Computational Bio

Spend less time preparing lab data and more time testing hypotheses.

Scispot’s Digital Brain makes connected lab data usable. Automate data assembly and agreed analysis tasks while keeping input versions, methods, source relationships and outputs available for scientific review and reuse.
Book a consultation
Explore LabOS

Trusted by 200+ life science labs

Supporting 250+ instrument types, more than 1,000 experiments each month, and millions of samples across diagnostics, genomics, biotech, and regulated testing.

Your best fit

Built for computational teams repeating data work before each analysis.

Connect omics, assay processing and research handoffs across lab records, notebooks and cloud storage. Start with one recurring analysis delayed by missing metadata or manual joins.

Map your analysis workflow
Features

Published Customer Outcomes

Usable data, less repeated handling, clearer analysis records.

2 days → <6 hrs

Shorter data-retrieval time

Cut data-access time for one computational biology lab

40%

Fewer manual data errors

Reduced manual errors through automated instrument-data ingestion.

30%

Fewer project delays

Connected structured data across one computational biology lab’s teams.

10h → <1h

Shorter data-processing time

Cut one biotech workflow’s processing time below one hour.

One system, three layers

Turn scattered lab records into reviewed analysis outputs.

Assemble usable analysis inputs

Assemble measurements and biological metadata through supported connections. Apply agreed sample identifiers, units and required-field checks, flagging gaps or inconsistencies while retaining original sources.

Record input versions and inclusion criteria. Analysts start from a defined dataset, with unresolved records visible, instead of rebuilding joins across notebooks, exports and folders.

Run and track agreed analyses

Use configured Smart Actions, embedded Jupyter workflows or your existing analysis environment. Record input versions, method or code references, parameters, reference-data releases and relevant environment details.

Automate agreed preparation or calculations, then preview and approve proposed updates before commitment. Returned files and values retain execution context for scientific assessment and later comparisons.

Review outputs and prepare reuse

Assemble outputs, QC evidence and exclusions for scientific review. Prepare figures, tables or handoff summaries with source relationships available to reviewers.

After required review, share the output version and intended use. Preserve earlier analyses when inputs or methods change, so receiving teams can follow how each result was produced.

One connected operating layer

A useful analysis starts before the first line of code.

Missing labels, unclear units and outdated files delay analysis. Prepare agreed data with methods and outputs connected, so the next analysis starts with usable context.

Ask what changed between analyses.
Prepare the next review.

Use connected evidence across data, methods, and outputs.

Meet Scibot
Scibot, the Scispot AI coworker
Which datasets are missing required sample metadata?
Trace this figure to its input version
Compare recorded methods for these two runs
Find analyses using reference release REF-04
Summarize exclusions in dataset CB-D014
Draft a review handoff for output CB-O077
Which datasets are missing required sample metadata?
Trace this figure to its input version
Compare recorded methods for these two runs
Find analyses using reference release REF-04
Summarize exclusions in dataset CB-D014
Draft a review handoff for output CB-O077
AI-native lab transformation

Reduce repeated preparation while preserving the evidence behind results.

Data Prepared for Your Method

GLUE assembles supported lab data, sample metadata and source references. Apply agreed identifiers, units and completeness checks before records enter the computational workflow.

Agreed Analysis Tasks, Automated

Smart Actions runs configured preparation and calculations alongside connected analysis tools. Preview and approve proposed updates before saving, retaining inputs and method settings for review.

Controlled Access for AI Tools

Scispot MCP gives approved external AI tools permitted records and supported actions. Define access and review rules so assistants help within the agreed scope.

Analysis Workflows With Expert Delivery

Our scientists and engineers connect sources, test methods and train users. Jazz gives reviewers focused task screens, keeping results and questions together on shared Scispot data.

Automate the recurring data work around scientific analysis.

01

Omics Dataset Assembly

Prepare the input set without repeating the same manual joins.

Combine measurements, sample groups, metadata and sources through agreed mappings. Check fields, units and inclusion criteria, keeping unresolved records visible rather than silently filling gaps.

Record the selected input version and review decision. Analysts start from a defined set; reviewers can trace measurements to biological sources and understand inclusion or exclusion decisions.

Explore dataset assembly workflows

02

Method Changes and Reanalysis

Prepare comparisons that distinguish changed data from a changed method.

Link each new analysis to its inputs, code or notebook, parameters and outputs. Reuse agreed preparation while recording changed methods or reference releases explicitly.

Bring both runs, output differences and exclusions into scientific review. Your team interprets the change. Earlier results retain their own input and method history; reanalysis does not overwrite it.

Explore reanalysis workflows

03

Reviewed Research Data Handoffs

Give the receiving team the output and the evidence needed to use it.

Prepare the selected output, dataset, method references and review comments. A focused Jazz screen can keep missing information and questions visible on shared records.

After required review, share through an agreed export or supported interface. Record the version and intended use so recipients can identify the exact analysis and ask questions against it.

Explore analysis handoff workflows

Choose the analysis your team keeps preparing from scratch.

Bring a dataset, notebook or pipeline, and required output. Our scientists and engineers will scope one workflow and measure preparation time or review completeness.
Book a free consultation

FAIR data • pipelines • automation

Instruments • ELN/LIMS/SDMS • 200+ connectors

FAIR data • pipelines • automation

Instruments • ELN/LIMS/SDMS • 200+ connectors

FAIR data • pipelines • automation

Instruments • ELN/LIMS/SDMS • 200+ connectors

FAIR data • pipelines • automation

Instruments • ELN/LIMS/SDMS • 200+ connectors

Frequently asked questions

How is computational biology data management different from sample tracking?

Sample tracking follows material and laboratory history. Computational biology data management adds input datasets, methods, parameters, references and derived outputs. Scispot automates agreed preparation across those records. Genomics covers library preparation, pooling and sequencing; Computational / TechBio covers the broader loop between model proposals and experiments.

Can we keep our Python, R, notebooks, and existing compute environment?

Yes. Use embedded Jupyter workflows or supported connections while keeping your scientific methods and compute environment. Define input delivery and returned run references and outputs. External scheduling, environment management and monitoring depend on the actual tools. Approved AI assistants can use Scispot MCP within configured access and action rules.

Does recording analysis provenance make an analysis reproducible?

Provenance connects outputs to input versions, code or methods, parameters, reference releases and environment details. Scispot helps capture and reuse that context. An identical rerun also depends on the original methods and execution environment remaining available and behaving consistently; provenance alone cannot guarantee reproducibility.

How do we start when data is spread across databases and cloud folders?

Choose one recurring analysis. Our scientists and engineers map sources, identifiers, fields, methods and review needs with your team. Agree which files stay in existing storage and how data is accessed or exported. Configure and test representative cases, train users, then measure preparation time and adoption before expanding.

How are access, review history, and regulated analysis needs handled?

Configure permissions, audit history, source links and review workflows, with Trust Vault organizing evidence. Define access, execution, change approval, output review and sharing roles. Your organization sets regulated validation and acceptance criteria. Scientists and Quality retain assigned decisions; connected records do not certify method or model performance.

Software platforms manage tools. We manage outcomes.

Maintain access controls, history, source traceability and review evidence. Your team owns data permissions, methods, interpretation, reproducibility checks and any intended-use validation.