Inferring the full genealogical history of a set of DNA sequences is a core problem in evolutionary biology as it encodes information about the events and forces that have influenced a species. However, current methods are limited, with the most accurate able to process no more than a hundred samples. With data sets consisting of millions of genomes being collected, there is a need for scalable and efficient inference methods to fully utilise these resources. We introduce an algorithm to infer whole-genome histories with comparable accuracy to the state-of-the-art but able to process four orders of magnitude more sequences. The approach also provides an “evolutionary encoding” of the data, enabling efficient calculation of relevant statistics. We apply the method to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the inferred genealogies are rich in biological signal and efficient to process.

Transcription factors regulate the molecular, morphological, and physiological characteristics of neurons and generate their impressive cell-type diversity. To gain insight into the general principles that govern how transcription factors regulate cell-type diversity, we used large-scale single-cell RNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single cells and assigned them to 52 clusters. We validated and annotated many clusters using RNA sequencing of FACS-sorted single-cell types and cluster-specific genes. To identify transcription factors responsible for inducing specific terminal differentiation features, we generated a "random forest" model, and we showed that the transcription factors Apterous and Traffic-jam are required in many but not all cholinergic and glutamatergic neurons, respectively. In fact, the same terminal characters often can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.

SummaryTranscription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a 'random forest' model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. We used a transcriptome-wide approach to show that the same terminal characters, including but not restricted to neurotransmitter identity, can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.peer-reviewed)

In the version of this article initially published, in Table 1, the final three numbers in the 'Trees' column were listed as '× 10 6 ' but should have been '× 10 3 '. In addition, the Acknowledgements had omitted support from the UK Biobank Resource, application number 12788. The errors have been corrected in the HTML and PDF versions of the article.