[White Paper] Optimized Workflows in the Life Sciences

Optimized Workflows: Towards Reproducible, Extensible and Scalable Bioinformatics Pipelines

Enthought Life Sciences | Bioinformatics Pipelines White Paper

A bioinformatics pipeline is an analysis workflow that takes input data files in unprocessed raw form through a series of transformations to produce output data in a human-interpretable form.

A bioinformatics pipeline evolves through five phases. Pipeline bioinformaticians first seek to collect and explore the essential components, including raw data, tools, and  references (Conception Phase). Then, they automate the analysis steps and investigate pipeline results (Survival Phase). Once satisfied, they move on to seek reproducibility and robustness (Stability Phase), extensibility (Success Phase), and finally scalability (Significance Phase).

Where are you in the development of the optimized bioinformatics pipeline?

To learn more, download the "Optimized Workflows: Towards Reproducible, Extensible and Scalable Bioinformatics Pipelines" white paper brought to you by the Enthought Life Sciences Solutions Group, and explore:

  • The five phases of bioinformatics pipelines and how to get to the next level
  • The implications for labs in a competitive marketplace
  • Real examples of challenges and solutions for each phase, related to: exploring protein reference databases, automating pipeline results visualization, improving a transcriptome analysis pipeline, transforming a metagenome pipelines framework, and handling memory exhaustion when scaling


Questions or want a consultation? Contact info@enthought.com to discuss how Enthought can help transform what’s possible in your R&D lab through optimizing workflows with machine learning and AI-assisted custom solutions.

Download White Paper

Share this article:

Related Content

Why Python?

Why Python? Of all of the questions that I have been asked as the instructor of an Enthought Python course, this has been one of…

Read More

3 Trends for Scientists To Watch in 2023

As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our…

Read More

Accelerating Science: the Classical Mechanics Perspective

When thinking about enhancing R&D processes, Newton’s second law of motion provides the perfect framework. Classical mechanics teaches us that putting a body into motion…

Read More

Retuning the Heavens: Machine Learning and Ancient Astronomy

What can we learn about machine learning from ancient astronomy? When thinking about Machine Learning it is easy to be model-centric and get caught up…

Read More

Announcing Enthought Academy

Dear Students and Friends of Enthought,  I am pleased to announce Enthought Academy—the culmination of over twenty years of teaching Scientific Python. Since our founding…

Read More

Extracting Target Labels from Deep Learning Classification Models

In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here,…

Read More

True DX in the Pharma R&D Lab Defined by Enthought

Enthought’s team in Japan exhibited at the Pharma IT & Digital Health Expo 2022 life sciences conference in Tokyo, to meet with pharmaceutical industry leaders…

Read More

Exploring Python Objects

Introduction When we teach our foundational Python class, one of the things we do is make sure that our students know how to explore Python…

Read More

Choosing the Right Number of Clusters

Introduction When I first started my machine learning journey, K-means clustering was one of the first algorithms I was introduced to – and it is…

Read More

Prospecting for Data on the Web

Introduction At Enthought we teach a lot of scientists and engineers about using Python and the ecosystem of scientific Python packages for processing, analyzing, and…

Read More

Join Our Mailing List!

Sign up below to receive email updates including the latest news, insights, and case studies from our team.