データベースとデータ解析の融合

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This document discusses the key factors needed to induce pluripotency in differentiated cells: c-Myc, Oct3/4, Klf4, and Sox2. It also mentions DeepGOscnn, which is likely a method or tool for studying the induced pluripotent stem cell reprogramming process.

The document describes performing dimensionality reduction, cell-cell interaction analysis, and reporting on single-cell RNA-seq data from a study of germline cells in male samples. It loads the gene expression, dimensionality reduced data, and cell type labels into a SingleCellExperiment object. Cell-cell interaction ranks are calculated and used for decomposition. A report is generated on the cell-cell interactions within each cell type at a threshold of 80%.

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弘毅露崎

ビール砲の放ち方

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Large-scale imputation of epigenetic datasets for systematic annotation of di...

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Phenomics assisted breeding in crop improvement

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As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus, high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology, and analysis methods, numerous infrastructure platforms have been developed for phenotyping.

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