RNA-Seq and protein structure prediction are essential tools in modern biological research, facilitating insights into the molecular mechanisms of diseases and the development of potential therapies. RNA-Seq is a technique for profiling gene expression, enabling researchers to better understand gene regulation and complex interactions between genes. Protein structure prediction, on the other hand, provides information about a protein’s function and interactions with other molecules, which is invaluable in drug development by identifying target binding sites and optimizing drug candidates.
The Challenge
Handling the large volumes of data generated by RNA-Seq and protein structure prediction poses significant challenges for researchers. These techniques require substantial computational and storage resources for accurate analysis. Protein structure prediction is particularly complex due to the size and intricate binding dynamics of many proteins, making precise prediction difficult. Furthermore, the lack of ground truth structural data for many proteins complicates the validation of computational predictions, adding to the task’s complexity. Therefore, achieving accurate and reliable results necessitates a scalable and reproducible analytical approach.
The Solution
Recent advances in machine learning and cloud computing have opened new avenues for addressing these challenges. AlphaFold, a deep learning program developed by DeepMind, can accurately predict a protein’s 3D structure and its binding dynamics with other molecules from an input amino acid sequence. Nextflow, an open-source workflow management system, and Google Batch, a fully managed service to schedule, queue, and execute batch jobs on cloud computing resources, facilitate scalable and reproducible analysis of genomic data using Docker containers. Leveraging the power of Google Cloud, we have developed an end-to-end pipeline for RNA-Seq and protein structure prediction that utilizes BigQuery and Vertex AI to efficiently handle and process terabyte-scale data. By sharing our experience, we aim to provide insights into how Google Cloud can be used to tackle the computational challenges in modern biology and medicine, ultimately paving the way for new discoveries and innovations.