Equipment

Available Software and Hardware

  • Standard statistical packages, such as SAS, Stata and R
  • Sample size and power computation applications, including PASS, Solo Power Analysis and custom-written applications
  • Tools for methodological research, such as Matlab, Python, Perl, Fortran and C.
  • Bioinformatics tools, including Ingenuity Pathway Analysis (IPA), R/Bioconductor, Python/Anaconda, GSEA, Cytoscape, and a large number of public domain tools for analyzing transcriptomic, genomic, and proteomics data.
  • A 480-core high performance computing cluster equipped with modules for all stages of NGS data analysis (QC, alignment, variant calling, differential expression, peak calling, annotation, visualization, etc.).
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