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Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.

Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated “base editing” being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 3-fold lower efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-governing factors on a novel mouse dataset using machine learning. We found the sequence composition of the single-stranded oligodeoxynucleotide (ssODN), i.e. the repair template, to be a governing factor. Furthermore, different regions of the ssODN have variable influence, which reflects the underlying mechanism of the repair process. Our model improves HDR efficiency by 83% compared to traditionally chosen targets. Using our findings, we developed CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or – for-the-first-time – HDR-mediated nucleotide changes.

Background Genomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. The widely used Hadoop MapReduce architecture and associated machine learning library, Mahout, provide the means for tackling computationally challenging tasks. However, many genomic analyses do not fit the Map-Reduce paradigm. We therefore utilise the recently developed Spark engine, along with its associated machine learning library, MLlib, which offers more flexibility in the parallelisation of population-scale bioinformatics tasks. The resulting tool, VariantSpark provides an interface from MLlib to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results. Results To demonstrate the capabilities of VariantSpark , we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VariantSpark is 80 % faster than the Spark -based genome clustering approach, adam , the comparable implementation using Hadoop/Mahout, as well as Admixture , a commonly used tool for determining individual ancestries. It is over 90 % faster than traditional implementations using R and Python. Conclusion The benefits of speed, resource consumption and scalability enables VariantSpark to open up the usage of advanced, efficient machine learning algorithms to genomic data.

Background CRISPR-Cas9 gene-editing technology has facilitated the generation of knockout mice, providing an alternative to cumbersome and time-consuming traditional embryonic stem cell-based methods. An earlier study reported up to 16% efficiency in generating conditional knockout (cKO or floxed) alleles by microinjection of 2 single guide RNAs (sgRNA) and 2 single-stranded oligonucleotides as donors (referred herein as “two-donor floxing” method). Results We re-evaluate the two-donor method from a consortium of 20 laboratories across the world. The dataset constitutes 56 genetic loci, 17,887 zygotes, and 1718 live-born mice, of which only 15 (0.87%) mice contain cKO alleles. We subject the dataset to statistical analyses and a machine learning algorithm, which reveals that none of the factors analyzed was predictive for the success of this method. We test some of the newer methods that use one-donor DNA on 18 loci for which the two-donor approach failed to produce cKO alleles. We find that the one-donor methods are 10- to 20-fold more efficient than the two-donor approach. Conclusion We propose that the two-donor method lacks efficiency because it relies on two simultaneous recombination events in cis , an outcome that is dwarfed by pervasive accompanying undesired editing events. The methods that use one-donor DNA are fairly efficient as they rely on only one recombination event, and the probability of correct insertion of the donor cassette without unanticipated mutational events is much higher. Therefore, one-donor methods offer higher efficiencies for the routine generation of cKO animal models.

The two Jaenisch laboratory studies published in Cell in 2013 were ground-breaking, demonstrating for the first time proof of principle CRISPR mediated gene editing in the mouse zygote to generate knockout and conditional alleles, and caused much excitement in the transgenic mouse community. [...]our study is not the first time concerns have been raised as to the efficiency of the 2-guides 2-oligo method, with anecdotal reports from others in the transgenic community (Science; 2016. doi:https://doi.org/10.1126/science.aal0334 [doi.org]), which stated that “What was disappointing is none of us could reproduce at the efficiencies reported by Jaenisch. Because efficiencies at different genomic loci often vary highly (which the Yang et al. authors state in their paragraph below point #3), it would be ideal to gather such side-by-side data for at least 6 to 10 loci or more to ensure reproducibility. [...]our observations call into question the robustness of the approach and its suitability for widespread use. Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, UK David Brough & Catherine B. Lawrence 13.

Editing individual nucleotides is a crucial component for validating genomic disease association. It currently is hampered by CRISPR-Cas-mediated \"base editing\" being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 4-fold lower efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-governing factors on a novel mouse dataset using machine learning. We found the sequence composition of the repair template (ssODN) to be a governing factor, where different regions of the ssODN have variable influence, which reflects the underlying biophysical mechanism. Our model improves HDR efficiency by 83% compared to traditionally chosen targets. Using our findings, we develop CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or -- for-the-first-time -- HDR-mediated nucleotide changes. CUNE can be run via the web at: https://gt-scan.net/cune .

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random-Forests are suitable for \"wide\" datasets, current implementations such as Google PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random-Forest. This paper introduces Cursed-Forest, a novel Random-Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. Cursed-Forest is 9 and up to 89 times faster than Google PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features. Footnotes * https://github.com/aehrc/VariantSpark * https://databricks.com/blog/2017/07/26/breaking-the-curse-of-dimensionality-in-genomics-using-wide-random-forests.html

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts ( CD16 + C1QA/B/C + ) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34 + hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8 + T cells and an increased ratio of CD8 + effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy. Transcriptomic and proteomic profiling of blood samples from individuals with COVID-19 reveals immune cell and hematopoietic progenitor cell alterations that are differentially associated with disease severity.

Prolonged seizures (status epilepticus, SE) may drive hippocampal dysfunction and epileptogenesis, at least partly, through an elevation in neurogenesis, dysregulation of migration and aberrant dendritic arborization of newly-formed neurons. MicroRNA-22 was recently found to protect against the development of epileptic foci, but the mechanisms remain incompletely understood. Here, we investigated the contribution of microRNA-22 to SE-induced aberrant adult neurogenesis. SE was induced by intraamygdala microinjection of kainic acid (KA) to model unilateral hippocampal neuropathology in mice. MicroRNA-22 expression was suppressed using specific oligonucleotide inhibitors (antagomir-22) and newly-formed neurons were visualized using the thymidine analog iodo-deoxyuridine (IdU) and a green fluorescent protein (GFP)-expressing retrovirus to visualize the dendritic tree and synaptic spines. Using this approach, we quantified differences in the rate of neurogenesis and migration, the structure of the apical dendritic tree and density and morphology of dendritic spines in newly-formed neurons.SE resulted in an increased rate of hippocampal neurogenesis, including within the undamaged contralateral dentate gyrus (DG). Newly-formed neurons underwent aberrant migration, both within the granule cell layer and into ectopic sites. Inhibition of microRNA-22 exacerbated these changes. The dendritic diameter and the density and average volume of dendritic spines were unaffected by SE, but these parameters were all elevated in mice in which microRNA-22 was suppressed. MicroRNA-22 inhibition also reduced the length and complexity of the dendritic tree, independently of SE. These data indicate that microRNA-22 is an important regulator of morphogenesis of newly-formed neurons in adults and plays a role in supressing aberrant neurogenesis associated with SE.

Pancreatic Ductal Adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the U.S. Both rare and common germline variants contribute to PDAC risk. Here, we fine-map and functionally characterize a common PDAC risk signal at chr1p36.33 (tagged by rs13303010) identified through a genome wide association study (GWAS). One of the fine-mapped SNPs, rs13303160 (OR = 1.23 (95% CI 1.15-1.32), P- value = 2.74×10 −9 , LD r 2  = 0.93 with rs13303010 in 1000 G EUR samples) demonstrated allele-preferential gene regulatory activity in vitro and binding of JunB and JunD in vitro and in vivo. Expression Quantitative Trait Locus (eQTL) analysis identified KLHL17 as a likely target gene underlying the signal. Proteomic analysis identified KLHL17 as a member of the Cullin-E3 ubiquitin ligase complex with vimentin and nestin as candidate substrates for degradation in PDAC-derived cells. In silico differential gene expression analysis of high and low KLHL17 expressing GTEx pancreas samples suggested an association between lower KLHL17 levels (risk associated) and pro-inflammatory pathways. We hypothesize that KLHL17 may mitigate cell injury and inflammation by recruiting nestin and vimentin for ubiquitination and degradation thereby influencing PDAC risk. Allele-preferential transcription factor binding can influence pancreatic ductal adenocarcinoma risk loci function. Here, the authors show allele-specific JunB and JunD binding at chr1p36.33 and propose a role for KLHL17 in protein homeostasis by mitigating inflammation.