RNA-Seq
Avadis NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes standard differential expression analysis for a variety of experiment conditions, as well as differential splicing analysis. It supports novel discovery steps including identifying novel genes and exons and novel splice junctions. It includes the ability to detect variants in the transcriptome, and the ability to detect gene fusions for cancer samples. Further downstream analysis such as GO, pathway analysis, etc can be performed on the set of interesting genes.
Download the RNA-Seq Highlights Guide
Watch the RNA-Seq Webinar Recording
Quantification and Normalization
Expression values at genes, exons and transcript level. DESeq, Quantile and TMM methods for normalization.
Differential Expression
t-Test, Mann-Whitney, n-way ANOVA, and MTC for identifying differentially expressed genes under different experimental conditions.
Differential Splicing
Determine gene and deconvoluted transcript expression profiles; identify alternative splicing patterns.
Novel Discovery
Analyze coverage patterns to detect novel genes and exons not present in NCBI, UCSC, and Ensembl annotations.
Transcriptome Variants
Identify variants in transcriptome, predict effect on transcripts, and perform differential SNP analysis.
Gene Fusion
Find gene fusions via spliced and paired reads spanning the fused genes. Results annotated with paralogs and pseudogenes information for identifying false positives.
Clustering Genes
Cluster genes on their expression values to identify groups of genes behaving similarly across conditions.
GO Analysis
Perform GO analysis on identified set of interesting genes.
Pathway Analysis
Use the packaged database of 2 million interactions (with supporting PubMed references) to find relationships between genes.
Compare gene lists
Compare different gene lists from multiple experiments and across organisms.
