Bioinformatics analysis service

Transcriptomics analysis services: bulk, single-cell, spatial, and long-read RNA-seq

Transcriptomics measures genome-wide RNA abundance to see which genes and transcripts are active under specific conditions. Pepkio runs version-pinned pipelines from raw FASTQs or vendor outputs to annotated results, with full code ownership, publication figures, and a Methods draft. Academic, biotech, and pharma teams can scope custom inputs, outputs, and non-standard analyses at kickoff.

Key facts

Key facts about transcriptomics analysis
FactValue
Data types supportedBulk RNA-seq (Illumina or MGI DNBSEQ FASTQs or count matrices); 10x Chromium scRNA-seq; 10x Visium/Xenium spatial transcriptomics; PacBio HiFi and Oxford Nanopore long-read RNA-seq
Reference builds or standards usedHuman GRCh38 (GENCODE v44 / Ensembl 110); mouse GRCm39 (GENCODE vM33 / Ensembl 110); ENCODE RNA-seq standards for bulk depth and replicate guidance [3]
Primary tools (with versions)STAR 2.7.11b; DESeq2 1.52.0; Cell Ranger 10.0.0; Scanpy 1.12.1; Seurat 5.2.1; Space Ranger 3.1.3; Squidpy 1.8.1; IsoQuant 3.13.0; SQANTI3 6.0.1; clusterProfiler 4.20.0
Typical turnaround range2-4 weeks (bulk); 3-5 weeks (scRNA-seq); 4-6 weeks (spatial or long-read); 5-10 weeks (multi-cohort integration or HD spatial), confirmed at kickoff
Deliverable formats.csv/.tsv count matrices; .rds/h5ad objects; .gtf isoform catalogs; PDF/SVG figures; HTML QC reports; commented R/Python scripts; Methods draft
Regulatory/reproducibility standards followedENCODE RNA-seq guidelines; MINSEQE-aligned sample metadata and reporting; version-pinned software with sessionInfo() or conda lock files; private Git or Zenodo archival on request
Custom / bespoke analysisNon-standard inputs (pre-aligned BAM, vendor matrices), output formats, methods, and analyses beyond standard workflows, for example cross-modality integration, fusion calling, trajectory inference, or client-specified contrasts, scoped at kickoff

What is transcriptomics?

Transcriptomics is the genome-wide measurement of RNA, including messenger RNA (mRNA), long non-coding RNA, and other transcript classes. It is used to quantify which genes are expressed, at what level, and in which cells or tissue regions. Unlike targeted assays such as qPCR, RNA sequencing (RNA-seq) can capture novel splice junctions and unannotated transcripts when depth permits [1]. Typical questions include which transcripts differ between conditions, cell types, or spatial domains, and which pathways those changes implicate. GTEx v8 profiled 17,382 RNA-seq samples across 52 tissues and two cell lines from 948 donors [2].

What transcriptomics analysis can answer

Published examples of questions transcriptomics can address:

Which genes differ between disease and control tissue?

Sun et al. [13] analyzed TCGA bulk RNA-seq from 103 primary and 368 metastatic melanoma samples, identifying 246 differentially expressed lncRNAs and 856 mRNAs between stages.

Which T cell populations expand during anti-PD-1 therapy in lung cancer?

Liu et al. [9] profiled 47 NSCLC biopsies from 36 patients before and after PD-1 therapy, showing precursor exhausted T cells accumulate in responders through clonal revival rather than reinvigoration of terminally exhausted cells.

How is gene expression organized across tissue architecture?

Arora et al. [14] applied spatial transcriptomics to oral squamous cell carcinoma, identifying conserved tumor core and leading-edge domains linked to survival and therapy response.

Which full-length isoforms switch between conditions?

Amarasinghe et al. [15] reviewed long-read RNA-seq applications showing that full-length reads resolve isoform structures that short-read fragments cannot reconstruct reliably.

Which genetic variants alter tissue-specific expression?

GTEx v8 linked cis-expression quantitative trait loci (eQTLs) across 52 tissues and two cell lines, enabling tissue-context interpretation of regulatory variants [2].

Services included in this category

This category includes bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and long-read RNA-seq. Each spoke page lists inputs, tools, and deliverables.

Transcriptomics services offered by Pepkio
ServiceDescriptionPrimary tools
Bulk RNA-seqPooled-tissue differential expression from FASTQs or count matrices to DESeq2 contrasts and pathway enrichmentSTAR 2.7.11b, featureCounts 2.0.8, DESeq2 1.52.0
Single-Cell RNA-seqPer-cell clustering, annotation, and differential expression from 10x Chromium FASTQs to annotated UMAPsCell Ranger 10.0.0, Scanpy 1.12.1, Seurat 5.2.1
Spatial TranscriptomicsTissue-coordinate expression, spatial domains, and cell-type deconvolution from Visium or Xenium dataSpace Ranger 3.1.3, Squidpy 1.8.1, cell2location 0.1.5
Long-Read RNA-seqFull-length isoform catalogs and isoform-level differential expression from PacBio HiFi or ONT readsminimap2 2.31, IsoQuant 3.13.0, SQANTI3 6.0.1

What Pepkio delivers

Each project includes processed data, figures, scripts, and a Methods draft. Summary slides are not the only output:

Processed data

  • Filtered count matrices; DESeq2 or annotated .rds/h5ad objects; SQANTI3-filtered GTF catalogs; sorted BAM on request

Figures and tables

  • MultiQC, PCA, volcano/MA, UMAP, and spatial maps (PDF/SVG); sample_manifest.csv; DEG and enrichment tables with log2FoldChange, padj, and gene descriptions

Code and documentation

  • Commented R/Python scripts; environment lock files; HTML QC report; README; Methods draft citing exact versions; you retain full ownership

Support

  • Milestone check-ins; reviewer clarification and minor revisions within agreed scope (typically up to 20% of deliverables)

Non-standard contrasts, cross-modality integration, or client-specified figures are scoped at kickoff.

How the analysis works — step by step

  1. 1. Scope study design and select modality

    Confirm biological question, replicates, platform, and modality. Flag confounded batch or a modality mismatch before analysis [1, 4].

    Tools and outputs

    Output: signed scope with contrasts

  2. 2. Validate inputs and record metadata

    Verify FASTQ integrity, chemistry, and sample IDs; check vendor outputs against annotation builds.

    Tools and outputs

    Tools used: FastQC 0.12.1

    Output: sample_manifest.csv

  3. 3. Run quality control and preprocessing

    Assess adapter contamination and library quality per sample; aggregate metrics for review [5].

    Tools and outputs

    Tools used: fastp 0.24.0; MultiQC 1.25.1; NanoPlot 1.42.0 (long-read)

    Output: multiqc_report.html

  4. 4. Quantify expression by modality

    Bulk: STAR 2.7.11b + featureCounts 2.0.8. scRNA-seq: Cell Ranger 10.0.0. Spatial: Space Ranger 3.1.3. Long-read: minimap2 2.31 + IsoQuant 3.13.0 [6, 7].

    Tools and outputs

    Output: count matrices and alignment summaries

  5. 5. Audit sample and batch integrity

    Compute library sizes, detection rates, and read-distribution metrics. PCA is used to separate condition from batch when possible [8, 9].

    Tools and outputs

    Tools used: DESeq2 1.52.0; RSeQC 5.0.3 [10]

    Output: sample_qc_summary.csv; PCA plots

  6. 6. Model differential expression or cell states

    Bulk: DESeq2 Wald tests with batch covariates [8]. scRNA-seq: clustering and cluster-wise DE. Spatial: Squidpy domains and pseudobulk statistics. Long-read: SQANTI3 filtering plus DESeq2 or DRIMSeq when replicates support isoform testing.

    Tools and outputs

    Output: deg_results.csv; annotated objects

  7. 7. Run functional and cell-type interpretation

    GO/KEGG enrichment; cell-type annotation; cell2location deconvolution when a reference atlas exists [10, 11].

    Tools and outputs

    Tools used: clusterProfiler 4.20.0; SingleR 2.12.0

    Output: enrichment and annotation tables

  8. 8. Generate publication figures and package deliverables

    Export figures at publication resolution; package scripts, environment locks, README, and Methods draft.

    Tools and outputs

    Output: PDF/SVG figures; Git repository or file bundle

  9. 9. Deliver results and support reviewer requests

    Transfer deliverables via agreed secure channels. Post-delivery support covers methods clarification and minor revisions; substantial new analyses are scoped separately.

Tools and standards we use

Software versions are pinned at kickoff and cited in the Methods draft. Tools used across modalities include:

Transcriptomics tools and standards
ToolVersionRolePrimary citation
STAR2.7.11bSplice-aware bulk RNA-seq alignmenthttps://doi.org/10.1093/bioinformatics/bts635
Subread/featureCounts2.0.8Gene-level read countinghttps://doi.org/10.1093/bioinformatics/btt656
DESeq21.52.0Bulk and isoform-level differential expressionhttps://doi.org/10.1186/s13059-014-0550-8
Cell Ranger10.0.010x scRNA-seq alignment and UMI counting10x Genomics [12]
Scanpy1.12.1scRNA-seq and spatial object processing (Python)https://doi.org/10.1186/s13059-017-1382-0
Seurat5.2.1scRNA-seq and spatial object processing (R)https://doi.org/10.1038/s41587-023-01767-y
Space Ranger3.1.310x Visium alignment and spot quantification10x Genomics [12]
Squidpy1.8.1Spatial statistics, neighborhood graphs, SVG detectionhttps://doi.org/10.1038/s41592-021-01358-2
minimap22.31Long-read RNA-seq alignmenthttps://doi.org/10.1093/bioinformatics/bty191
IsoQuant3.13.0Long-read transcript discovery and quantificationhttps://doi.org/10.1038/s41587-022-01565-y
SQANTI36.0.1Long-read isoform classification and filteringhttps://doi.org/10.1038/s41592-024-02229-2
clusterProfiler4.20.0GO/KEGG enrichmenthttps://doi.org/10.1089/omi.2011.0118
MultiQC1.25.1Aggregated QC reportinghttps://doi.org/10.1093/bioinformatics/btw354

Reference builds follow GENCODE v44 (human) and GENCODE vM33 (mouse) unless a project needs a custom annotation. For bulk depth, ENCODE recommends at least 30 million aligned reads per replicate [3]. Inferential designs typically need at least 3 biological replicates per condition when that is feasible [1].

Common challenges — and how we handle them

Common issues include pipeline choice, batch confounding, incomplete reporting, QC imbalances, and modality mismatch. Pepkio uses version-pinned workflows and checkpoint reviews.

Pipeline and tool choice overload.

192 pipeline combinations on 18 samples yielded materially different quantification and DE results [16]. Pepkio selects version-pinned tool chains at kickoff and documents choices in the Methods draft.

Batch effects and confounded processing.

Technical variation can mask biological signal [4, 17]. Pepkio includes batch in design formulas when it is not confounded with condition, and reviews PCA before modeling.

Insufficient methodological reporting.

Only 25% of RNA-seq articles describe all essential computational steps [18]. Pepkio delivers scripts, parameter logs, and a Methods draft with exact versions.

Hidden quality imbalances between groups.

35% of 40 clinical RNA-seq datasets had significant quality imbalances between groups [19]. Pepkio audits per-sample QC and flags asymmetry before testing.

Modality mismatch.

Bulk RNA-seq averages across cell types, so heterogeneous tissues may need scRNA-seq or spatial resolution [1, 11]. Pepkio discusses modality at kickoff.

Common questions

What data do I need to provide for a transcriptomics analysis project?

Provide FASTQs, vendor outputs (Cell Ranger, Space Ranger), or count matrices plus metadata listing condition, batch, and covariates. Pepkio confirms chemistry and reference build at kickoff. For bulk RNA-seq, ENCODE recommends at least 30 million aligned reads per replicate [3]. Custom formats are accepted when scoped in advance.

How long does transcriptomics analysis take at Pepkio?

Bulk projects usually take 2-4 weeks; scRNA-seq 3-5 weeks; spatial or long-read 4-6 weeks; and multi-cohort or HD spatial 5-10 weeks. Exact timelines are confirmed at kickoff.

What do Pepkio transcriptomics deliverables look like?

Deliverables include count matrices or annotated objects, DEG tables, PDF/SVG figures, an HTML QC report, commented scripts, and a Methods draft citing tool versions.

Can Pepkio handle my specific sequencing platform or instrument?

Yes. Supported inputs include Illumina and MGI DNBSEQ bulk data (T7, G400, G99 when scoped at kickoff), 10x Chromium, Visium/Xenium, and PacBio or ONT long-read data. Visium HD, BD Rhapsody, Parse, CosMx, and pre-built matrices are also supported when scoped at kickoff.

What if my RNA-seq data quality is poor?

Sub-threshold libraries can still be analyzed, with limitations documented in the QC report [3]. Outliers are flagged before modeling. Re-sequencing is discussed when yield may undermine the study question. Quality imbalances between condition groups are reported [19].

Do I receive the analysis code, and do I own it?

Yes. You retain full ownership. Pepkio delivers commented R/Python scripts with environment lock files via private Git or agreed file transfer.

Can I be involved during the transcriptomics analysis?

Yes. Checkpoint reviews follow QC and the sample audit, and another review happens before final delivery. You can review contrasts, covariates, and filtering thresholds within agreed scope.

What happens if a journal reviewer requests changes after delivery?

Methods clarification and minor revisions within agreed scope (typically up to 20% of deliverables) are covered. Substantial new analyses are scoped and priced separately.

How do I choose between bulk, single-cell, spatial, and long-read RNA-seq?

Bulk is often enough when tissue composition is stable. scRNA-seq is better for cell-type heterogeneity, spatial methods keep tissue architecture, and long-read data is better for isoforms [1, 4, 11, 15]. Pepkio can help choose at kickoff.

Can Pepkio run custom or non-standard transcriptomics analyses?

Yes, when scoped at kickoff: custom inputs, fusion calling, trajectory inference, cross-modality integration, alternative DE engines (edgeR, limma-voom), or client-specified outputs.

Related services

  • Genomics & variant analysis — Integrate expression with variant calls, eQTL mapping, or allele-specific expression when genotype data are available.
  • Proteomics — Check transcript-level findings at the protein level in multi-omics studies.
  • Machine learning — Build predictive models from expression features for biomarker discovery or patient stratification.
  • Statistical analysis — Experimental design, power estimation, and cohort modeling before library prep.
  • Bioinformatics consulting — Modality and depth selection, feasibility assessment, and pipeline planning before committing to a full analysis project.
References
  1. Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis. Genome Biology. 2016;17(1):13. https://doi.org/10.1186/s13059-016-0881-8 (PMID: 26813401)
  2. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369(6509):1318–1330. https://doi.org/10.1126/science.aaz1776 (PMID: 32913098)
  3. ENCODE Consortium. ENCODE Guidelines and Best Practices for RNA-Seq (Revised December 2016). https://www.encodeproject.org/documents/cede0cbe-d324-4ce7-ace4-f0c3eddf5972/@@download/attachment/ENCODE%20Best%20Practices%20for%20RNA_v2.pdf
  4. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology. 2019;15(6):e8746. https://doi.org/10.15252/msb.20188746 (PMID: 31217225)
  5. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (PMID: 27312411)
  6. Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635 (PMID: 23104886)
  7. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–930. https://doi.org/10.1093/bioinformatics/btt656 (PMID: 24227677)
  8. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8 (PMID: 25516281)
  9. Wang D, Liu Y, Zhang Y, et al. A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials. Nature Communications. 2024;15:6167. https://doi.org/10.1038/s41467-024-50420-y (PMID: 39039053)
  10. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287. https://doi.org/10.1089/omi.2011.0118 (PMID: 22455463)
  11. Moses L, Pachter L. Museum of spatial transcriptomics. Nature Methods. 2022;19(5):534–546. https://doi.org/10.1038/s41592-022-01409-2 (PMID: 35273392)
  12. 10x Genomics. Cell Ranger and Space Ranger release notes (2024). https://www.10xgenomics.com/support/software/cell-ranger/downloads
  13. Sun L, Guan Z, Wei S, Tan R, Li P, Yan L. Identification of long non-coding and messenger RNAs differentially expressed between primary and metastatic melanoma. Frontiers in Genetics. 2019;10:292. https://doi.org/10.3389/fgene.2019.00292 (PMID: 31024618)
  14. Arora R, Cao C, Kumar M, et al. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response. Nature Communications. 2023;14(1):4529. https://doi.org/10.1038/s41467-023-40271-4 (PMID: 37596273)
  15. Amarasinghe SL, Su S, Dong X, et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biology. 2020;21(1):30. https://doi.org/10.1186/s13059-020-1935-5 (PMID: 32033565)
  16. Corchete LA, Rojas EA, Alonso-López D, et al. Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Scientific Reports. 2020;10:19737. https://doi.org/10.1038/s41598-020-76881-x (PMID: 33184454)
  17. Deshpande D, Chhugani K, Chang Y, et al. RNA-seq data science: from raw data to effective interpretation. Frontiers in Genetics. 2023;14:997383. https://doi.org/10.3389/fgene.2023.997383 (PMID: 36999049)
  18. Simoneau J, Scott MS. Current RNA-seq methodology reporting limits reproducibility. Briefings in Bioinformatics. 2021;22(1):140–145. https://doi.org/10.1093/bib/bbz124 (PMID: 31813948)
  19. Sprang M, Möllmann J, Andrade-Navarro MA, Fontaine JF. Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets. Genome Biology. 2024;25(1):222. https://doi.org/10.1186/s13059-024-03331-6 (PMID: 39152483)

Individual services

Deep-dive pages for specific transcriptomics methods and workflows.

Let's Talk About Your Science

Tell us:

  • • Your biological question
  • • Data type and size
  • • Timeline constraints

We'll tell you:

  • • What's feasible
  • • How long it will take
  • • Exactly what it will cost
Discuss Your Project

Contact us to start with a free consultation. Need everyday bench calculators? Try our free lab tools.