A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1459-4) contains supplementary material, which is available to authorized users.

Two‐dimensional covalent organic frameworks (2D COFs) are considered as potential candidates for gas separation membranes, benefiting from permanent porosity, light‐weight skeletons, excellent stability and facilely‐tailored functionalities. However, their pore sizes are generally larger than the kinetic diameters of common gas molecules. One great challenge is the fabrication of single‐phase COF membranes to realize precise gas separations. Herein, three kinds of high‐quality β‐ketoenamine‐type COF nanosheets with different pore sizes were developed and aggregated to ultrathin nanosheet membranes with distinctive staggered stacking patterns. The narrowed pore sizes derived from the micro‐structures and selective adsorption capacities synergistically endowed the COF membranes with intriguing CO2‐philic separation performances, among which TpPa‐2 with medium pore size exhibited an optimal CO2/H2 separation factor of 22 and a CO2 permeance of 328 gas permeation units at 298 K. This membrane performance reached the target with commercial feasibility for syngas separations.

Membrane-bound ion channels are promising biological receptors since they allow for the stochastic detection of analytes at high sensitivity. For stochastic sensing, it is necessary to measure the ion currents associated with single ion channel opening and closing events. However, this calls for stability, high reproducibility, and long lifetimes. A critical issue to overcome is the low stability of the ion channel environment, that is, the bilayer membrane. A promising technique to surmount this is to connect the lower part of the membrane to a surface forming a tethered bilayer membrane. By reconstituting the synthetic ion channel, gramicidin A, into a tethered bilayer as part of a microchip design, we have been able to record the activity of single ion channels. The observed activity was compared with that obtained by a conventional electrophysiology method, tip dipping, to confirm its authenticity. These findings allow for the construction of stable biosensors based on ion channels and provide a novel technique for the characterization of ion channel activity.

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BackgroundCancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist.ResultsWe developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers.ConclusionsIn summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos, with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos.

Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

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