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Biotechnology PharmaceuticalsTop 9 Best Plasmid Design Software of 2026
Top 10 Best Plasmid Design Software ranking for lab workflows, covering Benchling, SnapGene, and Geneious with key strengths and tradeoffs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Benchling
Construct versioning with feature-level traceability across linked sequences and annotations.
Built for fits when mid-size teams need visual workflow automation without code..
SnapGene
Editor pickCloning simulations generate a plan using the plasmid feature map and annotated sites.
Built for fits when mid-size teams need annotated plasmid planning and consistent outputs without heavy API orchestration..
Geneious
Editor pickMap-based plasmid editing keeps feature locations aligned across restriction and annotation views.
Built for fits when teams need annotated plasmid design with strong visual validation and light orchestration..
Related reading
Comparison Table
This comparison table maps plasmid design tools across integration depth, data model, automation, and API surface, focusing on how each system represents sequences, annotations, and construct schemas. It also compares extensibility options plus admin and governance controls such as RBAC, provisioning workflows, and audit log coverage to show operational tradeoffs. Readers can use the table to assess throughput and configuration effort for lab-scale design pipelines.
Benchling
LIMS-adjacentBenchling provides plasmid and DNA sequence design workflows tied to a governed data model, versioning, and audit logs with admin controls for regulated laboratory operations.
Construct versioning with feature-level traceability across linked sequences and annotations.
Benchling supports plasmid assembly planning by linking sequences, features, and construct versions in a structured schema. It keeps traceability with versioning, change history, and searchable annotations tied to constructs and parts. Integration depth is driven by extensibility hooks and an API surface that enables external LIMS, ELN, and ordering systems to stay consistent with the design state.
A tradeoff is that strict governance and schema organization require upfront configuration to match lab conventions for parts, feature naming, and construct templates. Benchling fits best when teams need controlled collaboration on shared constructs while maintaining auditability through RBAC and audit logs. Usage is most effective when automation updates metadata after design changes and when external tooling needs deterministic identifiers for throughput.
- +Sequence-linked construct data model with version history
- +RBAC plus audit log for controlled plasmid design collaboration
- +API and extensibility supports automation between design and ordering
- +Searchable feature annotations tied to specific construct revisions
- –Schema and template setup requires time to align with lab standards
- –Automation requires API discipline and stable naming conventions
Molecular biology teams
Maintain versioned plasmid construct records
Reduced rework and misalignment
Lab operations managers
Standardize parts and assembly workflows
Higher throughput design output
Show 2 more scenarios
IT and system integrators
Sync design data into LIMS
Fewer manual handoffs
Use the API and identifiers to mirror construct state into external systems for downstream steps.
Compliance-focused organizations
Audit who changed constructs
Stronger governance and traceability
Use RBAC and audit logs to document access and modifications to plasmid design assets.
Best for: Fits when mid-size teams need visual workflow automation without code.
SnapGene
desktop editorSnapGene supports interactive plasmid sequence editing, restriction mapping, primer design, and export of annotated construct files suitable for automated laboratory handoffs.
Cloning simulations generate a plan using the plasmid feature map and annotated sites.
Teams use SnapGene to manage plasmid sequences with persistent annotations, feature tables, and simulated restriction or assembly workflows. The data model centers on named sequence records with feature schema, which keeps primers, sites, and cloning outcomes tied to the same underlying map. Integration depth is strongest through interchange with standard sequence files and lab-facing workflows that depend on consistent annotations. Automation and extensibility are more workbook-like than service-oriented, so it fits lab throughput where fewer steps need code-driven orchestration.
A tradeoff appears when governance and enterprise controls need to be enforced across many users and pipelines. SnapGene’s automation focus supports interactive planning well, but it does not replace an API-led provisioning model for RBAC, audit log retention, and multi-system job orchestration. In practice it works best when a small group curates plasmids and primer sets, then shares outputs to downstream orders, reports, or lab execution systems.
- +Feature map stays synchronized with sequence edits and primer outputs
- +Cloning simulations reuse the same annotated plasmid schema
- +Good interoperability with common sequence and map artifacts
- –Limited API and automation surface for end-to-end pipeline orchestration
- –Enterprise governance features like RBAC and audit logs are not workflow-native
- –Extensibility relies more on interactive steps than programmable jobs
Molecular biology teams
Plan restriction-based cloning from annotated plasmids
Fewer annotation mismatches
Core facilities
Standardize primer and plasmid records
Higher ordering consistency
Show 2 more scenarios
Small R&D groups
Iterate plasmid edits and revalidate cloning
Faster construct turnaround
Interactive sequence edits update maps and cloning checks in the same workflow context.
Bioinformatics-adjacent labs
Transfer annotated sequences between tools
Lower reannotation effort
Export and import of plasmid annotations preserves feature schema across lab workflows.
Best for: Fits when mid-size teams need annotated plasmid planning and consistent outputs without heavy API orchestration.
Geneious
sequence workbenchGeneious delivers plasmid-centric sequence annotation and assembly workflows with project-level organization, export formats for construct transfer, and automation via scripting and plugins.
Map-based plasmid editing keeps feature locations aligned across restriction and annotation views.
Geneious supports plasmid design by letting teams build sequences from components, then validate via restriction site checks, feature tables, and alignment views inside the same project. Geneious integrates import and export paths that preserve feature annotations, including GenBank-style feature locations and qualifiers that plasmid ordering pipelines commonly consume. The data model centers on sequences and annotations tied to projects, which reduces drift between an edited plasmid map and downstream analysis results.
A tradeoff appears in automation and integration depth. Geneious is strongest when design work stays inside the Geneious UI and project model, but external orchestration depends on what the available automation and API surface exposes for project and feature operations. Geneious fits labs that need consistent feature annotation and visual review across plasmid iterations, even when automation covers only the most repeatable steps.
- +Project data model keeps sequence and feature annotations synchronized
- +Visual plasmid maps tie directly to restriction planning and feature edits
- +Import and export preserve GenBank-like feature coordinates and qualifiers
- +Extensibility supports scripted analyses tied to sequence objects
- –Automation depth for full end-to-end plasmid pipelines can be limited
- –External integration work may require building around Geneious data objects
- –RBAC and governance controls are not as granular as enterprise workflow suites
Molecular biology teams
Iterative plasmid edits with feature validation
Fewer annotation mismatches
Genomics core facilities
Vector builds from annotated parts
Consistent downstream inputs
Show 2 more scenarios
Bioinformatics analysts
Scripted sequence transformations on projects
Repeatable plasmid workflows
Extensibility links scripted steps to shared sequence and annotation objects for repeatability.
Lab operations teams
Governed review of plasmid records
Traceable design outcomes
Project history and annotation structure support auditability of design decisions and edits.
Best for: Fits when teams need annotated plasmid design with strong visual validation and light orchestration.
CLC Workbench
analysis suiteCLC Workbench supports DNA sequence analysis, cloning-related assembly steps, and batch processing workflows for plasmid construct manipulation.
Scripted and batch workflow execution that reproduces plasmid design steps across many constructs.
CLC Workbench supports plasmid design workflows with sequence-aware editing, primer and feature annotation, and export-ready construct maps for downstream lab use. Its strength centers on integration depth inside the workbench environment, where plasmid records and sequence features form a coherent data model for edits, validation, and visualization.
Automation and extensibility are driven through scripted batch processing and configurable workflow steps that can be repeated across constructs at higher throughput. Administration and governance are strongest where project-level access controls and controlled sharing patterns are paired with reproducible workflow configurations and traceable run outputs.
- +Sequence feature data model keeps edits and annotations consistent across constructs
- +Batch workflow execution enables higher-throughput plasmid redesigns
- +Configurable pipeline steps standardize construct validation and map generation
- +Exportable construct artifacts fit common wet-lab handoff formats
- –API surface is limited for external automation compared with programmable lab platforms
- –Schema changes across complex plasmid schemas can be slow to operationalize
- –Governance controls are less granular than RBAC-first design environments
- –Sandboxing workflows for untrusted design scripts needs extra process
Best for: Fits when teams need repeatable plasmid redesign workflows with controlled data handling.
UGENE
open-source suiteUGENE offers plasmid and DNA sequence editing with annotation, alignment, and analysis workflows, plus extensibility via plugins and automated pipeline execution.
Restriction map and feature annotation stay linked to sequence edits during design workflows.
UGENE performs plasmid sequence analysis and assembly planning inside a shareable, scriptable GUI workflow. The core capabilities include sequence alignment, restriction site mapping, plasmid feature visualization, and assembly and cloning workspace operations.
UGENE also supports automation via scripting and project-level configuration, which helps scale repeated plasmid design tasks across datasets. Extensibility is driven by its data model and plugin-oriented architecture, which allows integration into lab-specific pipelines through APIs and scripting hooks.
- +Plasmid feature visualization with restriction maps tied to sequence coordinates
- +Scriptable workflows for repeatable cloning and assembly planning
- +Extensible plugin architecture for custom analyses and annotation rules
- +Project data model supports structured transfer of plasmid design context
- –Automation surface depends on scripting patterns rather than declarative job specs
- –Large projects can be heavy to load when many annotations and edits are present
- –RBAC and audit logging controls are limited compared with enterprise lab platforms
- –API-centric integration requires building wrappers around GUI-driven workflows
Best for: Fits when labs need visual plasmid design plus automation via scripts and plugins.
ApE (A plasmid editor)
specialist editorApE provides interactive plasmid sequence editing and annotation with restriction sites, primer assistance, and file export for downstream laboratory planning.
Macro scripting over the plasmid document model for repeatable annotation and analysis steps.
ApE (A plasmid editor) fits groups that need interactive plasmid annotation and sequence editing with tight control over GenBank-style features. The workflow centers on a data model of sequence plus annotated feature tables, which supports common operations like restriction analysis, primer design, and map rendering.
ApE provides automation through macro scripts and command-style repeatability, but it does not offer an enterprise-grade API surface for external systems. Integration depth is therefore strongest inside the editor via extensibility and repeatable workflows, rather than across an admin-controlled lab data platform.
- +GenBank feature tables map cleanly to visible plasmid annotations
- +Macros enable repeatable workflows for editing, labeling, and analysis
- +Restriction and primer tools reuse annotation context from the feature model
- –Limited API and data export automation for external pipelines
- –GUI-first workflow slows throughput for large batch design runs
- –No documented RBAC, audit log, or governance controls for teams
Best for: Fits when labs need annotation-driven editing and scripting inside a desktop workflow.
DNASTAR Lasergene
suite for cloningDNASTAR Lasergene supports DNA and plasmid sequence analysis, annotation, and cloning-related workflows with structured document management.
Tightly coupled primer design and restriction analysis driven by annotated plasmid feature maps.
DNASTAR Lasergene targets plasmid design with sequence annotation, restriction site analysis, primer design, and cloning workflow steps inside a shared project structure. Its strength is tighter integration between sequence features, plasmid maps, and design constraints so edits propagate through downstream calculations like primer candidates and digest views.
Automation is mostly user-driven through repeatable protocols and batch operations rather than code-first orchestration. Extensibility exists through scripting and imported sequence workflows, but the automation and API surface is less documented for enterprise provisioning than typical SaaS-grade design systems.
- +Project-level linkage keeps plasmid maps, features, and primers synchronized
- +Restriction digest and primer design share the same feature coordinates
- +Batch workflows reduce manual redesign for routine construct updates
- +Scripting supports automation around sequence parsing and report generation
- –Automation relies more on local workflows than API-first orchestration
- –Automation governance features like RBAC and audit logs are not integration-ready by default
- –Extensibility is stronger for internal scripting than external system integrations
- –Cross-team configuration and sandboxing controls are limited compared with managed systems
Best for: Fits when labs need local plasmid design repeatability with scripting and batch redesign.
DNA sequencing data to plasmid design pipelines via SeqKit and scripting
CLI utilitiesSeqKit provides command-line sequence utilities used in plasmid design automation pipelines for format normalization, extraction, and batch sequence operations.
SeqKit command composition for batch FASTA and FASTQ normalization before plasmid design handoff.
DNA sequencing data to plasmid design pipelines via SeqKit and scripting targets file-level transformation, validation, and handoff into plasmid design steps. SeqKit provides batch FASTA and FASTQ operations with predictable filters, statistics, and format conversions that fit pipeline automation.
Bioinf.shenwei.me style scripting adds orchestration, branching, and naming conventions for multi-sample throughput across stages. Integration depth depends on the workflow data model and on whether the design steps consume standardized outputs with stable schemas.
- +SeqKit batch operations support FASTA and FASTQ filtering, stats, and format conversion
- +Scripting enables deterministic sample naming and directory conventions across pipeline stages
- +Workflow handoffs stay file-based, reducing schema coupling between steps
- +Extensibility comes from chaining SeqKit commands with custom parser logic
- –Data model stays implicit in filenames and folder paths instead of enforceable schema
- –Auditability requires custom logging in scripts rather than built-in audit log controls
- –RBAC and governance features are not available without external orchestration
- –Higher complexity pipelines increase maintenance burden for custom glue code
Best for: Fits when labs need automated file transformations from sequencing inputs into plasmid design inputs.
Biopython
programmatic toolkitBiopython exposes programmatic DNA sequence parsing, feature handling, and file conversion primitives that support custom plasmid design automation and validations.
Restriction enzyme site search and sequence slicing via Biopython restriction and sequence utilities.
Biopython generates and manipulates plasmid sequence records in Python using feature tables, sequence translation, and restriction-site utilities. Its data model centers on Seq, SeqRecord, and feature annotations, which makes integration with external design inputs and analysis outputs straightforward through code.
Automation relies on Python scripts and library functions for parsing, validating, and transforming constructs, with a large automation surface via importable modules. Integration depth comes from extensibility through custom parsers, feature annotations, and workflow composition rather than a built-in admin system.
- +Python-first data model with SeqRecord and annotated features for plasmid workflows
- +Restriction analysis and sequence manipulation functions cover common construct design steps
- +Composable library modules support script-based automation and repeatable transformations
- +Extensible parsing and feature annotation enables custom lab schemas and pipelines
- –No native plasmid-specific UI or workflow provisioning for non-coders
- –No RBAC or audit log features for admin governance in design environments
- –API surface is code-level only, which limits throughput from external services
- –Built-in schemas are light, so cross-team governance depends on custom conventions
Best for: Fits when teams need code-driven plasmid design, validation, and analysis integration with automation.
How to Choose the Right Plasmid Design Software
This guide compares plasmid design software for governed sequence data models, feature map synchronization, and automation surfaces. It covers Benchling, SnapGene, Geneious, CLC Workbench, UGENE, ApE, DNASTAR Lasergene, SeqKit-based file pipelines, and Biopython.
The focus stays on integration depth, data model behavior, automation and API surface, and admin and governance controls that affect controlled laboratory workflows. Each tool is mapped to concrete mechanisms such as RBAC with audit logs in Benchling and interactive-only automation limits in SnapGene.
Plasmid construct design software for sequence-linked records and traceable feature maps
Plasmid design software creates and edits DNA constructs while keeping annotations, restriction sites, and primer outputs aligned to the same underlying sequence and feature coordinates. These tools solve problems such as version drift between plasmid maps and sequence edits, inconsistent cloning plans across teams, and fragile file handoffs that break downstream steps.
Benchling represents construct work as governed records with version history and feature-level traceability, which keeps downstream design steps synchronized. SnapGene represents plasmid planning as an interactive sequence plus feature map workflow that stays synchronized inside exported annotated construct files.
Evaluation criteria tied to data control, integration, and automation throughput
Integration depth and automation surface determine whether a plasmid design step can be triggered, validated, and audited inside a broader lab workflow. Data model details determine whether sequence edits propagate through annotations, restriction analysis, and cloning simulations without manual repair.
Admin and governance controls determine whether teams can separate design access, capture change history, and support controlled sharing of design assets. Benchling and SnapGene illustrate the split between governed design records and interactive editor-focused workflows.
Sequence-linked construct data model with revision and feature traceability
Benchling keeps edits tied to a sequence-aware construct record with version history and feature-level traceability across linked sequences and annotations. Geneious also maintains a project data model that keeps sequence features aligned with vector maps and restriction planning views.
Feature map synchronization across annotation and downstream outputs
SnapGene keeps the feature map synchronized with restriction analysis and primer design outputs so exported maps preserve aligned sites. UGENE maintains restriction map and feature annotation linkage to sequence edits during design workflows.
API and automation surface for end-to-end workflow triggering
Benchling provides APIs and configurable workflows that connect lab steps to external systems while requiring stable naming conventions for automation to remain predictable. SnapGene and Biopython rely more on interactive steps or code-level scripting rather than offering a broad API-first surface for pipeline orchestration.
Documented extensibility points that fit lab-specific pipelines
Geneious supports extensibility via scripting and plugins that tie custom analyses to sequence objects. ApE uses macro scripting over the plasmid document model for repeatable annotation and analysis steps.
Admin governance with RBAC and audit logs for controlled access
Benchling includes RBAC plus audit logging for controlled plasmid design collaboration. Tools like SnapGene, ApE, and Biopython lack workflow-native enterprise governance controls such as RBAC and audit logs.
Batch and throughput mechanisms for repeated redesign across many constructs
CLC Workbench supports batch workflow execution so scripted and repeatable steps can reproduce plasmid design actions across many constructs. SeqKit-based file pipelines enable batch FASTA and FASTQ normalization and deterministic sample naming for high-throughput handoffs.
Choose by mapping automation needs to data governance and integration constraints
Start by classifying the required integration depth into your lab workflow so design steps can be triggered, validated, and audited across systems. Benchling fits when integration needs extend beyond exporting files into governed records and API-driven automation.
Then verify that the data model behavior supports feature-level traceability so restriction planning, primer candidates, and cloning simulations do not drift after edits. SnapGene and Geneious can work well when interactive synchronization and export consistency matter more than admin governance and external API orchestration.
Match governance needs to RBAC and audit log requirements
Select Benchling when multiple roles must collaborate on plasmid design assets with RBAC plus audit logs and feature-level traceability. Select tools without workflow-native governance, such as SnapGene or ApE, only when controlled sharing and audit requirements are handled outside the editor.
Confirm whether plasmid edits propagate through restriction, primer, and cloning steps automatically
Choose SnapGene when a synchronized feature map drives restriction analysis and primer outputs inside a single interactive workflow. Choose Geneious or UGENE when map-based editing and sequence-coordinate-linked restriction maps must stay aligned across views.
Decide between API-first orchestration and editor-centered interactivity
Choose Benchling for API and extensibility that connects design records to external systems through configurable workflows. Choose SnapGene, which concentrates automation in interactive steps and offers limited API surface for end-to-end pipeline orchestration.
Evaluate automation patterns for scale and repeatability
Choose CLC Workbench when batch workflow execution is required so scripted pipeline steps reproduce plasmid redesign actions at higher throughput. Choose SeqKit-based file pipelines when the immediate need is deterministic FASTA and FASTQ normalization and file-based handoff into design steps.
Plan for plugin or macro extensibility only after data model alignment is validated
Choose Geneious when plugin and scripting hooks must attach to sequence objects that preserve feature coordinates and qualifiers. Choose ApE when macro scripting over GenBank-style feature tables supports repeatable annotation steps inside a desktop workflow.
Set integration boundaries for code-level automation
Choose Biopython when Python-first validation and transformation are required through SeqRecord and feature annotations and when integration can be handled in code. Choose UGENE or CLC Workbench when repeatability must be expressed as scriptable GUI workflows with project-level configuration rather than purely code-level orchestration.
Tool fit depends on team size, governance expectations, and automation integration depth
Different plasmid design teams prioritize different control mechanisms such as RBAC audit trails, synchronized feature maps, or batch throughput across many constructs. The best fit tracks directly to which system owns the workflow state.
Benchling and CLC Workbench target teams that need controlled data handling and repeatability at throughput scale. SnapGene, Geneious, and UGENE fit teams that need tight interactive map synchronization and light orchestration rather than admin-grade governance and API-driven automation.
Mid-size teams that need governed design records with visual workflow automation
Benchling fits because it links construct work to a governed data model with version history, RBAC, and audit logs, which enables controlled collaboration on sequence-linked plasmid assets.
Mid-size teams that need annotated plasmid planning with consistent export outputs
SnapGene fits because its feature map stays synchronized with sequence edits and supports cloning simulations that generate a plan using the plasmid feature map and annotated sites.
Teams that emphasize visual validation of restriction planning with map-driven editing
Geneious and UGENE fit because map-based plasmid editing keeps feature locations aligned across restriction and annotation views and because restriction maps remain linked to sequence edits during design workflows.
Labs that must run repeatable redesign steps across many constructs with controlled handling
CLC Workbench fits because it supports scripted and batch workflow execution that reproduces plasmid design steps across many constructs and standardizes validation and map generation through configurable steps.
Labs that need file-level pipeline integration from sequencing inputs into design handoffs
SeqKit-based file pipelines fit because SeqKit supports batch FASTA and FASTQ operations with predictable filters, statistics, and format conversions that stabilize file-based handoffs into downstream plasmid design steps.
Common selection pitfalls that break traceability and slow automation
Many selection errors happen when automation and governance requirements are treated as afterthoughts or when feature map synchronization assumptions are not verified. Other failures come from choosing tools with limited external automation surface and then expecting them to act as orchestration engines.
The reviewed tools show specific failure modes such as missing RBAC and audit log controls, reliance on file-path conventions for data integrity, or governance gaps when scripts run outside managed workflows.
Expecting editor-focused tools to provide enterprise API-driven orchestration
SnapGene and Biopython concentrate automation in interactive steps or code-level scripts rather than providing a broad API-first surface for end-to-end pipeline orchestration. Benchling is the safer choice when automation needs span governed records through documented APIs and configurable workflows.
Ignoring how feature maps stay synchronized after sequence edits
Geneious and UGENE keep feature locations aligned across restriction and annotation views, which prevents drift after edits. Tools that rely on manual updates or GUI-only workflows without strong linkage can force time-consuming map repairs when restriction sites shift.
Choosing code-level integrations without planning for auditability and governance
Biopython supports sequence parsing and feature handling through Python objects, but it does not provide native RBAC and audit logs for design environments. Benchling provides RBAC plus audit logging when auditability must be part of the design record.
Using implicit file naming conventions as a substitute for a schema-backed data model
SeqKit-based file pipelines normalize and transform files effectively, but its workflow data model stays implicit in filenames and folder paths. Benchling uses construct records and feature-level traceability so schema alignment remains enforceable rather than inferred from paths.
Underestimating batch and throughput constraints for large redesign runs
ApE and interactive desktop workflows can slow throughput when many annotations and edits are required because GUI-first editing can become heavy for large batch design runs. CLC Workbench and UGENE support scripted and batch-oriented workflows that reproduce design steps across many constructs.
How We Selected and Ranked These Tools
We evaluated Benchling, SnapGene, Geneious, CLC Workbench, UGENE, ApE, DNASTAR Lasergene, SeqKit-based file pipelines, and Biopython using features, ease of use, and value, then we built an overall score as a weighted average where features carries the most weight while ease of use and value each matter as secondary factors. This criteria-based scoring framework favors concrete integration depth, data model control, automation and API surface, and governance controls that affect controlled laboratory collaboration. Benchling set the pace because it ties plasmid design to governed construct records with version history, RBAC plus audit logs, and APIs that support automation between design and ordering workflows, which lifted both features and practical usability.
Frequently Asked Questions About Plasmid Design Software
Which tools support a shared data model that propagates plasmid edits across downstream steps?
What’s the cleanest workflow for labs that need an API or automation surface rather than manual interactive steps?
How do these tools handle integrations with external pipelines and what formats become handoff contracts?
Which options provide admin controls like RBAC and audit logs for design assets?
Can labs migrate existing plasmid annotations and feature maps without breaking feature coordinates?
Which tool is best when primer design must stay grounded in annotated feature locations and cloning simulations?
What’s the typical approach for batch redesign at higher throughput?
How do extensibility mechanisms differ across a desktop editor versus a scriptable analytics platform?
Which option fits code-driven plasmid design and validation with custom feature parsing?
Conclusion
After evaluating 9 biotechnology pharmaceuticals, Benchling stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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