LatchBio vs Pepkio: Bioinformatics Service Comparison

Quick Comparison Table

What Is LatchBio?

LatchBio is a cloud-native bioinformatics platform and Python workflow SDK built on top of the open-source Flyte workflow orchestrator. It functions as both a graphical web interface for wet-lab scientists and a developer framework for computational bioinformaticians.

The platform provides access to standard omics workflows, including bulk RNA-seq (STAR, Salmon, DESeq2), single-cell transcriptomics (Cell Ranger, Seurat, Scanpy), protein structure prediction (AlphaFold 2, ColabFold, ESMFold), DNA-seq variant calling (GATK, FreeBayes), epigenomics (MACS2), and metagenomics (Kraken2, Bracken).

Users access LatchBio through three main components:

• Web GUI: A point-and-click interface to launch pre-built workflows and adjust parameters via web forms.
• Developer SDK: A Python SDK and CLI (latch) that converts decorated Python functions into containerized Flyte workflows.
• Latch Pods: Interactive cloud workstations hosting browser-accessible JupyterLab, RStudio, CELLxGENE, or Streamlit linked to cloud storage via FUSE (/ldata).

LatchBio operates primarily on a pay-as-you-go model using Latch Credits ($1.00 USD = 1 Latch Credit), billing directly for CPU, RAM, GPU, and storage usage without mandatory per-seat subscription fees for self-serve users. Enterprise options with custom support SLAs and Bring-Your-Own-Compute (BYOC) infrastructure are also available.

What Is Pepkio?

Pepkio provides an outsourced bioinformatics service where Ph.D. bioinformaticians process Next-Generation Sequencing (NGS) and multi-omics datasets from raw sequencing files through manuscript submission.

Research teams provide raw data and experimental details. Experienced bioinformaticians conduct quality control, reference genome alignment, custom statistical modeling (including GLMs, multi-factor contrasts, and batch effect correction), and pathway enrichment analysis.

Standard deliverables include curated count and variant matrices, journal-formatted vector graphics, complete Materials & Methods writeups, and direct assistance during peer review. Custom R or Python scripts, Conda or Docker environment specifications, and Nextflow or Snakemake workflow definitions are available as optional deliverables upon request.

Pepkio vs LatchBio: Head-to-Head Comparison

How difficult is setup and onboarding?

Onboarding onto LatchBio depends on whether your team uses pre-built workflows or develops custom pipelines, whereas Pepkio requires no software installation or environment configuration.

For wet-lab researchers using LatchBio's pre-built GUI, starting an analysis takes 10 to 15 minutes to create an account, select a pipeline, connect cloud storage, and configure parameters. However, for bioinformaticians building custom workflows, setup requires installing Python and Docker locally, initializing project templates with latch init, decorating Python functions with Flyte task decorators (@small_task, @medium_task), and managing container builds during latch register.

With Pepkio, there is no software setup, container management, or CLI configuration. Work begins with an initial consultation between your team and Ph.D. bioinformaticians to define experimental groups, statistical contrasts, and analytical goals. Pepkio handles all environment setup, reference indexing, and pipeline execution internally.

Can it handle non-model organisms and complex experimental designs?

Pepkio provides custom statistical modeling for non-model organisms, unannotated genomes, and complex experimental designs out of the box, whereas LatchBio GUI users are limited to parameters exposed in pre-built web forms.

LatchBio's pre-built workflows execute efficiently for standard model organisms (such as human and mouse) using two-group differential expression designs. If your project involves unannotated genomic assemblies, multi-factor interaction terms, complex batch structure, or multi-omics integration—such as combining RNA-seq analysis with ATAC-seq or single-cell transcriptomics—a computational scientist must write custom Flyte workflows via the SDK or execute manual code inside interactive Latch Pods.

Pepkio builds custom linear models, tailored reference indexes, and multi-omics pipelines directly into project scoping. Bioinformaticians adjust statistical frameworks to account for sample imbalance, batch variation, and non-standard genomic annotations without requiring coding from your lab.

How fast can you get publishable results?

Pepkio delivers validated analysis packages with publication-ready vector figures in 1 to 3 weeks, whereas LatchBio provides rapid compute execution but leaves downstream plotting and figure assembly to the researcher.

On LatchBio, individual pipeline execution tasks finish in hours across cloud Kubernetes nodes. However, total project completion often extends across weeks because researchers must inspect intermediate tables, write custom R or Python scripts in Latch Pods to generate specialized plots, resolve statistical edge cases, and manually assemble figures for publication.

Pepkio manages processing, quality control, statistical contrasts, pathway analysis, and figure generation as a single integrated service. Bioinformaticians deliver validated result matrices, journal-formatted vector graphics, and draft manuscript sections within 1 to 3 weeks.

How do reproducibility and provenance tracking compare?

LatchBio tracks workflow provenance automatically using Flyte execution logs and Docker container digests, while Pepkio provides manuscript-ready Materials & Methods writeups alongside optional code and environment deliverables.

LatchBio logs every workflow run within Flyte execution trails, recording exact input parameters, task dependencies, execution timelines, and container digests. Workflows created with the Latch SDK exist as standard Python files and Dockerfiles, allowing version control in Git repositories to prevent software dependency drift over time.

Pepkio delivers detailed Materials & Methods writeups outlining tool selections, software parameters, and statistical logic for journal submission. Upon request, Pepkio also provides custom R or Python scripts, Conda or Docker environment specifications, and Nextflow or Snakemake workflow definitions as optional deliverables.

What is the true cost in software, compute, and researcher time?

Evaluating in-house vs outsourced bioinformatics requires balancing LatchBio's usage-based compute credits and internal labor against Pepkio's fixed per-project service fee.

LatchBio uses a credit currency ($1.00 USD = 1 Latch Credit) with published unit rates: CPU compute at 0.0000583 credits/core-second ($0.21/core-hr), RAM at 0.00000277 credits/GiB-second ($0.01/GiB-hr), GPU instances from $0.52/hr (NVIDIA T4) to $4.83/hr (NVIDIA V100), and storage at $0.023/GB/month for LatchData. Total operational costs on LatchBio also include cloud storage for intermediate files, compute time for running interactive Latch Pods, and 20 to 80+ hours of bioinformatician time to build, test, and maintain custom SDK pipelines.

Pepkio replaces usage-based credit tracking, storage accumulation, and internal personnel hours with a single, predictable project fee. This fee covers data processing, statistical modeling, custom figure design, manuscript drafting, and ongoing scientist support.

What support is provided when an analysis encounters issues?

Pepkio provides direct Ph.D. scientist consultation for biological edge cases and statistical interpretation, whereas LatchBio provides Slack and email support focused on platform infrastructure.

LatchBio offers technical support through a community Slack workspace and email for pay-as-you-go users, with dedicated bioinformaticians and formal support SLAs available on enterprise contracts. Platform support helps resolve Flyte execution errors, SDK registration bugs, or storage mounting issues, but does not provide biological guidance on experimental design choices or statistical modeling.

Pepkio assigns Ph.D. bioinformaticians to evaluate sample distributions using principal component analysis (PCA), identify technical outliers, adjust linear models, and consult directly with your team to address biological edge cases.

How do the services support figures, Methods writing, and peer review?

Pepkio provides journal-formatted vector graphics, manuscript text, and direct peer-review assistance, whereas LatchBio outputs standard MultiQC summaries and diagnostic plots that researchers format independently.

LatchBio workflows produce HTML summaries, raw data tables, diagnostic static plots (PNG, SVG, PDF), and object files (.h5ad, .rds) for exploration in Latch Pods. Researchers must download these output files and write custom R/Python code to format multi-panel figures and draft manuscript methods text.

Pepkio generates journal-formatted vector graphics (such as heatmaps, volcano plots, PCA layouts, and pathway diagrams), drafts complete Materials & Methods sections, and assists during peer review by executing requested sub-analyses and writing technical response text.

How does each option scale with sample volume and project complexity?

LatchBio scales compute resources dynamically in the cloud, while Pepkio scales project capacity without requiring your lab to manage infrastructure or pipeline code.

LatchBio uses cloud infrastructure to provision compute nodes based on task requirements, auto-scaling RAM for large single-cell datasets or allocating GPUs for AlphaFold 2 runs. However, scaling into new omics modalities requires researchers to learn new pipeline tools or write new SDK workflows.

Pepkio handles compute allocation and workflow expansion internally. Research groups can scale from pilot studies to large multi-cohort datasets across bulk RNA-seq, DNA-seq analysis, single-cell transcriptomics, and epigenomics without managing cloud nodes, Docker containers, or pipeline updates.

How is data security and storage managed?

Both Pepkio and LatchBio implement cloud security standards for genomic datasets, with LatchBio utilizing AWS S3-backed LatchData and Pepkio operating isolated, secure compute environments.

LatchBio manages cloud files via LatchData (backed by AWS S3), using latch:/// URIs that mount into Latch Pods via FUSE at /ldata and transfer using the latch cp CLI tool. Enterprise accounts can also configure Bring-Your-Own-Compute (BYOC) to run tasks inside their own AWS accounts.

Pepkio processes datasets within secure compute environments bound by strict confidentiality agreements. Upon project completion, final data matrices, tables, and vector graphics are delivered directly to your research team.

When to Choose Pepkio

Outsourcing to Pepkio is ideal when:

• Your lab lacks dedicated bioinformatics personnel, allowing wet-lab researchers to focus on experiments rather than learning workflow software.
• You require publication-ready vector figures, multi-panel layouts, and a complete Materials & Methods section alongside normalized count matrices.
• You work with non-model organisms, unannotated genomes, or complex experimental designs that require custom statistical modeling and linear contrasts.
• You need Ph.D.-level support during peer review to execute requested sub-analyses, adjust statistical thresholds, and draft technical response letters.
• You prefer a predictable project fee to eliminate time spent on workflow development, script debugging, and figure assembly.

When to Choose LatchBio

Using LatchBio is ideal when:

• You want a cloud platform that provides a no-code web GUI for wet-lab scientists alongside a Python SDK for bioinformaticians to build Flyte pipelines.
• Your lab frequently conducts routine, high-throughput assays (such as Bulk RNA-seq, scRNA-seq, or AlphaFold 2) on well-annotated model organisms like human or mouse.
• You require interactive cloud workstations (Latch Pods with JupyterLab or RStudio) linked directly to cloud object storage (/ldata) and CELLxGENE.
• You want automated internal provenance tracking that records every parameter, execution step, and Docker container digest in cloud logs.
• You prefer pay-as-you-go cloud compute pricing over fixed service contracts for routine pipeline execution.

Key Trade-Offs

LatchBio Advantages

• Complete hands-on control over pipeline parameters and custom script execution.
• Python SDK allows bioinformaticians to register custom containerized workflows with auto-generated web forms.
• Interactive Latch Pods (JupyterLab/RStudio) integrated with cloud object storage (/ldata).
• Pay-as-you-go credit system for compute execution without mandatory per-seat licensing fees.
• Automated Flyte execution logging and Docker containerization.

LatchBio Considerations

• Requires researcher time to analyze outputs, select statistical parameters, and format publication figures.
• Developing custom SDK workflows requires learning Flyte syntax and managing Docker builds (20–80+ hours).
• Ongoing cloud storage fees for intermediate files and compute charges for active Pods accumulate over time.
• Support focuses on platform infrastructure rather than biological interpretation or peer-review defense.

Pepkio Advantages

• Complete delivery of statistical modeling, normalized count matrices, and publication-ready vector figures.
• Dedicated Ph.D. bioinformaticians handle complex experimental designs, batch effect removal, and non-model organisms.
• Detailed Materials & Methods writeups and direct peer-review support included.
• Zero internal learning curve, coding requirements, or software maintenance.
• Predictable fixed per-project service fee.

Pepkio Considerations

• Turnaround time requires 1 to 3 weeks for complete service delivery.
• Less real-time control over day-to-day parameter tweaking compared to running interactive Pods locally.

Frequently Asked Questions

Can I get the pipeline code and scripts if I use Pepkio?

Yes. Pepkio provides custom R and Python scripts, Conda or Docker environment specifications, and Nextflow or Snakemake workflow definitions upon request as optional deliverables alongside standard count matrices and publication figures.

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

Running a pre-built Bulk RNA-seq pipeline via LatchBio's web GUI takes 10 to 15 minutes of initial setup. Building custom workflows with the Latch Python SDK requires 1 to 3 hours for initial registration and 20 to 80+ hours to master Docker containerization and Flyte task decorators.

What happens if a reviewer asks for a different normalization or statistical contrast?

If you use Pepkio, your assigned bioinformatician performs the requested re-analysis, updates statistical models, regenerates vector figures, and helps draft technical response text. If you use LatchBio, your team must re-open your workspace, modify pipeline settings or write new R/Python code in Latch Pods, and regenerate figures independently.

Is LatchBio free or does it require a subscription?

LatchBio uses a pay-as-you-go model based on Latch Credits ($1.00 USD = 1 Credit) with no mandatory monthly platform license fee for standard accounts. Compute and storage are billed based on resource usage, such as CPU ($0.21/core-hr), RAM ($0.01/GiB-hr), GPU ($0.52–$4.83/hr), and LatchData storage ($0.023/GB/month).

What workflow engine powers LatchBio under the hood?

LatchBio is built on top of the open-source Flyte workflow orchestrator. The latch CLI serializes Python code into Flyte workflow specifications, while developers can also register custom containerized tasks via the SDK.

Can LatchBio handle non-model organisms without pre-built reference genomes?

Yes, but doing so requires custom developer effort. While pre-built GUI workflows target standard reference genomes, analyzing unannotated non-model organisms or building custom reference indexes requires writing custom Python/Flyte workflows or executing manual scripts inside Latch Pods. Pepkio handles non-model organisms and custom reference indexing as part of standard project scoping.

What are Latch Pods?

Latch Pods are interactive, cloud-based computing workstations that host browser-accessible JupyterLab, RStudio, and web applications like CELLxGENE or Streamlit. They mount LatchData cloud storage via FUSE at /ldata, allowing researchers to write downstream scripts and inspect data objects directly in the cloud.

How does Pepkio handle batch effect correction compared to LatchBio?

Pepkio bioinformaticians evaluate data distributions using principal component analysis (PCA) and sample distance metrics, constructing customized linear models (such as DESeq2 or Limma/Voom) to account for multi-factor batch effects. On LatchBio, GUI users rely on parameters exposed in pre-built forms, while custom batch corrections require writing downstream R or Python code in Latch Pods.

What deliverables does Pepkio provide at project completion?

Pepkio delivers processed count and variant matrices, publication-ready vector figures (heatmaps, volcano plots, PCA plots), complete Materials & Methods text, and ongoing peer-review support. Custom R or Python scripts, Conda or Docker environments, and Nextflow or Snakemake workflow definitions are available as optional deliverables.

How does workflow provenance and reproducibility work in LatchBio?

LatchBio records workflow runs in Flyte audit logs, capturing input parameters, execution steps, task logs, and Docker container digests. Workflows written with the Latch SDK exist as version-controlled Python code and Dockerfiles, preventing software dependency drift.

How do turnaround times compare between Pepkio and LatchBio?

LatchBio executes compute tasks in hours, but total project completion often spans several weeks as researchers select parameters, debug scripts, analyze intermediate tables, and format figures. Pepkio delivers a complete, validated analysis package with journal-formatted vector graphics within 1 to 3 weeks.

Which option is better for a biotech startup without a dedicated bioinformatician?

Pepkio is better suited for startups without dedicated computational staff because it eliminates the need to interpret statistics, debug code, or construct publication figures internally. LatchBio is an effective choice for startups planning to hire a bioinformatician who can use the SDK to build reusable cloud workflows for wet-lab colleagues.

Bottom Line

The choice between Pepkio vs LatchBio comes down to whether your lab prefers to manage cloud workflow execution independently or outsource data processing and figure creation to experienced bioinformaticians. LatchBio is a modern cloud platform that provides a no-code GUI for standard pipelines alongside a Python/Flyte SDK and interactive Pods for computational teams. It is well suited for labs with internal bioinformatics bandwidth looking to streamline cloud execution and share workflows across teams. Pepkio provides an outsourced bioinformatics service where Ph.D. bioinformaticians take raw data and deliver validated statistics, journal-formatted vector figures, Materials & Methods text, and peer-review support within 1 to 3 weeks. If your goal is to minimize researcher time spent on software operation and secure publishable results, Pepkio is the more direct path.