hermes

hermes provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed RNA-seq data.

Key Features:

• Data Import: Data can be imported from SummarizedExperiment as well as matrix objects.
• Gene Annotation: Data can be annotated from BioMart.
• Gene Filtering: Filtering for genes without too low expression or containing required annotations is supported.
• Sample Filtering: Filtering for samples with sufficient correlation to other samples or total number of reads is supported.
• Normalization Methods: Standard normalization methods including cpm, rpkm and tpm can be used.
• Differential Expression Analysis: DESeq2 as well as voom differential expression analyses are available.

Scientific Applications:

• RNA-seq Data Preprocessing: Quality control, filtering and normalization of pre-processed RNA-seq data for downstream analysis.
• Differential Expression Analysis: Statistical identification of differentially expressed genes using DESeq2 or voom methods.

Methodology:

The tool provides classes and functions that implement quality control, filtering, normalization and differential expression analysis workflows for pre-processed RNA-seq data.