MetaWRAP Documentation

MetaWRAP is a suite of standalone modules for metagenomic analysis. Each module can be used independently depending on your specific needs.

Metagenomic data pre-processing modules:

1. Read_QC: Read trimming and host (e.g., human) read removal.
2. Assembly: Metagenomic assembly and QC using metaSPAdes or MegaHit.
3. Kraken/Kraken2: Taxonomy profiling and visualization of reads or contigs.

Bin processing modules:

1. Binning: Initial bin extraction using MaxBin2, metaBAT2, and/or CONCOCT.
2. Bin_refinement: Consolidates multiple binning predictions into a superior bin set.
3. Reassemble_bins: Reassembles bins to improve completion and N50, and reduce contamination.
4. Quant_bins: Estimates bin abundance across samples.
5. Blobology: Visualizes the community and extracted bins with blobplots.
6. Classify_bins: Provides conservative but accurate taxonomy prediction for bins.
7. Annotate_bins: Functionally annotates genes within a set of bins.

MetaWRAP provides advanced methods for improving the quality of extracted draft genomes (bins).

Bin Refinement

MetaWRAP uses a hybrid approach to consolidate multiple binning predictions (e.g., from metaBAT2, CONCOCT, and MaxBin2). The Bin_refinement module outperforms individual binning approaches and other consolidation tools like Binning_refiner or DAS_Tool. Because this module is standalone, you can use your own preferred binning software predictions as input.

Bin Reassembly

The Reassemble_bins module allows you to improve bin quality by extracting the reads belonging to each bin and reassembling them with a more permissive, non-metagenomic assembler. This process:

• Improves the N50 of the bins.
• Modestly increases bin completion.
• Drastically reduces contamination.

Recommendation: Run Reassemble_bins on the final bin set produced by the Bin_refinement module.

To prepare a manual installation, you must first set up the required channels and install the dependencies.

Note: The order of channel configuration is important.

For Unix/Linux only:

# Set up channels
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels ursky

# Install dependencies
conda install --only-deps -c ursky metawrap-mg

Universal method (works on other systems):

conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels ursky

conda install biopython blas=2.5 blast=2.6.0 bmtagger bowtie2 bwa checkm-genome fastqc kraken=1.1 krona=2.7 matplotlib maxbin2 megahit metabat2 pandas prokka quast r-ggplot2 r-recommended salmon samtools=1.9 seaborn spades trim-galore

Note: You may need to manually install concoct=1.0 and pplacer when using the universal method.

To remove host reads in the read_qc module, you must create a bmtagger index.

For Human Hosts (hg38)

1. Download and merge the hg38 genome:

mkdir BMTAGGER_INDEX
cd BMTAGGER_INDEX
wget ftp://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/*fa.gz
gunzip *fa.gz
cat *fa > hg38.fa
rm chr*.fa

2. Index the genome. Crucial: The output files must be named exactly hg38.bitmask and hg38.srprism for metaWRAP to recognize them.

bmtool -d hg38.fa -o hg38.bitmask
srprism mkindex -i hg38.fa -o hg38.srprism -M 100000

3. Configure metaWRAP: In config-metawrap, set BMTAGGER_DB to the path of your BMTAGGER_INDEX directory.

For Non-Human Hosts

Follow the same protocol as above. If your index files are named [prefix].srprism and [prefix].bitmask (e.g., pig.srprism and pig.bitmask), you must specify the prefix when running the read_qc module using the -x flag:

metawrap read_qc -x pig ...

# Example for human hg38
mkdir BMTAGGER_INDEX
cd BMTAGGER_INDEX
# ... download and cat hg38.fa ...
bmtool -d hg38.fa -o hg38.bitmask
srprism mkindex -i hg38.fa -o hg38.srprism -M 100000

# In config-metawrap:
BMTAGGER_DB=/path/to/your/index/BMTAGGER_INDEX

MetaWRAP supports Linux64. It is strongly recommended to install MetaWRAP within a dedicated Conda environment to avoid conflicts with existing packages, as it installs over 140 dependencies.

Recommended Setup (Custom Environment):

# Create and activate environment
conda create -y -n metawrap-env python=2.7
source activate metawrap-env

# Configure channels (Order is important)
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels ursky

# Install MetaWRAP
conda install -y -c ursky metawrap-mg

# Fix CONCOCT warning messages for MetaWRAP 1.2
conda install -y blas=2.5=mkl

Basic Setup (Direct Installation):

# Install Miniconda2
wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh
bash Miniconda2-latest-Linux-x86_64.sh

# Configure channels
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels ursky

# Install MetaWRAP
conda install -y -c ursky metawrap-mg

# Fix CONCOCT warning messages for MetaWRAP 1.2
conda install -y blas=2.5=mkl