Ok, there's a lot involved in what you listed and I'm not sure how low level you want to get with understanding the underlying principles right from the start. Do you want to stop at algorithms for now or do you want to take it further into the math? Are you interested in understanding why certain algorithms were developed they way they were + their constraints?

Analysis in general will be driven by the data quality as well as experimental constraints. Published tools and pipelines cater to many setups encountered empirically, but one pipeline or tools can't capture everything. Thus knowing fundamentally a tool's scope and underlying assumptions/biases is what I'd recommend in the same way that you would for an experiment protocol's reagents. So I'd structure it as:

1. fundamentals/theory: statistics, data structures, algorithms, logic + proofs
2. omics: experimental techniques + limitations
3. latest tools in the field and what they aim to solve/improve

Reference Books

In my opinion, a good start would be statistics + statistical genomics to serve as a foundation. That will help with your interest (raw reads -> downstream analysis) since much of what you'll likely end up doing during quality control and subsequent analyses will be directed by what test assumptions you can make given the experimental design and the data quality. If you're already comfortable with theoretical stats, then a book I just discovered today that seems to be a really good reference is The Handbook of Statistical Genomics by Balding, Moltke, and Marioni.

If you're also comfortable with pseudocode and proofs, then Introduction to Algorithms ("CLRS") chapters 4, 15-16, 22-26, and 32 are useful for laying out the pseudocode + proofs to convince you that the algorithms solve the posited problems. If I remember correctly, Pevzner's Bioinformatics textbook also reproduces or pushes learners to reason through & derive the major algorithms discussed in CLRS but scoped to a biology context. You might need to look for another secondary supporting resource when you encounter HMMs in Pevzner's Bioinformatics though since CLRS doesn't cover that.

Additionally, there's Ismail's Bioinformatics: A Practical Guide to Next Generation Sequencing Data Analysis focused on NGS. I have that in my TBR so I can't review it yet, but it seems to be recommended and the description covers explanations of why certain decisions are made during NGS analysis.

Nanopore Tools & Workflows

If learning Snakemake and/or Nextflow is something you're interested in, you can take a look at the nf-core community. They also publish pipelines that you can either use to apply or to study by tracing the modules' tools and reading their respective papers. This is a good way to discover tools, familiarize yourself with pipeline management, and get a sense of how others organize and approach certain analyses. Even if a pipeline doesn't quite work for your case as-is, you can later fork it and adjust, or write your own.

Courses

Pevzner's Bioinformatics book is also the textbook for the Coursera specialization, Bioinformatics. It has a hands-on honours track that you can complete and I recommend doing that anyway because there is a capstone project at the end of the specialization. There are also quite a few courses on Coursera for various levels, but you do have to be careful to not fall into the tutorial rabbit hole.

Galaxy is another helpful educational platform since it has tutorials for various analyses. I will say that not all of these tutorials get into explaining why certain tools are used. In my opinion, this is fine since you can look up the the software's papers to reason through the explanations yourself.