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| <h1>DRC ComputOmics</h1> | |
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| <h2>Computational Biology</h2> | |
| <p><img src="https://th.bing.com/th/id/OIG.tUfnEp4OWoDONNSs4Z7w?w=270&h=270&c=6&r=0&o=5&pid=ImgGn" height="220px" width="220px"> | |
| Computational biology is an interdisciplinary field that applies mathematical and computational methods to analyze biological data. Computational biology | |
| aims to understand the structure, function, evolution and interaction of biological systems at different levels of organization, from molecules to ecosystems. | |
| Computational biology can also help to design and test new hypotheses, experiments and therapies in biotechnology and medicine. | |
| Some of the main topics of computational biology include bioinformatics, genomics, proteomics, metabolomics, systems biology, phylogenetics, | |
| structural biology and synthetic biology.</p> | |
| <p>Bioinformatics is the application of computational methods to analyze biological data. It involves the use of algorithms, databases, | |
| software tools and statistical techniques to process, store and interpret information from various sources such as DNA sequences, protein | |
| structures, gene expression and metabolic pathways. | |
| Bioinformatics can help answer questions such as how genes function, how diseases develop and how organisms evolve.</p> | |
| <p>Genomics is the study of the structure, function, and evolution of genomes, which are the complete sets of genetic information in an organism. | |
| Genomics can help us understand the molecular basis of life, disease, and diversity. Genomics can also enable the development of new technologies | |
| and applications in fields such as medicine, agriculture, biotechnology, and environmental science.</p> | |
| <p>Proteomics is a field of science that studies the structure, function, and interactions of proteins in living organisms. Proteins are essential | |
| for many biological processes, such as metabolism, signaling, immunity, and gene expression. Proteomics uses various techniques, such as mass spectrometry, | |
| protein purification, and bioinformatics, to identify and quantify proteins and their modifications. Proteomics can provide insights into the molecular | |
| mechanisms of diseases, drug targets, biomarkers, and cellular pathways. | |
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| <p>Metabolomics is the study of the chemical processes that occur in living organisms, such as the production and degradation of metabolites. Metabolomics can | |
| provide insights into the metabolic state, function, and regulation of cells, tissues, and organs. | |
| Metabolomics can also be used to identify biomarkers, discover new pathways, and understand disease mechanisms.</p> | |
| <p>Phylogenetics is the study of the evolutionary history and relationships among different groups of organisms. It uses various methods, such | |
| as molecular sequences, morphological traits, and fossils, to infer the patterns of descent and divergence among taxa. Phylogenetic trees are | |
| graphical representations of these relationships, showing the branching order and relative time of speciation events. Phylogenetics has many | |
| applications in biology, such as understanding the origin and diversity of life, | |
| reconstructing the ancestral features of organisms, and testing hypotheses about adaptation and coevolution.</p> | |
| <h2>Transcriptomics</h2> | |
| <p>Transcriptomics is the study of the transcriptome, which is the complete set of RNA transcripts produced by the genome of an organism or a cell. | |
| Transcriptomics can reveal the expression levels of genes and their regulation under different conditions, such as development, stress, disease, or treatment. | |
| Transcriptomics can also identify novel transcripts, such as non-coding RNAs, alternative splicing variants, and fusion genes. | |
| Transcriptomics relies on high-throughput sequencing technologies, such as RNA-seq, to capture and analyze the transcriptome data.</p> | |
| <h2>Spatial Transcriptomics</h2> | |
| <p>Park HE, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and | |
| Their Applications in Neuroscience and Cancer Research. Adv Sci (Weinh). 2023 Apr 7:e2206939. doi: 10.1002/advs.202206939. Epub ahead of print. | |
| PMID: 37026425.</p> | |
| <p><img src="https://onlinelibrary.wiley.com/cms/asset/e7d09386-ea3e-4465-a6ea-a3a255a41caa/advs5479-fig-0001-m.jpg" width="900 px" height="800px"></p> | |
| <p>Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related | |
| analyses in various applications for biological systems. By transitioning from conventional biological studies to “in situ” biology, spatial | |
| transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles | |
| of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and | |
| its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new | |
| developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and | |
| discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.</p> | |
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| Last updated on April 13 2023<br> | |
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