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Taiwan Bioinformatics Institute

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Unit 3 : Cancer genomics and clinical studies

This unit also focuses on developing Bayes methods for prediction/prognosis in cancer genomics and clinical studies.  Platforms have been made available to provide customized bioinformatics services for the study of pharmacogenomics, pharmacogenetics and other studies using high-throughput technologies, including SNP array, expression array, methylation array, microRNA array, RNAi screening and next generation sequencing. We also conduct genome annotation and databases mining to prepare for functional and mechanism studies.

A.On-line services

(1) Core portal web service and consultation

(2) On-line sequence analysis tools, GCG and EMBOSS suite.

(3) Workflow and software for copy number variation study and array-CGH data analysis

(4) Workflow and software for analyzing data from cell-based two channel RNAi high throughput screening

(5) Workflow and software for genome-wide time-course expression profile of virus genes

(6) Workflow and software to compute false discovery rate for large scale family-based association tests


 
B.Customized services

(1) Array based genomics data analysis: Expression array, DNA methylation array, microRNA array, SNP array, and arrayCGH. Illumina, Affymetrix, Agilent, Nimblegen, cDNA microarray. Data-preprocessing, data analysis for association studies or classification/prediction. Study design, platform selection, experimentation, data analysis and follow-up studies.

(2) Next generation sequencing data analysis: SNP finding for fine mapping after GWAS, RNA-seq for expression studies.

(3) Genetic studies data analysis: GWAS (Genome-Wide Association Studies), eQTL(expression Quantitative Trait Loci), and false discovery rate in large scale family based association tests.

(4) Prediction model for Pharmacogenetics and pharmacogenomics, based on array data. Technical validity, clinical validity and clinical utility.

(5) RNAi data analysis service: Data preprocessing and hit selection for two-channel cell-based RNAi high-throughput screening.

(6) Cancer genes mining and analysis: Data mining of cancer related genes in the public domain databases. Analysis of clinical relevance of candidate cancer genes.

(7) Biological or functional interpretation for array or NGS data: Gene ontology analysis, pathway analysis, gene set enrichment analysis, genome assembly annotation, epigenomics regulation analysis, regulatory network analysis, gene connectivity analysis.

(8) Consultation on statistical genetics studies.