We assembled the sequences from deep RNA sequencing experiments by the Genotype-Tissue Expression (GTEx) project, to create a new catalog of human genes and transcripts, called CHESS. The new database contains 42,611 genes, of which 20,352 are potentially protein-coding and 22,259 are noncoding, and a total of 323,258 transcripts. These include 224 novel protein-coding genes and 116,156 novel transcripts. We detected over 30 million additional transcripts at more than 650,000 genomic loci, nearly all of which are likely nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells. The CHESS database is available at http://ccb.jhu.edu/chess.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1590-2) contains supplementary material, which is available to authorized users.

Understanding HIV-1–host interactions can identify the cellular environment supporting HIV-1 reactivation and mechanisms of clonal expansion. We developed HIV-1 SortSeq to isolate rare HIV-1–infected cells from virally suppressed, HIV-1–infected individuals upon early latency reversal. Single-cell transcriptome analysis of HIV-1 SortSeq+ cells revealed enrichment of nonsense-mediated RNA decay and viral transcription pathways. HIV-1 SortSeq+ cells up-regulated cellular factors that can support HIV-1 transcription (IMPDH1 and JAK1) or promote cellular survival (IL2 and IKBKB). HIV-1–host RNA landscape analysis at the integration site revealed that HIV-1 drives high aberrant host gene transcription downstream, but not upstream, of the integration site through HIV-1–to–host aberrant splicing, in which HIV-1 RNA splices into the host RNA and aberrantly drives host RNA transcription. HIV-1–induced aberrant transcription was driven by the HIV-1 promoter as shown by CRISPR-dCas9–mediated HIV-1–specific activation and could be suppressed by CRISPR-dCas9–mediated inhibition of HIV-1 5′ long terminal repeat. Overall, we identified cellular factors supporting HIV-1 reactivation and HIV-1–driven aberrant host gene transcription as potential therapeutic targets to disrupt HIV-1 persistence.

The ability to detect recombination in pathogen genomes is crucial to the accuracy of phylogenetic analysis and consequently to forecasting the spread of infectious diseases and to developing therapeutics and public health policies. However, in case of the SARS-CoV-2, the low divergence of near-identical genomes sequenced over a short period of time makes conventional analysis infeasible. Using a novel method, we identified 225 anomalous SARS-CoV-2 genomes of likely recombinant origins out of the first 87,695 genomes to be released, several of which have persisted in the population. Bolotie is specifically designed to perform a rapid search for inter-clade recombination events over extremely large datasets, facilitating analysis of novel isolates in seconds. In cases where raw sequencing data was available, we were able to rule out the possibility that these samples represented co-infections by analyzing the underlying sequence reads. The Bolotie software and other data from our study are available at https://github.com/salzberg-lab/bolotie.

We assembled the sequences from 9,795 RNA sequencing experiments, collected from 31 human tissues and hundreds of subjects as part of the GTEx project, to create a new, comprehensive catalog of human genes and transcripts. The new human gene database contains 43,162 genes, of which 21,306 are protein-coding and 21,856 are noncoding, and a total of 323,824 transcripts, for an average of 7.5 transcripts per gene. Our expanded gene list includes 4,998 novel genes (1,178 coding and 3,819 noncoding) and 97,511 novel splice variants of protein-coding genes as compared to the most recent human gene catalogs. We detected over 30 million additional transcripts at more than 650,000 sites, nearly all of which are likely to be nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells.

The ability to detect recombination in pathogen genomes is crucial to the accuracy of phylogenetic analysis and consequently to forecasting the spread of infectious diseases and to developing therapeutics and public health policies. However, previous methods for detecting recombination and reassortment events cannot handle the computational requirements of analyzing tens of thousands of genomes, a scenario that has now emerged in the effort to track the spread of the SARS-CoV-2 virus. Furthermore, the low divergence of near-identical genomes sequenced in short periods of time presents a statistical challenge not addressed by available methods. In this work we present Bolotie, an efficient method designed to detect recombination and reassortment events between clades of viral genomes. We applied our method to a large collection of SARS-CoV-2 genomes and discovered hundreds of isolates that are likely of a recombinant origin. In cases where raw sequencing data was available, we were able to rule out the possibility that these samples represented co-infections by analyzing the underlying sequence reads. Our findings further show that several recombinants appear to have persisted in the population.