Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.16
jupyter-notebook, version 6.0.1 (Kluyver et al. 2016)
Python, version 3.7.4 (https://docs.python.org/release/3.7.4/)
Modules:
-	pandas, version 0.25.1 (McKinney 2010)
-	NumPy, version 1.17.2 (Harris et al. 2020)
-	Matplotlib, version 3.1.0 (Hunter 2007)
-	scikit-learn, version 0.21.3 (Pedregosa et al. 2011)
-	statsmodels, version 0.10.1 (Seabold and Perktold 2010)
-	SciPy, version 1.3.1 (Virtanen et al. 2020)
-	seaborn, version 0.9.0 (Waskom 2021)
-	RNA, version 2.4.13 (Lorenz et al. 2011)
-	re, version 2.2.1 (https://docs.python.org/release/3.7.4/library)
-	itertools, version 7.2.0 (https://docs.python.org/release/3.7.4/library)
-	functools, version 1.6.4 (https://docs.python.org/release/3.7.4/library)
-	multiprocessing, version 0.70.9 (https://docs.python.org/release/3.7.4/library)

R, version 3.6.3 (R Core Team 2020)
Libraries:
-	ggplot2, version 3.3.5 (Wickham 2016)
-	ggseqlogo, version 0.1 (Wagih 2017)

Reference:
Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, et al. 2020. Array programming with NumPy. Nature 585: 357–362.
Hunter JD. 2007. Matplotlib: A 2D graphics environment. Comput Sci Eng 9: 90–95.
Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, Kelley K, Hamrick J, Grout J, Corlay S, et al. 2016. Jupyter Notebooks – a publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87–90, IOS Press.
Lorenz R, Bernhart SH, Höner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL. 2011. ViennaRNA Package 2.0. Algorithms Mol Biol 6: 26.
McKinney W. 2010. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference (eds. S. Van der Walt and J. Millman), pp. 56–61.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. 2011. Scikit-learn: Machine Learning in Python. J Mach Learn Res 12: 2825–2830.
R Core Team. 2020. R: A Language and Environment for Statistical Computing. https://www.r-project.org/.
Seabold S, Perktold J. 2010. Statsmodels: Econometric and Statistical Modeling with Python. In Proceedings of the 9th Python in Science Conference (eds. S. Van der Walt and J. Millman), Vol. SCIPY 2010 of, pp. 92–96.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al. 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17: 261–272.
Wagih O. 2017. ggseqlogo: a versatile R package for drawing sequence logos ed. J. Hancock. Bioinformatics 33: 3645–3647.
Waskom M. 2021. seaborn: statistical data visualization. J Open Source Softw 6: 3021.
Wickham H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer International Publishing, Cham.

