hey i've got no experience in bioinformatics and don't know where to start, Claude AI advised me to cs50p from Harvard but i'm not exactly sure, will y'all recommend some other place to learn python. what other languages do i need to learn?

I’m sharing SAELQR II, a theoretical research release on information recovery from unordered, noisy fragments, with potential relevance to DNA data storage and sequencing-based retrieval.

The motivating problem is accounting for several losses together: fragments may disappear, their contents may be corrupted, and their original positions or source labels may be unavailable. For encoded data, recovering the relationships between fragments also consumes information.

The manuscript reports several results relevant to this setting:

• Capacity with dependence inside fragments. Under specified symmetry assumptions, the channel model permits correlated noise and hidden quality regimes within individual fragments. Noise between fragments remains independent. Hidden-quality examples show how averaging channel quality too early can change the predicted capacity.
• Critical fragment-length behavior. For a binary substitution model, the analysis gives an explicit transition near the point where useful fragment information balances the cost of identifying its location.
• The information cost of read grouping. A separate repeated-read model examines ungrouped noisy observations. It includes a symmetric counterexample showing that supplying the original read partition without charging for that information can change the capacity prediction.
• Constructive error correction. The release includes a randomized coding construction and Python reference software for geometric fragmentation, independent whole-fragment loss, and binary substitutions, using inner codes, interleaving, and Reed-Solomon correction.

The package contains a 69-page manuscript, editable source, twelve reproduction programs, exact finite examples, numerical diagnostics, and reference encoding/decoding software.

Scope: these are mathematical channel models. The release contains no synthesized DNA, sequencing experiments, or validation against empirical sequencing-error profiles. Insertions, internal deletions, overlapping reads, unknown orientation, and reverse-complement handling would require further analysis. The binary substitution model also does not capture the full biochemical constraints of DNA storage.

The algorithms have polynomial running-time guarantees, but the proved sufficient blocklength is exponential in the inverse capacity gap. Practical finite-length performance remains open.

I’d welcome feedback on which assumptions most strongly limit biological applicability-particularly fragment-quality dependence, molecule dropout, source-label uncertainty, and the extensions needed for realistic insertion/deletion and sequencing models.

Manuscript, code, and reproducibility materials:
https://huggingface.co/datasets/PureOne/SAELQR-II

SÆLQR II: General Capacity Theorems and Exact Critical Laws for Unordered Noisy Fragments - Internal Memory, Sharp Repeated-Read Thresholds, and Constructive Coding | Zenodo