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Biological Data Science Ueda Laboratory

Biological Big Data to Knowledge, using Data Science

Biological Data Science

With the advancement of sequencing technologies, it has become a challenging task to process large volumes of data with conventional methods. In order to extract knowledge from biological big data, (ex. Multi-omics data) it is necessary to incorporate the latest Data Science technology, such as cloud computing and machine learning. We are developing cloud based Single Cell NGS analysis pipeline using Hadoop / Spark, (cloud computing framework) and developing the method to identify RNA modifications using deep learning method.
Our research include following:
(1) Cancer genomics
(2) Single Cell genomics
(3) Proteomics and post translational modification
(4) Epitranscriptome (RNA modifications) analysis using nanopore sequencer

Hepatitis B Virus (HBV) integration sites (blue) and DNA copy number break points (red) on human genome
Hepatitis B Virus (HBV) integration sites (blue) and DNA
copy number break points (red) on human genome
RNA Sequencing and Whole genome sequencing using Hadoop
RNA Sequencing and Whole genome sequencing using Hadoop
RNA epi-transcriptome analysis using nanopore sequencer and deep learning
RNA epi-transcriptome analysis using nanopore
sequencer and deep learning

Member

  • Hiroki UEDA
  • Specialized field:Computational Biology, Cancer Genomics, Machine Learning
<As of May 2020>

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