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Baltica: integrated splice junction usage analysis

Baltica logo

Baltica is a framework that facilitates the execution and enables the integration of results from multiple differential junction usage (DJU) methods. The core of the framework is Snakemake workflows 1, a python command-line interface, and R/Bioconductor scripts for analysis 2345. The workflows are include methods for RNA-Seq quality control 678, four DJU methods: RMATs 9 JunctionSeq 10, Majiq 11 and Leafcutter 12. We use Stringtie2 13 de novo transcriptome assembly to re-annotate the results. Baltica's main goal is to provide an integrative view of the results of these methods. To do so, Baltica produces an RMarkdown report with the integrated results and links to UCSC GenomeBrowser for further exploration.

Features

- Snakemake workflows for DJU: junctionseq, majiq, rmats, and leafcutter
- Snakemake workflow for de novo transcriptome annotation with stringtie
- Process, integrate and annotate the results from the methods
- Summarise AS class of differently spliced junctions
- DJU method benchmarks
- Report on the integrative analysis

To get started, use the menu on the left-hand side or search function to navigate over this documentation.

Citation

Britto-Borges T, Boehm V, Gehring NH, Dieterich C. Baltica: integrated splice junction usage analysis. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.12.23.473966

Baltica is based on the work of many scientists and developers. Thus, if you use the results of their tools in your analysis, consider citing their work.

License

Baltica is free, open-source software released under an MIT License.

Contact

Please get in touch with us the GitHub issue tracker.

References

  1. Felix Mölder, Kim Philipp Jablonski, Brice Letcher, Michael B. Hall, Christopher H. Tomkins-Tinch, Vanessa Sochat, Jan Forster, Soohyun Lee, Sven O. Twardziok, Alexander Kanitz, and et al. Sustainable data analysis with snakemake. F1000Research, 10:33, Apr 2021. URL: http://dx.doi.org/10.12688/f1000research.29032.2, doi:10.12688/f1000research.29032.2. ↩

  2. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2021. URL: https://www.R-project.org/. ↩

  3. Michael Lawrence, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan, and Vincent J. Carey. Software for computing and annotating genomic ranges. PLoS Computational Biology, 9(8):e1003118, Aug 2013. URL: http://dx.doi.org/10.1371/journal.pcbi.1003118, doi:10.1371/journal.pcbi.1003118. ↩

  4. M. Lawrence, R. Gentleman, and V. Carey. Rtracklayer: an r package for interfacing with genome browsers. Bioinformatics, 25(14):1841–1842, May 2009. URL: http://dx.doi.org/10.1093/bioinformatics/btp328, doi:10.1093/bioinformatics/btp328. ↩

  5. Hadley Wickham, Mara Averick, Jennifer Bryan, Winston Chang, Lucy McGowan, Romain François, Garrett Grolemund, Alex Hayes, Lionel Henry, Jim Hester, and et al. Welcome to the tidyverse. Journal of Open Source Software, 4(43):1686, Nov 2019. URL: http://dx.doi.org/10.21105/joss.01686, doi:10.21105/joss.01686. ↩

  6. Liguo Wang, Shengqin Wang, and Wei Li. RSeQC: quality control of RNA-seq experiments. Bioinformatics, 28(16):2184–2185, June 2012. URL: https://doi.org/10.1093/bioinformatics/bts356, doi:10.1093/bioinformatics/bts356. ↩

  7. Simon Andrews, Felix Krueger, Anne Segonds-Pichon, Laura Biggins, Christel Krueger, and Steven Wingett. FastQC. Babraham Institute, January 2012. URL: https://qubeshub.org/resources/fastqc. ↩

  8. Philip Ewels, Måns Magnusson, Sverker Lundin, and Max Käller. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047–3048, Jun 2016. URL: http://dx.doi.org/10.1093/bioinformatics/btw354, doi:10.1093/bioinformatics/btw354. ↩

  9. Shihao Shen, Juw Won Park, Zhi-xiang Lu, Lan Lin, Michael D. Henry, Ying Nian Wu, Qing Zhou, and Yi Xing. Rmats: robust and flexible detection of differential alternative splicing from replicate rna-seq data. Proceedings of the National Academy of Sciences, 111(51):E5593–E5601, Dec 2014. URL: http://dx.doi.org/10.1073/pnas.1419161111, doi:10.1073/pnas.1419161111. ↩

  10. Stephen W. Hartley and James C. Mullikin. Detection and visualization of differential splicing in RNA-seq data with JunctionSeq. Nucleic Acids Research, pages gkw501, June 2016. URL: https://doi.org/10.1093/nar/gkw501, doi:10.1093/nar/gkw501. ↩

  11. Jorge Vaquero-Garcia, Alejandro Barrera, Matthew R Gazzara, Juan Gonzalez-Vallinas, Nicholas F Lahens, John B Hogenesch, Kristen W Lynch, and Yoseph Barash. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife, February 2016. URL: https://doi.org/10.7554/elife.11752, doi:10.7554/elife.11752. ↩

  12. Yang I. Li, David A. Knowles, Jack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, and Jonathan K. Pritchard. Annotation-free quantification of RNA splicing using LeafCutter. Nature Genetics, 50(1):151–158, December 2017. URL: https://doi.org/10.1038/s41588-017-0004-9, doi:10.1038/s41588-017-0004-9. ↩

  13. Sam Kovaka, Aleksey V. Zimin, Geo M. Pertea, Roham Razaghi, Steven L. Salzberg, and Mihaela Pertea. Transcriptome assembly from long-read rna-seq alignments with stringtie2. Genome Biology, Dec 2019. URL: http://dx.doi.org/10.1186/s13059-019-1910-1, doi:10.1186/s13059-019-1910-1. ↩