Biology subjects

Hsu, J. Y.

Publications and source records attributed to Hsu, J. Y..

5 recordsLinked to original sources

bioRxiv2018

CRISPR-SURF: Discovering regulatory elements by deconvolution of CRISPR tiling screen data

Tiling screens using CRISPR-Cas technologies provide a powerful approach to map regulatory elements to phenotypes of interest, but computational methods that effectively model these experimental approaches for different CRISPR technologies are not readily available. Here we present CRISPR-SURF, a deconvolution framework to identify functional regulatory regions in the genome from data generated by CRISPR-Cas nuclease, CRISPR interference (CRISPRi), or CRISPR activation (CRISPRa) tiling screens. We validated CRISPR-SURF on previously published and new data, identifying both experimentally validated and new potential regulatory elements. With CRISPR tiling screens now being increasingly used to elucidate the regulatory architecture of the non-coding genome, CRISPRSURF provides a generalizable and accessible solution for the discovery of regulatory elements.

bioinformatics↗

bioRxiv2018

STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

genomics↗

bioRxiv2017

BrainImageR: Spatiotemporal gene set analysis referencing the human brain

RATIONALENeurological molecular analyses such as transcriptomics, epigenetics, and genome-wide association studies must be assessed in the context of the human brain in order to generate biologically meaningful inferences. It is often difficult to access primary human brain tissue; therefore, approximations are made using in vitro modeling or by identifying disease-associated genes from DNA extracted from blood. Gene sets from these studies are then compared to the post-mortem human brain to provide an assessment of the brain region and the developmental time point that a gene set is most closely associated with. However, most analyses of postmortem datasets are achieved by building new computational tools each time in-house, which can cause discrepancies from study to study, indicating that the field is in need of a user-friendly suite of tools to examine spatiotemporal expression with respect to the postmortem brain. Such a tool will be of use to the molecular interrogation of neurological and psychiatric disorders, with direct advantages for the disease-modeling and human genetics communities.\n\nRESULTSWe have developed brainImageR, an R package that calculates both the spatial and temporal association of a dataset with post-mortem gene expression data from the Allen Brain Atlas. BrainImageR performs a robust analysis of gene set enrichment to identify enriched anatomical regions, provides a global high-resolution visualization of these enrichments across the human brain, and predicts when in developmental time the sample is most closely matched to, a task that has become increasingly important in the field of in vitro neuronal modeling. These functionalities of brainImageR enable a quick and efficient characterization of a given dataset across normal human brain development.\n\nAVAILABILITY AND IMPLEMENTATIONBrainImageR is released under the Creative Commons CC BY-SA 4.0 license and the source code can be downloaded through github at https://github.com/saralinker/brainImageR.\n\nCONTACTgage@salk.edu

bioinformatics↗

bioRxiv2017

"Unexpected mutations after CRISPR-Cas9 editing in vivo" are most likely pre-existing sequence variants and not nuclease-induced mutations

Schaefer et al. recently advanced the provocative conclusion that CRISPR-Cas9 nuclease can induce off-target alterations at genomic loci that do not resemble the intended on-target site.1 Using high-coverage whole genome sequencing (WGS), these authors reported finding SNPs and indels in two CRISPR-Cas9-treated mice that were not present in a single untreated control mouse. On the basis of this association, Schaefer et al. concluded that these sequence variants were caused by CRISPR-Cas9. This new proposed CRISPR-Cas9 off-target activity runs contrary to previously published work2-8 and, if the authors are correct, could have profound implications for research and therapeutic applications. Here, we demonstrate that the simplest interpretation of Schaefer et al.s data is that the two CRISPR-Cas9-treated mice are actually more closely related genetically to each other than to the control mouse. This strongly suggests that the so-called \"unexpected mutations\" simply represent SNPs and indels shared in common by these mice prior to nuclease treatment. In addition, given the genomic and sequence distribution profiles of these variants, we show that it is challenging to explain how CRISPR-Cas9 might be expected to induce such changes. Finally, we argue that the lack of appropriate controls in Schaefer et al.s experimental design precludes assignment of causality to CRISPR-Cas9. Given these substantial issues, we urge Schaefer et al. to revise or re-state the original conclusions of their published work so as to avoid leaving misleading and unsupported statements to persist in the literature.

molecular biology↗

bioRxiv2017

Inducible, tunable and multiplex human gene regulation using CRISPR-Cpf1-based transcription factors

Targeted and inducible regulation of mammalian gene expression is a broadly important research capability that may also enable development of novel therapeutics for treating human diseases. Here we demonstrate that a catalytically inactive RNA-guided CRISPR-Cpf1 nuclease fused to transcriptional activation domains can up-regulate endogenous human gene expression. We engineered drug-inducible Cpf1-based activators and show how this system can be used to tune the regulation of endogenous gene transcription in human cells. Leveraging the simpler multiplex capability of the Cpf1 platform, we show that we can induce both synergistic and combinatorial gene expression in human cells. Our work should enable the creation of other Cpf1-based gene regulatory fusion proteins and the development of multiplex gene perturbation library screens for understanding complex cellular phenotypes.

molecular biology↗