Science Score: 67.0%
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Repository
Simple bacterial assembly and annotation pipeline
Basic Info
- Host: GitHub
- Owner: nf-core
- License: mit
- Language: Nextflow
- Default Branch: master
- Homepage: https://nf-co.re/bacass
- Size: 5.23 MB
Statistics
- Stars: 76
- Watchers: 165
- Forks: 61
- Open Issues: 25
- Releases: 9
Topics
Metadata Files
README.md
Introduction
nf-core/bacass is a bioinformatics best-practice analysis pipeline for simple bacterial assembly and annotation. The pipeline is able to assemble short reads, long reads, or a mixture of short and long reads (hybrid assembly).
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.
Pipeline summary
Short Read Assembly
This pipeline is primarily for bacterial assembly of next-generation sequencing reads. It can be used to quality trim your reads using FastP and performs basic sequencing QC using FastQC. Afterwards, the pipeline performs read assembly using Unicycler. Contamination of the assembly is checked using Kraken2 and Kmerfinder to verify sample purity.
Long Read Assembly
For users that only have Nanopore data, the pipeline quality trims these using PoreChop and assesses basic sequencing QC utilizing NanoPlot and PycoQC. Contamination of the assembly is checked using Kraken2 and Kmerfinder to verify sample purity.
The pipeline can then perform long read assembly utilizing Unicycler, Miniasm in combination with Racon, Canu or Flye by using the Dragonflye(*) pipeline. Long reads assembly can be polished using Medaka or NanoPolish with Fast5 files.
[!NOTE] Dragonflye is a comprehensive pipeline designed for genome assembly of Oxford Nanopore Reads. It facilitates the utilization of Flye (default), Miniasm, and Raven assemblers, along with Racon (default) and Medaka polishers. For more information, visit the Dragonflye GitHub repository.
Hybrid Assembly
For users specifying both short read and long read (NanoPore) data, the pipeline can perform a hybrid assembly approach utilizing Unicycler (short read assembly followed by gap closing with long reads) or Dragonflye (long read assembly followed by polishing with short reads), taking the full set of information from short reads and long reads into account.
Assembly QC and annotation
In all cases, the assembly is assessed using QUAST. The resulting bacterial assembly is furthermore annotated using Prokka, Bakta or DFAST.
If Kmerfinder is invoked, the pipeline will group samples according to the Kmerfinder-estimated reference genomes. Afterwards, two QUAST steps will be carried out: an initial ('general') QUAST of all samples without reference genomes, and subsequently, a 'by reference genome' QUAST to aggregate samples with their reference genomes.
[!NOTE] This scenario is supported when Kmerfinder analysis is performed only.
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.tsv:
```tsv ID R1 R2 LongFastQ Fast5 GenomeSize shortreads ./data/S1R1.fastq.gz ./data/S1R2.fastq.gz NA NA NA longreads NA NA ./data/S1longfastq.gz ./data/FAST5 2.8m shortNlong ./data/S1R1.fastq.gz ./data/S1R2.fastq.gz ./data/S1longfastq.gz ./data/FAST5 2.8m
```
Each row represents a fastq file (single-end) or a pair of fastq files (paired end).
Default: Short read assembly with Unicycler, --kraken2db can be any compressed database (.tar.gz/.tgz):
console
nextflow run nf-core/bacass -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.tsv --kraken2db "https://genome-idx.s3.amazonaws.com/kraken/k2_standard_8gb_20210517.tar.gz"
Long read assembly with Miniasm:
console
nextflow run nf-core/bacass -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.tsv --assembly_type 'long' --assembler 'miniasm' --kraken2db "https://genome-idx.s3.amazonaws.com/kraken/k2_standard_8gb_20210517.tar.gz"
bash
nextflow run nf-core/bacass \
-profile <docker/singularity/.../institute> \
--input samplesheet.tsv \
--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 and the parameter documentation.
Pipeline output
To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.
Credits
nf-core/bacass was initiated by Andreas Wilm, originally written by Alex Peltzer (DSL1), rewritten by Daniel Straub (DSL2) and maintained by Daniel Valle-Millares.
Contributions and Support
If you would like to contribute to this pipeline, please see the contributing guidelines.
For further information or help, don't hesitate to get in touch on the Slack #bacass channel (you can join with this invite).
Citations
If you use nf-core/bacass for your analysis, please cite it using the following doi: 10.5281/zenodo.2669428
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.
You can cite the nf-core publication as follows:
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Owner
- Name: nf-core
- Login: nf-core
- Kind: organization
- Email: core@nf-co.re
- Website: http://nf-co.re
- Twitter: nf_core
- Repositories: 84
- Profile: https://github.com/nf-core
A community effort to collect a curated set of analysis pipelines built using Nextflow.
Citation (CITATIONS.md)
# nf-core/bacass: Citations ## [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]. - [FastP](https://github.com/OpenGene/fastp) > Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018 Sep 1;34(17):i884-i890. doi: 10.1093/bioinformatics/bty560. PMID: 30423086; PMCID: PMC6129281. - [Porechop](https://github.com/rrwick/Porechop) - [NanoPlot](https://doi.org/10.1093/bioinformatics/bty149) > De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M., & Van Broeckhoven, C. (2018). NanoPack: visualizing and processing long-read sequencing data. Bioinformatics, 34(15), 2666-2669. doi: 10.1093/bioinformatics/bty149. - [pycoQC](https://github.com/tleonardi/pycoQC) - [Unicycler](https://pubmed.ncbi.nlm.nih.gov/28594827/) > Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017 Jun 8;13(6):e1005595. doi: 10.1371/journal.pcbi.1005595. PMID: 28594827; PMCID: PMC5481147. - [Miniasm](https://github.com/lh3/miniasm) with [Racon](https://github.com/isovic/racon) > Li H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics. 2016 Jul 15;32(14):2103-10. doi: 10.1093/bioinformatics/btw152. Epub 2016 Mar 19. PMID: 27153593; PMCID: PMC4937194. - [Canu](https://pubmed.ncbi.nlm.nih.gov/28298431/) > Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017 May;27(5):722-736. doi: 10.1101/gr.215087.116. Epub 2017 Mar 15. PMID: 28298431; PMCID: PMC5411767. - [QUAST](https://pubmed.ncbi.nlm.nih.gov/23422339/) > Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013 Apr 15;29(8):1072-5. doi: 10.1093/bioinformatics/btt086. Epub 2013 Feb 19. PMID: 23422339; PMCID: PMC3624806. - [Prokka](https://pubmed.ncbi.nlm.nih.gov/24642063/) > Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014 Jul 15;30(14):2068-9. doi: 10.1093/bioinformatics/btu153. Epub 2014 Mar 18. PMID: 24642063. - [DFAST](https://pubmed.ncbi.nlm.nih.gov/29106469/) > Tanizawa Y, Fujisawa T, Nakamura Y. DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics. 2018 Mar 15;34(6):1037-1039. doi: 10.1093/bioinformatics/btx713. PMID: 29106469; PMCID: PMC5860143. - [Medaka](https://github.com/nanoporetech/medaka) - [Nanopolish](https://github.com/jts/nanopolish) - [SAMtools](https://doi.org/10.1093/bioinformatics/btp352) > Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. doi: 10.1093/bioinformatics/btp352. - [Kraken2](https://doi.org/10.1186/s13059-019-1891-0) > Wood, D et al., 2019. Improved metagenomic analysis with Kraken 2. Genome Biology volume 20, Article number: 257. doi: 10.1186/s13059-019-1891-0. - [MultiQC](https://www.ncbi.nlm.nih.gov/pubmed/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 ## Data - [Full-size test data](https://pubmed.ncbi.nlm.nih.gov/32561582/) > Blackwell N, Bryce C, Straub D, Kappler A, Kleindienst S. Genomic Insights into Two Novel Fe(II)-Oxidizing Zetaproteobacteria Isolates Reveal Lifestyle Adaption to Coastal Marine Sediments. Appl Environ Microbiol. 2020 Aug 18;86(17):e01160-20. doi: 10.1128/AEM.01160-20. PMID: 32561582; PMCID: PMC7440796. ## 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.
GitHub Events
Total
- Create event: 8
- Release event: 1
- Issues event: 49
- Watch event: 15
- Delete event: 14
- Issue comment event: 105
- Push event: 41
- Pull request review event: 80
- Pull request review comment event: 47
- Pull request event: 75
- Fork event: 28
Last Year
- Create event: 8
- Release event: 1
- Issues event: 49
- Watch event: 15
- Delete event: 14
- Issue comment event: 105
- Push event: 41
- Pull request review event: 80
- Pull request review comment event: 47
- Pull request event: 75
- Fork event: 28
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Alexander Peltzer | a****r@g****m | 202 |
| d4straub | d****b@u****e | 69 |
| Daniel-VM | d****s@g****m | 25 |
| nf-core-bot | c****e@n****e | 25 |
| Alexander Peltzer | a****r@q****e | 21 |
| Alexander Peltzer | a****r | 17 |
| Andreas Wilm | w****a@g****g | 15 |
| Dani VM | d****e@i****s | 12 |
| MaxUlysse | m****a@g****m | 4 |
| Ben Taylor | b****r@s****k | 3 |
| Angel Angelov | a****o@g****m | 2 |
| xlinxlin | y****u@x****e | 2 |
| kevinmenden | k****n@t****e | 1 |
| Phil Ewels | p****s@s****e | 1 |
| Cloud User | c****s@n****l | 1 |
| Ramon Rivera | r****s@g****m | 1 |
| Daniel Straub | 4****b | 1 |
| runner | r****r@f****0 | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 67
- Total pull requests: 103
- Average time to close issues: 7 months
- Average time to close pull requests: about 1 month
- Total issue authors: 33
- Total pull request authors: 26
- Average comments per issue: 2.46
- Average comments per pull request: 1.67
- Merged pull requests: 58
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 20
- Pull requests: 32
- Average time to close issues: 2 months
- Average time to close pull requests: 10 days
- Issue authors: 8
- Pull request authors: 14
- Average comments per issue: 1.05
- Average comments per pull request: 1.16
- Merged pull requests: 15
- Bot issues: 0
- Bot pull requests: 0
Top Authors
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- Daniel-VM (25)
- SchwarzMarek (5)
- xlinxlin (4)
- yeroslaviz (4)
- apeltzer (3)
- d4straub (3)
- zdk123 (3)
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- llk578496 (1)
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- ThHarbig (1)
- varunshamanna (1)
Pull Request Authors
- Daniel-VM (61)
- nf-core-bot (44)
- apeltzer (10)
- d4straub (6)
- lborcard (4)
- rpalcab (3)
- KevinMenden (2)
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