Methods in molecular biology
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The analysis of genome-wide epigenomic alterations including DNA methylation has become a subject of intensive research for many complex diseases. Whole-genome bisulfite sequencing (WGBS) using next-generation sequencing technologies can be considered the gold standard for a comprehensive and quantitative analysis of cytosine methylation throughout the genome. Several approaches including tagmentation- and post bisulfite adaptor tagging (PBAT)-based WGBS have been devised. ⋯ Spike-in of unmethylated DNA allows for the precise estimation of bisulfite conversion rates. We also provide a step-by-step description of the data analysis using publicly available bioinformatic tools. The described protocol has been successfully applied to different human samples as well as DNA extracted from plant tissues and yields robust and reproducible results.
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Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.
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Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. ⋯ Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.
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Spinal muscular atrophy (SMA) is an autosomal recessive disorder caused by a mutation in SMN1 that stops production of SMN (survival of motor neuron) protein. Insufficient levels of SMN results in the loss of motor neurons, which causes muscle weakness, respiratory distress, and paralysis. A nearly identical gene (SMN2) contains a C-to-T transition which excludes exon 7 from 90% of the mature mRNA transcripts, leading to unstable proteins which are targeted for degradation. ⋯ Nusinersen (Spinraza), the first FDA-approved antisense oligonucleotide drug targeting SMA, was designed based on this concept and clinical studies have demonstrated a dramatic improvement in patients. Novel chemistries including phosphorodiamidate morpholino oligomers (PMOs) and locked nucleic acids (LNAs), as well as peptide conjugates such as Pip that facilitate accurate targeting to the central nervous system, are explored to increase the efficiency of exon 7 inclusion in the appropriate tissues to ameliorate the SMA phenotype. Due to the rapid advancement of treatments for SMA following the discovery of ISS-N1, the future of SMA treatment is highly promising.
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Recent advances in the CRISPR/Cas9 system have dramatically facilitated genome engineering in various cell systems. Among the protocols, the direct delivery of the Cas9-sgRNA ribonucleoprotein (RNP) complex into cells is an efficient approach to increase genome editing efficiency. ⋯ Here, we describe our routine methods for RNP complex-mediated gene deletion including the protocols to prepare the purified Cas9 protein and the in vitro transcribed sgRNA. Subsequently, we also describe a protocol to confirm the edited genomic positions using the T7E1 enzymatic assay and next-generation sequencing.