Transcriptomics
Long-read RNA-seq from FASTQ or BAM to SQANTI3-filtered isoform catalogs when short reads cannot reconstruct full transcripts [1], with pinned pipelines and a Methods draft.
| Category | Details |
|---|---|
| Supported platforms / instruments | Primary: PacBio Revio / Sequel II (Kinnex Iso-Seq HiFi). Oxford Nanopore PromethION / MinION (direct RNA, amplification-free cDNA, PCR-cDNA). MGI CycloneSEQ-WT02 / WY01 (nanopore-class, when scoped at kickoff). Single-cell long-read (MAS-ISO-seq, 10x-compatible cDNA) on request |
| Input requirements | ≥300 ng total RNA, RIN >7, DNase-treated; ≥3 biological replicates recommended for differential expression; 5M HiFi reads/sample (high-abundance isoform discovery), 10M (moderate-to-rare isoforms or isoform-level DE) for PacBio Kinnex [2, 3] |
| Reference builds supported | Human GRCh38 + GENCODE v44; mouse GRCm39 + GENCODE vM33; custom references on request |
| Primary tools (with versions) | minimap2 2.31; IsoQuant 3.13.0; SQANTI3 6.0.1; isoseq3 (SMRT Link 13.1); NanoPlot 1.42.0; DESeq2 1.44.0; DRIMSeq 1.30.0; stageR 1.28.0 |
| Typical turnaround time | 4-6 weeks (standard bulk cohort, reference-guided); 6-10 weeks (annotation-free, multi-condition isoform DE, or orthogonal short-read validation). Confirmed at kickoff |
| Deliverable formats | .gtf/.gff3, .bam, count matrices (.tsv/.csv); PDF/SVG figures; HTML QC report; documented R/Python scripts; Methods draft |
| Key cited best-practice reference | Amarasinghe et al. [1], Genome Biology; Pardo-Palacios et al. [4], Nature Methods (SQANTI3); LRGASP Consortium [5], Nature Methods |
| Custom / bespoke analysis | Non-standard inputs, outputs, and methods scoped at kickoff, for example pre-aligned BAM, client GTF, fusion prioritization, ORF prediction, short-read junction validation, or single-cell long-read extensions |
Long-read RNA sequencing maps individual RNA molecules that can span complete exon chains, so isoforms can be reconstructed rather than inferred from ~150 bp short-read fragments [1, 6]. Pepkio aligns PacBio HiFi or ONT reads with minimap2, builds transcript models with IsoQuant, and classifies isoforms as FSM, ISM, NIC, or NNC with SQANTI3 [4, 7, 8]. SG-NEx profiled seven human cell lines across five RNA-seq protocols and reported that long-read sequencing more robustly identifies major isoforms than short-read cDNA sequencing [6]. Custom deliverables outside the standard workflow are scoped at kickoff. See the long-read RNA-seq glossary.
Long-read RNA-seq is a fit when the question needs isoform-resolved expression, such as alternative splicing in disease, annotation of uncharacterized loci, or fusion validation. The table contrasts lrRNA-seq with bulk short-read RNA-seq and short-read scRNA-seq.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Long-read RNA-seq (PacBio / ONT) | Full-length isoform discovery, novel transcripts, fusion validation, ORF and UTR annotation | Higher per-sample sequencing cost than bulk short-read; artifact-rich catalogs need curation; lower sample throughput | Library prep, sequencing, and bioinformatics vary by platform, Kinnex multiplexing, and depth |
| Bulk short-read RNA-seq | Condition-level gene DE on well-annotated genomes | Often cannot resolve overlapping isoforms from junction fragments alone | Lower per-sample cost for modest cohorts |
| Single-cell RNA-seq (short-read) | Cell-type heterogeneity and rare populations | 3′ capture bias; typically no native full-length isoform resolution | Mid-range; per-cell cost exceeds bulk long-read |
Pepkio confirms FASTQ or BAM integrity, platform, chemistry, and metadata. Read depth, RIN, replicates, and contrast design are recorded in sample_manifest.csv. Sub-threshold yield or truncated reads are flagged before alignment [1, 2].
Tools used: fastqc / fastp as needed; samtools quickcheck for BAM inputs
Output: sample_manifest.csv with library IDs, platform, read counts, and QC flags
Read-length distributions, pass-filter rates, and full-length fractions are computed per sample. ONT reports include N50 read length and median quality. PacBio HiFi reports include mean read length and CCS pass rates. Truncation that inflates isoform catalogs is flagged [1, 4].
Tools used: NanoPlot 1.42.0; pycoQC 2.5.2 (ONT); PacBio dataset reports (HiFi)
Output: read_qc_summary.csv; read-length histograms; per-sample QC flags
When clients deliver PacBio subreads or CCS BAMs, Pepkio runs the Iso-Seq workflow: demultiplex, refine poly-A tails, and cluster to HiFi reads [2]. Barcode crosstalk and low CCS yield are documented before alignment.
Tools used: isoseq3 (SMRT Link 13.1)
Output: Demultiplexed HiFi .fastq.gz per sample; isoseq_stats.csv
Reads are mapped in splice-aware mode: minimap2 splice:hq for PacBio HiFi and splice with k-mer size 14 for ONT, with annotated GENCODE splice junctions supplied as BED input [7, 8]. Mapping rates, chimeric fractions, and primary vs. secondary alignments are audited per sample.
Tools used: minimap2 2.31; samtools 1.21
Output: Coordinate-sorted, indexed .bam per sample; alignment_summary.csv
IsoQuant runs in reference-guided mode with GENCODE, extending the reference with sample-specific isoforms [7]. Gene-, isoform-, exon-, and intron-level counts are generated. Saturation is compared to platform depth targets [2, 6].
Tools used: IsoQuant 3.13.0
Output: extended_annotation.gtf; isoform_counts.tsv; gene_counts.tsv; saturation curves
Long-read pipelines often emit highly redundant transcript models that differ by terminal exons or indels ([4]; ConesaLab SQANTI3 wiki). When redundancy exceeds project thresholds, Pepkio collapses near-identical models before SQANTI3 classification.
Tools used: TAMA collapse or cDNA_Cupcake collapse_isoforms_by_sam as appropriate
Output: collapsed_annotation.gtf; collapse audit log
SQANTI3 assigns structural categories (FSM, ISM, NIC, NNC, genic, intergenic, antisense) and quality metrics on TSS, TTS, and splice junctions [4]. Rules-based filtering is the default. ML filtering is documented when selected.
Tools used: SQANTI3 6.0.1
Output: SQANTI3_classification.txt; SQANTI3_filter_report.html; filtered corrected.gtf
SQANTI3 expression estimates are used for QC only, not for differential testing (ConesaLab SQANTI3 wiki; [4]). Pepkio re-runs IsoQuant quantification against the SQANTI3-filtered GTF per sample to produce final count matrices.
Tools used: IsoQuant 3.13.0 (--reference with filtered GTF)
Output: filtered_isoform_counts.tsv; filtered_gene_counts.tsv; TPM tables
Gene-level contrasts use DESeq2 with Benjamini-Hochberg FDR correction when ≥3 biological replicates per condition are available. Isoform-level testing with DRIMSeq and stageR is run only when replicate count and read depth support isoform-resolved power [5, 10, 11]. Batch is included in the design matrix when the same contrast spans multiple sequencing runs [12].
Tools used: DESeq2 1.44.0; DRIMSeq 1.30.0; stageR 1.28.0
Output: deg_results.csv; differential_isoform_usage.csv; MA and volcano plots
Pepkio assembles figures, exports count tables, writes commented scripts, and drafts a Methods section citing software versions. Custom milestones are included when scoped at kickoff.
Tools used: R 4.4.x / Python 3.12 scripts; ggplot2 3.5.1
Output: Final deliverable bundle; HTML QC report; README; Methods draft
Pepkio recommends ≥300 ng total RNA with RIN >7 and DNase treatment for PacBio Kinnex Iso-Seq libraries [2, 3]. PacBio recommends 5 million HiFi reads per sample for high-abundance isoform discovery and 10 million for moderate-to-rare isoforms or isoform-level differential expression [2]. ONT depth is scaled using SG-NEx protocol benchmarks [6]. ≥3 biological replicates per condition are recommended for DE. Exact targets are confirmed at kickoff.
Yes, with limitations documented in the QC report. Samples below RIN 7 often produce truncated reads that inflate artifact isoforms in uncured catalogs [1]. Pepkio flags elevated truncation and low full-length fractions before SQANTI3 filtering. Re-extraction or re-sequencing is discussed when yield cannot support the planned contrasts.
Yes. PacBio Revio and Sequel II Kinnex HiFi data are processed via isoseq3 (when needed) and minimap2 splice:hq. ONT direct RNA, amplification-free cDNA, and PCR-cDNA libraries use minimap2 splice with ONT-appropriate parameters [6, 7, 8]. MGI CycloneSEQ-WT02/WY01 FASTQs use minimap2 splice when scoped at kickoff [13]. Library-type-specific QC metrics are reported in the deliverable bundle.
A standard bulk cohort (roughly 6-12 samples, reference-guided, one contrast) typically completes in 4-6 weeks from data receipt. Annotation-free discovery, multi-condition isoform DE, or orthogonal short-read junction validation may take 6-10 weeks. Exact timelines are confirmed at kickoff.
For gene-level DESeq2, batch is included as a covariate in the design matrix when the same biological contrast spans multiple sequencing runs [12]. Isoform-level DRIMSeq models include batch when replicate structure supports it. After fitting batch covariates, Pepkio inspects top DE genes and marker isoforms to check that condition-associated signal is still present before final delivery.
Yes. You retain full ownership of all code, scripts, and results. Pepkio delivers commented R/Python scripts and environment lock files (sessionInfo(), conda, or pip). Count matrices and GTF files use standard formats readable in IGV, tappAS, or Bioconductor. R Markdown or Jupyter delivery is available on request.
Yes. Checkpoint reviews occur after read QC, after SQANTI3 filtering, and before final delivery. You can review structural-category distributions, adjust filter stringency within agreed scope, and request additional contrasts. A PhD-level scientific contact leads the project and incorporates your tissue-specific knowledge.
Clarification of computational methods, SQANTI3 filter thresholds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Pepkio drafts Methods and Supplementary text for analyses we performed. Substantial new analyses requested by reviewers are scoped separately.
No. Pepkio operates as a fee-for-service provider and does not require co-authorship unless that is discussed in advance. Acknowledgment of bioinformatics support in the Acknowledgments section is the usual practice. Co-authorship is considered only when Pepkio scientists make substantial intellectual contributions beyond routine analysis.
PacBio recommends 5 million HiFi reads per sample for high-abundance isoform discovery and 10 million for moderate-to-rare transcripts on Kinnex libraries [2]. Downsampling of heart and brain Kinnex data suggests ~80% of known genes and isoforms may be detectable at 10-20 million reads per sample ([2], ESHG poster). LRGASP advises additional replicates and orthogonal data when the goal is rare or novel transcript detection [5].
SQANTI3 read counts and TPM values are for structural QC only, not for statistical testing (ConesaLab SQANTI3 wiki; [4]). Pepkio re-runs IsoQuant against the SQANTI3-filtered GTF per sample to produce DESeq2- and DRIMSeq-ready count matrices. When matched short-read BAMs are available, SQANTI3 --short_reads input adds junction validation metrics [4]. RT-PCR primer design for experimental validation is scoped separately.
Yes. Beyond the standard FASTQ-to-filtered-GTF workflow, Pepkio scopes bespoke work at kickoff: custom inputs (pre-aligned BAM, client GTF), fusion prioritization, ORF prediction, allele-specific isoform phasing, single-cell long-read (MAS-ISO-seq), or integration with short-read or spatial data [1]. Milestone pricing and timelines are confirmed before work begins.
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