Barkerkloster3955

RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8-36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. Cell Cycle inhibitor The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.Genome editing has transformed the life sciences and has exciting prospects for use in treating genetic diseases. Our laboratory developed base editing to enable precise and efficient genome editing while minimizing undesired byproducts and toxicity associated with double-stranded DNA breaks. Adenine and cytosine base editors mediate targeted A•T-to-G•C or C•G-to-T•A base pair changes, respectively, which can theoretically address most human disease-associated single-nucleotide polymorphisms. Current base editors can achieve high editing efficiencies-for example, approaching 100% in cultured mammalian cells or 70% in adult mouse neurons in vivo. Since their initial description, a large set of base editor variants have been developed with different on-target and off-target editing characteristics. Here, we describe a protocol for using base editing in cultured mammalian cells. We provide guidelines for choosing target sites, appropriate base editor variants and delivery strategies to best suit a desired application. We further describe standard base-editing experiments in HEK293T cells, along with computational analysis of base-editing outcomes using CRISPResso2. Beginning with target DNA site selection, base-editing experiments in mammalian cells can typically be completed within 1-3 weeks and require only standard molecular biology techniques and readily available plasmid constructs.Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.Digested genome sequencing (Digenome-seq) is a highly sensitive, easy-to-carry-out, cell-free method for experimentally identifying genome-wide off-target sites of programmable nucleases and deaminases (also known as base editors). Genomic DNA is digested in vitro using clustered regularly interspaced short palindromic repeats ribonucleoproteins (RNPs; plus DNA-modifying enzymes to cleave both strands of DNA at sites containing deaminated base products, in the case of base editors) and subjected to whole-genome sequencing (WGS) with a typical sequencing depth of 30×. A web-based program is available to map in vitro cleavage sites corresponding to on- and off-target sites. Chromatin DNA, in parallel with histone-free genomic DNA, can also be used to account for the effects of chromatin structure on off-target nuclease activity. Digenome-seq is more sensitive and comprehensive than cell-based methods for identifying off-target sites. Unlike other cell-free methods, Digenome-seq does not involve enrichment of DNA ends through PCR amplification.