Partek Flow vs Pepkio: Bioinformatics Software vs Service Comparison | Pepkio Compare

Quick Comparison Table

Aspect	Pepkio (Outsourced Service)	Partek Flow (In-House GUI Software)
Analysis types	Bulk RNA-seq analysis, single-cell omics, spatial transcriptomics, epigenomics, and custom non-model assemblies	Bulk RNA-seq, scRNA-seq, spatial transcriptomics, DNA-seq variant calling, ChIP-seq/ATAC-seq, microarrays
Bioinformatics skills needed	Zero coding or statistical setup required	Zero coding required for GUI; moderate-to-high statistical domain knowledge needed for experimental contrasts
Infrastructure needed	None; all compute and high-performance storage managed by service provider	Dedicated 64-bit Linux server or HPC/cloud node (min 48 GB RAM, 4 CPU cores, 100 GB root, >2 TB storage); browser client
Time to first result	Turnkey delivery in 1–3 weeks without internal labor	15–30 minutes to launch pipeline after server setup; 5–20 hours of hands-on researcher effort to optimize and analyze
Customisation flexibility	High; bespoke scripts, custom pipeline logic, non-standard reference indexing, and tailored statistical models	Moderate; constrained by built-in graphical node algorithms and GUI options
Reproducibility tooling	Written Methods text, parameter logs, data matrices; optional Nextflow/Snakemake workflows or Docker containers	Visual Directed Acyclic Graph (DAG), automated step-by-step audit trails, and exportable project pipeline templates
Delivered code	Custom open-source R or Python scripts delivered upon request	No standalone open-source code; visual workflows remain locked within Partek Flow platform
Publication figure support	Publication-ready figure panels, vector graphics, and figure legends included	Interactive visual charts, heatmaps, and UMAP plots exportable as PNG, SVG, or PDF; manual formatting required
Reviewer-response support	Direct scientist-to-scientist support for revised contrasts, additional analyses, and response drafting	Self-service; researcher must manually adjust GUI parameters and re-run pipelines in-house
Monetary cost	Flat pay-per-project service fee; no ongoing software or hardware maintenance fees	Quote-based annual subscription plus Linux server, cloud compute, and IT maintenance costs
Personnel-time cost	Minimal lab effort (initial scoping consultation and data transfer)	5–20 hours of hands-on researcher time per project, plus ongoing system administrator maintenance
Technical support	Direct communication with assigned bioinformaticians to troubleshoot data anomalies	Vendor commercial technical support (email, phone, ticketing) and local system administrator for server bugs
Best suited for	Labs needing rapid publishable results, complex experimental designs, non-model organisms, or lacking compute setup	Core facilities and wet labs wanting independent point-and-click visual exploration on recurring standard datasets

What Partek Flow Is

Partek Flow is an enterprise web-based graphical user interface (GUI) bioinformatics software platform designed to process, analyze, and visualize high-throughput sequencing and microarray data without requiring command-line or programming experience.

Acquired by Illumina in 2023, the platform caters to bench scientists, core facility directors, and clinical researchers. It covers major omics workflows, including bulk RNA-seq, single-cell multiomics (scRNA-seq, scATAC-seq, CITE-seq), spatial transcriptomics (10x Visium, Xenium, CosMx, MERSCOPE), DNA-seq variant calling, epigenomics (ChIP-seq, ATAC-seq, WGBS methylation), and microarrays.

Under the hood, Partek Flow wraps standard open-source tools and native engines into a node-based visual workflow canvas. Integrated algorithms include STAR, Bowtie2, BWA-MEM, and Minimap2 for alignment; DESeq2, limma-voom, and Partek Gene Set Analysis (GSA) for differential expression; Seurat anchors, Harmony, and Scanpy-interoperable routines for single-cell processing; and MACS2 and FreeBayes for peak and variant calling. Users interact via web browsers (Chrome, Firefox, Safari, Edge) connected to a central server. Partek Flow is deployed via Docker container on 64-bit Linux host servers or cloud instances and licensed under an annual commercial subscription.

What Pepkio Offers

Pepkio provides a full-service bioinformatics model (CRO) where research teams delegate data processing, statistical modeling, and visualization to expert bioinformaticians.

Rather than licensing software or maintaining computational infrastructure, researchers provide raw sequencing data alongside experimental metadata. Pepkio handles the end-to-end analytical workflow, returning processed count and variant matrices, publication-ready figure panels, written Materials & Methods text, and parameter documentation.

This service-based approach eliminates software setup, server maintenance, cloud compute management, and the need for bench scientists to configure complex statistical parameters. Pepkio also provides scientist-to-scientist support during manuscript revision, addressing peer reviewer feedback and performing requested re-analyses. Custom open-source R or Python scripts, as well as optional Nextflow or Snakemake workflows and Docker container environments, are delivered upon request.

Partek Flow vs Pepkio: Head-to-Head Comparison

How do setup and infrastructure requirements compare?

Partek Flow requires initial server deployment and system administration before biologists can begin visual analysis, whereas Pepkio requires zero technical setup.

Deploying Partek Flow is a multi-step administrative task. A system administrator installs the daemon on a 64-bit Linux server (Ubuntu, RHEL, CentOS) or Docker container, configures storage permissions, downloads genome reference indices (such as hg38 or mm10), and manages licensing. Once provisioned, a bench biologist needs 15 to 30 minutes to create a project, import raw data, connect visual pipeline nodes, and launch an execution. While end users do not write code, they must understand biological experimental design and statistical contrast selection to set up nodes correctly.

Pepkio eliminates infrastructure setup entirely. Researchers consult with bioinformaticians to define experimental contrasts and analytical goals, transfer raw data files, and receive finalized results without installing software or managing servers.

How do analytical flexibility and customization compare?

Pepkio supports unconstrained custom pipeline development and statistical modeling for complex edge cases, while Partek Flow focuses on standard pipelines wrapped within its visual GUI canvas.

Partek Flow includes pre-packaged algorithms through graphical nodes. It works well for common human and mouse datasets such as bulk RNA-seq and standard 10x single-cell workflows. Parameter changes outside built-in options, such as non-model references, non-standard GTF annotations, or nested multi-factor batch designs, usually require workarounds within the GUI limits.

Pepkio provides custom analytical tailoring. Experienced bioinformaticians write custom scripts, build bespoke reference indices, execute de novo transcript assembly, and design tailored statistical linear models specifically for non-standard experimental layouts or unique biological questions.

How do delivery timelines and researcher labor compare?

Partek Flow can finish compute steps in hours on strong servers, but total researcher time for QC review, interpretation, and figure assembly often stretches to 1 to 3 weeks. Pepkio delivers complete results in a similar 1 to 3 week window without requiring internal analysis labor.

Partek Flow executes computational steps rapidly on high-RAM Linux host servers (for example, STAR alignment loads human reference genome indices into ~32 GB RAM). However, the biologist must dedicate 5 to 20 hours of hands-on labor per project to build workflow diagrams, evaluate QC metrics, fine-tune UMAP cluster resolutions, set statistical thresholds, and assemble charts into publication panels.

Pepkio operates on a turnkey delivery schedule of 1 to 3 weeks total. The service team executes processing, statistical testing, pathway enrichment, and figure formatting, delivering finalized results without pulling bench scientists away from wet-lab experiments.

How do reproducibility and workflow exports compare?

Partek Flow provides automated internal graphical provenance tracking within its software environment, while Pepkio delivers external written methods, parameter documentation, and optional open-source code scripts.

Partek Flow excels at internal auditing. The platform automatically generates an interactive visual Directed Acyclic Graph (DAG) for every project, logging tool versions, input file lineages, parameter choices, user IDs, and timestamps. Graphical pipelines can be saved as internal template files. However, Partek Flow does not export workflows as standalone open-source R, Python, Nextflow, or WDL scripts; the execution logic remains within the proprietary platform.

Pepkio delivers publication-ready Materials & Methods text tailored for journal submission, alongside parameter records and count or variant data matrices. Upon request, Pepkio provides complete open-source workflow scripts, while Nextflow or Snakemake pipeline files and Conda or Docker environments can also be included as optional deliverables.

What is the true cost of each option?

Partek Flow incurs annual software subscription licensing, compute hardware overhead, and researcher salary hours; Pepkio charges a flat, project-based service fee.

Partek Flow licensing uses an annual subscription per lab group or enterprise site. Public academic records suggest shared group licenses often fall around $3,000 to $5,000+ per lab group per year, while full server licenses can cost more. Modules such as the Single Cell Toolkit are billed separately. Labs also carry Linux server or cloud compute costs, IT administration, and about 5 to 20 hours of researcher labor per project.

Pepkio operates on a pay-per-project CRO service fee. Research groups avoid annual software subscription renewals, server maintenance overhead, cloud compute charges, and internal personnel time allocation.

How do peer-review support and manuscript preparation compare?

Pepkio includes publication-ready figure panel formatting, manuscript Methods drafting, and reviewer-response assistance, whereas Partek Flow outputs visual charts that researchers must format themselves.

Partek Flow generates interactive visual outputs—such as heatmaps, 2D and 3D UMAP plots, volcano plots, and violin plots—exportable as PNG, SVG, EPS, or PDF files. Researchers must independently assemble these graphics into manuscript figure panels, write the Materials & Methods section, and re-run GUI workflows if reviewers request revised analyses.

Pepkio provides complete publication support. Deliverables include publication-ready vector figure panels, figure legends, and manuscript Methods text. If peer reviewers request additional statistical contrasts or altered normalization parameters, Pepkio performs the re-analysis and assists in drafting responses to reviewer comments.

When to Choose Pepkio

Outsourcing to Pepkio is usually a better fit when your team needs publication-ready results without investing in software subscriptions, server hardware, or internal analysis training:

Tight publication deadlines: You need manuscript-ready figures and Methods text quickly for submission or renewal timelines.
Complex edge cases: Your project includes non-model organisms, custom GTF/GFF annotations, single-cell multiomics, or multi-factor batch correction.
No dedicated bioinformatics team: Wet-lab researchers need to stay focused on experiments instead of spending 5 to 20 hours per project in GUI setup and QC loops.
Project-based budgeting: Funding is tied to specific deliverables rather than long-term software and infrastructure upkeep.
Reviewer-response support: You want expert help with revised contrasts, additional analyses, and updated figure packages.

When to Choose Partek Flow

Running Partek Flow in-house is usually a better fit for core facilities and multi-lab groups with recurring data volume that want researchers to explore data directly in a point-and-click interface:

Core facilities and multi-lab groups: You support many scientists with a shared self-service software stack.
High-volume standard datasets: You process routine human or mouse bulk RNA-seq and scRNA-seq continuously.
Direct visual exploration preference: Researchers want to tune clustering and plots interactively in the GUI.
Existing Linux infrastructure: You already run high-RAM Linux servers, Docker environments, and admin support.

Trade-Offs Summary

Partek Flow (In-House GUI Software):
- Pros: Interactive point-and-click GUI; rich visual exploration capabilities; automated DAG provenance tracking; accessible to non-programmer biologists; vendor documentation and technical support.
- Cons: Quote-based annual subscription licensing (~$3,000–$5,000+ academic lab/year); requires 48 GB+ RAM Linux server infrastructure; proprietary vendor lock-in (cannot export open-source scripts); requires 5–20 researcher hours per project; no manuscript writing support.
Pepkio (Outsourced Bioinformatics Service):
- Pros: Zero software licensing or infrastructure required; turnkey delivery in 1–3 weeks; handles complex edge cases and non-model organisms; includes publication-grade figures, written Methods, and reviewer support; delivers open-source scripts upon request with optional Nextflow/Snakemake pipelines.
- Cons: Flat service fee per project; less direct real-time interactive parameter tweaking by bench scientists during initial data exploration.

Frequently Asked Questions

Can I obtain reproducible code when using Pepkio?

Yes. Unlike proprietary GUI platforms, Pepkio provides complete workflow documentation and custom open-source scripts upon request. Nextflow or Snakemake pipeline files and Docker container environments can also be delivered as optional deliverables, ensuring full analytical transparency.

How long does it take to learn Partek Flow for RNA-seq analysis?

Once a system administrator provisions the server, a wet-lab biologist can launch a standard RNA-seq pipeline within 15 to 30 minutes using the node-based GUI. However, mastering statistical contrast setup, batch correction, and parameter interpretation typically requires several days of hands-on practice.

What happens if a manuscript reviewer requests revised statistical contrasts?

If you use Pepkio, the service team performs requested re-analyses, updates figure panels, and drafts response text for the reviewers. If you use Partek Flow, your lab must re-open the project in the software, adjust normalization nodes, re-run execution steps, and re-export updated figures manually.

Does Partek Flow run locally on a Mac or Windows laptop?

No. Partek Flow uses a client-server architecture requiring a dedicated 64-bit Linux server, HPC cluster node, or cloud instance (AWS/Azure). Local Mac or Windows laptops function strictly as web browser thin clients.

What are the host server hardware requirements for Partek Flow?

The host Linux server requires at least a 64-bit CPU (quad-core minimum; 16 cores recommended for STAR alignment), 48 GB RAM (64–128+ GB for single-cell multiomics), 100 GB root storage, and >2 TB high-speed storage. Splice-aware aligners like STAR require ~32 GB RAM just to load the human genome reference index.

Can Partek Flow export pipelines as open-source R scripts or Nextflow workflows?

No. While Partek Flow tracks provenance via an interactive visual DAG and allows saving template pipeline files inside its system, it does not export standalone open-source R code, Python scripts, or Nextflow pipelines.

How is Partek Flow software licensed?

Partek Flow uses a quote-based annual subscription model per lab group or enterprise site. Public academic records indicate shared group licenses often range between $3,000 and $5,000+ per year, with specialized modules like the Single Cell Toolkit billed as add-ons.

Which option is better suited for non-model organism datasets?

Outsourcing to Pepkio is typically better for non-model organisms. Building custom reference indices, handling incomplete GTF annotations, and performing de novo transcript assembly are easier for experienced bioinformaticians using custom pipelines than forcing non-standard datasets through fixed GUI nodes.

Which underlying algorithms are integrated into Partek Flow?

Partek Flow integrates standard open-source tools into its visual canvas, including STAR, Bowtie2, and BWA for alignment; DESeq2, limma-voom, and Partek GSA for differential expression; Seurat anchors and Harmony for single-cell integration; and MACS2 for peak calling.

How much researcher labor is required for an analysis in Partek Flow?

While computational pipeline execution takes hours on a Linux server, a researcher typically spends 5 to 20 hours hands-on per project importing data, configuring contrasts, inspecting QC plots, fine-tuning cluster resolutions, and formatting output figures.

How do Partek Flow and Pepkio compare for single-cell RNA-seq?

Partek Flow offers an interactive Single Cell Toolkit with visual UMAP, t-SNE, and cell-type profiling nodes. Pepkio provides end-to-end scRNA-seq processing, custom doublet filtering, cell-type annotation, trajectory modeling, and publication-ready composite panels without requiring high-RAM local infrastructure.

Bottom Line

The core decision is whether your team wants to run software in-house or hand execution to an external bioinformatics team.

Partek Flow is a practical visual platform for core facilities and wet-lab teams that regularly process standard sequencing datasets and want direct control of analysis steps. It still comes with annual licensing, high-RAM Linux infrastructure, IT maintenance, and researcher time. Pepkio offers a CRO alternative that delivers publishable figures, written Methods text, and reviewer-response support without local infrastructure management. If speed, complex edge cases, and manuscript readiness matter more than operating software internally, Pepkio is often the simpler path.