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.

Allogeneic hematopoietic stem cell transplantation is a widely used and effective therapy for hematopoietic malignant diseases and numerous other disorders. High-resolution human leukocyte antigen (HLA) haplotype frequency distributions not only facilitate individual donor searches but also determine the probability with which a particular patient can find HLA-matched donors in a registry. The frequencies of the HLA-A, -B, -C, -DRB1, and -DQB1 alleles and haplotypes were estimated among 169,995 Chinese volunteers using the sequencing-based typing (SBT) method. Totals of 191 HLA-A, 244 HLA-B, 146 HLA-C, 143 HLA-DRB1 and 47 HLA-DQB1 alleles were observed, which accounted for 6.98%, 7.06%, 6.46%, 9.11% and 7.91%, respectively, of the alleles in each locus in the world (IMGT 3.16 Release, Apr. 2014). Among the 100 most common haplotypes from the 169,995 individuals, nine distinct haplotypes displayed significant regionally specific distributions. Among these, three were predominant in the South China region (i.e., the 20th, 31st, and 81sthaplotypes), another three were predominant in the Southwest China region (i.e., the 68th, 79th, and 95th haplotypes), one was predominant in the South and Southwest China regions (the 18th haplotype), one was relatively common in the Northeast and North China regions (the 94th haplotype), and one was common in the Northeast, North and Northwest China (the 40th haplotype). In conclusion, this is the first to analyze high-resolution HLA diversities across the entire country of China, based on a detailed and complete data set that covered 31 provinces, autonomous regions, and municipalities. Specifically, we also evaluated the HLA matching probabilities within and between geographic regions and analyzed the regional differences in the HLA diversities in China. We believe that the data presented in this study might be useful for unrelated HLA-matched donor searches, donor registry planning, population genetic studies, and anthropogenesis studies.

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Full-temperature all-solid-state flexible symmetrical fiber supercapacitors (FSCs) are assembled by using montmorillonite flake/polyvinyl alcohol organic hydrogel (F-MMT/PVA OHGE) as the electrolyte and separator and Ti 3 C 2 T x /ANF-5% (T/A-5) fiber as the electrode, in which T/A-5 fiber is prepared by using delaminated Ti 3 C 2 T x nanosheets as assembled units and 5% of aramid nanofiber (ANF) as the functional additive using a wet spinning method in a coagulated bath with 0.5 m FeCl 2 solution. The T/A-5 hybrid fiber exhibits a specific capacity of 807 F cm −3 in 3 m H 2 SO 4 electrolyte, a superior mechanical strength of 104 MPa, and a high conductivity of 1025 S cm −1 . The assembled F-MMT/PVA OHGE T/A-5 FSC not only shows a specific capacitance of 295 F cm −3 and a capacitance retention of 91% at a current density of 5 A cm −3 after 10 000 charging/discharging cycles, but also a maximum volumetric energy density of 26.2 mWh cm −3 . Meanwhile, the assembled device displays good flexibility and excellent capacitance in a wide temperature range of −40 to 80 °C, the electrochemical performance of the FSC is maintained under varying degrees of bending. This study provides an effective strategy for designing and assembling of full-temperature all-solid-state symmetrical flexible FSCs with the optimal balance of capacitive performance and flexibility.

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.

The aim of this study was to assess the impacts of different land use practices on physicochemical characteristics and macroinvertebrate functional feeding groups (FFGs) in the Dongjiang River basin, southeast China and also to evaluate if macroinvertebrate FFGs match the river continuum concept (RCC) predictions. For this aim, a total of 30 sampling sites were selected that comprised three different land use types (10 forest, 10 agricultural, and 10 urban sites) and sampled during the dry season in January 2013. Analysis of variance results showed evident differences in the environmental factors among the three land use types, particularly between the forest and urban sites. The forest sites had significantly lower water temperature, lower stream order, higher pH, higher dissolved oxygen, higher elevation, and coarser substrates than the other land use sites. Conversely, the urban sites showed significantly higher mean values for electrical conductivity, nitrogen and phosphorus compounds. Significant differences in the shredder and predator richness and density were observed among land uses with more shredders and predators found in forest sites. Redundancy analysis showed a clear separation of forest sites from riparian modified areas (agriculture and urban use sites). Our results were broadly in accordance with the central RCC theme. However, the longitudinal distribution of predators and collectors did not completely match the prediction of the RCC. These results confirm that macroinvertebrate FFG structure has been altered by agricultural and urbanization practices in the Dongjiang River basin. Moreover, shredders and predators could be potential candidates for monitoring and assessing land use impacts on water quality in this basin to improve future watershed management.

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BackgroundHistone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study.ResultsThe results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the leftacterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors.ConclusionsOur best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect.