequences (NCBI build 36.1) and determined 34 699 human gene clusters, which could define 34 057 (98.1%) protein-coding and 642 (1.9%) non-protein-coding loci; 858 (2.5%) transcribed loci overlapped with predicted pseudogenes. For all these transcripts and genes, we provide comprehensive annotation including gene structures, gene functions, alternative splicing variants, functional non-protein-coding RNAs, functional domains, predicted sub cellular localizations, metabolic pathways, predictions of protein 3D structure, mapping of SNPs and microsatellite repeat motifs, co-localization with orphan diseases, gene expression profiles, orthologous genes, protein-protein interactions (PPI) and annotation for gene families. The current H-InvDB annotation resources consist of two main views: Transcript view and Locus view and eight sub-databases: the DiseaseInfo Viewer, H-ANGEL, the Clustering Viewer, G-integra, the TOPO Viewer, Evola, the PPI view and the Gene family/group.