Category: About LabKey

Antibody Discovery Software for Emerging Biotechs

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The biologics development process is a data-driven endeavor. A vast amount of data is generated from many teams with the hopes of answering questions and informing decision-making along the way. Centralizing this data and their analyses across experiments amplifies the power and impact that data has on biologics research and development. 

Of course, centralizing biologics assay data means much more than simply storing data in the same place. Data centralization increases the value of biologics data beyond one particular experiment or result by defining data relationships. The ability to aggregate data across multiple samples, assays, and experiments, with metadata describing the relationship of all of these to each other, is where modern data science resides. Whereas the analysis of a single value from a single assay shows a distribution, joining that assay with others reveals a richer data landscape that is ripe for analysis. These data landscapes yield even more insights when they are related to one another across many controlled variables and conditions.

Below are more ways that implementing tools and strategies for data centralization can impact the speed and efficiency of biotherapeutic development. 

Data Integrity 

Having a single source of truth for data makes it easier for organizations to track how (and by whom) data is generated, accessed, and modified. With the assistance of a biologics data management application serving as a central hub-  permissions, auditing, and backups provide control over how, when, where, and by whom data is used. Centralization of data also encourages standardization of data formats across research teams. Standardizing data structures helps validate the data being entered and helps preserve the relationships between data. This leads to downstream efficiency and maximizes the utility of experiments beyond the individual or team from which it was derived. 

Collaboration 

Centralizing biologics research data also helps promote collaboration between research teams. By storing data in one central location in standardized formats, scientists can easily find, compare and reference existing data in their research. A central repository for data also makes it much easier to see what data is missing or needed to inform decisions. Hand-offs of data are also made easier when all data is centrally stored. 

About our Biologics LIMS

LabKey Biologics provides researchers with a set of tools to centralize biological entity registration, workflow management, and data exploration. 

  • Bioregistry – Register and track molecular entities, nucleotide sequences, protein sequences, expression systems, constructs, vectors, and cell lines
  • Biologics Assay Management – Connect design data to related multi-dimensional assay results for a complete data landscape.
  • Biologics Workflow Manager – Centrally manage biologics development workflows to help your team collaborate
  • Electronic Lab Notebook – Highlight and connect your research entities and data with our biologics ELN

Click Here to take a tour of LabKey Biologics.

Antibody Development with Biologics LIMS

Posted on by admin

The biologics development process is a data-driven endeavor. A vast amount of data is generated from many teams with the hopes of answering questions and informing decision-making along the way. Centralizing this data and their analyses across experiments amplifies the power and impact that data has on biologics research and development. 

Of course, centralizing biologics assay data means much more than simply storing data in the same place. Data centralization increases the value of biologics data beyond one particular experiment or result by defining data relationships. The ability to aggregate data across multiple samples, assays, and experiments, with metadata describing the relationship of all of these to each other, is where modern data science resides. Whereas the analysis of a single value from a single assay shows a distribution, joining that assay with others reveals a richer data landscape that is ripe for analysis. These data landscapes yield even more insights when they are related to one another across many controlled variables and conditions.

Below are more ways that implementing tools and strategies for data centralization can impact the speed and efficiency of biotherapeutic development. 

Data Integrity 

Having a single source of truth for data makes it easier for organizations to track how (and by whom) data is generated, accessed, and modified. With the assistance of a biologics data management application serving as a central hub-  permissions, auditing, and backups provide control over how, when, where, and by whom data is used. Centralization of data also encourages standardization of data formats across research teams. Standardizing data structures helps validate the data being entered and helps preserve the relationships between data. This leads to downstream efficiency and maximizes the utility of experiments beyond the individual or team from which it was derived. 

Collaboration 

Centralizing biologics research data also helps promote collaboration between research teams. By storing data in one central location in standardized formats, scientists can easily find, compare and reference existing data in their research. A central repository for data also makes it much easier to see what data is missing or needed to inform decisions. Hand-offs of data are also made easier when all data is centrally stored. 

About our Biologics LIMS

LabKey Biologics provides researchers with a set of tools to centralize biological entity registration, workflow management, and data exploration. 

  • Bioregistry – Register and track molecular entities, nucleotide sequences, protein sequences, expression systems, constructs, vectors, and cell lines
  • Biologics Assay Management – Connect design data to related multi-dimensional assay results for a complete data landscape.
  • Biologics Workflow Manager – Centrally manage biologics development workflows to help your team collaborate
  • Electronic Lab Notebook – Highlight and connect your research entities and data with our biologics ELN

Click Here to take a tour of LabKey Biologics.

Evaluating an SDMS for Life Science Research

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For life science organizations, evaluating and selecting a data management system is a critical process that can have a lasting impact on their research. Research data is among the most valuable assets that an organization possesses due to the enormous amount of time, effort, and resources spent in its collection and analysis. Whether from lab instruments, a clinical trial or an observational study, data is central to decision-making, and ultimately to scientific discovery. 

When evaluating data management systems, focus on scalability and flexibility.

Efficiently managing research data requires a data management system that is specifically tailored to the ever-changing needs of scientific research. Planning for the future needs of your organization is essential when choosing a data management system. Research organizations may need to plan for:

  • Expanding their lab, research team or collaborators
  • Shifting into new experiments, instruments, assays, therapeutic areas or types of data
  • Dealing with exponential growth in the volume of data being captured
  • Experiencing increased needs for compliance, security, privacy, data sharing, provenance tracking, reproducibility

The data management system you choose should be able to handle any of the scenarios above. By focusing on your future needs you can avoid being locked into a system that will not grow and adapt to new requirements. Under this guiding principle, Our recent webinar outlined six areas that are critical to choosing the right data management system for your organization.


When evaluating a data management system look for:

  1. Robust security and compliance controls
    The first requirement for any data management system is that it meets the security and compliance needs of your organization and area of research. Without meeting these requirements the system will likely hit internal roadblocks and may be deemed insufficient for your organization and use case.
  2. Ability to handle large volumes of diverse data
    Your data management system should be designed to efficiently bring together and integrate large volumes (millions of rows, hundreds of columns) of diverse data. A clinical trial may include demographics data, sample data, instrument data, and clinical data. Your system should be able to centralize and align all of these data types for efficient and accurate analysis.
  3. Support for automated, high throughput data acquisition
    Large volumes of data necessitate automation for scalable and efficient workflows. However, data files are often imperfect and automating their import requires robust quality control functions to ensure the accuracy and integrity of data within the system.
  4. Integration of disparate data and metadata
    Beyond acquiring large volumes of data using automation, the system must integrate and annotate data in scientifically relevant ways. For example, a clinical trial may need to align sample, assay, and clinical data via participants and visits. By using clinical ontologies, your system should be able to harmonize your data in meaningful ways.
  5. Support for data analytics systems
    When deciding what system support is required for analytic tools, it is imperative that you include your research team and the larger organization. The system you choose should support a wide diversity of analytic tools. This includes both native tools within the system as well as third-party analysis software.
  6. Actively developed and supported by the vendor
    Choosing a system that is actively being developed and supported by the vendor is key to the ability of the system to grow and adapt to the changing needs of your organization. Like scientific research, technology is consistently evolving and your vendor should be dedicated to the success of your organization and research goals.

Watch the webinar below to learn more:[vc_video link=”https://youtu.be/jIubcWkiPwo” align=”center”]

Does Your Biotech Startup Need a LIMS?

Posted on by will

Antibody Development with LabKey Biologics

A key function of LabKey Biologics is assisting with antibody development and registration. Registration allows all data from antibody production processes to be associated with the candidate. It also allows for the optimization of processes at each stage of antibody development by facilitating the comparison of antibody experiment data.

Registering Antibodies and Components

To register antibodies, LabKey Biologics provides an easy-to-use Bioregistry. Users can add an antibody description, aliases, and details about the molecule’s lineage. Then, users select the components of this molecule, generally composed of one or more protein sequences, and finalize the stoichiometry. The Bioregistry verifies that this entity, defined by its components and their stoichiometry, has not previously been registered. 

Users can also register molecules in bulk through the user interface, or via API. This includes support for importing GenBank files.

Tracking Component Relationships

Antibody development typically utilizes an expression system to produce samples of a molecule and its variations. LabKey Biologics stores key physical characteristics about registered molecules and presents users with captured information about its components, with linkage to the expression system from which it is derived. The relationship between an expression system and its target molecule can be determined from the construct, media, and gene inserts used in the expression system. Users are able to import biologics assay data for samples derived from that expression system for comparison and deeper analytics. When a user registers a new expression system in LabKey Biologics, the application makes an explicit connection between it and the molecule of interest. This relationship is valuable for visualizing and navigating between the related expression system data and the molecule data.

Antibody Development Workflow 

Proper coordination of the multifaceted activities involved with antibody development is essential to ensure high quality results. Research managers must orchestrate all activities in their lab to facilitate communication, prevent bottlenecks, and prevent data from becoming siloed. LabKey Biologics provides a centralized system for managing ongoing research workflows and storing data generated across team members. This includes features to generate configurable work requests and track laboratory tasks. By maintaining the relationship between tasks and their resulting data LabKey Biologics ensures smooth handoffs and provides researchers with a holistic view of research operations and workflow progress. For example, a user can navigate from assay result data to the original request for it, facilitating operational visibility.

Additional Resources

LabKey biologics has features to assist researchers in nearly every stage of the biotherapeutic development pipeline. To learn more about LabKey Biologics, please explore the links below.

> Bioregistry
> Biologics Workflow Manager
> Biologics Assay Management
> Electronic Lab Notebook

To request a demo of LabKey Biologics, click here.

Antibody Development with LabKey Biologics

Posted on by admin

Antibody Development with LabKey Biologics

A key function of LabKey Biologics is assisting with antibody development and registration. Registration allows all data from antibody production processes to be associated with the candidate. It also allows for the optimization of processes at each stage of antibody development by facilitating the comparison of antibody experiment data.

Registering Antibodies and Components

To register antibodies, LabKey Biologics provides an easy-to-use Bioregistry. Users can add an antibody description, aliases, and details about the molecule’s lineage. Then, users select the components of this molecule, generally composed of one or more protein sequences, and finalize the stoichiometry. The Bioregistry verifies that this entity, defined by its components and their stoichiometry, has not previously been registered. 

Users can also register molecules in bulk through the user interface, or via API. This includes support for importing GenBank files.

Tracking Component Relationships

Antibody development typically utilizes an expression system to produce samples of a molecule and its variations. LabKey Biologics stores key physical characteristics about registered molecules and presents users with captured information about its components, with linkage to the expression system from which it is derived. The relationship between an expression system and its target molecule can be determined from the construct, media, and gene inserts used in the expression system. Users are able to import biologics assay data for samples derived from that expression system for comparison and deeper analytics. When a user registers a new expression system in LabKey Biologics, the application makes an explicit connection between it and the molecule of interest. This relationship is valuable for visualizing and navigating between the related expression system data and the molecule data.

Antibody Development Workflow 

Proper coordination of the multifaceted activities involved with antibody development is essential to ensure high quality results. Research managers must orchestrate all activities in their lab to facilitate communication, prevent bottlenecks, and prevent data from becoming siloed. LabKey Biologics provides a centralized system for managing ongoing research workflows and storing data generated across team members. This includes features to generate configurable work requests and track laboratory tasks. By maintaining the relationship between tasks and their resulting data LabKey Biologics ensures smooth handoffs and provides researchers with a holistic view of research operations and workflow progress. For example, a user can navigate from assay result data to the original request for it, facilitating operational visibility.

Additional Resources

LabKey biologics has features to assist researchers in nearly every stage of the biotherapeutic development pipeline. To learn more about LabKey Biologics, please explore the links below.

> Bioregistry
> Biologics Workflow Manager
> Biologics Assay Management
> Electronic Lab Notebook

To request a demo of LabKey Biologics, click here.

What’s New in LabKey 22.7!

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LabKey Server

  • The Text Choice data type provides an easy way to define a set of expected text values for a field. (docs)
  • Sample naming patterns are validated during sample type definition. Example names are visible to users creating new samples. (docs)
  • Queries can be included in published studies. (docs)
  •  Ontology insert and update forms now support type ahead filtering for quick input into ontology lookup fields. (docs)

Sample Manager

  •  Sample Finder: Find samples based on source and parent properties, giving users the flexibility to locate samples based on relationships and lineage details. (docs)
  •  New Storage Editor and Storage Designer roles, allowing admins to assign different users the ability to manage freezer storage and manage sample and assay definitions. (docs)
  •  Redesigned main dashboard featuring storage information and prioritizing what users use most. (docs)

Biologics

  •  ELN Improvements:
    • Notebook entry locking and protection: Users are notified if someone else is already editing a notebook entry and prevented from accidentally overwriting their work.
    • Find notebooks easily based on a variety of filtering criteria. (docs)
  • Freezer Management for Biologics: Create a virtual match of your physical storage system, and then track sample locations, availability, freezer capacities, and more. (docs)
  •  User-defined barcodes can also be included in sample definitions and search-by-barcode results. (docs)

Premium Resources

See the full Release Notes 22.3 (March 2022)

Clinical Sample Management & Compliance in the Lab

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Clinical sample management requires laboratories to put a significant amount of thought and effort into record-keeping and reporting in order to meet compliance requirements. Clinical sample management complianceRegardless of the type of compliance (HIPAA, Part 11, GLP, etc),  managing clinical samples requires a commitment to actively maintain an audit ready-state. For many labs that are getting started with clinical sample management, this can be a daunting task. Check out the 3 tips below to get started. 

Clinical Sample Management Compliance Tips

1. Traceability of Clinical Samples 

Your staff should be able to the “what, when, how, who” of every human-derived sample in the lab. Sample tracking should include the capture of all details around the collection, receipt, storage, processing and handling samples.

What? – Document the type(s) of samples you will receive/create including the descriptive metadata that you want to collect for each sample type.
When? – Capture dates for all events in the lifecycle of a sample including when it was collected, received, processed, stored, and shipped.
How? – Track step-by-step how tasks were performed in the lab in relation to a sample. Utilize sample management software to capture SOPs in workflows and audit trails.
Who? – Capture who in the lab performed any work or handling of the samples. This is both valuable for accountability and troubleshooting.

2. Sample Identification & Barcoding

Clinical samples should have clear and unique identifiers that are appropriate to the laboratory type. Among other considerations, do your samples need to be de-identified for a specific laboratory or can they have PHI in the identifier? Do your naming patterns easily provide information and context for a given sample? 

Sample Receipt – Consider how samples will be labeled from the collection site and received into the laboratory. Do they need to be de-identified before use in the lab?

Laboratory-Specific Identification – Define naming patterns that account for the uniqueness of your samples, aliquots, derivatives, and repeat samples. Although you may also use barcoding, consider having your identifier be “human-readable” so that lab staff can identify samples on-the-fly. 

3. Standard Operating Procedures

When managing clinical samples it is essential to have clear SOPs that document sample processing tasks and workflows.  SOPs should accurately document the work to be done in the lab as well as capture any results from sample processing tasks.

Document – With the help of your team, draft an example of the sample lifecycle in the lab. Be sure to capture areas of possible deviation, key variables and decision points.
Create/Update SOPs – Compare the sample lifecycle with your SOPs to ensure procedures are aligned with what is taking place in the lab.
Evaluate – Conduct a “trial run” of your clinical sample intake, processing and storage to ensure SOPs accurately reflect real-world procedures.
Maintain – Define a schedule to regularly review processes and track any changes to SOPs using documented change requests. Internal audits should also be scheduled at set intervals to keep your lab in an “audit-ready” state.

4. Record Keeping

All work performed on clinical samples should be documented and include the same  “what, when, how, who” details mentioned before. This should be a record of the actual work that was done, versus your SOPs, which layout a plan and methodology for the work. Keep regulatory requirements in mind when determining how this documentation will be captured, stored and shared.

Clinical Sample Management with LabKey Sample Manager

Efficient and compliant management of clinical samples requires sample management software that is both powerful and easy to use. Sample Manager is a cloud-based sample management system that helps:

  • Track the full history and chain-of-custody for each sample.
  • Register and definine samples using all relevant metadata 
  • Manage task-based workflows that accurately reflect SOPs
  • Support barcoding and unique, human-readable sample ID creation
  • Retain records of the complete life-cycle of each sample.
  • Easily manage freezers and sample storage 

Click here to learn about Sample Manager and take a product tour!

We asked lab managers… What do you love most about Sample Manager?

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Sample Manager, sample management softwareIt’s no surprise that our users are overwhelmingly happy with the ease and efficiency that Sample Manager has brought to their labs. After all, Sample Manager was created in close consultation with labs of all shapes and sizes to ensure that we were creating the best sample management software on the market. We recently asked lab managers what they love most about Sample Manager and how it has impacted their labs. Their responses varied, but all shared a common theme- Sample Manager makes lab work easier and faster.   

“Sample Manager is so easy to use. It makes all of our days easier and more productive.”

With a beautifully simple user interface and intuitive features that are essential for modern laboratories, Sample Manager is designed to bring ease and efficiency to sample management tasks. From bulk importing and storing samples to tracking lab workflows and managing data, Sample Manager makes the management of lab samples faster and easier than ever before. 

“The Sample Timeline is amazing, I can finally see the complete history of a sample and who in the lab has touched it.”

Our Sample Timeline feature provides an audit-ready view of the complete history of each sample. By reviewing the chain-of-custody, you can easily see the “who, what, when and where” for each event in the timeline of a sample. Events may include sample registration, storage changes, sample check-in/check-out events, assay runs and more. The sample timeline can also be exported to Excel for further analysis and review.

“No more searching for lost samples! The freezer management features alone have saved us so much time.”

Helping our users efficiently search their sample inventory and manage their freezer storage is a core function of Sample Manager. Our flexible freezer management tool allows you to create an exact match of your physical freezer storage options within the application. You can also use our Sample Finder tool to build custom queries to search, group and take action on related samples.

“The lineage tracking capability has been a gamechanger. We can now easily tie samples back to their sources and aliquots that have been created.“

Tracking samples and their derivatives has been made as easy and intuitive as possible within Sample Manger. Using a visual lineage tree, users can easily see the context and details of samples including all sources and aliquots. We’ve also streamlined the creation of aliquots from samples to boost efficiency in the lab. 

Learn More About Sample Manager:

Clinical Sample Management – 4 Essential Questions for Better Tracking & Compliance in the Lab

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Clinical sample management poses unique compliance challenges for laboratories. Sample collection, tracking and consent all become more regulated (GLP, GCLP, HIPAA) when managing clinical samples, and require a greater depth of record-keeping and reporting. Although keeping up with these regulations can seem like a daunting task, you can simplify and get a good start on improving regulatory compliance by focusing on 4 basic sample tracking questions: What? When? How? and Who?.

Clinical Sample Management

Lab staff should be able to answer these questions for every clinical sample managed in the lab. In order to do this, your lab will need to capture all details around the collection, receipt, storage, processing and handling of samples. With each sample requiring the capture of so many data points, it is essential to have modern sample management software designed to support the management of clinical samples.

What? – Fully and accurately define your samples, sample types and sources.
Document the type(s) of samples you will receive/create including the descriptive metadata that you want to collect for each sample type. Make sure to consider the source of the sample (perhaps a clinical trial participant), derivatives, and any other related details you need to capture. 

When? – Track dates for everything related to your samples.
Capture dates for all events in the lifecycle of a sample including when it was collected, received, processed, stored, and shipped. This data should be readily available and exportable for further analysis during an audit.

How? – Capture everything that has been done to your samples in detail.
Track step-by-step how tasks were performed in the lab in relation to a sample. This includes all of the steps taken during sample receipt, checking of tubes for breakages or possible contamination and documenting storage conditions. Utilize a sample management system to capture SOPs in lab workflows and audit trails.

Who? – Capture who has worked with your samples.
Capture who in the lab performed any work or handling of the samples. This is both valuable for accountability and troubleshooting in the lab as well as for supplying auditors with additional information that they may need.

Clinical Sample Management with LabKey Sample Manager

Clinical Sample Management and TrackingEfficient and compliant management of clinical samples requires sample management software that is both powerful and easy to use. Sample Manager is a cloud-based sample management system that helps:

  • Track the full history and chain of custody for each sample.
  • Register and define samples using all relevant metadata 
  • Manage task-based workflows that accurately reflect SOPs
  • Support barcoding and unique, human-readable sample ID creation
  • Easily manage freezers and sample storage 

Click here to learn about Sample Manager and take a product tour!