Biology subjects

Lareau, C.

Publications and source records attributed to Lareau, C..

4 recordsLinked to original sources

bioRxiv2017

hichipper: A preprocessing pipeline for assessing library quality and DNA loops from HiChIP data

Mumbach et al. recently described HiChIP, a novel protein-mediated chromatin conformation assay that lowers cellular input requirements while simultaneously increasing the yield of informative reads compared to previous methods (1). To facilitate the dissemination and adoption of this assay, we introduce hichipper (http://aryeelab.org/hichipper), an open-source HiChIP data preprocessing tool, with features that include bias-corrected peak calling, library quality control, DNA loop calling, and output of processed data for downstream analysis and visualization (Figure 1a).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC=\"FIGDIR/small/192302_fig1.gif\" ALT=\"Figure 1\">\nView larger version (23K):\norg.highwire.dtl.DTLVardef@1d3d143org.highwire.dtl.DTLVardef@14fb9eforg.highwire.dtl.DTLVardef@1381541org.highwire.dtl.DTLVardef@fb6114_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO a) Overview of the hichipper analysis pipeline. hichipper requires aligned and annotated in-teraction files from preprocessing tools such as Hi-C Pro (2) as well as a .bed file of restriction sites. The hichipper pipeline identifies loop anchors using a background model that accounts for the effect of restriction site proximity on read density. The output of hichipper can be used for quality control, visualization, and downstream topology analysis of HiChIP data. b) Ratio of per-base coverage to local MACS-estimated local window background signal as a function of distance to nearest MboI cutsite for a HiChIP sample (blue) and a ChIP-seq sample (red). Both samples represent published mouse ESC with SMC1 (cohesin) ChIP. C_FIG

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

Single-cell epigenomics maps the continuous regulatory landscape of human hematopoietic differentiation

AbstractNormal human hematopoiesis involves cellular differentiation of multipotent cells into progressively more lineage-restricted states. While epigenomic landscapes of this process have been explored in immunophenotypically-defined populations, the single-cell regulatory variation that defines hematopoietic differentiation has been hidden by ensemble averaging. We generated single-cell chromatin accessibility landscapes across 8 populations of immunophenotypically-defined human hematopoietic cell types. Using bulk chromatin accessibility profiles to scaffold our single-cell data analysis, we constructed an epigenomic landscape of human hematopoiesis and characterized epigenomic heterogeneity within phenotypically sorted populations to find epigenomic lineage-bias toward different developmental branches in multipotent stem cell states. We identify and isolate sub-populations within classically-defined granulocyte-macrophage progenitors (GMPs) and use ATAC-seq and RNA-seq to confirm that GMPs are epigenomically and transcriptomically heterogeneous. Furthermore, we identified transcription factors and cis-regulatory elements linked to changes in chromatin accessibility within cellular populations and across a continuous myeloid developmental trajectory, and observe relatively simple TF motif dynamics give rise to a broad diversity of accessibility dynamics at cis-regulatory elements. Overall, this work provides a template for exploration of complex regulatory dynamics in primary human tissues at the ultimate level of granular specificity - the single cell.\n\nOne Sentence SummarySingle cell chromatin accessibility reveals a high-resolution, continuous landscape of regulatory variation in human hematopoiesis.

genomics↗

bioRxiv2016

diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data

The three-dimensional architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in loop structure are associated with differences in gene expression and cell state. Here, we introduce diffloop, an R/Bioconductor package for identifying differential DNA looping between samples. The package additionally provides a suite of functions for the quality control, statistical testing, annotation and visualization of DNA loops. We demonstrate this functionality by detecting differences in DNA loops between ENCODE ChIA-PET datasets and relate looping to differences in epigenetic state and gene expression.

bioinformatics↗