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Transcriptomics Services

Neorabio provides transcriptomics services designed to support systematic analysis of gene expression and transcriptional regulation in biological research. The introduction of RNA sequencing fundamentally transformed transcriptome profiling by enabling quantitative measurement of gene expression and transcript structure at genome-wide scale, as demonstrated in early methodological syntheses by Wang, Gerstein, and Snyder (2009). Subsequent large-scale applications established transcriptomics as a core approach for investigating how genetic background and environmental conditions shape cellular states and biological processes, positioning RNA-seq–based expression profiling as a foundational layer for modern functional studies.

About Service

Neorabio's transcriptomics services are built on an integrated workflow encompassing standardized RNA preparation, next-generation sequencing, and controlled computational analysis. In data processing and expression analysis, Neorabio follows analytical principles consistent with community-validated RNA-seq workflows, in which read quality, normalization strategy, and statistical modeling are treated as interdependent determinants of biological interpretability. Transcript quantification, differential expression testing, and functional annotation are performed within reproducible pipelines, incorporating quality control checkpoints that monitor RNA integrity, sequencing performance, and variance structure to reduce technical bias and enhance cross-sample comparability.

Our Scope

● Bulk RNA Sequencing
● Genome-wide quantification of gene and transcript expression across experimental conditions
● Differential Expression Analysis
● Identification of genes and pathways exhibiting statistically significant expression changes
● Functional and Pathway Annotation
● Biological interpretation of expression patterns through enrichment and pathway-level analyses

Applications

● Analysis of transcriptional responses to genetic or environmental perturbations
● Investigation of gene regulatory programs and cellular state transitions
● Pathway- and process-level interpretation of expression changes
● Comparative transcriptomic studies across tissues, treatments, or conditions
● Transcriptomic data generation for integrative genomics, epigenomics, or proteomics research

Workflow

Exploratory Consultation → Experimental Design & Comparison Strategy → RNA Sequencing & Data Quality Assessment → Expression Quantification & Differential Analysis → Pathway & Functional Interpretation → Final Report Delivery & Result Review

References

Wang, Z., Gerstein, M., Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 2009, 10(1): 57–63. DOI: 10.1038/nrg2484
Conesa, A., et al. A survey of best practices for RNA-seq data analysis. Genome Biology, 2016, 17: 13. DOI: 10.1186/s13059-016-0881-8
Love, M. I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 2014, 15: 550. DOI: 10.1186/s13059-014-0550-8
Bray, N. L., Pimentel, H., Melsted, P., Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 2016, 34(5): 525–527. DOI: 10.1038/nbt.3519

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Project execution at Neorabio emphasizes traceable data processing and transparent reporting to support downstream biological interpretation. To ensure reliable quantification of transcript abundance, analytical outputs are generated using expression estimation and normalization concepts that have been formalized in widely adopted transcriptomic methods, including statistical models for RNA-seq count data described by Love et al. (2014) and transcript-level quantification strategies developed by Bray et al. (2016). By anchoring deliverables to these established analytical standards, Neorabio provides expression matrices, differential gene lists, and functional annotations that can be confidently reused in mechanistic studies and integrative multi-omics analyses.

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