WideVariant: Lieberman Lab SNP calling pipeline

Overview

This pipeline and toolkit is used to detect and analyze single nucleotide differences between closely related bacterial isolates.

• Noteable features

  • Avoids false-negative mutations due to low coverage; if a mutation is found in at least one isolate in a set, the evidence at that position will be investigated to make a best-guess call.
  • Avoids false-positives mutations by facilitating visualization of raw data, across samples (whereas pileup formats must be investigated on a sample-by-sample basis) and changing of threshold to best fit your use case.
  • Enables easy evolutionary analysis, including phylogenetic construction, nonsynonmous vs synonymous mutation counting, and parallel evolution

• Inputs (to Snakemake cluster step):

  • short-read sequencing data of closely related bacterial isolates
  • an annotated reference genome

• Outputs (of local analysis step):

  • table of high-quality SNVs that differentiate isolates from each other
  • parsimony tree of how the isolates are related to each other

The pipeline is split into two main components, as described below. A complete tutorial can be found at the bottom of this page.

1. Snakemake pipeline

The first portion of WideVariant aligns raw sequencing data from bacterial isolates to a reference genome, identifies candidate SNV positions, and creates useful data structure for supervised local data filtering. This step is implemented in a workflow management system called Snakemake and is executed on a SLURM cluster. More information is available here.

2. Local python analysis

The second portion of WideVariant filters candidate SNVs based on data arrays generated in the first portion and generates a high-quality SNV table and a parsimony tree. This step is implemented with a custom python script. More information can be found here.

Tutorial Table of Contents

Main WideVariant pipeline README

• Snakemake pipeline
  • Overview and how to run the snakemake pipeline
  • Technical details about the snakemake pipeline
  • Wishlist for snakemake pipeline upgrades
  • Helpful hints for using the command line
• Local analysis
  • How to run the local analysis script
  • Wishlist for local analysis upgrades
  • Python best practices

Example use cases

Previous iterations of this pipeline have been used to study:

• C. acnes biogeography in the human skin microbiome
• Adaptive evolution of S. aureus on patients with atopic dermatitis
• Adaptive evolution of B. fragilis on healthy people