Biopython (Python3)

Biopython represents an ecosystem of related biology tools written in the Python3 programming language
Unlike with BioJulia, Biopython tools are installed together as modules into a single biopython package
Launched over two decades ago with major releases covering the Python2/3 transition
Capabilities cover a number of molecular biology applications including sequence alignment, population genetics, and machine learning

A few equivalent application tools between BioJulia and Biopython:

Application	BioJulia packages	Biopython objects/modules
Sequence annotation	GenomicFeatures, GenomicAnnotations	`Seq`, `SeqRecord`
Input/Output	FASTX, XAM, BigWig,...	`Bio.SeqIO`, `Bio.AlignIO`
Sequence alignment	BioSequences, BioAlignments	`Bio.Align`
Population genetics	PopGen	`Bio.PopGen`

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