RNA-seq with next-generation sequencing (NGS) has become the method of choice for researchers interested in characterizing genome-wide gene and transcript expression in a single experiment.

RNA-seq is widely used for profiling expression of protein-coding and non-coding transcripts (small and long) in both model species (human, mouse, Arabidopsis, maize, etc.) with well-defined transcriptome, and species with no reference transcriptome. RNA-Seq methods can provide precise measurement of gene and transcript levels, determine strand orientation, map alternate transcripts with high confidence, characterize gene fusions, identify single nucleotide variants and help discover novel gene isoforms.

RNA-seq has been utilized for diverse application areas in biology. For example, in pathobiology of cancer to dissect the link between tumor genotypes and molecular subtypes of cancer, to aid in tumor classification and progression, and to characterize multiple drug susceptible tumorigenic pathways towards discovering new biomarkers or therapeutic strategies. In agriculture, RNA-seq methods have been used to assess transcript diversity across different varieties, characterize mode of action of trait genes, and discover new genes or targets for crop/trait improvement (yield and other agronomic traits, disease resistance, insect tolerance, quality traits, etc).

Tertiary analysis of expression datasets is necessary to understand affected genes and pathways and this is typically accomplished through differential expression analysis, enrichment analysis, and co-expression based network analysis, to create a global picture of cellular function.

Our integrated data solutions lets you manage and explore your primary expression data, as well as outputs from tertiary analysis tools. Explore data outputs from transcriptome characterization tools by simply uploading your expression matrix or results from differential expression analysis. Search over your data, subset your data, perform statistical analysis and visualize the data.


bowtie2Ultra-fast, sensitive gapped, short-read aligner.
STARUltra-fast universal RNA-seq aligner.
RSEMQuantify gene and transcript expression from RNA-seq data.
TopHat2Sensitive and accurate spliced-read aligner.
HISAT2Fast spliced aligner with low memory requirements.
CufflinksAssemble transcripts, estimate transcript abundances, and test for differential expression.
KallistoQuantify transcript abundance from RNA-Seq data.
DESeq2Test for differential expression based on a model using the negative binomial distribution.
EdgeRExamine differential expression of replicated count data based on several statistical methods including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests.
EBSeqEmpirical Bayes approach to identify differentially expressed genes and isoforms.
limma/voomLinear model analysis tools for differential expression assessment of genes from RNA-seq read counts.
TrinityDe novo reconstruction of transcriptomes from RNA-seq data.
TransDecoderIdentify candidate coding regions within transcript sequences.
BUSCOAssessment of genome assembly, gene set, and transcriptome completeness.
DETONATEEvaluate de novo transcriptome assemblies from RNA-seq data.
TrinotateAutomatic functional annotation of de novo assembled transcriptomes.
EricScriptComputational framework for the discovery of gene fusions in paired end RNA-seq data.
GSEAGene Set Enrichment Analysis, a knowledge-based approach for interpreting genome-wide expression profiles.
WGCNAWeighted Gene Correlation Network Analysis, a systems biology method for describing the correlation patterns among genes from expression data.
CytoscapeOpen source software platform for visualizing complex networks and integrating these with any type of attribute data.

File types

Group 2983CSV
Group 2994TSV
Group 3005SAM
Group 3016BAM
Group 3027CRAM
Group 3049FASTA
Group 3038FASTQ
Group 3060BED
Group 3071Wig
Group 3082bigBED
Group 3093bigWig
Group 3137bedGraph
Group 3104GTF
Group 3126GFF3
Group 3148GCT

Visual analytics

HeatmapExpression similarity of genes that can be clustered and reordered to reveal patterns among samples.
DendrogramTree-like diagrams from clustering samples by expression correlation.
Volcano plotVisualize DEG on a scatter-plot of fold change vs its significance (negative log of the p value).
Venn diagramCompare gene lists across tissue types or conditions.
Parallel plotVisualize standardized expression data from time series experiments or longitudinal analyses.
Principal Component Analysis (PCA)Reduce dimension of expression data, filter noise, visualize similarities between the biological samples and relate to experimental conditions.
Pathway mapMaps of biochemical capability, or signal transduction of enriched gene lists.
NetworkUnderstand gene co-expression and gene-trait relationships.
Genome browserView your transcriptome alignments from a BAM file or as a graph along with reference annotation.
CytoscapeOpen source software platform for visualizing complex networks and integrating these with any type of attribute data.