Fig. 1

The workflow of RNAlysis. Top section: a typical analysis with RNAlysis can start at any stage from raw/trimmed FASTQ files, through more processed data tables such as count matrices, differential expression tables, or any form of tabular data. Middle section: data tables can be filtered, normalized, and transformed with a wide variety of functions, allowing users to clean up their data, fine-tune their analysis to their biological questions, or prepare the data for downstream analysis. RNAlysis also provides users with a broad assortment of customizable clustering methods to help recognize genes with similar expression patterns, and visualization methods to aid in data exploration. All of these functions can be arranged into customized Pipelines that can be applied to multiple tables in one click, or exported and shared with others. Bottom section: Once users have focused their data tables into gene sets of interest, or imported such gene sets from another source, they can use RNAlysis to visualize the intersections between different gene sets, extract lists of genes from any set operations applied to their gene sets and data tables, and perform enrichment analysis for their gene sets, using either public datasets such as GO and KEGG or customized, user-defined enrichment attributes