What input data do you need from me?

We generally require raw FASTQ files from your RNA-seq experiment. If you have already performed alignment or quantification, we can also work from BAM or count matrices. A sample metadata sheet with conditions and batch information is essential for proper statistical modeling.

Do you support non-model organisms?

Yes. For organisms with an annotated genome, we align using STAR or HISAT2 and map gene annotations. For species without a reference genome, we can perform de novo transcriptome assembly and then quantify expression using the assembly as a reference.

How do you handle batch effects in multi-batch studies?

We incorporate batch information as a covariate in the differential expression model (e.g., DESeq2's design formula) or apply ComBat for explicit batch-effect removal when appropriate. The chosen method is documented in your project report.

Can you include pathway analysis in the same project?

Yes, functional enrichment analysis (GO, KEGG, Reactome, GSEA) is included as part of our standard pipeline. Additional custom analyses, such as transcription factor enrichment or gene set variation analysis (GSVA), can be scoped per project.

What counts as a sufficiently powered sample size?

Adequate sample size depends on the expected effect size, biological variability, and desired statistical power. We recommend at least three biological replicates per condition for differential expression. Our team can help assess your study design during the scoping phase.

Do you provide raw intermediate files or only final reports?

All intermediate files—cleaned FASTQ, BAM alignments, count matrices, and normalized expression tables—are delivered alongside the final report. This allows you to reuse data for future analyses or regulatory submissions.

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Transcriptome Sequencing Data Analysis

Transcriptome Bioinformatics

Extract actionable differential expression, splicing, and pathway insights from your RNA-seq data with a robust, reproducible pipeline.

FASTQ quality control and adapter trimming
Splice-aware alignment and transcript quantification
Differential expression and splicing analysis
Functional enrichment and pathway mapping
Customized multi-omics integration support

Start Your Analysis

From Raw Reads to Biological Meaning

Transcriptome sequencing data analysis transforms raw RNA-seq reads into insights about gene expression, splicing, and pathway activity. Our pipeline covers the entire workflow from quality control to biological interpretation, using industry-standard tools and rigorous statistical methods to ensure reproducibility. Designed for any eukaryotic organism, the analysis scales from small pilot studies to large cohort projects, with flexible options for reference-based or de novo approaches.

Proven tools: STAR, DESeq2, edgeR, GSEA
Batch-effect correction and multiple-testing control
Flexible annotation support for model and non-model species

Reproducible

Scalable

Actionable

Our Methodology

End-to-End Transcriptome Analysis Pipeline

We combine industry-standard bioinformatics tools with project-specific customization to deliver consistent, auditable results for each transcriptome experiment.

Core RNA-seq Pipeline

A fully documented workflow that starts with raw FASTQ files, performs quality assessment and trimming, aligns reads to a reference genome or transcriptome, quantifies gene and transcript expression, and identifies differentially expressed genes using statistical models.

FastQC + Trimmomatic for read preprocessing
STAR or HISAT2 for splice-aware alignment
featureCounts, Salmon, or RSEM for quantification
DESeq2 or edgeR for differential expression

Multi-Omics Integration

Extend transcriptome findings by integrating with proteomic, epigenomic, or metabolomic datasets. We support correlation analyses, pathway co-mapping, and network-based interpretation to reveal cross-layer regulatory mechanisms.

Correlation of transcript and protein abundance
Pathway enrichment using KEGG, GO, and Reactome
Integration with chromatin accessibility (ATAC-seq) or methylation data

Quality Control

Every step includes automated and manual QC checks: sequence quality metrics, alignment rates, gene body coverage, and sample-level clustering to flag outliers before statistical analysis.

Statistical Rigor

We apply appropriate multiple-testing correction (e.g., Benjamini-Hochberg) and batch-effect detection (ComBat, limma) to minimize false discovery and ensure reproducible results across batches.

Biological Interpretation

Downstream analyses include gene set enrichment, transcription factor inference, splicing pattern analysis (rMATS), and publication-ready visualizations such as volcano plots, heatmaps, and pathway diagrams.

What You Receive

Comprehensive Data Deliverables and Support

Results are provided in accessible formats that facilitate further analysis and regulatory review, with full documentation of every parameter and tool version.

Data Deliverables

Cleaned FASTQ files with QC reports (FastQC, MultiQC)
Aligned BAM/SAM files and count matrices (gene- and transcript-level)
Differential expression tables with fold-change, p-value, and FDR
Batch-corrected normalized expression values (TPM, FPKM)

Reporting & Support

Interactive HTML report summarizing analysis and visualizations
Custom figures: PCA plots, volcano plots, heatmaps, enrichment bar charts
Methodology section suitable for manuscript or grant submission
Project consultation to review results and plan next steps

Service Scope

Flexible Parameter Selection

The table below outlines typical analysis parameters; exact choices are confirmed during project scoping to match your experimental design and biological questions.

Parameter	Typical Project Scope
Raw Data QC & Trimming	FastQC, MultiQC, Trimmomatic or cutadapt
Alignment Method	STAR, HISAT2 (or Bowtie2 for transcriptome-only)
Quantification Strategy	We use featureCounts for gene-level quantification and Salmon (alignment-free) or RSEM for transcript-level quantification.
Differential Expression	DESeq2, edgeR, limma+voom (with batch correction as needed)
Splicing Analysis	We use rMATS to detect and quantify alternative splicing events.
Functional Enrichment	GO, KEGG, Reactome, GSEA (ssGSEA or ORA)
Isoform Discovery	Scoped per project; Cufflinks/StringTie for novel transcripts
Visualization & Reporting	Volcano, MA, heatmap, enrichment dot plot, PCA
Species Support	Any eukaryotic species with an annotated genome (including non-model organisms via de novo transcriptome assembly)

Project Workflow

Five-Stage Analysis Process

Each stage is fully documented and reviewed with you before proceeding, ensuring transparency and alignment with your research objectives.

Raw Data QC & Preprocessing

We assess sequencing quality using FastQC, trim adapters and low-quality bases with Trimmomatic or cutadapt, and generate a MultiQC report for a global overview of read quality across all samples.

Align reads and quantify expression

Reads are aligned to the reference genome or transcriptome using STAR or HISAT2. Transcript-level quantification is performed with Salmon (alignment-free) or RSEM for accurate isoform abundance estimates.

Differential Expression Analysis

We model gene counts with DESeq2 or edgeR, applying appropriate normalization and multiple-testing correction (Benjamini-Hochberg). Batch effects, if present, are corrected using ComBat or included as covariates.

Functional & Pathway Enrichment

The list of differentially expressed genes is tested for overrepresentation in Gene Ontology, KEGG, and Reactome pathways. Gene Set Enrichment Analysis (GSEA) provides ranked-based pathway-level insights.

Report & Biological Interpretation

We compile an interactive HTML report with all results, figures, and a methods section. A consultation meeting is held to discuss findings and propose follow-up experiments or validation targets.

Applications

Where Transcriptome Analysis Drives Discovery

Our analysis pipeline has been applied across diverse research contexts, from cancer biomarker development to agricultural trait mapping.

Identify differentially expressed genes and splicing variants linked to tumor progression or drug resistance.

In oncology studies, transcriptome analysis reveals gene signatures that stratify patient risk or predict treatment response. Our pipeline has been used in studies of lung adenocarcinoma, cervical cancer, and colorectal cancer.

Differential expression between tumor and normal tissues
Alternative splicing events associated with oncogenic pathways
Gene set enrichment for immune infiltration and ferroptosis signatures

Characterize transcriptional changes induced by drug treatment and identify mechanism-of-action markers.

From cell-line experiments to preclinical animal models, transcriptome sequencing captures the downstream effects of compounds. We help clients link gene expression changes to phenotypic outcomes.

Dose–response expression patterns
Pathway enrichment for target engagement
Biomarker identification for patient stratification

Uncover the regulatory networks driving biological processes such as development, stress response, or disease progression.

Our pipeline supports time-course and multi-condition experiments, enabling reconstruction of transcriptional cascades and network inference.

Time-series differential expression
Transcription factor target enrichment
Co-expression network construction (WGCNA)

Detect novel isoforms, gene fusions, and long non-coding RNAs from transcriptome data.

Using splice-aware aligners and fusion detection tools, we identify transcript variants that may serve as biomarkers or therapeutic targets.

rMATS for differential splicing events
Fusion detection using STAR-Fusion or Arriba
Novel transcript assembly with StringTie

Frequently Asked Questions

Common Questions About Transcriptome Data Analysis

Answers are based on typical project scoping; specific details are confirmed during consultation.

We generally require raw FASTQ files from your RNA-seq experiment. If you have already performed alignment or quantification, we can also work from BAM or count matrices. A sample metadata sheet with conditions and batch information is essential for proper statistical modeling.
Yes. For organisms with an annotated genome, we align using STAR or HISAT2 and map gene annotations. For species without a reference genome, we can perform de novo transcriptome assembly and then quantify expression using the assembly as a reference.
We incorporate batch information as a covariate in the differential expression model (e.g., DESeq2's design formula) or apply ComBat for explicit batch-effect removal when appropriate. The chosen method is documented in your project report.
Yes, functional enrichment analysis (GO, KEGG, Reactome, GSEA) is included as part of our standard pipeline. Additional custom analyses, such as transcription factor enrichment or gene set variation analysis (GSVA), can be scoped per project.
Adequate sample size depends on the expected effect size, biological variability, and desired statistical power. We recommend at least three biological replicates per condition for differential expression. Our team can help assess your study design during the scoping phase.
All intermediate files—cleaned FASTQ, BAM alignments, count matrices, and normalized expression tables—are delivered alongside the final report. This allows you to reuse data for future analyses or regulatory submissions.

Scientific Literature

References

Lau MML, Lim LWK, Chung HH, et al. The first transcriptome sequencing and data analysis of the Javan mahseer (Tor tambra). Data in brief. 2021;39:107481. View on PubMed
Tsitrina AA, Halimani N, Andreichenko IN, et al. 4-Methylumbelliferone Targets Revealed by Public Data Analysis and Liver Transcriptome Sequencing. International journal of molecular sciences. 2023;24(3). View on PubMed
Gregersen LH, Mitter R, Svejstrup JQ. Using TT(chem)-seq for profiling nascent transcription and measuring transcript elongation. Nature protocols. 2020;15(2):604-627. View on PubMed
Zhao S, Zhang P, Niu S, et al. Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data. Functional & integrative genomics. 2024;24(2):72. View on PubMed
Wang X, Du Q, Mai Q, et al. Targeting FASN enhances cisplatin sensitivity via SLC7A11-mediated ferroptosis in cervical cancer. Translational oncology. 2025;56:102396. View on PubMed
Sun Z, Zhou M, Wang C, et al. Inhibition of DLX6 sensitizes lung adenocarcinoma to cisplatin via GPX4-dependent ferroptosis. Cellular oncology (Dordrecht, Netherlands). 2026 Jul 15. View on PubMed

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