All Publications


  • RNA splicing programs define tissue compartments and cell types at single cell resolution. eLife Olivieri, J. E., Dehghannasiri, R., Wang, P. L., Jang, S., de Morree, A., Tan, S. Y., Ming, J., Ruohao Wu, A., Tabula Sapiens Consortium, Quake, S. R., Krasnow, M. A., Salzman, J. 2021; 10

    Abstract

    The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.

    View details for DOI 10.7554/eLife.70692

    View details for PubMedID 34515025

  • Specific splice junction detection in single cells with SICILIAN. Genome biology Dehghannasiri, R., Olivieri, J. E., Damljanovic, A., Salzman, J. 2021; 22 (1): 219

    Abstract

    Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN is a general method that can be applied to bulk or single-cell data, but has particular utility for single-cell analysis due to that data's unique challenges and opportunities for discovery. SICILIAN's precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, and increases agreement between biological replicates. SICILIAN detects unannotated splicing in single cells, enabling the discovery of novel splicing regulation through single-cell analysis workflows.

    View details for DOI 10.1186/s13059-021-02434-8

    View details for PubMedID 34353340

  • SICILIAN: Precise and unbiased detection of gene fusions at the resolution of single cells using improved statistical modeling Dehghannasiri, R., Olivieri, J., Salzman, J. AMER ASSOC CANCER RESEARCH. 2020
  • Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics BIOINFORMATICS McInnes, G., Tanigawa, Y., DeBoever, C., Lavertu, A., Olivieri, J., Aguirre, M., Rivas, M. A. 2019; 35 (14): 2495–97
  • Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. Bioinformatics (Oxford, England) McInnes, G., Tanigawa, Y., DeBoever, C., Lavertu, A., Olivieri, J. E., Aguirre, M., Rivas, M. A. 2018

    Abstract

    Summary: Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests, and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities.Availability and implementation: GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

    View details for PubMedID 30520965

  • Molecular mechanism of GPCR-mediated arrestin activation NATURE Latorraca, N. R., Wang, J. K., Bauer, B., Townshend, R. L., Hollingsworth, S. A., Olivieri, J. E., Xu, H., Sommer, M. E., Dror, R. O. 2018; 557 (7705): 452-+

    Abstract

    Despite intense interest in discovering drugs that cause G-protein-coupled receptors (GPCRs) to selectively stimulate or block arrestin signalling, the structural mechanism of receptor-mediated arrestin activation remains unclear1,2. Here we reveal this mechanism through extensive atomic-level simulations of arrestin. We find that the receptor's transmembrane core and cytoplasmic tail-which bind distinct surfaces on arrestin-can each independently stimulate arrestin activation. We confirm this unanticipated role of the receptor core, and the allosteric coupling between these distant surfaces of arrestin, using site-directed fluorescence spectroscopy. The effect of the receptor core on arrestin conformation is mediated primarily by interactions of the intracellular loops of the receptor with the arrestin body, rather than the marked finger-loop rearrangement that is observed upon receptor binding. In the absence of a receptor, arrestin frequently adopts active conformations when its own C-terminal tail is disengaged, which may explain why certain arrestins remain active long after receptor dissociation. Our results, which suggest that diverse receptor binding modes can activate arrestin, provide a structural foundation for the design of functionally selective ('biased') GPCR-targeted ligands with desired effects on arrestin signalling.

    View details for PubMedID 29720655

  • From cacti to carnivores: Improved phylotranscriptomic sampling and hierarchical homology inference provide further insight into the evolution of Caryophyllales AMERICAN JOURNAL OF BOTANY Walker, J. F., Yang, Y., Feng, T., Timoneda, A., Mikenas, J., Hutchison, V., Edwards, C., Wang, N., Ahluwalia, S., Olivieri, J., Walker-Hale, N., Majure, L. C., Puente, R., Kadereit, G., Lauterbach, M., Eggli, U., Flores-Olvera, H., Ochoterena, H., Brockington, S. F., Moore, M. J., Smith, S. A. 2018; 105 (3): 446–62

    Abstract

    The Caryophyllales contain ~12,500 species and are known for their cosmopolitan distribution, convergence of trait evolution, and extreme adaptations. Some relationships within the Caryophyllales, like those of many large plant clades, remain unclear, and phylogenetic studies often recover alternative hypotheses. We explore the utility of broad and dense transcriptome sampling across the order for resolving evolutionary relationships in Caryophyllales.We generated 84 transcriptomes and combined these with 224 publicly available transcriptomes to perform a phylogenomic analysis of Caryophyllales. To overcome the computational challenge of ortholog detection in such a large data set, we developed an approach for clustering gene families that allowed us to analyze >300 transcriptomes and genomes. We then inferred the species relationships using multiple methods and performed gene-tree conflict analyses.Our phylogenetic analyses resolved many clades with strong support, but also showed significant gene-tree discordance. This discordance is not only a common feature of phylogenomic studies, but also represents an opportunity to understand processes that have structured phylogenies. We also found taxon sampling influences species-tree inference, highlighting the importance of more focused studies with additional taxon sampling.Transcriptomes are useful both for species-tree inference and for uncovering evolutionary complexity within lineages. Through analyses of gene-tree conflict and multiple methods of species-tree inference, we demonstrate that phylogenomic data can provide unparalleled insight into the evolutionary history of Caryophyllales. We also discuss a method for overcoming computational challenges associated with homolog clustering in large data sets.

    View details for PubMedID 29738076

  • Drawing DNA Sequences Networks Olivieri, J. Electronic Thesis or Dissertation. 2016 1–21
  • Game-of-Life Mosaics Bosch, R., Olivieri, J. 2014: 325–28
  • Designing Game of Life mosaics with integer programming Journal of Mathematics and the Arts Bosch, R., Olivieri, J. 2014; 8 (3-4): 120-132