Nathan Grubaugh, PhD

This pipeline takes raw Illumina read data in the form of fastq files, maps them against provided bed files and then provides a series of outputs for further analysis including consensus sequences. The following pipeline works with ONT data: https://github.com/josephfauver/DENV_MinION_Script

IMPORTANT: the bed files must correspond to the wet lab protocol that you used and the reference sequence used to generate them otherwise the sequences generated will be incorrect.

It calls input files as a virus type if it has more than 50% coverage of the reference genome provided. If running on a server, it is highly recommended to run it using screen/tmux or similar.

Faculty: Nathan Grubaugh, PhD, Verity Hill, PhD, Chrispin Chaguza, PhD

Download: GitHub / DENV_pipeline package

Platform: Python

Reference: doi.org (DENV_pipeline)

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iVar is a computational package that contains functions broadly useful for viral amplicon-based sequencing. Additional tools for metagenomic sequencing are actively being incorporated into iVar. While each of these functions can be accomplished using existing tools, iVar contains an intersection of functionality from multiple tools that are required to call iSNVs and consensus sequences from viral sequencing data across multiple replicates. We implemented the following functions in iVar: (1) trimming of primers and low-quality bases, (2) consensus calling, (3) variant calling - both iSNVs and insertions/deletions, and (4) identifying mismatches to primer sequences and excluding the corresponding reads from alignment files.

Faculty: Nathan Grubaugh, PhD

Download: GitHub / iVar package

Platform: Other

Reference: doi.org (iVar)

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