- Omics Data Analysis
With the tremendous growth in biological data, omics - genomics, transcriptomics, proteomics, metabolomics,
metagenomics, epigenomics - is rapidly expanding and creating new perspectives. The development of high-throughput technologies and big data analysis enabled a comprehensive understanding of many biological phenomena by omics
research. However, integrating these large-scale multi-omics data and discovering functional insights are challenging tasks. To address these challenges, machine learning has been applied to analyze multi-omics.
HelloGene provides a wide range of tools for omics data analysis which derives biological meaning from the data. Our platform also integrates the results and offers solutions for multi-omics and system biological data analysis.
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Genomics has been broadly used in biomedical research to
find new genes and susceptible loci associated with different
traits or diseases. Proteomics is the study related to the
structure, function, and modification of proteins, especially
focusing in post-transcriptional modifications which lead to
the diversity in proteomes that originate from the same
genome.
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Epigenomics researches about the epigenetic modifications
of the genome and the regulations of the gene expression.
Transcriptomics studies the genome-wide expression
patterns of the complete set of RNA transcriptomes.
Metabolomics characterizes the metabolites present in cell,
tissue, and body fluid. Finally, multi-omics approaches aim to
integrate different omics data to understand their
interrelation and the functioning of larger systems.
