I approach bioinformatics workflows in a whole different way now that I’ve used Nextflow. It has evolved into a vital tool in my toolbox, capable of handling massive genomic datasets and guaranteeing repeatability and scalability. Thanks to Nextflow, I can automate and streamline tasks that used to take days or even weeks, whether it’s merging disparate software applications or simplifying intricate pipelines. To go beyond the constraints of conventional scripting (bash, perl, Python, R) for genomic data analysis was the driving force for my decision to work with Nextflow. Since then, it’s opened up new possibilities, leading me to collaborations, innovation, and now this exciting new role as an Ambassador! I am Pritam Kumar Panda, a bioinformatician working at the German Cancer Research Center DKFZ, Heidelberg, Germany.
My journey with Nextflow started almost by coincidence in 2023, immediately following the completion of my doctorate and the start of my job as a bioinformatician. With hindsight, I worked on several bioinformatics projects that needed me to coordinate several tools for analyzing genomics data. My initial approach (the only approach) was a patchwork of bash/R/Python scripts, each meticulously crafted but fragile in their dependence on specific software environments. As anyone who has ventured into bioinformatics knows, managing dependencies in such an environment is akin to walking a tightrope – one wrong move, and the whole thing can come crashing down.
At DKFZ, I was initially assigned tasks to streamline the alignment and variant calling workflow, which was scripted using multiple languages and had various issues with version conflicts. So to say, it was complicated and hard to understand. It was during one of these frustrating moments, while I was grappling with yet another software version conflict, that I stumbled upon Nextflow. During a casual YouTube search, I came across some byte-sized talks and other videos on Nextflow. Intrigued by what I saw and the possibilities, I decided to give it a try and see how it could help streamline my workflows.
I was initially baffled by the syntax and ideas that Nextflow presented; channels, processes, and the concept of containers were all unfamiliar to me. But as I kept going, the puzzle started to come together. The first pipeline I built was rudimentary, but it worked by following the famous RNA-seq pipeline tutorial by Marcel Ribeiro-Dantas. It handled the same steps my bash scripts did, but with far greater elegance and reliability. No more dependency issues, no more version conflicts – everything just worked. For me, this was a paradigm shift.
As I continued to explore Nextflow, I discovered the nf-core community. This was another turning point in my journey. Not only did nf-core provide a collection of high-quality, peer-reviewed pipelines, but it also introduced me to a network of like-minded individuals who were just as passionate about bioinformatics and open science as I was. I began contributing to the community by testing pipelines, providing feedback, and eventually, making my own pipelines. Each contribution, no matter how small, was met with encouragement and support, which only fueled my desire to learn and contribute more.
Fast forward to today, Nextflow has become an integral part of my bioinformatics toolkit. I’ve developed pipelines for various projects, ranging from genomics/transcriptomics to multi-omics integration (even suggested to my fellow mates to use Nextflow on a daily basis). The ability to scale these workflows from a local machine to a high-performance computing cluster or even the cloud (especially with the introduction of Data Studios in Seqera Platform) has opened up new possibilities for the kinds of analyses I can undertake. More importantly, the reproducibility that Nextflow ensures has made my research more robust and reliable.
I see Nextflow being extremely important to my next initiatives. I’m really enthusiastic about Nextflow’s interoperability with AI/ML technologies. Thanks to Seqera’s recent acquisition of tinybio, it is now feasible to use human-centric AI and LLMs for bioinformatics, which can translate complex biological data into actionable insights and expedite scientific advancement. If this is feasible, I would be more interested in developing AI/ML workflows to predict treatment responses for patients using public data sets like the Broad Institute’s DepMap.
As we all know, tailoring optimal treatment for individual cancer patients remains a significant challenge. Researchers trying to decipher or craft ML models based on single-cell (sc) expression data to showcase patient stratification using sc-expression profiles of patients’ tumors. Integrating these profiles into machine learning (ML) models to personalize treatments holds tremendous potential in precision oncology. Currently, the combination of multi-omics, especially single-cell techniques like scRNA-seq and scATAC-seq, offers deep insights into tumor heterogeneity, immune cell infiltration, and other microenvironment factors. However, the manual execution of R/Python scripts for each pipeline step can indeed be cumbersome and prone to error. If we leverage Nextflow to streamline this entire process, from data preprocessing to feature extraction, model training, and evaluation, it would significantly reduce the complexity of deploying these models. Automating the execution of multi-omics analysis pipelines would not only eliminate the need for manual intervention but also standardize the workflows. That standardization ensures reproducibility, consistency, and scalability, which are crucial for clinical adoption. Nextflow’s compatibility with various cloud computing environments, such as AWS and Google Cloud, also makes it easier to process large-scale single-cell and multi-omics data, further advancing the ML models. This approach would provide researchers and clinicians with ready-to-use, well-defined pipelines that take them from raw data to actionable insights, enabling them to focus on model development and interpretation rather than logistics. Integrating machine learning-driven, multi-omics-based stratification workflows into Nextflow could indeed be a game-changer for precision oncology, accelerating the development of personalized treatment strategies based on the deep molecular features of each patient’s tumor. The standardization that Nextflow brings to bioinformatics workflows could be a perfect complement to the data-driven approaches of machine learning, potentially leading to new insights and discoveries.
Recently, I was honored to become a Nextflow Ambassador, which has opened up new opportunities for me to share my knowledge and experiences with a broader audience. As part of the ambassador program, I began creating YouTube videos to help others learn and navigate Nextflow. Through these videos, I aim to break down complex concepts into more accessible content, guiding newcomers through the basics and offering tips to more advanced users (following the principles of Richard Feynman; “The Feynman Technique”). This role has not only allowed me to contribute more to the community but has also deepened my own understanding of Nextflow as I explore new ways to teach and inspire others.
In retrospect, discovering Nextflow has been a pivotal moment in my career, marking a significant transformation in the way I approach bioinformatics workflows. Before Nextflow, I was bound by the limitations of conventional scripting languages like bash, Python, and R—each tool serving its purpose but often leading to inefficiencies, version conflicts, and endless troubleshooting. However, Nextflow offered a fresh perspective, simplifying not only the technical execution of pipelines but also providing a more structured and scalable way to manage complex bioinformatics analyses. What began as a solution to streamline my own workflows quickly evolved into a key part of my professional identity. Nextflow didn’t just simplify my day-to-day tasks—it opened doors to collaboration with a global community of like-minded researchers and developers, fostering a sense of connection and shared purpose. I’ve learned, contributed, and grown alongside this community, which has been incredibly rewarding both professionally and personally. Without Nextflow, I doubt I would have been able to accomplish as much as I have in such a short time. It has allowed me to break through the limitations of manual scripting, enabling me to work on more ambitious projects involving multi-omics data, single-cell analysis, and even spatial transcriptomics. The potential for innovation in this space energizes me, and I’m eager to see how Nextflow will continue to elevate my research to new heights. With its powerful capabilities and ever-growing community, I feel confident that the future holds even greater possibilities for bioinformatics, precision oncology, and beyond.
This post was contributed by a Nextflow Ambassador. Ambassadors are passionate individuals who support the Nextflow community. Interested in becoming an ambassador? Read more about it here.