KIURI 바이오인공지능센터


Sequencing service

유전체분석 전문기업

와의 MOU 체결

  • AKB-AI

    • 연구설계 상담
  • AKB-AI

    • Custom analysis
  • 연구자

    • Data sample
    • NGS Sequencing

Bio-medical data analysis service

  • Transcriptomics

  • Proteomics

  • Genomics

  • Drug

    • - Gene expression profiles for small molecules and drugs
    • - protein expression profiles for small molecules and drugs

1. Primary data analysis (1차 데이터 분석)

각 다중 오믹스 시퀀싱의 FASTQ 형태를 비롯한 원데이터( raw data) 로 부터 서열정렬, 변이 동정, 각 유전자별 발현량 동정등의 과정을 진행하는 preprocessing 과정

  • Genome
    (WES, WGS)
    • Raw data quality control
    • Alignment of raw data to genome or proteome
    • Variant calling
  • Transcriptome
    (RNA-seq, scRNA-seq)
    • Raw data quality control
    • Alignment of raw data to genome or proteome
    • Expression matrix generation
  • Proteome
    (LC-ms/ms)
    • Raw data quality control
    • Alignment of raw data to genome or proteome
    • Expression matrix generation
  • Raw data quality control
  • Alignment of raw data to genome or proteome
  • Variant calling
  • Expression matrix generation

2. Secondary data analysis (2차 데이터 분석)

preprocessing 과정이 끝난 데이터를 사용하여 유전 변이 별 주석 처리, 차별 발현 유전자 분석, 단일 세포 군집분석 등 각 실험에 맞는 통계 기법을 사용하여 데이터를 분석하는 과정

  • Genome
    (WES, WGS)
    • Basic variant annotation
      • Disease-Phenotype annotation
      • Clinical annotation
      • Functional score annotation(SIFT..)
      • Gene identifier annotation
  • Transcriptome
    (RNA-seq, scRNA-seq)
    • RNA-seq, LC-ms/ms
      Differentially expressed gene analysis
      • Quality control
      • Normalization
      • Statistical test
      • Multiple correction
    • Single cell RNA-seq(scRNA-seq)
      • QC
      • Normalization
      • Dimensional reduction (PCA, UMAP, t-SNE)
      • DEG
      • Cell type annotation
  • Proteome
    (LC-ms/ms)
    • RNA-seq, LC-ms/ms
      Differentially expressed gene analysis
      • Quality control
      • Normalization
      • Statistical test
      • Multiple correction
  • Basic variant annotation
    • Disease-Phenotype annotation
    • Clinical annotation
    • Functional score
      annotation(SIFT..)
    • Gene identifier annotation
  • RNA-seq, LC-ms/ms Differentially expressed gene analysis
    • Quality control
    • Normalization
    • Statistical test
    • Multiple correction
  • Single cell RNA-seq(scRNA-seq)
    • QC
    • Normalization
    • Dimensional reduction (PCA, UMAP, t-SNE)
    • DEG
    • Cell type annotation

3. Advanced Data analysis (고급 데이터 분석)

각 대량의 유전자 발현 변화를 다양한 pathway , GO(Gene ontology), 단백질/전사체 상호작용(PPI, TRI) 네트워크 등의 지식베이스를 기반으로 다양한 실험 목적에 맞는 생물학적, 의학적, 약물학적 의미를 분석하는 과정

  • Genome
    (WES, WGS)
    • Gene set enrichment annotation (GSEA)
    • Multiple gene identifier annotation
    • Network analysis( PPI, TRN)
    • Clinical, Pharmacological insight knowledge annotation (Disease/Drug-gene)
  • Transcriptome
    (RNA-seq, scRNA-seq)
    • Gene set enrichment annotation (GSEA)
    • Multiple gene identifier annotation
    • Network analysis( PPI, TRN)
    • Clinical, Pharmacological insight knowledge annotation (Disease/Drug-gene)
  • Proteome
    (LC-ms/ms)
    • Gene set enrichment annotation (GSEA)
    • Multiple gene identifier annotation
    • Network analysis( PPI, TRN)
    • Clinical, Pharmacological insight knowledge annotation (Disease/Drug-gene)
  • Gene set enrichment annotation (GSEA)
  • Multiple gene identifier annotation
  • Network analysis( PPI, TRN)
  • Clinical, Pharmacological insight knowledge annotation (Disease/Drug-gene)

4. Collaboration Research (협력 연구)

고급 통계 분석, 최신 bio-medical, 데이터 사이언스, AI 기술 활용 분석 등 다양한 고차원 분석 기술기반 데이터 맞춤 분석을 진행하는 과정

  • Genome
    (WES, WGS)
    • Variant(gene) prioritization (custom variant filtering and prioritization)
    • Exploratory analysis to identify clusters and patterns in the data sets using methods (PCA, Regression, SVM..)
    • Advanced Quantitative Research Methods (Survival analysis, time series data)
    • Prediction of Clinical outcomes based on RNA-seq data(Disease risk prediction)
    • Integrative analysis and causal inference of multiple omics data sets in order to gain mechanistic insights into diseases and biological processes
    • Integrative network and pathways analysis for omics data
    • Advanced analysis of single cell genomics data, including scRNA-seq data using the state-of-art methods
    • Computational pharmacogenomics analysis(drug repositioning, target discovery)
  • Transcriptome
    (RNA-seq, scRNA-seq)
    • Variant(gene) prioritization (custom variant filtering and prioritization)
    • Exploratory analysis to identify clusters and patterns in the data sets using methods (PCA, Regression, SVM..)
    • Advanced Quantitative Research Methods (Survival analysis, time series data)
    • Prediction of Clinical outcomes based on RNA-seq data(Disease risk prediction)
    • Integrative analysis and causal inference of multiple omics data sets in order to gain mechanistic insights into diseases and biological processes
    • Integrative network and pathways analysis for omics data
    • Advanced analysis of single cell genomics data, including scRNA-seq data using the state-of-art methods
    • Computational pharmacogenomics analysis(drug repositioning, target discovery)
  • Proteome
    (LC-ms/ms)
    • Variant(gene) prioritization (custom variant filtering and prioritization)
    • Exploratory analysis to identify clusters and patterns in the data sets using methods (PCA, Regression, SVM..)
    • Advanced Quantitative Research Methods (Survival analysis, time series data)
    • Prediction of Clinical outcomes based on RNA-seq data(Disease risk prediction)
    • Integrative analysis and causal inference of multiple omics data sets in order to gain mechanistic insights into diseases and biological processes
    • Integrative network and pathways analysis for omics data
    • Advanced analysis of single cell genomics data, including scRNA-seq data using the state-of-art methods
    • Computational pharmacogenomics analysis(drug repositioning, target discovery)
  • Variant(gene) prioritization (custom variant filtering and prioritization)
  • Exploratory analysis to identify clusters and patterns in the data sets using methods (PCA, Regression, SVM..)
  • Advanced Quantitative Research Methods (Survival analysis, time series data)
  • Prediction of Clinical outcomes based on RNA-seq data(Disease risk prediction)
  • Integrative analysis and causal inference of multiple omics data sets in order to gain mechanistic insights into diseases and biological processes
  • Integrative network and pathways analysis for omics data
  • Advanced analysis of single cell genomics data, including scRNA-seq data using the state-of-art methods
  • Computational pharmacogenomics analysis(drug repositioning, target discovery)

5. Comprehensive integrated data analysis (종합적인 데이터 통합 분석)

1차 데이터 분석부터 고급 데이터 분석까지 각 실험 디자인에 맞는 종합적이고 포괄적인 데이터 분석을 제공하는 과정

  • Primary data analysis
  • Secondary data analysis
  • Advanced Data analysis

Bio-medical data analysis service

  • 연구 디자인 맞춤형 기계학습(ML/DL) 모델 설계
  • ML/DL 기반 진단 및 치료를 위한 모델 구현
  • Network based ML/DL 기반의 신약 후보물질 예측 및 제시