The MIMIC Metagenome simulator creates simulated Oxford Nanopore (ONT) reads from any existing metagenomic community by taking in a fastq file and analyzing the naturally occurring microbial abundances.
This project was conceptualized and built during the Baylor College of Medicine Human Genome Sequencing Center (HGSC) 2024 Hackathon.
Oxford Nanopore (ONT) sequencing is rapidly becoming a widely used sequencing technology in metagenomic studies due to its cost, long reads, and significantly improved error rate1. We built The MIMIC Metagenome simulator to create a simple tool to facilitate the benchmarking and testing of metagenomic profiles and taxonomic classifiers on compositions that mimic real-world microbiome samples (sequenced with ONT reads). Specifically, MIMIC will simulate Oxford Nanopore (ONT) reads from any existing metagenomic community by taking in an ONT FASTQ file and analyzing it with Lemur and Magnet2. It will then simulate reads based on the Lemur and Magnet mimicked profile with Nanosim3. Once the truth tables are generated for simulated data based on real microbiome samples, MIMIC offers a simple-to-use evaluation framework for comparing the results of existing methods to the known truth data simulated by Nanosim.
- Implement mimic.py script with basic functionality
- Create workflow diagram depicting the main components of MIMIC
- Generate simulated data for two ONT soil metagenome samples (SRR30413550, SRR29660113)
- Run Kraken2+Bracken and Sourmash on the MIMIC output for an ONT soil metagenome sample
- Add recall and precision metrics for the ONT soil metagenome simulated data
- Add installation and usage details to readme
- Get MIMIC set up and running in DNAnexus environment
- Add gene gain/gene loss events into the reference genomes
The MIMIC workflow takes in Oxford Nanopore metagenomic reads in fastq format, these reads are then processed by Lemur2, which performs an initial taxonomic profiling and generates an abundance table. The profile is then fed into Magnet 2, which downloads all of the reference genomes and performs competitive read-alignment in order to determine final presence/absence calls. Abundances from Lemur are then mapped to the present genomes, which are inputted into Nanosim. Nanosim outputs a simulated file in fasta files, as well as the error profiles (if --perfect not used). A full description of the outputs is included below.
The second aspect in the pipeline is to compare to existing tools. ADD INFO HERE
Mimic can be installed using the following steps:
## Clone the git repository
git clone https://github.com/collaborativebioinformatics/Mimic.git
## cd into the repository
cd Mimic/
## Setup the conda environment
conda env create -f mimic_env.yml
conda activate mimic
The Mimic simulator can be run using the following command:
python mimic.py -i [fastq] -o [output_loc] -r [num_reads] --db [lemur_db] -t [threads]
arguments:
-h, --help show help message and exit
-i FASTQ, --fastq FASTQ
Path to first fastq file (required)
-I FASTQ2, --fastq2 FASTQ2
Path to second fastq file for paired-end reads
-o OUTPUT, --output OUTPUT
Path to the output directory (required)
--db DB Lemur database location (required)
-t THREADS, --threads THREADS
Number of threads for multithreading (Default: 1)
-r READS, --reads READS
Number of simulated reads to generate (Default: 100)
--simulate-only
Only runs simulation (must have already run pipeline on sample once, will override existing simulated data)
--perfect
Will generate perfect reads from the genomes, ignores nanosim profiles
Some helpful things to keep in mind:
- Depending on the file size, the lemur/magnet/nanosim model generation steps are the slowest. Thus, once run on a sample, use the
--simulate-onlytag to generate new simulated data based off that profile
The following provides a brief description of important outputs from the Mimic simulator, assuming -o output
output/lemur/relative_abundance.tsv -- Initial taxonomic profiling file from Lemur. Column 1 is taxonomy ID, and 'F' column is the relative abundance
output/magnet/cluster_representative.tsv -- Magnet details, including reference details as well as the presence/absence calls in the final column
output/magnet/reference_genomes/* -- Location of downloaded reference genomes from Magnet, note that not all will be included in Nanosim simulation
output/nanosim/abundances.tsv -- contains species and abundances inputted into nanosim, top row contains number of reads generated
output/nanosim/genome_list1.tsv -- contains species and reference genome location, as well as input abundance
output/nanosim/genome_list2.tsv -- same as above, without abundances
output/simulated_data/simulated.fasta -- the simulated fasta files. See the nanosim documentation for description on the read headers
The truth table, based off of the abundances in abundances.tsv, is used for both input into nanosim as well as validating
Size 100000
Paraburkholderia fungorum 0.97
Paraburkholderia phenazinium 2.9
Caballeronia udeis 2.42
Paraburkholderia sp. BCC1885 0.48
Mycobacterium cookii 8.77
Mycobacterium sp. 1245111.1 7.85
Candidatus Mycobacterium methanotrophicum 40.82
Trebonia kvetii 35.78
We ran the Mimic pipeline on the following SRA samples (SRR29660113 and SRR30413550). Metagenomes were simulated at 1k, 50k, and 100k reads for both 'perfect' reads and default error-prone reads.
For the 1k reads we can evaluate the accuracy of Kraken2's classification of each read at every taxonomic rank. For the 'perfect' reads, we get:
rank FN TP FP TN Prec Rec
species 104 511 385 0 0.570 0.511
genus 47 799 154 0 0.838 0.799
family 41 862 97 0 0.899 0.862
order 39 868 93 0 0.903 0.868
class 33 955 12 0 0.988 0.955
phylum 29 965 6 0 0.994 0.965
While for the error-prone reads, we get:
rank FN TP FP TN Prec Rec
species 930 22 48 0 0.314 0.022
genus 923 24 53 0 0.312 0.024
family 924 28 48 0 0.368 0.028
order 921 32 47 0 0.405 0.032
class 920 37 43 0 0.463 0.037
phylum 915 49 36 0 0.576 0.049
This illustrates the heavy impact that sequencing error can have on the accuracy of Kraken2 taxonomic classifications. For reads without error, Kraken's only shortcoming is that at very low taxonomic ranks it fails to classify in roughly half of the cases, but does quite well at genus and above. With error, it fails to classify over 90% of the reads at every rank and incorrectly classifies more than half of the rest!
Hackathon team: Todd Treangen, Shwetha Kumar, Ryan Doughty, Sumaiya Khan, Iva Kotásková, Arthur Shem Kasambula, Mike Nute
Footnotes
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Agustinho, Daniel P., Yilei Fu, Vipin K. Menon, Ginger A. Metcalf, Todd J. Treangen, and Fritz J. Sedlazeck. "Unveiling microbial diversity: harnessing long-read sequencing technology." Nature Methods (2024): 1-13. ↩
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Sapoval, Nicolae, Yunxi Liu, Kristen Curry, Bryce Kille, Wenyu Huang, Natalie Kokroko, Michael G. Nute et al. "Lightweight taxonomic profiling of long-read sequenced metagenomes with Lemur and Magnet." bioRxiv (2024): 2024-06. ↩ ↩2 ↩3
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Yang, Chen, Justin Chu, René L. Warren, and Inanç Birol. "NanoSim: nanopore sequence read simulator based on statistical characterization." GigaScience 6, no. 4 (2017): gix010. ↩