Client
Dr. David Joly, Associate Professor, Biology, Université de Moncton
ACENET Research Consultant
Dr. Serguei Vassiliev
Objective
Reduce the time involved in processing and analyzing rapidly growing volumes of DNA sequencing data.
Challenge
While modern sequencers can generate enormous datasets, turning that information into usable results requires coordinating more than a dozen specialized software tools, each with its own installation requirements and hardware demands. What should have been a straightforward analysis was often involving weeks of manual setup and troubleshooting.
Results
Working as an embedded technical specialist, Serguei collaborated closely with the researchers to understand their workflow and develop a solution tailored to their needs. He designed and implemented an automated pipeline that transforms what once required weeks of manual effort into a workflow that can be launched with a single command and completed in just one to two days.
The solution integrates key bioinformatics applications—including Dorado for DNA sequence processing, Flye for genome assembly, BUSCO for completeness assessment, and QUAST for quality evaluation—into a seamless workflow that converts raw sequencing data into complete, high-quality genome assemblies. Serguei also installed and configured additional machine learning–based tools to support advanced analyses such as genome annotation and detection of specific DNA patterns.
Running on Canada’s national high-performance computing infrastructure, the system distributes work efficiently between CPUs and GPUs to maximize performance. Serguei also resolved complex software compatibility issues, installed large reference databases, and created clear documentation and easy-to-use submission scripts that allow researchers to focus on science rather than software management.
The impact was immediate. Analyses that previously demanded weeks of manual preparation can now be launched with minimal effort and completed in one to two days, producing reproducible results that are easy to validate. Using the new pipeline, the researchers generated the first complete genome assemblies of the fungal pathogen Septoria cannabis, which causes leaf spot disease in hemp and cannabis plants.
Future
Through this collaboration, a robust and flexible bioinformatics analysis platform was developed that can readily incorporate new tools and workflows as research questions and computational methods evolve. The work has contributed to a scientific manuscript and established a scalable framework for comparative genomic analyses.
Building on this foundation, the group is now analyzing additional genomes from a wide range of cannabis pathogens and pests, enabling comparative studies that were previously impractical due to computational and resource limitations. Access to these analytical pipelines has fundamentally expanded their research capacity, allowing them to maximize the value of existing genomic datasets while identifying key knowledge gaps that will guide future data generation.
More importantly, the expertise and guidance provided through ACENET have opened new research avenues by making previously inaccessible experiments feasible, fostering the development of novel hypotheses and laying the groundwork for future collaborative projects in plant pathology, comparative genomics, and host–microbe interactions.