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GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.

John A HadishTyler D BiggsBenjamin T ShealyM Reed BenderColeman B McKnightConnor WytkoMelissa C SmithF Alex FeltusLoren HonaasStephen P Ficklin
Published in: BMC bioinformatics (2022)
Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.
Keyphrases
  • rna seq
  • single cell
  • gene expression
  • electronic health record
  • big data
  • dna methylation
  • data analysis
  • deep learning