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PhyloGrok / VCFgenerator

Automated variant calling app for NextGen evolutionary genomics
GNU General Public License v3.0
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VCFgenerator

Automated variant calling and Shiny dashboard for NextGen evolutionary genomics.

Environment: Ubuntu 20.02 VM configured with required software packages described in OmicsVMconfigure (https://github.com/PhyloGrok/OmicsVMconfigure)

Usage

  1. Clone VCFgenerator repo git clone https://github.com/PhyloGrok/VCFgenerator in your Ubuntu 20.02 Linux user/home directory.
  2. move cd VCFgenerator/Python_Hub
  3. run sudo python Controller.py
  4. User will be prompted for 5 inputs, used to run the workflow:
Workflow-Chart

Workflow Description

  1. download.py Data Retrieval. Downlaods reference genome and BioProject-linked SRA files base on user-provided data. Currently works only with Illumina paired-end .fastq files, sequenced from genomics DNA data from a whole genome sequencing strategy. Uses ncbi EDirect, ncbi-datasets, and sra-toolkit APIs.
  2. trimmomatic.py Data QC - Runs trimmomatic and fastqc on the .fastq sra files.
  3. variants.py Assembly and Variant Calling - Performs alignment of .fastq sequences to the reference genome using bwa. Performed variant calling with SAMtools and BCFtools, generating variant calling format (.vcf) files as output.
  4. annotations.py VCF annotation - Annotates the .vcf files using SNPeff and reference genome .gff/.gtf annotation files.
  5. Shiny Dashboard (nonfunctional, in development) - Transfers annotated .vcf data to a SQLite database, imports into an R dataframe and plots genomes in R Shiny dashboard with a stacked barplot of mutation types by sample, and displays a circos-style plot annotated showing called variants from multiple (up to 5 genomic BioSamples).

Demonstration Data

  1. NCBI BioProject PRJNA541441 (15 .fastq SRA files). "Iron and Acid Adapted Strains of Halobacterium sp. NRC-1 obtained by Experimental Evolution" initial testing
  2. NCBI BioProject PRJNA844510 (67 .fastq SRA files). "Halobacterium mutation acumulation lines. testing for BGIseq and for scaled-up throughput

Future Goals

  1. Incorporate a Mummer branch into the workflow.
  2. Peform Metagenomics and Comparative Genomics.
  3. Publish plots in a Shiny web app as a Science Gateway.

Acknowledgements

  1. Data Carpentry Genomics Workshop (https://datacarpentry.org/genomics-workshop/) was the original template for the QC, alignment and variant calling steps. Here we focused on a command-line implementation, with user specification, and high-throughput automated processing in Linux Ubuntu-based cloud vm.
  2. Lenski Long-Term E. coli Evolution (LTEE) experiment. The analysis of genomic variants follows the concept of Tenaillon et al. 2016 and other publications and content from the LTEE (https://lenski.mmg.msu.edu/ecoli/genomicsdat.html).
  3. See Citations.md for many additional citations and resources.

Funding

This work used Jetstream2 at Indiana University (IU) through research allocation BIO220099 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

This work used Jetstream at Indiana Universery/Texas Advanced Computing Center (IU/TACC) through research startup allocation BIO210100 from the Extreme Science and Engineering Discovery Environment (XSEDE), which was supported by National Science Foundation grant number #1548562.

This work used Jetstream at Indiana Universery/Texas Advanced Computing Center (IU/TACC) through educational allocation MCB200044 from the Extreme Science and Engineering Discovery Environment (XSEDE), which was supported by National Science Foundation grant number #1548562.

UMBC Translational Life Science Technology (TLST) student interns Lloyd Jones III, Nhi Luu, and Jan Le supported by Merck Data Science Fellowship for Observational Research Program and the UMBC College of Natural and Mathematical Sciences. Lloyd Jones III developed the variant calling workflow framework and workflow integration. Nhi Luu developed the annotation scripts and R-Shiny framework and integration. Jan Le prepared the "Iron and Acid Adaptation" analysis, developed EDirect scripts, and troubleshooted throughout the workflow. Additionally, TLST student Gina Hwang contributed to the MUMMER branch of the workflow.

Citations

Erin Alison Becker, Tracy Teal, François Michonneau, Maneesha Sane, Taylor Reiter, Jason Williams, et al. (2019, June). datacarpentry/genomics-workshop: Data Carpentry: Genomics Workshop Overview, June 2019 (Version v2019.06.1). Zenodo. http://doi.org/10.5281/zenodo.3260309

David Y. Hancock, Jeremy Fischer, John Michael Lowe, Winona Snapp-Childs, Marlon Pierce, Suresh Marru, J. Eric Coulter, Matthew Vaughn, Brian Beck, Nirav Merchant, Edwin Skidmore, and Gwen Jacobs. 2021. “Jetstream2: Accelerating cloud computing via Jetstream.” In Practice and Experience in Advanced Research Computing (PEARC ’21). Association for Computing Machinery, New York, NY, USA, Article 11, 1–8. DOI: https://doi.org/10.1145/3437359.3465565

Stewart, C.A., Cockerill, T.M., Foster, I., Hancock, D., Merchant, N., Skidmore, E., Stanzione, D., Taylor, J., Tuecke, S., Turner, G., Vaughn, M., and Gaffney, N.I., “Jetstream: a self-provisioned, scalable science and engineering cloud environment.” 2015, In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure. St. Louis, Missouri. ACM: 2792774. p. 1-8. DOI: https://dx.doi.org/10.1145/2792745.2792774

Tenaillon O, Barrick JE, Ribeck N, et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature. 2016;536(7615):165-170. doi:10.1038/nature18959

Towns, J, and T Cockerill, M Dahan, I Foster, K Gaither, A Grimshaw, V Hazlewood, S Lathrop, D Lifka, GD Peterson, R Roskies, JR Scott. “XSEDE: Accelerating Scientific Discovery”, Computing in Science & Engineering, vol.16, no. 5, pp. 62-74, Sept.-Oct. 2014, doi:10.1109/MCSE.2014.80