fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing
Derelle, R.; Lees, J.; Phelan, J.; Lalvani, A.; Arinaminpathy, N.; Chindelevitch, L.
Show abstract
Lineage typing of the Mycobacterium tuberculosis complex (MTBC) has evolved from traditional phenotypic methods to advanced molecular and genomic techniques. In this study we present fastlin, a bioinformatics tool designed for rapid MTBC lineage typing. Fastlin utilises an ultra-fast alignment-free approach to detect previously identified barcode single nucleotide polymorphisms (SNPs) associated with specific MTBC lineages directly from fastq files. In a comprehensive benchmarking against existing tools, fastlin demonstrated high accuracy and significantly faster running times. Analysis of large MTBC datasets revealed fastlins capability not only to predict MTBC lineages, but also to detect mixed-lineage strain mixtures and estimate their proportions. Fastlin offers a user-friendly and efficient solution for MTBC lineage typing, complementing existing tools and facilitating large-scale analysis.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel 94%
- SNPPar: identifying convergent evolution and other homoplasies from microbial whole-genome alignments 93%
- Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data 93%
Similar papers in this journal
Similar papers in this journal
- PyOrthoANI, PyFastANI, and Pyskani: a suite of Python libraries for computation of average nucleotide identity 91%
- Deep kinetoplast genome analyses result in a novel molecular assay for detecting Trypanosoma brucei gambiense-specific minicircles 91%
- EASYstrata: An All-in-One Workflow for Genome Annotation and Genomic Divergence Analysis 91%
Similar papers in this journal
- The Mycobacterium tuberculosis complex pangenome is small and shaped by sub-lineage-specific regions of difference 93%
- Hierarchical machine learning predicts geographical origin of Salmonella within four minutes of sequencing 92%
- Population-based sequencing of Mycobacterium tuberculosis reveals how current population dynamics are shaped by past epidemics 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.