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bintaxonomy

https://github.com/barbarahelena/bintaxonomy

Science Score: 44.0%

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Repository

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  • Host: GitHub
  • Owner: barbarahelena
  • License: mit
  • Language: Nextflow
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Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog License Code of conduct Citation

README.md

Nextflow run with conda run with docker run with singularity

Introduction

saltprofiler is a bioinformatics pipeline for assembly, binning and annotation of metagenomes focused on salt tolerance genes. The pipeline is based on the nf-core mag pipeline.

Pipeline summary

This pipeline uses paired-end short-read fastq files (with or without assemblies) as input, quality trims the reads and adapters with fastp and Porechop, and performs basic QC with FastQC, and merge multiple sequencing runs.

The pipeline then: - assigns taxonomy to reads using Centrifuge and/or Kraken2 - performs assembly using SPAdes, and checks their quality using Quast - performs metagenome binning using MetaBAT2, MaxBin2, and checks the quality of the genome bins using Busco, or CheckM. - predicts protein-coding genes for the assemblies using Prodigal, and bins with Prokka - assigns taxonomy to bins using GTDB-Tk and/or CAT

Furthermore, the pipeline creates various reports in the results directory specified, including a MultiQC report summarizing some of the findings and software versions.

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 test before running the workflow on actual data.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow 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.

Group-wise co-assembly and co-abundance computation

Each sample has an associated group ID (see input specifications). This group information can be used for group-wise co-assembly with MEGAHIT or SPAdes and/or to compute co-abundances for the binning step with MetaBAT2. By default, group-wise co-assembly is disabled, while the computation of group-wise co-abundances is enabled. For more information about how this group information can be used see the documentation for the parameters --coassemble_group and --binning_map_mode.

When group-wise co-assembly is enabled, SPAdes is run on accordingly pooled read files, since metaSPAdes does not yet allow the input of multiple samples or libraries. In contrast, MEGAHIT is run for each group while supplying lists of the individual readfiles.

Credits

The salt tolerance-specific modules were written by Barbara, but most of the pipeline is based on the nf-core/mag pipeline. This pipeline was written by Hadrien Gourlé at SLU, Daniel Straub and Sabrina Krakau at the Quantitative Biology Center (QBiC). James A. Fellows Yates and Maxime Borry at the Max Planck Institute for Evolutionary Anthropology joined in version 2.2.0.

Citations

If you use this pipeline, I suggest that you cite the nf-core/mag preprint, since most of this pipeline is based on their work:

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

Sabrina Krakau, Daniel Straub, Hadrien Gourlé, Gisela Gabernet, Sven Nahnsen.

NAR Genom Bioinform. 2022 Feb 2;4(1):lqac007. doi: 10.1093/nargab/lqac007.

Additionally you can cite the pipeline directly with the following doi: 10.5281/zenodo.3589527

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: Barbara Verhaar
  • Login: barbarahelena
  • Kind: user
  • Location: Amsterdam

PhD candidate @ Amsterdam UMC Vascular medicine

Citation (CITATIONS.md) 

# nf-core/mag: 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

- [AdapterRemoval2](https://doi.org/10.1186/)

  > Schubert, M., Lindgreen, S., and Orlando, L. 2016. "AdapterRemoval v2: Rapid Adapter Trimming, Identification, and Read Merging." BMC Research Notes 9 (February): 88. doi: 10.1186/s13104-016-1900-2

- [BBnorm/BBTools](http://sourceforge.net/projects/bbmap/)

- [BCFtools](https://doi.org/10.1093/gigascience/giab008)

  > Danecek, Petr, et al. "Twelve years of SAMtools and BCFtools." Gigascience 10.2 (2021): giab008. doi: 10.1093/gigascience/giab008

- [Bowtie2](https:/dx.doi.org/10.1038/nmeth.1923)

  > Langmead, B. and Salzberg, S. L. 2012 Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), p. 357–359. doi: 10.1038/nmeth.1923.

- [Busco](https://doi.org/10.1007/978-1-4939-9173-0_14)

  > Seppey, M., Manni, M., & Zdobnov, E. M. (2019). BUSCO: assessing genome assembly and annotation completeness. In Gene prediction (pp. 227-245). Humana, New York, NY. doi: 10.1007/978-1-4939-9173-0_14.

- [CAT](https://doi.org/10.1186/s13059-019-1817-x)

  > von Meijenfeldt, F. B., Arkhipova, K., Cambuy, D. D., Coutinho, F. H., & Dutilh, B. E. (2019). Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome biology, 20(1), 1-14. doi: 10.1186/s13059-019-1817-x.

- [Centrifuge](https://doi.org/10.1101/gr.210641.116)

  > Kim, D., Song, L., Breitwieser, F. P., & Salzberg, S. L. (2016). Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome research, 26(12), 1721-1729. doi: 10.1101/gr.210641.116.

- [CheckM](https://doi.org/10.1101/gr.186072.114)

  > Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25(7), 1043–1055. doi: 10.1101/gr.186072.114

- [CONCOCT](https://doi.org/10.1038/nmeth.3103)

  > Alneberg, J., Bjarnason, B. S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., Lahti, L., Loman, N. J., Andersson, A. F., & Quince, C. (2014). Binning metagenomic contigs by coverage and composition. Nature Methods, 11(11), 1144–1146. doi: 10.1038/nmeth.3103

- [DAS Tool](https://doi.org/10.1038/s41564-018-0171-1)

  > Sieber, C. M. K., et al. 2018. "Recovery of Genomes from Metagenomes via a Dereplication, Aggregation and Scoring Strategy." Nature Microbiology 3 (7): 836-43. doi: 10.1038/s41564-018-0171-1

- [FastP](https://doi.org/10.1093/bioinformatics/bty560)

  > Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics , 34(17), i884–i890. doi: 10.1093/bioinformatics/bty560.

- [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

  > Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [Filtlong](https://github.com/rrwick/Filtlong)

- [Freebayes](https://arxiv.org/abs/1207.3907)

  > Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:1207.3907 [q-bio.GN] 2012

- [geNomad](https://doi.org/10.1101/2023.03.05.531206)

  > Camargo, A. P., et al. (2023). You can move, but you can’t hide: identification of mobile genetic elements with geNomad. bioRxiv preprint. doi: https://doi.org/10.1101/2023.03.05.531206

- [GTDB-Tk](https://doi.org/10.1093/bioinformatics/btz848)

  > Chaumeil, P. A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2020). GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics , 36(6), 1925–1927. doi: 10.1093/bioinformatics/btz848.

- [GUNC](https://doi.org/10.1186/s13059-021-02393-0.)

  > Orakov, A., Fullam, A., Coelho, A. P., Khedkar, S., Szklarczyk, D., Mende, D. R., Schmidt, T. S. B., and Bork, P.. 2021. “GUNC: Detection of Chimerism and Contamination in Prokaryotic Genomes.” Genome Biology 22 (1): 178. doi: 10.1186/s13059-021-02393-0.

- [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.

- [Krona](https://doi.org/10.1186/1471-2105-12-385)

  > Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011). Interactive metagenomic visualization in a Web browser. BMC bioinformatics, 12(1), 1-10. doi: 10.1186/1471-2105-12-385.

- [MaxBin2](https://doi.org/10.1093/bioinformatics/btv638)

  > Yu-Wei, W., Simmons, B. A. & Singer, S. W. (2015) MaxBin 2.0: An Automated Binning Algorithm to Recover Genomes from Multiple Metagenomic Datasets. Bioinformatics 32 (4): 605–7. doi: 10.1093/bioinformatics/btv638.

- [MEGAHIT](https://doi.org/10.1016/j.ymeth.2016.02.020)

  > Li, D., Luo, R., Liu, C. M., Leung, C. M., Ting, H. F., Sadakane, K., ... & Lam, T. W. (2016). MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods, 102, 3-11. doi: 10.1016/j.ymeth.2016.02.020.

- [MetaBAT2](https://doi.org/10.7717/peerj.7359)

  > Kang, D. D., Li, F., Kirton, E., Thomas, A., Egan, R., An, H., & Wang, Z. (2019). MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ, 7, e7359. doi: 10.7717/peerj.7359.

- [MetaEuk](https://doi.org/10.1186/s40168-020-00808-x)

> Levy Karin, E., Mirdita, M. & Söding, J. MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020). https://doi.org/10.1186/s40168-020-00808-x

- [MMseqs2](https://www.nature.com/articles/nbt.3988)

> Steinegger, M., Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017). https://doi.org/10.1038/nbt.3988

- [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.

- [NanoLyse](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.

- [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.

- [Porechop](https://github.com/rrwick/Porechop)

- [Prodigal](https://pubmed.ncbi.nlm.nih.gov/20211023/)

  > Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010 Mar 8;11:119. doi: 10.1186/1471-2105-11-119. PMID: 20211023; PMCID: PMC2848648.

- [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.

- [PyDamage](https://doi.org/10.7717/peerj.11845)

  > Borry M, Hübner A, Rohrlach AB, Warinner C. 2021. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ 9:e11845 doi: 10.7717/peerj.11845

- [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.

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

- [SPAdes](https://doi.org/10.1101/gr.213959.116)

  > Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). metaSPAdes: a new versatile metagenomic assembler. Genome research, 27(5), 824-834. doi: 10.1101/gr.213959.116.

- [Tiara](https://doi.org/10.1093/bioinformatics/btab672)

  > Karlicki, M., Antonowicz, S., Karnkowska, A., 2022. Tiara: deep learning-based classification system for eukaryotic sequences. Bioinformatics 38, 344–350. doi: 10.1093/bioinformatics/btab672

## Data

- [Full-size test data](https://doi.org/10.1038/s41587-019-0191-2)
  > Bertrand, D., Shaw, J., Kalathiyappan, M., Ng, A. H. Q., Kumar, M. S., Li, C., ... & Nagarajan, N. (2019). Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nature biotechnology, 37(8), 937-944. doi: 10.1038/s41587-019-0191-2.

## 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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Dependencies

subworkflows/nf-core/utils_nextflow_pipeline/meta.yml cpan
subworkflows/nf-core/utils_nfcore_pipeline/meta.yml cpan