UMInator

KO | EN

gitlite — search

#hacktoberfest #typescript #llm #golang #android #machine-learning #docker #nodejs #awesome #kubernetes #awesome-list #pytorch #mcp #open-source

(★ 9)

A Nextflow pipeline for generating consensus sequences from Nanopore reads tagged with UMIs

UMInator Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# UMInator
**UMInator** is a [Nextflow](https://www.nextflow.io) 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**

* [Nextflow](https://nf-co.re/usage/installation)
* [Docker](https://docs.docker.com/engine/install/) or [Singularity](https://sylabs.io/guides/3.0/user-guide/installation.html)                                                                                  
                                                                                   
**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 forks
```

## Pipeline testing

The UMInator pipeline can be tested on a small subset of [Zymo mock rRNA data](https://github.com/SorenKarst/longread_umi/blob/master/test_data/test_reads.fq) available from [Karst et al.](https://www.nature.com/articles/s41592-020-01041-y) 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 docker
```
## Pipeline benchmarking

The UMInator pipeline was benchmarked on [Zymo mock rRNA data](https://www.ebi.ac.uk/ena/data/view/ERR3813594) available from [Karst et al.](https://www.nature.com/articles/s41592-020-01041-y) 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 docker
```
Raw 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](https://github.com/SorenKarst/longread_umi) pipelines, supported by at least 30 reads, were analysed with [MetaBlast](https://github.com/MaestSi/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

[medaka](https://github.com/nanoporetech/medaka)

[seqtk](https://github.com/lh3/seqtk)

UMInator

UMInator Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

UMInator

UMInator Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README