scikit-bio (Python3)

scikit-bio represents a Python3 library providing data structures, algorithms, and additional bioinformatics resources
Unlike with BioJulia, scikit-bio tools are installed together as modules into a single skbio package
Launched in 2014 based on a BSD-relicensing of PyCogent and QIIME code and is currently in beta/maintenance mode

A few equivalent application tools between BioJulia and scikit-bio:

Application	BioJulia packages	scikit-bio modules
Sequence alignment	BioSequences, BioAlignments	`skbio.sequence`, `skbio.alignment`
Input/Output	FASTX, XAM, BigWig,...	`skbio.io`
Data structures	IntervalTrees	`skbio.tree`

A few package/ecosystem equivalents between Julia and Python3:

Application	Julia	Python3
Data manipulation/analysis	DataFrames, CSV, Query	`pandas`, `Blaze`
Plotting/visualization	Plots, Gadfly, Makie, TidierPlots	`matplotlib`, `seaborn`, `bokeh`, `plotnine`, `VisPy`
Statistical analysis	Statistics, HypothesisTests, GLM	`SciPy`, `statsmodels`
Machine learning	Flux, SciML, MLJ, Zygote	`PyTorch`, `TensorFlow`, `scikit-learn`, `JAX`
Numerical mathematics	LinearAlgebra, Symbolics	`NumPy`, `SciPy`, `SymPy`
Notebook/report generator	IJulia, Pluto, Weave	`IPython`, `Jupyter Book`
Web applications	Genie, Franklin	`Django`, `Flask`, `Pelican`, `Dash`

A few notable differences between Julia and Python3:

Julia	Python3
High-level, general-purpose compiled language	High-level, general-purpose interpreted language
Expression-based (statements yield a value)	Statement-based (assignment as a statement)
Dynamically typed with multiple dispatch and optional type annotations	Dynamically typed with optional type annotations
Built-in parallelism via threads, coroutines (Tasks)	Limited (GIL) built-in parallelism via threads, async/await
Extensive metaprogramming (Lisp-like macros, homoiconicity,...)	Less extensive metaprogramming (decorators, metaclasses,...)
Included default package manager	No default package manager (poetry, conda, and other alternatives available)
Single implementation available (JuliaLang)	Multiple implementations available (PyPy, Numba,...)
Arrays are column-major (columns are contiguous in memory)	(Numpy) arrays are row-major by default (rows are contiguous in memory)

To transition from Python3 to Julia:

See Noteworthy differences from Python in the Julia manual for a more in-depth comparison
Use PythonCall.jl/CondaPkg.jl to seamlessly integrate Python3 code into your Julia project
Use juliacall to seamlessly integrate Julia code into your Python3 project