Dataset Information

Nf-LO: A Scalable, Containerized Workflow for Genome-to-Genome Lift Over.

ABSTRACT: The increasing availability of new genome assemblies often comes with a paucity of associated genomic annotations, limiting the range of studies that can be performed. A common workaround is to lift over annotations from better annotated genomes. However, generating the files required to perform a lift over is computationally and labor intensive and only a limited number are currently publicly available. Here we present nf-LO (nextflow-LiftOver), a containerized and scalable Nextflow pipeline that enables lift overs within and between any species for which assemblies are available. nf-LO will consequently facilitate data interpretation across a broad range of genomic studies.

SUBMITTER: Talenti A

PROVIDER: S-EPMC8412297 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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nf-LO: A Scalable, Containerized Workflow for Genome-to-Genome Lift Over.

Talenti Andrea A Prendergast James J

Genome biology and evolution 20210901 9

The increasing availability of new genome assemblies often comes with a paucity of associated genomic annotations, limiting the range of studies that can be performed. A common workaround is to lift over annotations from better annotated genomes. However, generating the files required to perform a lift over is computationally and labor intensive and only a limited number are currently publicly available. Here we present nf-LO (nextflow-LiftOver), a containerized and scalable Nextflow pipeline th ...[more]

PMID: 34383887

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Project description:Next generation sequencing technologies are becoming more accessible and affordable over the years, with entire genome sequences of several pathogens being deciphered in few hours. However, there is the need to analyze multiple genomes within a short time, in order to provide critical information about a pathogen of interest such as drug resistance, mutations and genetic relationship of isolates in an outbreak setting. Many pipelines that currently do this are stand-alone workflows and require huge computational requirements to analyze multiple genomes. We present an automated and scalable pipeline called BAGEP for monomorphic bacteria that performs quality control on FASTQ paired end files, scan reads for contaminants using a taxonomic classifier, maps reads to a reference genome of choice for variant detection, detects antimicrobial resistant (AMR) genes, constructs a phylogenetic tree from core genome alignments and provide interactive short nucleotide polymorphism (SNP) visualization across core genomes in the data set. The objective of our research was to create an easy-to-use pipeline from existing bioinformatics tools that can be deployed on a personal computer. The pipeline was built on the Snakemake framework and utilizes existing tools for each processing step: fastp for quality trimming, snippy for variant calling, Centrifuge for taxonomic classification, Abricate for AMR gene detection, snippy-core for generating whole and core genome alignments, IQ-TREE for phylogenetic tree construction and vcfR for an interactive heatmap visualization which shows SNPs at specific locations across the genomes. BAGEP was successfully tested and validated with Mycobacterium tuberculosis (n = 20) and Salmonella enterica serovar Typhi (n = 20) genomes which are about 4.4 million and 4.8 million base pairs, respectively. Running these test data on a 8 GB RAM, 2.5 GHz quad core laptop took 122 and 61 minutes on respective data sets to complete the analysis. BAGEP is a fast, calls accurate SNPs and an easy to run pipeline that can be executed on a mid-range laptop; it is freely available on: https://github.com/idolawoye/BAGEP.

Project description:BackgroundAmplicon sequencing (metabarcoding) is a common method to survey diversity of environmental communities whereby a single genetic locus is amplified and sequenced from the DNA of whole or partial organisms, organismal traces (e.g., skin, mucus, feces), or microbes in an environmental sample. Several software packages exist for analyzing amplicon data, among which QIIME 2 has emerged as a popular option because of its broad functionality, plugin architecture, provenance tracking, and interactive visualizations. However, each new analysis requires the user to keep track of input and output file names, parameters, and commands; this lack of automation and standardization is inefficient and creates barriers to meta-analysis and sharing of results.FindingsWe developed Tourmaline, a Python-based workflow that implements QIIME 2 and is built using the Snakemake workflow management system. Starting from a configuration file that defines parameters and input files-a reference database, a sample metadata file, and a manifest or archive of FASTQ sequences-it uses QIIME 2 to run either the DADA2 or Deblur denoising algorithm; assigns taxonomy to the resulting representative sequences; performs analyses of taxonomic, alpha, and beta diversity; and generates an HTML report summarizing and linking to the output files. Features include support for multiple cores, automatic determination of trimming parameters using quality scores, representative sequence filtering (taxonomy, length, abundance, prevalence, or ID), support for multiple taxonomic classification and sequence alignment methods, outlier detection, and automated initialization of a new analysis using previous settings. The workflow runs natively on Linux and macOS or via a Docker container. We ran Tourmaline on a 16S ribosomal RNA amplicon data set from Lake Erie surface water, showing its utility for parameter optimization and the ability to easily view interactive visualizations through the HTML report, QIIME 2 viewer, and R- and Python-based Jupyter notebooks.ConclusionAutomated workflows like Tourmaline enable rapid analysis of environmental amplicon data, decreasing the time from data generation to actionable results. Tourmaline is available for download at github.com/aomlomics/tourmaline.

Dataset Information

Nf-LO: A Scalable, Containerized Workflow for Genome-to-Genome Lift Over.

Publications

nf-LO: A Scalable, Containerized Workflow for Genome-to-Genome Lift Over.

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