Buyer guide
Bulk RNA-seq calendars often diverge: Explicyte targets custom reports in about four weeks (Explicyte, n.d.), while Pitt's Genomics Analysis Core asks labs to book capacity ≥3 months before data arrive (University of Pittsburgh GAC, n.d.)—even when Coriell lists analyst hours as low as four (Coriell Institute, n.d.). You will estimate timelines by modality, deliverable depth, and provider model.
Three clocks drive every bioinformatics project: pipeline compute and analyst hours, queue and contracting time, and iteration toward publication-ready deliverables. Anchor your schedule on the figures below—not on the fastest marketing claim you see.
| Fact | Detail | Source |
|---|---|---|
| Bulk RNA-seq analyst hours (standard pipeline) | 4 h (6 samples) to 12 h (25 samples), plus 1 h review meeting | Coriell Institute, n.d. |
| Bulk RNA-seq calendar turnaround (standalone analysis, scoped) | Often ~4 weeks for custom analysis reports; focused packages can be shorter | Explicyte, n.d. |
| End-to-end commercial RNA-seq project | Often ~2–4 weeks (project scope includes wet lab through data delivery) | Discovery Life Sciences, n.d. |
| scRNA-seq analyst hours | 8 h (6 samples) to 16 h (25 samples) | Coriell Institute, n.d. |
| Multi-omics integration (~6 samples) | 3–6 months of analyst effort | University of Pittsburgh GAC, n.d. |
| Academic core queue lead time | Contact analysis core ≥3 months before anticipated data availability | University of Pittsburgh GAC, n.d. |
| In-house hire lag; core staffing pressure | 60–95 days to fill bioinformatics-related roles (G-Force Life Sciences, 2024); adequate staffing to keep up with analysis was the most pressing challenge among small cores (Dragon et al., 2020) | G-Force Life Sciences, 2024; Dragon et al., 2020 |
Grant and manuscript deadlines are set months before FASTQ files exist. Budgeting only for sequencing leaves data in queue while hiring cycles run 60–95 days (G-Force Life Sciences, 2024) and small cores report adequate staffing to keep up with analysis as their most pressing challenge (Dragon et al., 2020). Rushed analysis shifts delay to peer review—more than 70% of surveyed scientists failed to reproduce another lab's work (Baker, 2016)—and NIH Data Management and Sharing Plans must specify when and how scientific data will be shared (NIH, 2023).
Calendar turnaround depends on whether you need a primary pipeline report or a publication-ready package with figures, Methods text, and reproducible code. Analyst-hour estimates from academic rate cards reflect compute and standard review—not queue time, biological interpretation, or reviewer-response rounds.
| Analysis type | Primary pipeline (calendar) | Analyst hours (where published) | Publication-ready add-on |
|---|---|---|---|
| Bulk RNA-seq (6–15 samples) | ~4 weeks standalone analysis report (Explicyte, n.d.); ~2–4 weeks end-to-end RNA-seq project (Discovery Life Sciences, n.d.) | 4–10 h (Coriell Institute, n.d.) | Additional weeks for figures, added contrasts, Methods draft—confirm in SOW |
| scRNA-seq | Often several months at academic cores (University of Pittsburgh GAC, n.d.); quote-based commercially | 8–16 h tiers (Coriell Institute, n.d.) | Annotation and biological interpretation often dominate calendar time (He et al., 2022) |
| ATAC-seq | Several months for typical academic-core projects (University of Pittsburgh GAC, n.d.) | Quote-based | Integration with RNA-seq extends further |
| ChIP-seq | Quote-based at most cores | Quote-based | Often scoped with multi-omics projects |
| WGS / WES (research) | Weeks to months; scope-dependent | Quote-based | Variant interpretation and reporting drive the tail |
| Multi-omics integration (~6 samples) | 3–6 months (University of Pittsburgh GAC, n.d.) | Effort-intensive | Custom ML or pathway modelling adds time |
| Proteomics / metabolomics | ~3–4 weeks for standard core projects (Cleveland Clinic Proteomics Core, n.d.) | Included in core workflow | Larger projects may run longer (Cleveland Clinic Proteomics Core, n.d.) |
| Spatial transcriptomics | Quote-based | Limited published TAT | Plan explicitly in SOW |
Clinical pipelines have reported nine-day cancer WGS+transcriptome turnaround (Shukla et al., 2022) and eleven workdays for routine clinical WGS (Samsom et al., 2024)—dedicated infrastructure, not a typical academic default. Proteomics cores often deliver preliminary data in ~3 weeks and reports in ~4 weeks (Cleveland Clinic Proteomics Core, n.d.).
Bioinformatics timelines stretch across four phases, each with independent delay risk. Treating "analysis" as a single step is why PIs are surprised when a four-hour pipeline quote becomes a four-month wait.
**1. Pre-data** — design, agreements, core booking, DMS planning. Pitt GAC advises contact ≥3 months before data (University of Pittsburgh GAC, n.d.). Under-powered replicate counts can yield unreliable differential-expression results or force expanded analysis (Schurch et al., 2016).
**2. Data arrival** — transfer, QC, failed-lane decisions. Contract re-run policy before data land.
**3. Active analysis** — primary processing is often shortest; biological interpretation and cluster annotation are often the most time-consuming steps in scRNA-seq (He et al., 2022).
**4. Post-delivery** — feedback, added contrasts, figures, deposit, reviewer re-analysis. If the SOW ends at "standard pipeline," this phase is scope creep or a new PO.
Use this when scoping a grant, core request, or CRO statement of work:
Work backward from here.
Pitt GAC model.
Primary report vs. publication package with code.
Confirm both in the statement of work before signing.
Exclude, partial delivery, re-sequence recommendation.
Weeks to months.
Unplanned reprocessing is common at peer review.
(NIH, 2023).
No model is fastest for every project. Start dates vary by provider; in-house hires take 60–95 days to fill plus onboarding (G-Force Life Sciences, 2024); academic cores may have low analyst-hour estimates but long calendar queues (Dragon et al., 2020).
| Factor | Bioinformatics CRO | In-house hire | Academic core |
|---|---|---|---|
| Time to start after decision | Varies; confirm start date in SOW | 60–95 days to hire, plus onboarding | Weeks to months; book ≥3 months ahead (University of Pittsburgh GAC, n.d.) |
| Standard bulk RNA-seq | ~4 weeks standalone report (Explicyte, n.d.) | Depends on individual bandwidth | Low analyst hours (Coriell Institute, n.d.); calendar wait often dominates |
| scRNA-seq / multi-omics | Quote-based; typically weeks to months | Fastest if dedicated specialist on staff | scRNA-seq: several months typical (University of Pittsburgh GAC, n.d.) |
| Rush / surge capacity | Ask whether rush is offered and what QA changes | Overtime or reprioritisation | Usually queue-limited |
| Iteration speed | Contract-defined review rounds | Fastest with dedicated FTE and clear priorities | Slowest when queue-bound between rounds |
| Continuity risk | Defined in SOW | Risk if employee leaves mid-project | Shared staff across labs; staffing pressure (Dragon et al., 2020) |
Coriell's four-to-twelve-hour bulk RNA-seq tiers (Coriell Institute, n.d.) are not "results in four hours."
Pitt GAC requires contact ≥3 months before data (University of Pittsburgh GAC, n.d.); sequencing and analysis cores bill separately.
Splicing, pathway, and custom visualisation are additional effort (University of Pittsburgh GAC, n.d.).
Re-analysis contingency is not optional.
Many bundled pipelines lack adequate software-version or parameter documentation (Piccolo & Frampton, 2016).
Define how long re-analysis stays included after delivery.
Work backward: **manuscript target** → reviewer buffer (weeks to months—confirm with provider) → figure/Methods iteration (scope-dependent) → primary analysis → queue lead (0 to ≥3 months at busy cores; University of Pittsburgh GAC, n.d.) → sequencing and sample prep. Send these questions to two or three providers:
Often ~4 weeks for a standalone custom analysis report (Explicyte, n.d.); end-to-end RNA-seq projects at commercial providers may run ~2–4 weeks depending on scope (Discovery Life Sciences, n.d.). Academic tiers list four to ten analyst hours for 6–15 samples (Coriell Institute, n.d.) but queue wait can require booking ≥3 months ahead (University of Pittsburgh GAC, n.d.). Publication figures and Methods add calendar time beyond primary differential-expression output—confirm in SOW.
Eight to sixteen analyst hours at published academic tiers (Coriell Institute, n.d.), but Pitt GAC estimates several months for typical Cell Ranger/Seurat projects (University of Pittsburgh GAC, n.d.). He et al. (2022) state that biological interpretation and cluster annotation are often the most time-consuming steps in scRNA-seq—not primary processing alone. Commercial turnaround is quote-based; publication-ready annotation validation adds calendar time beyond pipeline output.
Different clocks. Coriell hours cover pipeline execution plus one review meeting (Coriell Institute, n.d.)—not queue, transfer, interpretation, or figures. Explicyte targets custom analysis reports within ~4 weeks (Explicyte, n.d.); Discovery Life Sciences quotes ~2–4 weeks for end-to-end RNA-seq projects (Discovery Life Sciences, n.d.). Always ask which phase a number measures.
Academic cores: before sequencing—Pitt GAC requests contact ≥3 months before anticipated data availability (University of Pittsburgh GAC, n.d.), and advises contacting during initial sequencing-core consultation so analysis is queued. CROs: when experimental design and deliverables are fixed, not when FASTQ files arrive. NIH grantees must submit a DMS Plan specifying when and how scientific data will be shared—budget analysis completion accordingly (NIH, 2023).
Research WGS/WES turnaround is scope-dependent; weeks to months is a safer default when variant interpretation, cohort size, or integrative reporting are included. Clinical optimised workflows report nine-day cancer WGS+transcriptome (Shukla et al., 2022) and eleven workdays routine clinical WGS (Samsom et al., 2024)—both rely on dedicated infrastructure, not a typical academic core queue. Scope variant interpretation, reporting format, and revision rounds in the SOW.
Possible for a narrow primary pipeline on clean, queued data—not publication-ready delivery. Coriell's shortest tier is four analyst hours plus one review hour (Coriell Institute, n.d.), assuming data are already in hand. Treat one-week promises for large cohorts or scRNA-seq sceptically unless the SOW lists exact deliverables.
Not always—but uncosted speed often skips documentation or review. Reproducibility failures are common (Baker, 2016); many NGS papers lack adequate version or parameter documentation (Piccolo & Frampton, 2016). Fast is fine when scope is narrow and the SOW still requires pinned environments and parameter logs. Rush without clarified QA is a red flag.
Plan weeks to months per major reviewer round—longer when editors request new contrasts, subgroup splits, or batch reprocessing. No published benchmark applies across journals; timeline depends on scope and queue. Contract post-submission support up front; many providers bill reviewer sprints separately. Primary-only SOWs make reviewer work a new purchase order and timeline.
Among the slowest project types: Pitt GAC estimates three to six months of effort for ~6 samples integrating ATAC-seq, ChIP-seq, and RNA-seq (University of Pittsburgh GAC, n.d.), with cost roughly $5,000–$6,000 at that core. Dragon et al. (2020) note rising demand for multi-omics integration at bioinformatics cores. Custom ML, harmonisation across platforms, or publication figures extend calendars further—confirm milestones in writing.
Only with a productive analyst and short queue. Hiring takes 60–95 days (G-Force Life Sciences, 2024); outsourced or core capacity may be faster until then. Onboarded in-house staff iterate quickest on familiar data. Cores win when queues are short; they lose when staffing cannot keep up with demand (Dragon et al., 2020).
Tell us:
We'll tell you:
Contact us to start with a free consultation. Need everyday bench calculators? Try our free lab tools.
