Jovian
A user-friendly Viromics toolkit

For Citations, please use the following DOI:

See the documentation:

Or view an example notebook:

IMPORTANT: manuscript is in preparation

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Table of contents

• About Jovian
  • Key features of Jovian:
• Commands
• Features
  • General features
  • Metagenomics specific features
  • Reference-alignment specific features
  • Visualizations
  • Virus typing
  • Audit trail
• Requirements
• Installation
• Usage instructions
• FAQ
• Example Jovian report
• Acknowledgements
• Authors

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About Jovian

Jovian is a Public Health toolkit to automatically process raw NGS data from human clinical matrices (faeces, serum, etc.) into clinically relevant information. It has three main components:

• Illumina based Metagenomics:
  Includes (amongst other features) data quality control, assembly, taxonomic classification, viral typing, and minority variant identification (quasispecies).
  📝 Please refer to the documentation page for the Illumina Metagenomics workflow for more information.

• Illumina based Reference-alignment:
  Includes (amongst other features) data quality control, alignment, SNP identification, and consensus-sequence generation.
  ❗ A reference fasta is required.
  📝 Please refer to the documentation page for the Illumina Reference based workflow for more information.

• Nanopore based Reference-alignment:
  Includes (amongst other features) data quality control, alignment, SNP identification, and consensus-sequence generation.
  ❗ A reference fasta is required.
  ❗ A fasta with primer sequences is required.
  📝 Please refer to the documentation page for the Nanopore Reference based workflow for more information.

Key features of Jovian:

• User-friendliness:
  Wetlab personnel can start, configure and interpret results via an interactive web-report. Click here for an example report.
  This makes doing Public Health analyses much more accessible and user-friendly since minimal command-line skills are required.

• Audit trail:
  All pipeline parameters, software versions, database information and runtime statistics are logged. See details below.

• Portable:
  Jovian is easily installed on off-site computer systems and at back-up sister institutes. Allowing results to be generated even when the internal grid-computer is down (speaking from experience).

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Commands

📝 Please see the full Command Line Reference on the documentation site for a more detailed explanation of each command, including example commands for starting an analysis or common usage examples.

Here, we have a short list of commands and use cases that are used very frequently.

Use case 1:
Metagenomic analylsis based on Illumina data:

bash jovian illumina-metagenomics -i <INPUT DIRECTORY>

Use case 2:
Align Illumina data against a user-provided reference to generate a consensus genome:

bash jovian illumina-reference -i <INPUT DIRECTORY> -ref <REFERENCE FASTA>

Use case 3:
Align Nanopore (multiplex) PCR data against a user-provided reference, remove overrepresented primer sequences, and generate a consensus genome:

bash jovian nanopore-reference -i <INPUT DIRECTORY> -ref <REFERENCE FASTA> -pr <PRIMER FASTA>

use bash jovian -h to see a full list of commands applicable to the Jovian version that you're using.

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Features

📝 Please refer to our documentation for the full list of features

General features

• Data quality control and cleaning.
  • Including library fragment length analysis, useful for sample preparation QC.
• Removal of human* data (patient privacy). *^{_{You can use whichever reference you would like. However, Jovian is intended for human clinical samples.}}
• Removal of PCR-duplicates for Illumina data.

Metagenomics specific features

• Assembly of short reads into bigger scaffolds (often full viral genomes).
• Taxonomic classification:
  • Every nucleic acid containing biological entity (i.e. not only viruses) is determined up to species level.
  • Lowest Common Ancestor (LCA) analysis is performed to move ambiguous results up to their last common ancestor, which makes results more robust.
• Viral typing:
  • Several viral families and genera can be taxonomically labelled at the sub-species level as described here.
• Viral scaffolds are cross-referenced against the Virus-Host interaction database and NCBI host database.
• Scaffolds are annotated in detail:
  • Depth of coverage.
  • GC content.
  • Open reading frames (ORFs) are predicted.
  • Minority variants (quasispecies) are identified.
• Importantly, results of all processes listed above are presented via an interactive web-report including an audit trail.

Reference-alignment specific features

• All cleaned reads are aligned against the user-provided reference fasta.
• In the case of Nanopore (multiplex) PCR data, the overrepresented primer sequences are removed.
• SNPs are called and a consensus genome is generated.
• Consensus genomes are filtered at the following coverage cut-off thresholds: 1, 5, 10, 30 and 100x.
• A tabular overview of the breadth of coverage (BoC) at the different coverage cut-off thresholds is generated.
• Alignments and visualized via IGVjs and allow manual assessment and validation of consensus genomes.

Visualizations

All data are visualized via an interactive web-report, as shown here, which includes:

• A collation of interactive QC graphs via MultiQC.
• Taxonomic results are presented on three levels:
  • For an entire (multi sample) run, interactive heatmaps are made for non-phage viruses, phages and bacteria. They are stratified to different taxonomic levels.
  • For a sample level overview, Krona interactive taxonomic piecharts are generated.
  • For more detailed analyses, interactive tables are included. Similar to popular spreadsheet applications (e.g. Microsoft Excel).
    • Classified scaffolds
    • Unclassified scaffolds (i.e. "Dark Matter")
• Virus typing results are presented via interactive spreadsheet-like tables.
• An interactive scaffold alignment viewer (IGVjs) is included, containing:
  • Detailed alignment information.
  • Depth of coverage graph.
  • GC content graph.
  • Predicted open reading frames (ORFs).
  • Identified minority variants (quasispecies).
• All SNP metrics are presented via interactive spreadsheet-like tables, allowing detailed analysis.

Virus typing

After a Jovian analysis is finished you can perform virus-typing (i.e. sub-species level taxonomic labelling). These analyses can be started by the command bash jovian -vt [virus keyword], where [virus keyword] can be:

Keyword	Taxon used for scaffold selection	Notable virus species
NoV	Caliciviridae	Norovirus GI and GII, Sapovirus
EV	Picornaviridae	Enteroviruses (Coxsackie, Polio, Rhino, etc.), Parecho, Aichi, Hepatitis A
RVA	Rotavirus A	Rotavirus A
HAV	Hepatovirus A	Hepatitis A
HEV	Orthohepevirus A	Hepatitis E
PV	Papillomaviridae	Human Papillomavirus
Flavi	Flaviviridae	Dengue (work in progress)
all	All of the above	All of the above

Audit trail

An audit trail, used for clinical reproducibility and logging, is generated and contains:

• A unique methodological fingerprint: allowing to exactly reproduce the analysis, even retrospectively by reverting to old versions of the pipeline code.
• The following information is also logged:
  • Database timestamps
  • (user-specified) Pipeline parameters

However, it has limitations since several things are out-of-scope for Jovian to control:

• The virus typing-tools version
  • Currently we depend on a public web-tool hosted by the RIVM. These are developed in close collaboration with - but independently of - Jovian. A versioning system for the virus typing-tools is being worked on, however, this is not trivial and will take some time.
• Input files and metadata
  • We only save the names and location of input files at the time the analysis was performed. Long-term storage of the data, and documenting their location over time, is the responsibility of the end-user. Likewise, the end-user is responsible for storing datasets with their correct metadata (e.g. clinical information, database versions, etc.). We recommend using iRODS for this as described by Nieroda et al. 2019. While we acknowledge that database versions are vital to replicate results, the databases Jovian uses have no official versioning, hence why we include timestamps only.

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Jovian Illumina Metagenomics workflow visualization

Click the image for a full-sized version

Jovian Illumina Reference alignment workflow visualization

Click the image for a full-sized version

Jovian Nanopore Reference alignment workflow visualization

Click the image for a full-sized version

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Requirements

📝 Please refer to our documentation for a detailed overview of the Jovian requirements here

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Installation

📝 Please refer to our documentation for detailed instructions regarding the installation of Jovian here

Usage instructions

General usage instructions vary for each workflow that we support.
Please refer to the link below corresponding to the workflow that you wish to use

• Illumina Metagenomics workflow - Usage instructions and information
• Illumina Reference alignment workflow - Usage instructions and information
• Nanopore Reference alignment workflow - Usage instructions and information

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FAQ

Can be found here.

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Example Jovian report

Can be found here.

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Acknowledgements

Name	Publication	Website
BBtools	NA	https://jgi.doe.gov/data-and-tools/bbtools/
BEDtools	Quinlan, A.R. and I.M.J.B. Hall, BEDTools: a flexible suite of utilities for comparing genomic features. 2010. 26(6): p. 841-842.	https://bedtools.readthedocs.io/en/latest/
BLAST	Altschul, S.F., et al., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. 1997. 25(17): p. 3389-3402.	https://www.ncbi.nlm.nih.gov/books/NBK279690/
BWA	Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997.	https://github.com/lh3/bwa
BioConda	Grüning, B., et al., Bioconda: sustainable and comprehensive software distribution for the life sciences. 2018. 15(7): p. 475.	https://bioconda.github.io/
Biopython	Cock, P. J., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., ... & De Hoon, M. J. (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11), 1422-1423.	https://biopython.org/
Bokeh	Bokeh Development Team (2018). Bokeh: Python library for interactive visualization.	https://bokeh.pydata.org/en/latest/
Bowtie2	Langmead, B. and S.L.J.N.m. Salzberg, Fast gapped-read alignment with Bowtie 2. 2012. 9(4): p. 357.	http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
Conda	NA	https://conda.io/
DRMAA	NA	http://drmaa-python.github.io/
FastQC	Andrews, S., FastQC: a quality control tool for high throughput sequence data. 2010.	https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
gawk	NA	https://www.gnu.org/software/gawk/
GNU Parallel	O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014.	https://www.gnu.org/software/parallel/
Git	NA	https://git-scm.com/
igvtools	NA	https://software.broadinstitute.org/software/igv/igvtools
Jupyter Notebook	Kluyver, Thomas, et al. "Jupyter Notebooks-a publishing format for reproducible computational workflows." ELPUB. 2016.	https://jupyter.org/
Jupyter_contrib_nbextension	NA	https://github.com/ipython-contrib/jupyter_contrib_nbextensions
Jupyterthemes	NA	https://github.com/dunovank/jupyter-themes
Krona	Ondov, B.D., N.H. Bergman, and A.M. Phillippy, Interactive metagenomic visualization in a Web browser. BMC Bioinformatics, 2011. 12: p. 385.	https://github.com/marbl/Krona/wiki
Lofreq	Wilm, A., et al., LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. 2012. 40(22): p. 11189-11201.	http://csb5.github.io/lofreq/
MGkit	Rubino, F. and Creevey, C.J. 2014. MGkit: Metagenomic Framework For The Study Of Microbial Communities. . Available at: figshare [doi:10.6084/m9.figshare.1269288].	https://bitbucket.org/setsuna80/mgkit/src/develop/
Minimap2	Li, H., Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 2018.	https://github.com/lh3/minimap2
MultiQC	Ewels, P., et al., MultiQC: summarize analysis results for multiple tools and samples in a single report. 2016. 32(19): p. 3047-3048.	https://multiqc.info/
Nb_conda	NA	https://github.com/Anaconda-Platform/nb_conda
Nb_conda_kernels	NA	https://github.com/Anaconda-Platform/nb_conda_kernels
Nginx	NA	https://www.nginx.com/
Numpy	Walt, S. V. D., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22-30.	http://www.numpy.org/
Pandas	McKinney, W. Data structures for statistical computing in python. in Proceedings of the 9th Python in Science Conference. 2010. Austin, TX.	https://pandas.pydata.org/
Picard	NA	https://broadinstitute.github.io/picard/
Prodigal	Hyatt, D., et al., Prodigal: prokaryotic gene recognition and translation initiation site identification. 2010. 11(1): p. 119.	https://github.com/hyattpd/Prodigal/wiki/Introduction
Python	G. van Rossum, Python tutorial, Technical Report CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam, May 1995.	https://www.python.org/
Qgrid	NA	https://github.com/quantopian/qgrid
SAMtools	Li, H., et al., The sequence alignment/map format and SAMtools. 2009. 25(16): p. 2078-2079.	http://www.htslib.org/
SPAdes	Nurk, S., et al., metaSPAdes: a new versatile metagenomic assembler. Genome Res, 2017. 27(5): p. 824-834.	http://cab.spbu.ru/software/spades/
seqkit	Shen, Wei, et al. "SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation." PloS one 11.10 (2016).	https://github.com/shenwei356/seqkit
Seqtk	NA	https://github.com/lh3/seqtk
Snakemake	Köster, J. and S.J.B. Rahmann, Snakemake—a scalable bioinformatics workflow engine. 2012. 28(19): p. 2520-2522.	https://snakemake.readthedocs.io/en/stable/
Tabix	NA	www.htslib.org/doc/tabix.html
tree	NA	http://mama.indstate.edu/users/ice/tree/
Trimmomatic	Bolger, A.M., M. Lohse, and B. Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014. 30(15): p. 2114-20.	www.usadellab.org/cms/?page=trimmomatic
Virus-Host Database	Mihara, T., Nishimura, Y., Shimizu, Y., Nishiyama, H., Yoshikawa, G., Uehara, H., ... & Ogata, H. (2016). Linking virus genomes with host taxonomy. Viruses, 8(3), 66.	http://www.genome.jp/virushostdb/note.html
Virus typing tools	Kroneman, A., Vennema, H., Deforche, K., Avoort, H. V. D., Penaranda, S., Oberste, M. S., ... & Koopmans, M. (2011). An automated genotyping tool for enteroviruses and noroviruses. Journal of Clinical Virology, 51(2), 121-125.	https://www.ncbi.nlm.nih.gov/pubmed/21514213

Authors

• Dennis Schmitz (RIVM and EMC)
• Sam Nooij (RIVM and EMC)
• Robert Verhagen (RIVM)
• Thierry Janssens (RIVM)
• Jeroen Cremer (RIVM)
• Florian Zwagemaker (RIVM)
• Mark Kroon (RIVM)
• Erwin van Wieringen (RIVM)
• Harry Vennema (RIVM)
• Annelies Kroneman (RIVM)
• Marion Koopmans (EMC)

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This project/research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 643476. and the Dutch working group on molecular diagnostics (WMDI).

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