This week, Harshil Patel, Director of Scientific Development at Seqera, joins the Data in Biotech podcast to discuss the importance of collaborative, open-source projects in scientific research and how they support the need for reproducibility.

Harshil lifts the lid on how Nextflow has become a leading open-source workflow management tool for scientists and the benefits of using an open-source model. He talks in detail about the development of Nextflow and the wider Seqera ecosystem, the vision behind it, and the advantages and challenges of this approach to tooling.

He discusses how the nf-core community collaboratively develops and maintains over 100 pipelines using Nextflow and how the decision to constrain pipelines to one per analysis type promotes collaboration and consistency and avoids turning pipelines into the “wild west.”

We also look more practically at Nextflow adoption as Harshil delves into some of the challenges and how to overcome them.

He explores the wider Seqera ecosystem and how it helps users manage pipelines, analysis, and cloud infrastructure more efficiently, and he looks ahead to the future evolution of scientific research.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

---

Chapter Markers

[1:23] Harshil shares a quick overview of his background in bioinformatics and his route to joining Seqera.

[3:37] Harshil gives an introduction to Nextflow, including its origins, development, and the benefits of using the platform for scientists.

[9:50] Harshil expands on some of the off-the-shelf process pipelines available through NFcore and how this is continuing to expand beyond genomics.

[12:08] Harshil explains NFcore’s open-source model, the advantages of constraining pipelines to one analysis per type, and how the Nextflow community works.

[17:43] Harshil talks about Nextflow's custom DSL and the advantages it offers users

[20:23] Harshil explains how Nextflow fits into the broader Seqera ecosystem.

[26:08] Ross asks Harshil about overcoming some of the challenges that arise with parallelization and optimizing pipelines

[28:01] Harshil talks about the features of Wave, Seqera’s containerization solution.

[32:16] Ross asks Harshil to share some of the most complex and impressive things he has seen done within the Seqera ecosystem.

[35:42] Harshil gives his take on how he sees the future of biotech genomics research evolution.

---

Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

Visit this link: https://connect.corrdyn.com/biotech-ml

This week, we are pleased to have Stewart Fossceco, Head of Non-Clinical and Diagnostics Statistics at Zoetis and an expert in pharmaceutical manufacturing, join us on the Data in Biotech podcast.

We sat down with Stewart to discuss implementing and improving Quality Assurance (QA) processes at every stage of biotech manufacturing, from optimizing assay design and minimizing variability in early drug development to scaling this up when moving to full production. Stewart talks from his experiences on the importance of experimental design, understanding variability data to inform business decisions, and the pitfalls of over measuring.

Along with host Ross Katz, Stewart discusses the value of statistical simulations in mapping out processes, identifying sources of variability, and what this looks like in practice. They also explore the importance of drug stability modeling and how to approach it to ensure product quality beyond the manufacturing process.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

---

Chapter Markers

[1:39] Stewart starts by giving an overview of his career in biotech manufacturing.

[3:54] Stewart talks about optimizing processes to control product quality in the early stages of the drug development process.

[7:27] Ross asks Stewart to speak more about how to optimize and minimize the variability of assays to increase confidence in clinical results.

[12:11] Stewart explains the importance of understanding how assay variability influences results and how to handle this when making business decisions.

[14:13] Ross and Stewart discuss the issue of assay variability in relation to regulatory scrutiny.

[17:07] Stewart walks through the benefits of using statistical simulation tools to better understand how an assay performs.

[19:49] Stewart highlights the importance of understanding at which stage sampling has the greatest impact on decreasing variability

[22:09] Stewart answers the question of how monitoring processes change when moving to full production scale.

[26:39] Stewart outlines stability modeling and the importance of stability programs in biotech manufacturing.

[30:38] Stewart shares his views on the biggest challenges that biotech manufacturers face around data.

---

Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

Visit this link: https://connect.corrdyn.com/biotech-ml

This week, we are pleased to welcome to the Data in Biotech podcast Brendan McCorkle, CEO of SciNote, a cloud-based ELN (Electronic Lab Notebook) with lab inventory, compliance, and team management tools.

In this episode, we discuss how the priorities of ‘Research’ and ‘Development’ differ when it comes to the data they expect and how they use it, and how ELNs can work to support both functions by balancing structure and flexibility. We explore the challenges of developing an ELN that serves the needs and workflows of all stakeholders, making the wider business case for ELNs, and why, in the lab, paper and Excel need to be a thing of the past.

Brendan is upfront about the data challenges faced by biotechs, which do not have one-vendor solutions. He emphasizes the importance of industry collaboration and software vendors’ role in following the principles of FAIR data. We also get his take on the future of ELNs and how they can leverage AI and ML.

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

Chapter Markers

[1:40] Brendan gives a whistlestop tour of his career and the path to setting up SciNote.

[4:20] Brendan discusses the principles of FAIR data and the challenges of adhering to them in the biotech industry.

[6:15] Brendan talks about the need to balance flexibility and structure when collecting R&D data.

[13:34] Brendan highlights the challenge of catering to diverse workflows, even within the same company.

[16:05] Brendan emphasizes the importance of metadata and how vendors, like SciNote, can help collect it with flexible tools for data entry and post-processing.

[18:59] Ross and Brendan discuss how to create an ELN that serves all stakeholders within the organization without imposing creativity constraints on research scientists.

[21:57] Brendan highlights how benefits like improving loss reduction and efficiency form part of the business case for a tool like SciNote.

[24:25] Brendan shares real-world examples of how companies integrate SciNote into their organizations and the need to work with other systems and software.

[34:01] Ross asks for his advice to biotech companies considering implementing ELNs, particularly into their workflows.

[39:10] Brendan gives his take on incorporating ML and AI within SciNote.

---

Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.”

Visit this link: https://connect.corrdyn.com/biotech-ml