STRExpansionAnalyzer

Background Information

Short tandem repeats (STRs) are tracts of repetitive DNA. Typically, they are short sequences of 2-6 nucleotides repeated adjacently two or more times in a head to tail manner³. STR loci are prone to mutations and high rates of polymorphism¹. Expansions of STR loci can be pathogenic and have been attributed to more than 40 Mendelian diseases^2,4. As well, they have been strongly linked to more complex disorders like autism spectrum syndrome³.

Description

This package helps label short tandem repeat (STR) loci as “variable” or not (stable) for machine learning and statistical analysis. The function readIntoDF takes in a text file of identified STR loci locations from any species’ genome and creates a dataframe. The function createVariables calculates additional measure variables using STR motifs and locations, cleans the dataset and labels each STR loci as either “variable” or not depending a user defined z-score. The rest of the functions help graph the distribution of STR motifs, the most common/least common motifs and most variable/least variable motifs.

Installation

To install:

require("devtools")
install_github("michaelzwong/STRExpansionAnalyzer", build_vignettes = TRUE)
library("STRExpansionAnalyzer")

To run Shiny app:

STRExpansionAnalyzer::runSTRExpansionAnalyzer()

Overview

STRExpansionAnalyzer contains 6 functions and 1 dataset.

ls("package:STRExpansionViewer")
data(package = "ShortTandemRepeatsLoci")

To load text file containing STR loci positions of any species’ genome into a dataframe, use: readIntoDF.

To label loci as variable and add measure variable columns like motif mean and sd, use: createVariables.

To graph distribution, see common/least common motifs, or to see variable or least variable motifs, use the graphing functions: plotMotifsDistribution, plotTopNCommonMotifs, plotTopNLeastVariableMotifs, plotTopNMostVariableMotifs.

plotMotifsDistribution Display the distribution of certain motifs

plotTopNMostCommonMotifs Display the common motifs

plotTopNLeastVariableMotifs Display the least variable or (stable) motifs

plotTopNMostVariableMotifs Display the most variable motifs

Use the Shiny App to view results from the ShortTandemRepeatsLoci dataset. You can specify the Z-Score used in calculating variability of a motif and plot the different types of graphs to understand the data better.

Tutorials

browseVignettes("STRExpansionAnalyzer")

Citation for Package

citation("STRExpansionAnalyzer")

Contributions

The author of package is Michael Wong. The readr package is used to create the read in data from a file. The ggplot package is used to produce plot visualizations. The R shiny package was used to create the Shiny app.

References

1. Gemayel, R., Vinces MD., Legendre, M., Verstrepen, K.J.. Variable tandem repeats accelerate evolution of coding and regulatory sequences. Annu Rev Genet 44, 445–77 (2010).

2. López Castel, A., Cleary, J. D. & Pearson, C. E. Repeat instability as the basis for human diseases and as a potential target for therapy. Nat. Rev. Mol. Cell Biol. 11, 165–170 (2010).

3. Trost, B., Engchuan, W., Nguyen, C.M. et al. Genome-wide detection of tandem DNA repeats that are expanded in autism. Nature 586, 80–86 (2020). https://doi.org/10.1038/s41586-020-2579-z

4. Hao F., Chu J. A Brief Review of Short Tandem Repeat Mutation. Genomics, Proteomics & Bioinformatics. 5, 7-14 (2007). https://doi.org/10.1016/S1672-0229(07)60009-6.

5. Church, D. M., Schneider, V. A., Graves, T., Auger, K., Cunningham, F., Bouk, N., Chen, H. C., Agarwala, R., McLaren, W. M., Ritchie, G. R., Albracht, D., Kremitzki, M., Rock, S., Kotkiewicz, H., Kremitzki, C., Wollam, A., Trani, L., Fulton, L., Fulton, R., Matthews, L., … Hubbard, T. (2011). Modernizing reference genome assemblies. PLoS biology, 9(7), e1001091. https://doi.org/10.1371/journal.pbio.1001091

6. Schneider, V. A., Graves-Lindsay, T., Howe, K., Bouk, N., Chen, H. C., Kitts, P. A., Murphy, T. D., Pruitt, K. D., Thibaud-Nissen, F., Albracht, D., Fulton, R. S., Kremitzki, M., Magrini, V., Markovic, C., McGrath, S., Steinberg, K. M., Auger, K., Chow, W., Collins, J., Harden, G., … Church, D. M. (2017). Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome research, 27(5), 849–864. https://doi.org/10.1101/gr.213611.116

7. Dolzhenko, E., van Vugt, J., Shaw, R. J., Bekritsky, M. A., van Blitterswijk, M., Narzisi, G., Ajay, S. S., Rajan, V., Lajoie, B. R., Johnson, N. H., Kingsbury, Z., Humphray, S. J., Schellevis, R. D., Brands, W. J., Baker, M., Rademakers, R., Kooyman, M., Tazelaar, G., van Es, M. A., McLaughlin, R., … Eberle, M. A. (2017). Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome research, 27(11), 1895–1903. https://doi.org/10.1101/gr.225672.117

8. Dolzhenko, E., Deshpande, V., Schlesinger, F., Krusche, P., Petrovski, R., Chen, S., Emig-Agius, D., Gross, A., Narzisi, G., Bowman, B., Scheffler, K., van Vugt, J., French, C., Sanchis-Juan, A., Ibáñez, K., Tucci, A., Lajoie, B. R., Veldink, J. H., Raymond, F. L., Taft, R. J., … Eberle, M. A. (2019). ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions. Bioinformatics (Oxford, England), 35(22), 4754–4756. https://doi.org/10.1093/bioinformatics/btz431

9. Benson G. (1999). Tandem repeats finder: a program to analyze DNA sequences. Nucleic acids research, 27(2), 573–580. https://doi.org/10.1093/nar/27.2.573

10. C Yuen, R., Merico, D., Bookman, M. et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci 20, 602–611 (2017). https://doi.org/10.1038/nn.4524

11. Trost, B., Engchuan, W., Nguyen, C.M. et al. Genome-wide detection of tandem DNA repeats that are expanded in autism. Nature 586, 80–86 (2020). https://doi.org/10.1038/s41586-020-2579-z

12. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

13. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

14. Hadley Wickham and Jim Hester (2020). readr: Read Rectangular Text Data. R package version 1.4.0. https://CRAN.R-project.org/package=readr

15. Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2020). shiny: Web Application Framework for R. R package version 1.5.0. https://CRAN.R-project.org/package=shiny

16. Yihui Xie (2020). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.30.

Maintainer

• Michael Wong (michaelz.wong@mail.utoronto.ca)

Acknowledgements

This package was developed as part of an assessment for 2020 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA. This package welcomes issues, improvement requests, and contributions. Please use issues section.