hybridassembly
Hybrid de novo assembly pipeline for whole-genome human datasets
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Hybrid de novo assembly pipeline for whole-genome human datasets
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README.md
Hybrid assembly
A public repository of hybrid de novo assembly pipeline maintained by ITER.
Introduction
hybridassembly is a bioinformatics pipeline that performs preprocessing, de novo assembly, polishing and evaluation steps to obtain high-quality human genomes using long-reads from Oxford Nanopore Technologies (ONT) and short-reads from Illumina. It takes a samplesheet with ONT and Illumina FASTQ files as input, perform quality control (QC), filtering, error-correction, assembly, polishing with self long-reads and short-reads, and post-assembly curation, among assembly evaluations with different tools.
The hybridassembly pipeline is built using Nextflow, following nf-core guidelines and templates.
Pipeline summary
- Illumina read QC (
FastQC) - ONT read QC (
NanoPlot) - Quality filtering long reads (
Filtlong) - Hybrid error correction of long reads (
Ratatosk) - De novo assembler (
Flye) - Assembly polishing using long-reads (
Racon) - Assembly polishing using short-reads (
Pilon) - Purge haplotigs and overlaps (
Purge_Dups) - Contig correction and scaffolding (
RagTag) - Close gaps (
TGS-GapCloser) - Quality assessment of genome assembly (
QUAST) - k-mer based assembly evaluation (
Merqury) - Single-Copy Orthologs based assessment (
BUSCO) - Aggregate QC results (
MultiQC)
Usage
[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with
-profile testbefore running the workflow on actual data.
First, prepare a samplesheet with your input data that looks as follows:
samplesheet.csv:
csv
sample,fastq_1,fastq_2,long_reads
SAMPLENAME,SAMPLENAME_R1_001.fastq.gz,SAMPLENAME_R2_001.fastq.gz,SAMPLENAME_LR.fastq.gz
[!TIP] If you are working with large genomes, as human, we recommend to include only one sample in the samplesheet at a time, due to the high computational requirements used by some pipeline steps.
Each row represents a sample with both paired-end FASTQ files (gzipped) and ONT long-read FASTQ file (gzipped).
Now, you can run the pipeline using:
bash
nextflow run main.nf \
-profile <default/docker/conda> \
--input samplesheet.csv \
--outdir <OUTDIR>
[!WARNING] Please provide pipeline parameters via the CLI or Nextflow
-params-fileoption. Custom config files including those provided by the-cNextflow option can be used to provide any configuration except for parameters; see docs.
For more details and further functionality, please refer to the usage documentation.
Running the pipeline with test data
To ensure the pipeline is set up correctly and functioning as expected, you can run it with the provided test data. The test data consists of a small subset of the human chromosome 22 sequence, specifically selected for quick and efficient testing. Follow these steps:
1. Clone the repository
First, clone the repository and navigate to the project directory:
bash
git clone https://github.com/genomicsITER/hybridassembly
cd hybridassembly
Test data is included in the repository under the test_data/ directory.
2. Run the pipeline
Execute the pipeline with the test data using the following command:
bash
nextflow run main.nf \
-profile test,<docker/singularity/conda> \
-config test.config
3. Check the results
Upon successful completion, the output files will be saved in the test_data/results directory. Review the output to verify that the pipeline ran correctly.
4. Troubleshooting
If you encounter errors, ensure:
- The required dependencies (e.g., Nextflow, Docker/Singularity/Conda) are installed.
- You are using a compatible environment.
- Use the
-resumeflag to retry failed tasks without re-running completed ones:
bash
nextflow run main.nf \
-profile test,<docker/singularity/conda> \
-config test.config \
-resume
For further assistance, feel free to open an issue in this repository.
Pipeline output
For more details about the output files and reports, please refer to the output documentation.
Code for genome preprocessing, assembly, polishing, and evaluation
See here a detailed use of each tool used for preprocessing, assembly, polishing, and evaluation.
Contributions and Support
If you would like to contribute to this pipeline, please see the contributing guidelines.
Follow us on X: @LabCflores
Credits
Preprint: Adrián Muñoz-Barrera, Luis A. Rubio-Rodríguez, David Jáspez, Almudena Corrales, Itahisa Marcelino-Rodriguez, José M. Lorenzo-Salazar, Rafaela González-Montelongo, and Carlos Flores. 2024. “Benchmarking of Bioinformatics Tools for the Hybrid de Novo Assembly of Human Whole-Genome Sequencing Data.” bioRxiv.
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.
Funding
This research was funded by Ministerio de Ciencia e Innovación (RTC-2017-6471-1; AEI/FEDER, UE), co-financed by the European Regional Development Funds ‘A way of making Europe’ from the European Union; Cabildo Insular de Tenerife (CGIEU0000219140); by the agreements OA17/008 and OA23/043 with Instituto Tecnológico y de Energías Renovables (ITER) to strengthen scientific and technological education, training, research, development and innovation in Genomics, Epidemiological surveillance based on sequencing, Personalized Medicine and Biotechnology; and by Convenio Marco de Cooperación Consejería de Educación-Cabildo Insular de Tenerife 2021–2025 (CGIAC0000014697).
Owner
- Name: Genomics Division, ITER
- Login: genomicsITER
- Kind: user
- Location: Tenerife, Canary Islands, SPAIN
- Company: ITER
- Website: http://www.iter.es/
- Repositories: 5
- Profile: https://github.com/genomicsITER
The area of Genomics of the Institute of Technology and Renewable Energy (ITER), at Tenerife, Canary Islands, SPAIN.
Citation (CITATIONS.md)
# nf-core/hybridassembly: Citations ## Nextflow and nf-core - [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/) > Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031. - [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/) > Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311. ## Pipeline tools - [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) > Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. - [NanoPlot](https://pubmed.ncbi.nlm.nih.gov/37171891/) > De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics. 2023 May 4;39(5):btad311. doi: 10.1093/bioinformatics/btad311. PMID: 37171891; PMCID: PMC10196664. - [Filtlong](https://github.com/rrwick/Filtlong) > Wick, R. 2017. Filtlong: Quality Filtering Tool for Long Reads [Online]. - [Ratatosk](https://pubmed.ncbi.nlm.nih.gov/33419473/) > Holley G, Beyter D, Ingimundardottir H, Møller PL, Kristmundsdottir S, Eggertsson HP, Halldorsson BV. Ratatosk: hybrid error correction of long reads enables accurate variant calling and assembly. Genome Biol. 2021 Jan 8;22(1):28. doi: 10.1186/s13059-020-02244-4. PMID: 33419473; PMCID: PMC7792008. - [Flye](https://pubmed.ncbi.nlm.nih.gov/30936562/) > Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol. 2019 May;37(5):540-546. doi: 10.1038/s41587-019-0072-8. Epub 2019 Apr 1. PMID: 30936562. - [Racon](https://pubmed.ncbi.nlm.nih.gov/28100585/) > 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. - [Pilon](https://pubmed.ncbi.nlm.nih.gov/25409509/) > Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, Cuomo CA, Zeng Q, Wortman J, Young SK, Earl AM. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014 Nov 19;9(11):e112963. doi: 10.1371/journal.pone.0112963. PMID: 25409509; PMCID: PMC4237348. - [Purge_dups](https://pubmed.ncbi.nlm.nih.gov/31971576/) > Guan D, McCarthy SA, Wood J, Howe K, Wang Y, Durbin R. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics. 2020 May 1;36(9):2896-2898. doi: 10.1093/bioinformatics/btaa025. PMID: 31971576; PMCID: PMC7203741. - [RagTag](https://pubmed.ncbi.nlm.nih.gov/36522651/) > Alonge M, Lebeigle L, Kirsche M, Jenike K, Ou S, Aganezov S, Wang X, Lippman ZB, Schatz MC, Soyk S. Automated assembly scaffolding using RagTag elevates a new tomato system for high-throughput genome editing. Genome Biol. 2022 Dec 15;23(1):258. doi: 10.1186/s13059-022-02823-7. PMID: 36522651; PMCID: PMC9753292. - [TGS-GapCloser](https://pubmed.ncbi.nlm.nih.gov/32893860/) > Xu M, Guo L, Gu S, Wang O, Zhang R, Peters BA, Fan G, Liu X, Xu X, Deng L, Zhang Y. TGS-GapCloser: A fast and accurate gap closer for large genomes with low coverage of error-prone long reads. Gigascience. 2020 Sep 1;9(9):giaa094. doi: 10.1093/gigascience/giaa094. PMID: 32893860; PMCID: PMC7476103. - [QUAST](https://pubmed.ncbi.nlm.nih.gov/29949969/) > Mikheenko A, Prjibelski A, Saveliev V, Antipov D, Gurevich A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics. 2018 Jul 1;34(13):i142-i150. doi: 10.1093/bioinformatics/bty266. PMID: 29949969; PMCID: PMC6022658. - [BUSCO](https://pubmed.ncbi.nlm.nih.gov/26059717/) > Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015 Oct 1;31(19):3210-2. doi: 10.1093/bioinformatics/btv351. Epub 2015 Jun 9. PMID: 26059717. - [Merqury](https://pubmed.ncbi.nlm.nih.gov/32928274/) > Rhie A, Walenz BP, Koren S, Phillippy AM. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol. 2020 Sep 14;21(1):245. doi: 10.1186/s13059-020-02134-9. PMID: 32928274; PMCID: PMC7488777. - [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/) > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924. ## Software packaging/containerisation tools - [Anaconda](https://anaconda.com) > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web. - [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/) > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506. - [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/) > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671. - [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241) > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241. - [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/) > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.
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