Pratt, M. | Search bioRxiv

bioRxiv2026

Evaluation of MeaSeq: comprehensive analysis and reporting of measles virus whole genome sequences.

Although vaccine-preventable, measles virus (MeV) continues to pose a significant public health challenge, with a substantial resurgence of cases worldwide. As whole-genome sequencing (WGS) becomes increasingly affordable and routinely adopted in public health laboratories, reliable and accessible analysis of next-generation sequencing (NGS) data is critical for outbreak investigation and molecular surveillance. Here, we present MeaSeq, a fast, user-friendly, open-source bioinformatics pipeline for MeV analysis using Illumina or Oxford Nanopore Technologies (ONT) NGS data. MeaSeq performs quality control assessments, consensus genome assembly and variant detection, optional genotype-specific reference selection, Distinct Sequence Identifier (DSId) assignment via user-provided databases or hashing, sub-consensus variant visualization, genome quality assessment, and standardized HTML reporting. We compared the performance of MeaSeq on NGS data generated from multiple sequencing platforms and targeted enrichment strategies against gold-standard Sanger data, reference genomes, and publicly available comparative data. This validation demonstrates that MeaSeq provides an accurate, reproducible, and accessible solution for routine MeV WGS analysis, supporting genomic surveillance and outbreak response workflows in public health and research settings. Impact StatementThe recent surge in measles cases worldwide, causing several countries to lose their measles elimination status, underscores the urgent need for effective and accessible genomic surveillance. Our manuscript introduces MeaSeq, a comprehensive and open-source bioinformatics pipeline specifically designed for analyzing MeV NGS data. MeaSeq includes MeV specific analyses such as genotype prediction from sequencing reads with optional genotype-specific reference selection; DSId assignment; quality control checks such as genome rule-of-six divisibility and gene CDS validation; subconsensus nucleotide analysis with mixed-site highlighting; and genomic plotting. By leveraging NGS technology, our pipeline can facilitate the identification of transmission chains and may provide critical insights into the dynamics of MeV outbreaks. This information is essential for public health officials and researchers to implement targeted interventions and optimize vaccine strategies. Additionally, the open-source nature of MeaSeq fosters collaboration and innovation within the scientific measles community along with providing access to a wider range of researchers. Data SummaryThe MeaSeq pipeline code is available on GitHub (https://github.com/phac-nml/measeq). Comparative datasets of publicly available WGS data were accessed through the NCBI Sequence Read Archive under the following BioProjects: PRJNA869081 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA869081) PRJNA480551 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA480551) PRJNA1017431 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1017431) PRJNA1241325 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1241325) PRJNA1174053 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1174053) PRJNA1293457 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1293457) PRJNA843031 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA843031) Whole-genome sequences were included in the validation analysis if they consisted of paired-end data (Illumina) and achieved [≥]95% genome completeness following trimming of the 5' and 3' untranslated regions (UTRs). This criterion ensured sufficient genome coverage for robust validation while allowing for limited missing data arising from regions of low sequencing depth or amplicon dropout. A complete list of sequences included in the validation, along with their accession numbers, is provided in Supplementary Table 1.

bioinformatics↗

bioRxiv2022

Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length ([~]300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

genomics↗

bioRxiv2017

Drosophila PLP forms centriolar-clouds that promote centriole stability, cohesion and MT nucleation

Pericentrin is a conserved centrosomal protein whose dysfunction has been linked to several human diseases. The precise function of Pericentrin, however, is controversial. Here, we examine Drosophila Pericentrin-like- protein (PLP) function in vivo, in tissues that form both centrosomes and cilia. PLP mutant centrioles exhibit four major defects: (1) They are too short and have subtle structural defects; (2) They separate prematurely, and so overduplicate; (3) They organise fewer MTs during interphase; (4) They fail to establish and/or maintain a proper connection to the plasma membrane-- although, surprisingly, mutant centrioles can still form an axoneme and recruit transition zone (TZ) proteins. We show that PLP helps to form \" pericentriolar clouds\" of electron-dense material that emanate from the central cartwheel spokes and spread outward to surround the mother centriole. The partial loss of these structures may explain the complex centriole, centrosome and cilium defects we observe in PLP mutant cells.

cell biology↗