# How the FineDNA biology text was found

The `bio_*` subsets of FineDNA are web pages, papers and forum posts that contain DNA sequences inside normal text.
They were found in two steps.

## 1. Regex filter: does the document contain DNA? (`1_regex_filter.py`)

We ran this filter over FineWeb-Edu, FinePDFs, PubMed Central, bioRxiv and Biostars. A document is kept when it

- contains a run of **15 or more nucleotide letters** (A, T, C, G, U, N, any case),
- that run uses **at least 3 different letters**, so `AAAAAAAAAAAAAAA` does not count,
- the run is **not inside curly braces**, which in scraped text is usually LaTeX, and
- the document mentions **at least one molecular-biology keyword**: dna, rna, nucleotide, genome, gene, base pair,
  codon, primer, pcr, amplification, sequencing, nucleic acid, oligonucleotide, plasmid, transcription,
  translation, mrna, ribosome, trna, rrna or uracil.

```bash
python 1_regex_filter.py --input hf://datasets/HuggingFaceFW/fineweb-edu/sample/10BT --output ./dna_regex/fineweb_edu
```

## 2. p90 split: how long is the DNA? (`2_p90_split.py`)

Many regex hits are short: a primer, a restriction site, a motif. Training on documents with real sequences worked
better, so we measure the **longest DNA word** in each document with `tag_dna_sequences.py`: a whitespace-delimited
token of at least 6 letters that is pure A/C/G/T (or IUPAC codes, 12+ letters, at most 20% ambiguous), with
punctuation and 5'/3' markers stripped.

- Longest DNA word **at least 39 bp** (the 90th percentile across the regex data): `bio_p90_*`.
- Everything else: `bio_rest`.

```bash
python 2_p90_split.py --input ./dna_regex/fineweb_edu --p90 ./p90/fineweb_edu --rest ./rest/fineweb_edu
```

In the Carbon-X mix, p90 gets as much weight as all of `bio_rest` and is read about seven times; `bio_rest` about
twice.

## Requirements

`pip install datatrove` for step 1. Step 2 needs only the Python standard library and `tag_dna_sequences.py`, which
sits next to it.