FruitFly4

Introduction

This package contains a set of minimal eukaryotic genome and transcriptome datasets from chromosme r of Drosophila melanogaster. They may, for example, be useful for rapidly testing a RNA-seq bioinformatics pipeline.

Installation

Ensure your computing environment includes Python 3.11 or later and the following bioinformatics tools:

• sra-tools
• pigz
• fastp
• kraken2

Conda is useful for setting up this environment. Then, fruitfly4 can be installed with Pip:

conda create -c conda-forge -c bioconda -n fruitfly4 \
  biopython pysradb sra-tools pigz fastp kraken2
conda activate fruitfly4
pip install fruitfly4

Usage

Run fruitfly4 without arguments to list the help text of all subcommands

fruitfly4

usage: fruitfly4 [-h] [--version] {init,path,metadata,download,sheet,trim,classify} ...

Small dataset of fruit fly chromosome 4

positional arguments:
  {init,path,metadata,download,sheet,trim,classify}
    init                fully initialize the dataset by downloading, trimming, and classifying RNA-seq reads
    path                show paths to data directory and files
    metadata            fetch sra metadata
    download            download RNA-seq reads
    sheet               sample sheet for fruitfly4 rna-seq dataset
    trim                trim downloaded reads
    classify            classify downloaded reads

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit

To download and downsample all RNA-seq data, run:

fruitfly4 init

Use fruitfly4 path to get the absolute path for a data file

fruitfly4 path data-dir
fruitfly4 path genome
fruitfly4 path cds
fruitfly4 path pep
fruitfly4 path gff
fruitfly4 path gtf
fruitfly4 path sheet

fruitfly4 path can also fetch paths to datasets for the two alternate species:

fruitfly4 path genome-dmauri
fruitfly4 path genome-dseche

To see the sample sheet listing the locations of the RNA-seq reads:

fruitfly4 sheet

If you need the data at another location, you can symbolically link to the fruitfly4 dataset, or copy the contents to another directory

ln -s `fruitfly4 path data-dir` test_data/
cp -r `fruitfly4 path data-dir` test_data/

To see the SRA metadata of the RNA-seq reads:

fruitfly4 metadata

To execute the initialization steps individually:

fruitfly4 download
fruitfly4 trim
fruitfly4 classify

Datasets

There are two groups of datasets:

1. The directly included package data of chromosome r sequence and annotation from D. melanogaster assembly DM6 at NCBI, including:

   • Genome sequence (FASTA)
   • Coding sequences (FASTA)
   • mRNA transcripts (FASTA)
   • Peptide sequences (FASTA)
   • Gene annotation (GFF3, GTF)
   • Sequences and annotations (chromosome 4) are also included for six other Drosophila species:
     • D. mauritiana
     • D. sechellia
     • D. santomea
     • D. simulans
     • D. teissieri
     • D. yakuba

2. The RNA-seq reads from Bajgiran et al. (2021), which are downloaded from SRA/NCBI BioProject PRJNA718442 and downsampled to include only chromosome 4 reads by running an included script.

   • The study sequenced mRNA from D. melanogaster at ages 1 day and 60 days.

Downsampling RNA-seq

During the initialization process, after the RNA-seq reads are downloaded they are downsampled by:

1. Adapter and quality trimming with fastp
2. Classification with kraken2 to filter out all reads that do not match chromosome I

Configuration

You can configure some fruitfly4 settings using environment variables or a TOML file.

Environment variables

export FRUITFLY4_DOWNLOAD_DIR = /absolute/path/to/directory/
export FRUITFLY4_TAXID = <integer>
export FRUITFLY4_SAMPLE_SHEET = /absolute/path/to/sample_sheet.csv
export FRUITFLY4_KRAKEN2_DB /absolute/path/to/kraken2_db/

Config file

By default, fruitfly4 looks for configuration in TOML format at $HOME/.fruitfly4.toml

download_dir = /absolute/path/to/directory/
taxid = <integer>
sample_sheet = /absolute/path/to/sample_sheet.csv
kraken2_db = /absolute/path/to/kraken2_db/

The location of the config file can also be set using an environment variable:

export FRUITFLY4_CONFIG_FILE=/absolute/path/to/alternate/config.toml

Python API

FruitFly4 resources can also be accessed from within python:

import fruitfly4
fruitfly4.init()
fruitfly4.path('genome')
fruitfly4.path('genome-skud')
metadata = fruitfly4.metadata()
sample_sheet = fruitfly4.create_sample_sheet(metadata)
fruitfly4.write_sample_sheet(sample_sheet, 'sample_sheet.csv')
fruitfly4.download()
fruitfly4.trim('reads_1.fastq.gz', 'reads_2.fastq.gz')
fruitfly4.classify('reads_1.fastq.gz', 'reads_2.fastq.gz')
fruitfly4.taxid
fruitfly4.busco

Example usage with nf-core pipelines

Conda environment

conda create -c conda-forge -c bioconda -n fruitfly4-nf-core \
  biopython pysradb sra-tools pigz fastp kraken2 nextflow nf-core
conda activate fruitfly4-nf-core
pip install fruitfly4

nf-core/genomeqc

echo '{"is_plant": false, "skip_busco": false, "skip_tidk": false}' > genomeqc-params.json
fruitfly4 sheet --pipeline nf-core-genomeqc > sample_sheet.csv
nextflow run nf-core/genomeqc -r 1.0.1 \
  -profile <docker/singularity/.../institute> \
  -params-file genomeqc-params.json \
  --input sample_sheet.csv \
  --outdir genomeqc_output_dir/ \
  --busco_lineage `fruitfly4 busco`

nf-core/seqinspector

fruitfly4 sheet --pipeline nf-core-seqinspector > sample_sheet.csv
nextflow run nf-core/seqinspector \
  -profile <docker/singularity/.../institute> \
  --input sample_sheet.csv \
  --outdir seqinspector_output_dir/ \
  --fasta `fruitfly4 path genome`

nf-core/rnaseq

fruitfly4 sheet > sample_sheet.csv
nextflow run nf-core/rnaseq \
  -profile <docker/singularity/.../institute> \
  --input sample_sheet.csv \
  --outdir rnaseq_output_dir/ \
  --fasta `fruitfly4 path genome` \
  --gtf `fruitfly4 path gtf`

nf-core/denovotranscript

fruitfly4 sheet > sample_sheet.csv
nextflow run nf-core/denovotranscript -r 1.2.1 \
  -profile <docker/singularity/.../institute> \
  --input sample_sheet.csv \
  --outdir denovotranscript_output_dir/ \
  --assemblers rnaspades