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### Orfipy ORF Prediction (`orfipy-prediction`) Scans one or more DNA sequences across the configured strand setting (three forward and three reverse reading frames by default) and returns every open reading frame that satisfies the configured start codon, stop codon, strand, and length filters. Each returned ORF carries its nucleotide sequence, translated amino-acid sequence, 1-indexed start and end positions on the parent sequence, strand, reading frame, and the parent sequence identifier. #### API Reference

Source DNA sequence(s) to analyze for open reading frames. Can be provided as:

Source Number of CPU threads to use for processing each sequence. Since processing is batched per-sequence, this controls intra-sequence parallelism. Must be at least 1. Default: 4. Start codons to recognize for ORF prediction. Multi-select from: Stop codons to recognize for ORF prediction. Multi-select from: Which strand(s) to scan for ORFs. Options: Available options: `f`, `r`, `b` Minimum ORF length in nucleotides (not including stop codon unless `include_stop=True`). ORFs shorter than this are filtered out. Common values: Maximum ORF length in nucleotides. ORFs longer than this are silently filtered out by orfipy; raise (e.g. `1_000_000_000`) for genome-scale inputs. Default: 10000. Whether to include the stop codon in the reported ORF nucleotide sequence. If `True`, the stop codon is included in both the nucleotide sequence and length calculations. If `False`, the stop codon is excluded. Default: `True`. Treat lowercase (soft-masked) nucleotides as ORF-eligible. Default: `False`. Report ORFs missing a stop codon at the 3' end of the sequence. Default: `False`. Report ORFs missing a start codon at the 5' end of the sequence. Default: `False`. Report ORFs spanning stop-to-stop (start codons ignored). Default: `False`. NCBI genetic code for translation. `None` uses the standard genetic code (table 1). Only tables supported by orfipy's built-in translation table dict are available (NCBI tables 1-6, 9-14, 16, 21-30). Common options: Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the tool on. Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded.

Source One entry per input sequence, in input order, each holding the ORFs found in that sequence. ORFs found in this sequence, in the caller's order.

#### Applications This tool is appropriate for the upstream ORF-enumeration step of any analysis that begins with raw DNA sequences and needs candidate coding regions. Representative applications include cataloguing all ORFs in a newly assembled genome before annotation, extracting coding-sequence candidates from a de novo transcriptome assembly, generating an exhaustive ORF set for downstream filtering by length, codon usage, or homology to a known protein, and producing translated protein sequences for downstream language-model scoring or domain annotation. #### Usage Tips * **`min_len` is the primary control on the number of reported ORFs.** At the default of `min_len=0`, every candidate region is reported, including many short ORFs that arise by chance in any DNA sequence and do not encode functional proteins. A threshold of `min_len=150` (approximately 50 amino acids) excludes the majority of these short ORFs. A threshold of `min_len=300` (approximately 100 amino acids) focuses the output on typical small proteins, and `min_len=900` (approximately 300 amino acids) restricts the output to larger proteins. The threshold is specified in nucleotides. * **`start_codons` should match the genetic context of the input.** The default of `["ATG", "GTG", "TTG"]` is appropriate for bacterial and archaeal sequences, in which alternative start codons account for approximately 15 to 20 percent of genes. A value of `["ATG"]` is appropriate for stringent eukaryotic ORF analyses, and the inclusion of `"CTG"` is appropriate for organisms that use a non-standard genetic code in which `CTG` functions as an alternative start codon. * **`strand` controls which DNA strands are scanned.** The default of `"b"` scans both strands and reports ORFs from both the forward sequence and its reverse complement. A value of `"f"` or `"r"` restricts the scan to a single strand, which approximately halves the number of ORFs returned and is appropriate when the coding strand of the input is known in advance. * **`translation_table` selects the genetic code used for amino-acid translation.** The default value of `None` applies the standard genetic code (NCBI table 1). A value of `"bacterial"` selects the bacterial, archaeal, and plant plastid code (NCBI table 11), `"vertebrate_mitochondrial"` selects the vertebrate mitochondrial code (NCBI table 2), and the remaining supported NCBI tables are appropriate for organisms that use the corresponding alternative codes. * **The partial-ORF flags allow incomplete reading frames at sequence boundaries.** A value of `partial_3=True` reports ORFs that begin at a recognised start codon and continue to the 3' end of the input without an in-frame stop codon. A value of `partial_5=True` reports ORFs that end at a recognised stop codon but begin at the 5' end of the input without a recognised start codon. Both flags are disabled by default and are appropriate when the input represents a fragment of a larger sequence, such as a transcriptome contig. * **`between_stops=True` reports every region between two in-frame stop codons regardless of whether a recognised start codon is present.** This is appropriate for ribosome-profiling analyses that aim to identify all potential translation regions, and implies that both `partial_3` and `partial_5` behave as if enabled. * **The output is exhaustive rather than curated.** ORFipy reports every candidate ORF that satisfies the configured filters. Confirming the biological relevance of any individual ORF requires subsequent analyses such as homology search with BLAST, domain annotation with HMMER, or gene prediction with Prodigal.