UMInator
UMInator is a Nextflow pipeline for generating consensus sequences from Nanopore reads tagged with UMIs. In particular, it first builds a database of high-quality UMIs based on UMI structure, length and presence of flanking adapters and PCR primers found in the reads. Then, it bins the reads into files based on the match to high-quality UMIs, and reads assigned to each UMI are collapsed to produce a consensus sequence. Finally, PCR primers are trimmed, producing a set of high-quality consensus sequences.
Getting started
Prerequisites
Installation
git clone https://github.com/MaestSi/UMInator.git
cd UMInator
chmod 755 *Overview
Usage
The UMInator pipeline requires you to open UMInator.conf configuration file and set the desired options. Then, you can run the pipeline using either docker or singularity environments just specifying a value for the -profile variable.
Usage:
nextflow -c UMInator.conf run UMInator.nf --fastq_files = "/path/to/files*.fastq" --scripts_dir = "/path/to/scripts_dir" --results_dir = "/path/to/results_dir" -profile docker
Mandatory argument:
-profile Configuration profile to use. Available: docker, singularity
Other mandatory arguments which may be specified in the UMInator.conf file
--fastq_files Path to fastq files, use wildcards to select multiple samples
--results_dir Path to a folder where to store results
--scripts_dir Directory containing all scripts
--FW_adapter Forward adapter sequence
--RV_adapter Reverse adapter sequence
--FW_primer Forward primer sequence
--RV_primer Reverse primer sequence
--searchLen Amount of bases at the beginning and end of each read to search for UMIs
--tolCutadaptErr Cutadapt maximum allowed error rate [0, 1]
--minLenOvlp Min overlap between read and adapter
--UMILen UMI length (before merging UMI1 and UMI2)
--UMIPattern UMI structure (after merging UMI1 and UMI2) in the form of a regex of the type: [nucl.]{cardinality}
--UMIClustID UMI clustering identity
--maxDiff BWA aln maximum number of differences
--max_NM_mean Maximum tolerated mean mapping difference between UMI and query reads
--max_NM_sd Maximum tolerated sd of mapping difference between UMI and query reads
--min_UMI_freq Minimum number of reads assigned to UMI for generating a consensus sequence
--target_reads_consensus Maximum number of reads used for consensus calling
--target_reads_polishing Maximum number of reads used for consensus polishing
--fast_consensus_flag Set fast_consensus_flag = 1 for obtaining draft consensus with VSEARCH, instead of MAFFT + EMBOSS cons
--fast_polishing_flag Set fast_polishing_flag = 1 for polishing with Racon, instead of Racon + Medaka
--plurality MAFFT plurality value: minimum fraction of aligned reads supporting a basis for including it in the preliminary consensus
--fast_alignment_flag Set fast_alignment_flag=1 if you want to perform fast multiple sequence alignment; otherwise set fast_alignment_flag=0
--medaka_model Medaka model for consensus polishing
--maxF Maximum forksPipeline testing
The UMInator pipeline can be tested on a small subset of Zymo mock rRNA data available from Karst et al. with the following command:
nextflow -c UMInator.conf run UMInator.nf \
--FW_adapter="CAAGCAGAAGACGGCATACGAGAT" \
--RV_adapter="AATGATACGGCGACCACCGAGATC" \
--FW_primer="AGRGTTYGATYMTGGCTCAG" \
--RV_primer="CGACATCGAGGTGCCAAAC" \
--fastq_files=/path/to/test_reads.fastq \
--results_dir=/path/to/results/dir \
--minQ=7 \
--minLen=3000 \
--maxLen=6000 \
--min_UMI_freq=10 \
--scripts_dir=/path/to/UMInator/scripts \
--medaka_model=r941_min_high_g330 \
-profile dockerPipeline benchmarking
The UMInator pipeline was benchmarked on Zymo mock rRNA data available from Karst et al. with the following command:
nextflow -c UMInator.conf run UMInator.nf -bg \
--FW_adapter="CAAGCAGAAGACGGCATACGAGAT" \
--RV_adapter="AATGATACGGCGACCACCGAGATC" \
--FW_primer="AGRGTTYGATYMTGGCTCAG" \
--RV_primer="CGACATCGAGGTGCCAAAC" \
--fastq_files=/path/to/ERR3336963_1.fastq \
--results_dir=/path/to/ERR3336963_1_UMInator_output \
--minQ=7 \
--minLen=3000 \
--maxLen=6000 \
--min_UMI_freq=30 \
--maxF=10 \
--scripts_dir=/path/to/UMInator/scripts \
--medaka_model=r941_min_high_g330 \
-profile dockerRaw reads obtained with R9.4.1 chemistry and base-called with Guppy v3.3.0 hac and consensus sequences obtained from such reads with both UMInator and longread_umi pipelines, supported by at least 30 reads, were analysed with MetaBlast using NCBI nt as a reference database. The relative abundances of species in the community and alignment identities were extracted from MetaBlast results. Overall, consensus sequences obtained with both pipelines allowed to increase the median alignment identity from 92.7% (Figure panel A) to >99.9% (Figure panel B). Consensus sequences obtained with both pipelines recapitulated relative abundance at the species level with strong correlation (Figure panels C, D).
Citation
For further information, please refer to the following manuscripts and repositories:
Karst, S.M., Ziels, R.M., Kirkegaard, R.H. et al. High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nat Methods 18, 165–169 (2021). https://doi.org/10.1038/s41592-020-01041-y
De Coster W, D'Hert S, Schultz DT, Cruts M, Van Broeckhoven C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics. 2018;34(15):2666-2669. doi:10.1093/bioinformatics/bty149
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018 Sep 15;34(18):3094-3100. doi: 10.1093/bioinformatics/bty191. PMID: 29750242; PMCID: PMC6137996.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: 10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PMID: 19505943; PMCID: PMC2723002.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016 Oct 18;4:e2584. doi: 10.7717/peerj.2584. PMID: 27781170; PMCID: PMC5075697.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013 Apr;30(4):772-80. doi: 10.1093/molbev/mst010. Epub 2013 Jan 16. PMID: 23329690; PMCID: PMC3603318.
Rice P., Longden I. and Bleasby A. EMBOSS: The European Molecular Biology Open Software Suite. Trends in Genetics. 2000 16(6):276-277
Vaser R, Sović I, Nagarajan N, Šikić M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 2017 May;27(5):737-746. doi: 10.1101/gr.214270.116. Epub 2017 Jan 18. PMID: 28100585; PMCID: PMC5411768.
Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316–319. doi:10.1038/nbt.3820