Methods in molecular biology
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DNA methylation is a transgenerational stable epigenetic modification able to regulate gene expression and genome stability. The analysis of DNA methylation by genome-wide bisulfite sequencing become the main genomic approach to study epigenetics in many organisms; leading to standardization of the alignment and methylation call procedures. ⋯ Therefore, in this chapter we propose a computational workflow for the analysis, visualization, and interpretation of data obtained from alignment of whole genome bisulfite sequencing of plant samples. Using almost exclusively the R working environment we will examine in depth how to tackle some plant-related issues during epigenetic analysis.
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CRISPR-associated nuclease (Cas) has been widely applied to modify the genomes of various cell types. As RNA-guided endonucleases, Cas enzymes can target different genomic sequences simply by changing the guide sequence of the CRISPR RNA (crRNA) or single guide RNA (sgRNA). Recent studies have demonstrated that DNA-RNA chimeric crRNA or sgRNA can efficiently guide the Cas9 protein for genome editing with reduced off-target effects. This chapter aims to describe a procedure for using chimeric RNA to modify the genomes of mammalian cells.
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With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. ⋯ Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications.
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The recently discovered clustered regularly interspaced short palindromic repeats (CRISPR)-Cpf1 system, now reclassified as Cas12a, is a DNA-editing platform analogous to the widely used CRISPR-Cas9 system. The Cas12a system exhibits several distinct features over the CRISPR-Cas9 system, such as increased specificity and a smaller gene size to encode the nuclease and the matching CRISPR guide RNA (crRNA), which could mitigate off-target and delivery problems, respectively, described for the Cas9 system. However, the Cas12a system exhibits reduced gene editing efficiency compared to Cas9. ⋯ To optimize the CRISPR-Cas12a system, we describe the inclusion of a self-cleaving ribozyme in the vector design to facilitate accurate 3'-end processing of the crRNA transcript to produce precise molecules. This optimized design enhanced not only the gene editing efficiency, but also the activity of the catalytically inactive Cas12a-based CRISPR gene activation platform. We thus generated an improved CRISPR-Cas12a system for more efficient gene editing and gene regulation purposes.
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The number of studies published in the biomedical literature has dramatically increased over the last few decades. This massive proliferation of literature makes clinical medicine increasingly complex, and information from multiple studies is often needed to inform a particular clinical decision. However, available studies often vary in their design, methodological quality, and population studied, and may define the research question of interest quite differently. ⋯ In addition, since even highly cited trials may be challenged over time, clinical decision-making requires ongoing reconciliation of studies which provide different answers to the same question. Because it is often impractical for readers to track down and review all the primary studies, systematic reviews and meta-analyses are an important source of evidence on the diagnosis, prognosis and treatment of any given disease. This chapter summarizes methods for conducting and reading systematic reviews and meta-analyses, as well as describes potential advantages and disadvantages of these publications.