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What is g.nome?

g.nome is an advanced cloud-native platform meticulously crafted to deliver efficient, scalable, and interoperable workflows specifically designed for next-generation sequencing analysis. With its low-code/no-code framework, users can effortlessly construct pipelines while taking advantage of a carefully curated library of pre-built workflows and toolkits. This functionality enables researchers to seamlessly integrate their custom code, effectively manage extensive datasets, and promote collaboration among team members, no matter where they are located. By adopting g.nome, researchers can overcome enduring challenges associated with workflow languages, process flow visibility, and quality assurance. As a result, they can fully dedicate their efforts to their scientific pursuits, while g.nome simplifies the complexities of genomic workflows into streamlined, efficient processes. This groundbreaking platform not only enhances the ease of conducting research but also cultivates an atmosphere where scientific exploration can flourish without barriers. Ultimately, g.nome represents a significant leap forward in the way researchers approach genomic analysis, paving the way for future innovations.

What is Elucidata Polly?

Harness the power of biomedical data with the Polly Platform, which is specifically crafted to improve the scalability of batch processing, workflows, coding environments, and data visualization. By enabling resource pooling, Polly smartly allocates resources based on your unique requirements while also utilizing spot instances when advantageous. This feature leads to better optimization, enhanced efficiency, faster response times, and lower costs related to resource consumption. Moreover, Polly includes a real-time dashboard that tracks resource usage and expenses, significantly alleviating the resource management workload for your IT team. A key component of Polly's architecture is its dedication to version control, which ensures that your workflows and analyses remain consistent through a strategic integration of dockers and interactive notebooks. Additionally, we have developed a system that allows for the seamless integration of data, code, and the computing environment, thus promoting collaboration and reproducibility. With the inclusion of cloud-based data storage and project sharing options, Polly assures that every analysis you perform can be consistently reproduced and verified. Consequently, Polly not only streamlines your workflow but also nurtures a collaborative atmosphere that encourages ongoing refinement and innovation. This platform empowers users to focus on their research and leverage cutting-edge tools to achieve their objectives more effectively.

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Integrations Supported

Amazon S3
Amazon Web Services (AWS)
Bitbucket
Box
Docker
Dropbox
GitHub
Google Drive
Jupyter Notebook

Integrations Supported

Amazon S3
Amazon Web Services (AWS)
Bitbucket
Box
Docker
Dropbox
GitHub
Google Drive
Jupyter Notebook

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Almaden Genomics

Date Founded

2022

Company Location

United States

Company Website

almaden.io

Company Facts

Organization Name

Elucidata

Date Founded

2015

Company Location

United States

Company Website

elucidata.io

Categories and Features

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

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