MetLab - Metagenomics Analysis Pipeline
MetLab is a convenient tool for performing basic metagenomic tasks. The tool has three distinct parts:
This part of the tool is based on Wendl, et al. "Coverage theories for metagenomic DNA sequencing based on a generalization of Stevens' theorem" (http://www.ncbi.nlm.nih.gov/pubmed/22965653). It can be used to approximate the amount of sequencing needed to answer a given metagenomic question.
Metagenomic sequencing simulator
This tool can be used to create a statistical profile from real world sequencing data, and may then be used to download random viral genomes and create simulated data sets.
Metagenomic analysis pipeline
The main function of the MetLab is to run a metagenomic classification pipeline. The pipeline is based on input from NGS sequencing data, and can perform data cleaning and pre-processing, host-genome mapping to remove contamination, assembly, as well as taxonomic binning.
For Installing MetLab, please refer to INSTALL.md
Start metlab by typing
in your terminal from the directory where you installed MetLab. It will launch a GUI, with separated tabs for the three distinct modules.
Alternatively, you can launch MetLab from the finder by right clicking on MetLab.py, and select open with -> python launcher
The experimental design part of MetLab can be used to approximate the amount of sequencing you need for your project.
Given an estimation of species diversity as well as estimated genome size range the module calculates the probability of covering all included genomes (such as at least one contig is produced from each genome) given a theoretical optimal assembly. If a single run is not sufficient to reach that probability the module goes into iterative state, consecutively adding simulated runs until coverage probability is reach or a maximum of 10 runs are simulated
From the experimental design tab, simply enter the estimated lowest abundance of the viruses you want to detect, and their estimated genome size, then click calculate.
Metagenomic sequencing simulator
The module produces viral datasets from sequencing profiles with realistic error profiling and known taxonomic content. It is especially useful if you want to test a new method of classification.
The module will output one (or two if you selected paired-end read) fastq file(s) and one key file containing the viral composition of your simulated dataset.
The “key file” includes “Genome ID”, “Tax ID”, “Definition”, “Project”, and “No. Reads”, where the “Tax ID” is the NCBI taxonomy identifier, “Project” is the sequencing project identifier, and “No. Reads” is the number of reads from the species that is included in the dataset. The fastq file includes read headers formatted as
"<record id>|ref:<genome id>-<read nr.>|pos:<start>-<end>”, where the “record_id” and “genome_id” are the NCBI accession number and genome id respectively, and “pos” is the read position in the record sequence.
Alternatively, the module can be used at a command-line application, by running
from the metlab main directory.
the options available for the command-line simulator are:
usage: metamaker.py [-h] [-c CREATE [CREATE ...]] [-d DISTRIBUTION] [-i INSERT] [-k KEYFILE] [-l LENGTH_VAR] [-o OUTPUT] [-p] [-m] [-n NO_READS] [-r READ_LENGTH] [-s NO_SPECIES] [-f PROFILE] [-x TAXA] [-a ERROR_VARIANCE [ERROR_VARIANCE ...]] [-e ERROR_FUNCTION [ERROR_FUNCTION ...]] [-v] [-q] optional arguments: -h, --help show this help message and exit -c CREATE [CREATE ...], --create CREATE [CREATE ...] Create new profile from file(s). (default: None) -d DISTRIBUTION, --distribution DISTRIBUTION Read distribution, 'uniform' or 'exponential' (default: uniform) -i INSERT, --insert INSERT Matepair insert size. (default: 3000) -k KEYFILE, --keyfile KEYFILE key filename. (default: None) -l LENGTH_VAR, --length_var LENGTH_VAR Length variance. (default: 0.0) -o OUTPUT, --output OUTPUT Output filename (default: output) -p, --progress Display progress information for long tasks. (default: False) -m, --matepair Generate matepairs. (default: False) -n NO_READS, --no_reads NO_READS Number of reads. (default: 50M) -r READ_LENGTH, --read_length READ_LENGTH Read length (default: 200) -s NO_SPECIES, --no_species NO_SPECIES Number of species. (default: 10) -f PROFILE, --profile PROFILE Sequencing profile to use for read generation. Changes default for reads, read_length and error_function. Valid options are Illumina MiSeq, IonTorrent, IonProton, IonTorrent 200bp or IonTorrent 400bp (default: None) -x TAXA, --taxa TAXA Taxonomic identifier of the species to download. (default: viruses) -v, --verbose Increase output Verbosity (default: 0) -q, --quiet Decrease output Verbosity (default: 0) quality function arguments: Factors for the quality and variance functions -a ERROR_VARIANCE [ERROR_VARIANCE ...], --error_variance ERROR_VARIANCE [ERROR_VARIANCE ...] Factors for the error variance approximation equation. (default: ) -e ERROR_FUNCTION [ERROR_FUNCTION ...], --error_function ERROR_FUNCTION [ERROR_FUNCTION ...] Factors for the error approximation equation. (default: [25.0])
Metagenomic analysis pipeline
This module is the core of MetLab and offers different options to analyse metagenomes
The metagenomic analysis pipeline is based on a set of programs suited for metagenomic analysis, where a number of steps are optional, depending on the analysis. The pipeline starts with data pre-processing with Prinseq-Lite. Trimming and filtering options are set to default values (extrapolated from a normal need), but you can easily modify them by expending the Data filtering menu. The next steps is host genome mapping with Bowtie2, designed for metagenomic analysis from animal samples. Reads that don’t map to the host genome are extracted using SAMTOOLS, and the analysis continues with these unmapped reads.
The next step is de novo assembly with SPAdes, which is not default but can improve classification in cases where high assembly coverage is available in the sample. The analysis ends with taxonomic classification.
If you want to only assign taxonomic information to your data and skip the quality control and trimming, filtering of the host genome and assembly steps, untick the Data filtering, Reference mapping and Assembly boxes, upload your reads and click run!
By default, the standard kraken database is used. If you wish to use our custom database (which we highly recommend!), please refer to INSTALL.md
In the ouput directory, you can find Krona charts describing both the classification by kraken and by hmmer.