Web-TOAST

Design

Web-TOAST

Design

TOAST (Tuberculosis Optimised Amplicon Sequencing Tool) is a powerful and flexible computational tool designed to generate targeted amplicons for bacterial sequencing, with a specialized focus on Mycobacterium tuberculosis. While TOAST can be adapted for a range of bacterial species, it has been specifically optimized for TB genomic studies, integrating a global drug resistance database containing mutations from over 50,000 M. tuberculosis isolates. This ensures that the designed amplicons effectively cover clinically relevant resistance markers across 13 anti-TB drugs, supporting rapid and accurate resistance profiling.

TOAST automates the design of targeted amplicons by using a rolling window algorithm to strategically place primers at positions that maximize mutation coverage while minimizing redundancy. It starts by scanning a large-scale genomic database of over 50,000 M. tuberculosis isolates to identify high-frequency drug resistance mutations. The rolling window approach ensures that amplicons are evenly distributed across target regions, prioritizing clinically relevant SNPs while avoiding excessive overlap. TOAST then employs Primer3 to design primers within these selected regions, optimizing for melting temperature (Tm), GC content, and binding efficiency. To ensure robust PCR performance, the tool incorporates a quality control step, filtering out primers with hairpins, homodimers, heterodimers, and non-specific binding sites. The final output includes detailed primer sequences, mutation inclusion reports, gene coverage summaries, and visualization-ready files for genome browsers like IGV..
TOAST accepts a mutation priority file where users can specify SNPs or mutations of interest. The tool will then optimize amplicon design to ensure high coverage of the selected mutations while minimizing primer redundancy and unwanted interactions. Further information about file format can be found on the input page of the tool.
Yes, you can upload your own reference genome(.fast) and gene label(.gff) file.
While TOAST designs amplicons of constant sizes. TOAST also allows for segmented amplicon size design, which refers to a deliberate strategy in PCR-based workflows. A set of amplicons designed with various sizes gives the user more information about the amplification of certain amplicons from gel electrophoresis. This sanity check may prevent time and resource loss of sequencing
WebToast allows users to control the amplicon number, size and specific coverage of gene (following gene name from .gff file). - A more advanced input, including TM, DNA concentration, dNTP concentration, Salt divalent, Annealing temperature, and primer size, can be controlled to further direction for this can be seen in the input page of WebTOAST.
Yes, if you have access to a Linux or OSX operating system, then you can download the command-line version of TOAST. For more information on this, please visit the GitHub repository.
The more experimental functionalities are implemented in the command line version of TOAST. Feel free to explore the PyPi and GitHub pages of TOAST. The functionalities available include:
  • Estimating amplicon number requirement gives target and amplicon size.
  • Designing amplicons on top of a given set of amplicons.
  • More detailed error reporting, informing users of the exact reason for failure and potential change.
  • Allowing modification for frequent SNP to be changed for degenerate bases in primer sequences (Hence allowing toast to be used on bacteria other than TB).
Wang, L., Naphatcha Thawong, Thorpe, J., Higgins, M., Tan, M., Waritta Sawaengdee, Surakameth Mahasirimongkol, Perdigao, J., Campino, S., Clark, T.G. and Phelan, J.E. (2025). A novel tool for designing targeted gene amplicons and an optimised set of primers for high-throughput sequencing in tuberculosis genomic studies. bioRxiv (Cold Spring Harbor Laboratory).doi:https://doi.org/10.1101/2025.01.13.632698.
This tool was designed by Linfeng Wang

I am a PhD candidate in Computational Genomics at the London School of Hygiene and Tropical Medicine, specializing in tuberculosis (TB) drug resistance and transmission analysis. With a strong foundation in bioinformatics, machine learning, and genomic data analysis, my work focuses on developing computational tools and models to enhance the understanding and treatment of infectious diseases.