Lazypipe Bioinformatics Pipeline Series
The Lazypipe series is a collection of bioinformatics pipelines developed by our research group to enhance the discovery and analysis of viruses from metagenomic next-generation sequencing (mNGS) data.
Lazypipe was introduced as a novel pipeline aimed at identifying both known and novel viruses from a wide range of host-associated and environmental samples. This Unix-based pipeline automates the assembly and taxonomic profiling of NGS libraries using a combination of C++, Perl, and R scripts. It addresses the urgent need for reliable methods to detect previously unknown viruses, especially in light of emerging infectious diseases, such as those highlighted during the COVID-19 pandemic (Plyusnin et al., 2020).
Lazypipe 2 built upon the original framework, incorporating significant improvements in code stability and transparency. This updated version enhanced functionality and added support for new software components, making it more robust for virus detection across various sample types. Extensive benchmarking demonstrated its superior accuracy in detecting eukaryotic viruses, outperforming other pipelines in precision and recall, particularly in low viral genetic material scenarios (Pljusnin et al., 2023).
Lazypipe 3 further advanced the capabilities of the pipeline by introducing customizable annotation strategies tailored to specific datasets and research objectives. This version emphasized speed and efficiency, significantly reducing execution times while maintaining high accuracy in virus detection. Notably, Lazypipe 3 was able to discover and characterize multiple novel whole-genome viral sequences that had previously gone undetected. It also featured improved background filtering and interactive reporting tools for visualizing data, making it a versatile resource for virome analysis (Weinstein et al., 2025).
For more detailed information, please refer to the following publications:
HaVoC: Helsinki University Analyzer for Variants of Concern
HaVoC is a specialized bioinformatics pipeline developed for reference-based consensus assembly and lineage assignment of SARS-CoV-2 sequences. During the COVID-19 pandemic, HaVoC played a crucial role in the rapid detection and monitoring of variants of concern, integrating multiple bioinformatics tools to perform essential analyses that investigate genetic variance among SARS-CoV-2 samples.
In addition to its application in the COVID-19 pandemic, HaVoC has been slightly modified to assist in the analysis of the Mpox outbreak, demonstrating its versatility in addressing emerging infectious diseases. The pipeline's adaptable framework allows it to be utilized for similar viruses, making it a valuable resource for ongoing efforts in viral surveillance and outbreak management.
For more detailed information, please refer to the publication:
Nguyen, T. P., Plyusnin, I., Sironen, T., Vapalahti, O., Kant, R., & Smura, T. (2021). HAVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences. BMC Bioinformatics, 22, 373. https://doi.org/10.1186/s12859-021-04294-2
ClusTRace: A Bioinformatics Pipeline for Analyzing Clusters in Virus Phylogenies
ClusTRace is an innovative bioinformatics pipeline designed for the rapid and scalable analysis of sequence clusters or clades within large viral phylogenies. Developed in response to the global challenge posed by SARS-CoV-2, the highly transmissible virus responsible for COVID-19, ClusTRace addresses the urgent need for early detection and in-depth analysis of emerging variants. The emergence of new SARS-CoV-2 variants has raised significant concerns regarding prevention and treatment, making timely analysis essential for public health efforts.
ClusTRace offers a comprehensive suite of functionalities, including lineage assignment, outlier filtering, sequence alignment, phylogenetic tree reconstruction, cluster extraction, variant calling, visualization, and reporting. It was specifically developed to aid in tracing COVID-19 transmission chains in Finland, focusing on the rapid screening of phylogenies for markers indicative of super-spreading events and other critical features, such as high rates of cluster growth and the accumulation of novel mutations. Importantly, ClusTRace is versatile and can be adapted for use with any emerging virus, making it a valuable tool for a wide range of viral surveillance and outbreak management efforts.
By providing an effective interface, ClusTRace significantly reduces the learning curve and operational costs associated with complex bioinformatics analyses of large viral sequence datasets. The code for ClusTRace is freely available, promoting accessibility and collaboration within the scientific community.
For more detailed information, please refer to the publication:
Plyusnin, I., Truong Nguyen, P. T., Sironen, T., et al. (2022). ClusTRace, a bioinformatic pipeline for analyzing clusters in virus phylogenies. BMC Bioinformatics, 23, 196.
The code can be accessed at: ClusTRace Repository
LazypipeNP: A Bioinformatics Pipeline for Long-Read Oxford Nanopore Metagenomics Data Analysis
LazypipeNP is an emerging bioinformatics pipeline specifically designed for the analysis of long-read metagenomics data generated by the Oxford Nanopore platform. This pipeline not only facilitates the analysis of metagenomic samples but is also capable of performing full 16S taxonomic analysis, making it a versatile tool for researchers studying microbial diversity and pathogen detection.
In addition to its core functionalities, we are developing portable versions of both Lazypipe and LazypipeNP, which can be deployed in offline settings with local custom databases tailored for specific pathogens. This feature makes them ideal for use in field settings as part of a suitcase laboratory, enabling rapid and efficient analysis in real-time during outbreaks.
To enhance accessibility, we are also creating graphical user interfaces (GUIs) for these pipelines. This development aims to empower biologists without extensive knowledge of command line operations, scripting, or bioinformatics to utilize these powerful tools effectively.
Furthermore, we are developing a GUI for the Vicon pipeline, which uses K-mers to predict conserved regions in viral genomes. The Vicon pipeline is designed to streamline the identification of conserved sequences, aiding in the rapid detection of emerging viruses and their variants.
Overall, these advancements in LazypipeNP, along with the accompanying GUIs, represent a significant step toward making sophisticated bioinformatics tools more accessible and practical for a broader range of users, ultimately enhancing our capacity to respond to emerging infectious diseases.