Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.

Adam Siepel and colleagues estimate key parameters for ancient human demography using a Bayesian analysis of the whole-genome sequences of six individuals from diverse populations. They present new methods for coalescent-based inference of demographic parameters as well as a custom pipeline for genotype inference. Whole-genome sequences provide a rich source of information about human evolution. Here we describe an effort to estimate key evolutionary parameters based on the whole-genome sequences of six individuals from diverse human populations. We used a Bayesian, coalescent-based approach to obtain information about ancestral population sizes, divergence times and migration rates from inferred genealogies at many neutrally evolving loci across the genome. We introduce new methods for accommodating gene flow between populations and integrating over possible phasings of diploid genotypes. We also describe a custom pipeline for genotype inference to mitigate biases from heterogeneous sequencing technologies and coverage levels. Our analysis indicates that the San population of southern Africa diverged from other human populations approximately 108–157 thousand years ago, that Eurasians diverged from an ancestral African population 38–64 thousand years ago, and that the effective population size of the ancestors of all modern humans was ∼9,000.

We have integrated and analyzed a large number of data sets from a variety of genomic assays using a novel computational pipeline to provide a global view of estrogen receptor 1 (ESR1; a.k.a. ERα) enhancers in MCF-7 human breast cancer cells. Using this approach, we have defined a class of primary transcripts (eRNAs) that are transcribed uni- or bidirectionally from estrogen receptor binding sites (ERBSs) with an average transcription unit length of ∼3–5 kb. The majority are up-regulated by short treatments with estradiol (i.e., 10, 25, or 40 min) with kinetics that precede or match the induction of the target genes. The production of eRNAs at ERBSs is strongly correlated with the enrichment of a number of genomic features that are associated with enhancers (e.g., H3K4me1, H3K27ac, EP300/CREBBP, RNA polymerase II, open chromatin architecture), as well as enhancer looping to target gene promoters. In the absence of eRNA production, strong enrichment of these features is not observed, even though ESR1 binding is evident. We find that flavopiridol, a CDK9 inhibitor that blocks transcription elongation, inhibits eRNA production but does not affect other molecular indicators of enhancer activity, suggesting that eRNA production occurs after the assembly of active enhancers. Finally, we show that an enhancer transcription “signature” based on GRO-seq data can be used for de novo enhancer prediction across cell types. Together, our studies shed new light on the activity of ESR1 at its enhancer sites and provide new insights about enhancer function.

The complete genome sequences of an indigenous hunter-gatherer from Namibia's Kalahari Desert and of a Bantu from South Africa are presented in this issue, together with protein-coding regions from three other hunter-gatherer groups from the Kalahari. Analysis of genetic variance in what is probably the oldest known modern human lineage will contribute to understanding human diversity, and facilitate the inclusion of southern Africans in medical genomics research projects. Initial observations from the data include the fact that the Bushmen seem more different from each other, in terms of nucleotide substitutions, than typical Asians and Europeans. More speculatively, variants between these genomes and the existing data sets may point to genetic adaptations for an agricultural lifestyle. Until now, fully sequenced human genomes of the indigenous hunter-gatherer peoples of southern Africa have been limited to recently diverged populations. The complete genome sequences of an indigenous hunter-gatherer from the Kalahari Desert and of a Bantu from southern Africa are now presented. The extent of whole-genome and exome diversity is characterized; the observed genomic differences may help to pinpoint genetic adaptations to an agricultural lifestyle. The genetic structure of the indigenous hunter-gatherer peoples of southern Africa, the oldest known lineage of modern human, is important for understanding human diversity. Studies based on mitochondrial1 and small sets of nuclear markers2 have shown that these hunter-gatherers, known as Khoisan, San, or Bushmen, are genetically divergent from other humans1,3. However, until now, fully sequenced human genomes have been limited to recently diverged populations4,5,6,7,8. Here we present the complete genome sequences of an indigenous hunter-gatherer from the Kalahari Desert and a Bantu from southern Africa, as well as protein-coding regions from an additional three hunter-gatherers from disparate regions of the Kalahari. We characterize the extent of whole-genome and exome diversity among the five men, reporting 1.3 million novel DNA differences genome-wide, including 13,146 novel amino acid variants. In terms of nucleotide substitutions, the Bushmen seem to be, on average, more different from each other than, for example, a European and an Asian. Observed genomic differences between the hunter-gatherers and others may help to pinpoint genetic adaptations to an agricultural lifestyle. Adding the described variants to current databases will facilitate inclusion of southern Africans in medical research efforts, particularly when family and medical histories can be correlated with genome-wide data.

Using data from global run-on and sequencing (GRO-seq) or related methods, the dREG software identifies a new class of transcriptional regulatory elements with strong evidence for directing active transcription. Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5′-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment ( https://github.com/Danko-Lab/dREG/ ). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.