In CRISPR-Cas9 genome editing, the underlying principles for selecting guide RNA (gRNA) sequences that would ensure for efficient target site modification remain poorly understood. Here we show that target sites harbouring multiple protospacer adjacent motifs (PAMs) are refractory to Cas9-mediated repair in situ. Thus we refine which substrates should be avoided in gRNA design, implicating PAM density as a novel sequence-specific feature that inhibits in vivo Cas9-driven DNA modification.

Genomic organization is critical for proper gene regulation and based on a hierarchical model, where chromosomes are segmented into megabase-sized, cell-type-specific transcriptionally active (A) and inactive (B) compartments. Here, we describe SACSANN, a machine learning pipeline consisting of stacked artificial neural networks that predicts compartment annotation solely from genomic sequence-based features such as predicted transcription factor binding sites and transposable elements. SACSANN provides accurate and cell-type specific compartment predictions, while identifying key genomic sequence determinants that associate with A/B compartments. Models are shown to be largely transferable across analogous human and mouse cell types. By enabling the study of chromosome compartmentalization in species for which no Hi-C data is available, SACSANN paves the way toward the study of 3D genome evolution. SACSANN is publicly available on GitHub: https://github.com/BlanchetteLab/SACSANN

Background Understanding how transcription occurs requires the integration of genome-wide and locus-specific information gleaned from robust technologies. Chromatin immunoprecipitation (ChIP) is a staple in gene expression studies, and while genome-wide methods are available, high-throughput approaches to analyze defined regions are lacking. Results Here, we present carbon copy-ChIP (2C-ChIP), a versatile, inexpensive, and high-throughput technique to quantitatively measure the abundance of DNA sequences in ChIP samples. This method combines ChIP with ligation-mediated amplification (LMA) and deep sequencing to probe large genomic regions of interest. 2C-ChIP recapitulates results from benchmark ChIP approaches. We applied 2C-ChIP to the HOXA cluster to find that a region where H3K27me3 and SUZ12 linger encodes HOXA-AS2 , a long non-coding RNA that enhances gene expression during cellular differentiation. Conclusions 2C-ChIP fills the need for a robust molecular biology tool designed to probe dedicated genomic regions in a high-throughput setting. The flexible nature of the 2C-ChIP approach allows rapid changes in experimental design at relatively low cost, making it a highly efficient method for chromatin analysis. Electronic supplementary material The online version of this article (10.1186/s12864-019-5532-5) contains supplementary material, which is available to authorized users.

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Hi-C is a popular technique to map three-dimensional chromosome conformation. In principle, Hi-C’s resolution is only limited by the size of restriction fragments. However, insufficient sequencing depth forces researchers to artificially reduce the resolution of Hi-C matrices at a loss of biological interpretability. We present the Hi-C Interaction Frequency Inference (HIFI) algorithms that accurately estimate restriction-fragment resolution Hi-C matrices by exploiting dependencies between neighboring fragments. Cross-validation experiments and comparisons to 5C data and known regulatory interactions demonstrate HIFI’s superiority to existing approaches. In addition, HIFI’s restriction-fragment resolution reveals a new role for active regulatory regions in structuring topologically associating domains.Availability: https://github.com/BlanchetteLab/HIFI