MUFFIN is a hybrid assembly and differential binning workflow for metagenomics, transcriptomics and pathway analysis.
If you use MUFFIN in your research, please cite our paper
The documentation is available here https://rvandamme.github.io/MUFFIN_Documentation/#introduction
- Introduction
- Figure :
- Installation :
- Test the pipeline
- Usage :
- Troubleshooting
- Options
- Complete help and options
- Bibliography
- License
MUFFIN aims at being a reproducible pipeline for metagenome assembly of crossed illumina and nanopore reads.
MUFFIN uses the following software
| Task | Software | Version | Docker | Image version |
|---|---|---|---|---|
| QC illumina | fastp | 0.20.0 | LINK | 0.20.0--78a7c63 |
| QC ont | automated way to discard shortest reads | |||
| filtlong | 0.2.0 | LINK | v0.2.0--afa175e | |
| metagenomic composition of ont | sourmash | 2.0.1 | LINK | 2.0.1--6970ddc |
| Hybrid assembly | Meta-spades | 3.13.1 | LINK | 3.13.1--2c2a4c0 |
| unicycler | 0.4.7 | LINK | 0.4.7-0--c0404e6 | |
| Long read assembly | MetaFlye | 2.7 | LINK | 2.7--957a1a1 |
| polishing | racon | 1.4.13 | LINK | 1.4.13--bb8a908 |
| medaka | 1.0.3 | LINK | 1.0.3--7c62d67 | |
| pilon | 1.23 | LINK | 1.23--b21026d | |
| mapping | minimap2 | 2.17 | LINK | 2.17--caba7af |
| bwa | 0.7.17 | LINK | 1.23--b21026d | |
| samtools | 1.9 | LINK | 2.17--caba7af | |
| retrieve reads mapped to contig | seqtk | 1.3 | LINK | 1.3--dc0d16b |
| Binning | Metabat2 | 2.13 | LINK | 2.13--0e2577e |
| maxbin2 | 2.2.7 | LINK | 2.2.7--b643a6b | |
| concoct | 1.1.0 | LINK | 1.1.0--03a3888 | |
| metawrap | 1.2.2 | LINK | 1.2.2--de94241 | |
| qc binning | checkm | 1.0.13 | LINK | 1.0.13--248242f |
| Taxonomic Classification | sourmash using the gt-DataBase | 2.0.1 | LINK | 2.0.1--6970ddc |
| GTDB | version r89 | |||
| Annotations (bin and RNA) | eggNOG | 2.0.1 | LINK | 2.0.1--d5e0c8c |
| eggNOG DB | v5.0 | |||
| De novo transcript and quantification | Trinity | 2.9.1 | LINK | 2.9.1--82fe26c |
| Salmon | 0.15.0 | LINK | 2.9.1--82fe26c |
You need to install nextflow Version 20.07+ ( https://www.nextflow.io/ )
# verify Java version (at least version 8+)
java -version
# Setup nextflow (it will create a nextflow executable file in the current directory)
curl -s https://get.nextflow.io | bash
# If you want the pipeline installed locally use the following
git clone https://github.com/RVanDamme/MUFFIN.git
# If you want to not install the pipeline use the following when running nextflow
nextflow run RVanDamme/MUFFIN --parameters.....
If you use conda, you don't need extra installations. An error might occur with the installation of metawrap, if so please consult Troubleshooting.
If you use the google lifescience ressources you first need to setup a few parameters.
In the nextflow.config you need to change the parameters of gcloud to correspond to your project (line 67 to 78).
gcloud {
//workDir = "/tmp/nextflow-docker_pipelines-$USER"
process.executor = 'google-lifesciences'
process.memory = params.memory
bucketDir = 'gs:https://bucket/work-dir' // change this to your bucket where you want the workfile to be stored
google { project = 'project-name-111111'; zone = 'europe-north1-a' } // insert your project ID as well as the zone(s) you want to use
// you can also use {region = 'europe-north1'} instead of zone
google.lifeSciences.copyImage = 'google/cloud-sdk:latest'
google.lifeSciences.preemptible = true
google.lifeSciences.bootDiskSize = "10GB"
google.lifeSciences.debug = true
//includeConfig 'configs/preemptible.config'
}You will also have to change the bucket to store the different database in: /modules/checkmgetdatabases.nf ; /modules/eggnog_get_databases.nf ; /modules/sourmashgetdatabase.nf
To do so just edit the line using your bucket. keep the structure for more clarity (e.g. keep the "/databases-nextflow/sourmash" part).
Example:
if (workflow.profile.contains('gcloud')) {publishDir 'gs:https://gcloud_storage/databases-nextflow/sourmash', mode: 'copy', pattern: "genbank-k31.lca.json.gz" }
#becomes
if (workflow.profile.contains('gcloud')) {publishDir 'gs:https://MY_STORAGE/databases-nextflow/sourmash', mode: 'copy', pattern: "genbank-k31.lca.json.gz" }If you desire run on gcloud without the preemptible parameter activated just edit the line 74 of nextflow.config to false.
If you use containers either docker or singularity, you don't need extra installations.
You just need to have all the software used in the pipeline (see table above) installed and in your $PATH.
To test the pipeline we have a subset of 5 bins available at https://osf.io/9xmh4/ A detailed explanation of all the parameter is available in Usage, the most important for the test is the profile executor and engine. To run it you just need to add "test" in the -profile parameter e.g.:
#test locally with conda, you need to specify cpus and ram available
nextflow run RVanDamme/MUFFIN --output results_dir --cpus 8 --memory 32g -profile local,conda,test
#test locally with docker, you can change the cpus and ram in configs/containers.config
# this test also run the transcriptomics analysis with --rna
nextflow run RVanDamme/MUFFIN --output results_dir --rna -profile local,docker,test
#test using gcloud with docker, you can change the cpus and ram in configs/containers.config
# this test use flye instead of spades with the --assembler metaflye
nextflow run RVanDamme/MUFFIN --output results_dir --assembler metaflye -profile gcloud,docker,test
The subset contains also RNA data to test with transcriptomics analysis you just need to activate it using "--rna" The results of the different test run are available at https://osf.io/m5czv/
To avoid writing all the parameter in the CLI you can use the additional "-params-file" and provide a .yml file that contains all the parameters available for MUFFIN and described below. You can find the MUFFIN_params.yml file in the base of MUFFIN directory.
Exemple: MUFFIN_params.yml
assembler : "metaspades"
ouptut : "path/to/resultdir"
illumina : "fastq_ill/"
ont : "fastq_ont/"
cpus : 16
memory : "64g"
modular : "full"
MUFFIN comand:
path/to/nextflow run $MUFFIN_pipeline -params-file MUFFIN_params.yml -profile local,conda,test
$MUFFIN_pipeline is either "path/to/MUFFIN/main.nf" or "RVanDamme/MUFFIN"
path/to/nextflow run $MUFFIN_pipeline --output results_dir --assembler $assembler --illumina fastq_ill/ --ont fastq_ont/ --cpus 16 --memory 64g --modular full -profile $profile_executor,$profile_engine
$MUFFIN_pipeline is either "path/to/MUFFIN/main.nf" or "RVanDamme/MUFFIN"
$assembler is either:
- "metaspades" for hybrid assembly
- "metaflye" for long-read assembly with short-reads polishing
What assembly approach should i use? (metaspades vs flye)
Chose your assembly approach based on the amount of data (in Gigabases). If you have more short reads go for meta-spades, more long-reads? Go for flye. However, if you have over 15 Gigabases of long read data, flye might always be the better option regardless of Illumina throughput as you get good complete genome drafts. As the each sample influences the outcome heavily we recommend in trying both if you are unsure.
$profile_executor can be:
- "local" to run on your computer
- "gcloud" to run on google life science cloud computing (you need to setup your project in nextflow.config)
- "slurm" to run on HPC using slurm (e.g. UPPMAX)
$profile_engine can be:
- "local_engine" to execute the software installed locally
- "conda" to execute using conda installation
- "docker" and "singularity" to execute using the docker container
You can add "-resume" at the end of the command to restart it while keeping the process that succeeded This is often used in case or error in the pipeline or if you modify slightly the command and want to avoid running everything again. One exemple is to run the pipeline without RNA and rerun it adding RNA data, in this specific case the second time you add:
--rna path/to/rna -resume
Only the transcript processes and final parsing will be run
You can use RNA data to have transcriptomics analysis to do so add "--rna path/to/fastq_rna/"
You can run only partially the pipeline to do so change --modular to the right parameter:
- "full" run the 3 steps of MUFFIN (assemble, classify, annotate)
- "assemble" run the assembly and binning
- "classify" run the classification of the bins (require a different input)
- "annotate" run the annotation step of the bins (require a different input) and RNA if provided
- "assemb-class" run assemble and classify step
- "assemb-annot" run assemble and classify step
- "class-annot" run classify and annotate step (require a different input)
To run classify and annotate independently from the assemble you need to provide a CSV file of the bins The structure of the file should correspond to:
Samplename,path/to/bin1.fa
Samplename,path/to/bin2.fa
...
Samplename,path/to/binX.fa
Samplename,path/to/binY.fa
If you run "classify" with or without "annotation" use "--bin_classify" If you run "annotate" without "classify" use "--bin_annotate"
- If metawrap installation fail while using conda. A known fix is to install metawrap in a conda environment and put the path in "modules/metawrap_refine_bin.nf" To do so run the following command:
#create an env and install metawrap
conda create -y -p /path/to/install/metawrap-env python=2.7
conda activate /path/to/install/metawrap-env
conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels ursky
conda install -y -c ursky metawrap-mg
conda deactivate
#edit MAFIN/modules/metawrap_refine_bin.nf to use the env of metawrap
#you need to change the line 3 and 25 to the path of your env (/path/to/install/metawrap-env)-
If you run the pipeline with google life sciences and get error code 14 It means the process was killed by google, you just need to run the pipeline again don't forget to add "-resume"
-
If either Metawrap or checkm have the following error You need to increase the RAM in the command for local_engine and conda or in the "configs/containers.config"
IOError: [Errno 2] No such file or directory: 'binsA.checkm/storage/tree/concatenated.tre'
- For other issue please open a ticket on
- number of thread available
- default 2
- number of memory available
- default 16g
- which method to use
- can be Hybrid with metaspades or long read + polishing with metaflye
- required
- location of the dir containing the forward and reverse illumina reads in fasta or fastq
- required
- location of the dir containing the nanopore reads in fasta or fastq
- required
- output directory
- required
- engine and executor
- required
- engine: local_engine, conda, docker, singularity
- executor: local, gcloud, slurm
*********hybrid assembly and differential binning workflow for metagenomics, transcriptomics and pathway analysis*********
MUFFIN is still under development please wait until the first non edge version realease before using it.
Please cite us using https://www.biorxiv.org/content/10.1101/2020.02.08.939843v1
Mafin is composed of 3 part the assembly of potential metagenome assembled genomes (MAGs); the classification of the MAGs; and the annotation of the MAGs.
Usage example:
nextflow run RVanDamme/MUFFIN --output result --ont nanopore/ --illumina illumina/ --assembler metaspades --rna rna/ -profile local,docker
or
nextflow run RVanDamme/MUFFIN --output result --ont nanopore/ --illumina illumina/ --assembler metaflye -profile local,docker
Input:
--ont path to the directory containing the nanopore read file (fastq) (default: $params.ont)
--illumina path to the directory containing the illumina read file (fastq) (default: $params.illumina)
--rna path to the directory containing the RNA-seq read file (fastq) (default: none)
--bin_classify path to the directory containing the bins files to classify (default: none)
--bin_annotate path to the directory containing the bins files to annotate (default: none)
--assembler the assembler to use in the assembly step (default: $params.assembler)
Optional input:
--check_db path to the checkm database
--check_tar_db path to the checkm database tar compressed
--sourmash_db path to the LCA database for sourmash (default: GTDB LCA formated)
--eggnog_db path to the eggNOG database
Output:
--output path to the output directory (default: $params.output)
Outputed files:
You can see the output structure at https://osf.io/a6hru/
QC The reads file after qc
Assembly The assembly contigs file
Bins The bins produced by CONCOCT, MetaBAT2, MaxBin2 and MetaWRAP (the refining of bins)
Mapped bin reads The fastq files containing the reads mapped to each metawrap bin
Unmapped bin reads The fastq files containing the unmmaped reads of illumina and nanopore
Reassembly The reassembly files of the bins (.fa and .gfa)
Checkm Various file outputed by CheckM (summary, taxonomy, plots and output dir)
Sourmash The classification done by sourmash
Classify summary The summary of the classification and quality control of the bins (csv file)
RNA output The de novo assembled transcript and the quantification by Salmon
Annotation The annotations files from eggNOG (tsv format)
Parsed output HTML files that summarize the annotations and show graphically the pathways
Basic Parameter:
--cpus max cores for local use [default: $params.cpus]
--memory 80% of available RAM in GB for --metamaps [default: $params.memory]
Workflow Options:
--skip_ill_qc skip quality control of illumina files
--skip_ont_qc skip quality control of nanopore file
--short_qc minimum size of the reads to be kept (default: $params.short_qc )
--filtlong use filtlong to improve the quality furthermore (default: false)
--model the model medaka will use (default: $params.model)
--polish_iteration number of iteration of pilon in the polish step (default: $params.polish_iteration)
--extra_ill a list of additional ill sample file (with full path with a * instead of _R1,2.fastq) to use for the binning in Metabat2 and concoct
--extra_ont a list of additional ont sample file (with full path) to use for the binning in Metabat2 and concoct
--skip_metabat2 skip the binning using metabat2 (advanced)
--skip_maxbin2 skip the binning using maxbin2 (advanced)
--skip_concoct skip the binning using concoct (advanced)
Nextflow options:
-profile change the profile of nextflow both the engine and executor more details on github README
-resume resume the workflow where it stopped
-with-report rep.html cpu / ram usage (may cause errors)
-with-dag chart.html generates a flowchart for the process tree
-with-timeline time.html timeline (may cause errors)
BWA: Li H. and Durbin R. (2009) Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics, 25:1754-60. [PMID: 19451168]
CheckM: Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25: 1043–1055.
Concoct: Johannes Alneberg, Brynjar Smári Bjarnason, Ino de Bruijn, Melanie Schirmer, Joshua Quick, Umer Z Ijaz, Leo Lahti, Nicholas J Loman, Anders F Andersson & Christopher Quince. 2014. Binning metagenomic contigs by coverage and composition. Nature Methods, doi: 10.1038/nmeth.3103
Fastp: Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu; fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i884–i890, https://doi.org/10.1093/bioinformatics/bty560
Filtlong: https://github.com/rrwick/Filtlong
Flye: Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin and Pavel Pevzner, "Assembly of Long Error-Prone Reads Using Repeat Graphs", Nature Biotechnology, 2019 doi:10.1038/s41587-019-0072-8
HMMER: https://hmmer.org/
Maxbin2: Wu YW, Tang YH, Tringe SG, Simmons BA, and Singer SW, "MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm", Microbiome, 2:26, 2014.
Medaka: https://github.com/nanoporetech/medaka
Metabat2: Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165. doi:10.7717/peerj.1165
Metawrap: Uritskiy, G.V., DiRuggiero, J. and Taylor, J. (2018). MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome, 6(1). https://doi.org/10.1186/s40168-018-0541-1
Minimap2: Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100. doi:10.1093/bioinformatics/bty191
Pilon: Bruce J. Walker, Thomas Abeel, Terrance Shea, Margaret Priest, Amr Abouelliel, Sharadha Sakthikumar, Christina A. Cuomo, Qiandong Zeng, Jennifer Wortman, Sarah K. Young, Ashlee M. Earl (2014) Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. PLoS ONE 9(11): e112963. doi:10.1371/journal.pone.0112963
pplacer: Matsen FA, Kodner RB, Armbrust EV. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11: doi:10.1186/1471-2105-11-538.
prodigal: Hyatt D, Locascio PF, Hauser LJ, Uberbacher EC. 2012. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28: 2223–2230.
Racon: Vaser R, Sovic I, Nagarajan N, Sikic M. 2017. Fast and accurate de novogenome assembly from long uncorrected reads. Genome Res 27:737–746.https://doi.org/10.1101/gr.214270.116
Samtools: Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and 1000 Genome Project Data Processing Subgroup, The Sequence alignment/map (SAM) format and SAMtools, Bioinformatics (2009) 25(16) 2078-9 [19505943]
Seqtk: https://github.com/lh3/seqtk
Sourmash: Brown et al, (2016), sourmash: a library for MinHash sketching of DNA, Journal of Open Source Software, 1(5), 27, doi:10.21105/joss.00027
Spades: Lapidus A., Antipov D., Bankevich A., Gurevich A., Korobeynikov A., Nurk S., Prjibelski A., Safonova Y., Vasilinetc I., Pevzner P. A. New Frontiers of Genome Assembly with SPAdes 3.0. (poster), 2014
Unicycler: Wick RR, Judd LM, Gorrie CL, Holt KE (2017) Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13(6): e1005595. https://doi.org/10.1371/journal.pcbi.1005595
Code is GPL-3.0
We welcome contributions from the community! See our Contributing guidelines
The Muffin logo has been made by Tanguy Desmarez and is CC BY (Version 4) compliant