At a minimum, RNA-Seq data analysis requires the following steps, performed through various scripts and code, in order to understand the results of your experiment:

• Map your short (or long) sequence reads against a reference genome of your desired species.

• Quantify the expression of genes.

• Compare the expression of genes across your biological samples or different variables.

• Interpret the functional impact of any differentially expressed genes.

• Generate graphs to visualize the analyses

Alternatively, you could use Trovomics to analyze and visualize your RNA-Seq data to significantly streamline the process. No prior coding experience necessary.

At a minimum, ChIP-Seq data analysis requires the following steps, performed through various scripts and code, in order to understand the results of your experiment:

• Map your sequence reads against a reference genome of your desired species.

• Identify enriched regions (peaks) where proteins such as transcription factors or histones are bound to DNA.

• Compare binding profiles across different biological samples or experimental conditions.

• Annotate peaks to nearby genes or genomic features to determine potential regulatory targets.

• Perform functional enrichment analysis to understand the biological processes or pathways associated with these binding events.

• Visualize the results through genome browser tracks, peak heatmaps, or motif enrichment plots.

Alternatively, you can use Trovomics to analyze and visualize your ChIP-Seq data and significantly streamline the process. No prior coding experience necessary.

Trovomics has built a robust pipeline that uses various industry-standard tools to perform quality control, gene quantification, differential expression analysis, and functional analysis on your RNA-Seq samples. Below is a list of packages that we use to analyze your data:

• Quality control: FASTQC, AdapterRemoval

• Mapping/Alignment: HISAT2

• Gene quantification: HTseq

• Differential expression: DESeq2

• Functional analysis: clusterProfiler

  • Knowledge databases: Gene Ontology

In addition, our computational biologists have implemented proprietary scripts to enhance and automate the pipeline.

Trovomics has built a robust pipeline that uses a suite of industry-standard tools to perform quality control, peak calling, differential binding analysis, and functional annotation and analysis on your ChIP-Seq samples. Below is a list of packages that we use to analyze your data:

• Quality control: FASTQC, AdapterRemoval

• Mapping/Alignment: Bowtie2

• Peak calling: MACS2

• Differential binding analysis: DiffBind

• Functional analysis: ChIPseeker, clusterProfiler

  • Knowledge databases: Gene Ontology, WikiPathways

In addition, our computational biologists have implemented proprietary scripts to automate the entire workflow from raw reads to interpretable visualizations.

Analysis of your RNA-Seq data may take up to 48 hours* in Trovomics, depending on how many samples you have in your experiment, what type of analysis you’re running, and available resources. Once completed, all seven visualization types are available for viewing and customization.

*Processing time may be affected by the volume, complexity, and quality of the data submitted for analysis.

The difference between Trovomics and R is that in Trovomics, you don’t need to write code to analyze and visualize your omics data — such as RNA-Seq, ChIP-Seq, or other sequencing-based experiments — like you would in R. Instead, you can rely on Trovomics’s standardized, code-free pipelines built on industry-proven tools and maintained by our computational biologists.

If you were to perform the same analysis in R using the equivalent parameters and methods applied in Trovomics, you would obtain the same results. Trovomics saves you time, ensures consistency, and requires no coding experience — allowing you to focus on interpreting your data, not writing scripts.