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name: sequence-tools description: "Use gget for remote BLAST/BLAT searches, local MUSCLE/DIAMOND alignment, ELM motif analysis, RCSB PDB retrieval, and the optional AlphaFold prediction workflow." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

Sequence tools

Use this route when the task is sequence similarity/location, multiple-sequence alignment, protein motif discovery, structure retrieval, or protein structure prediction. The public Python API is gget.<tool>(...); the CLI is gget <tool> .... Start with a literal sequence or a small FASTA path and decide whether the operation is remote or local before running it.

Route and safety

• Route Ensembl/WormBase/FlyBase lookup or gene/transcript FASTA acquisition with gget.seq to gene-annotation.
• Route mutation generation to specialized-workflows; do not use alignment tools as a mutation engine.
• Route G2P portal annotations and disease/target queries to disease-structure. PDB retrieval and AlphaFold prediction stay here.
• blast and blat submit data to NCBI and UCSC. Expect network latency, changing databases, rate limits, and empty/no-match results.
• muscle and diamond are local subprocess workflows. The generated skill does not ship gget's platform binaries; verify executable availability and permissions before a production run.
• elm is local after a one-time ELM data download, but UniProt fallback and setup use the network. ELM data is licensed for non-commercial use according to the ELM license.
• AlphaFold is optional, computationally expensive, network-heavy, and no longer actively maintained by gget. Prefer ColabFold or the AlphaFold Server for new predictions.

Choose the operation

Goal	Tool	Main input	Main output
Remote nucleotide/protein similarity	blast	one sequence or first record of .fa/.txt	DataFrame, JSON records, or saved CSV/JSON
Genomic/protein location	blat	one sequence or first record of .fa/.txt	DataFrame, JSON records, or saved CSV/JSON
Local MSA	muscle	list of sequences or FASTA/text path	printed Clustal-style view or .afa
Local protein/translated search	diamond	query plus required reference	DataFrame, JSON records, or output folder
Eukaryotic linear motifs	elm	amino-acid sequence or UniProt accession	ortholog and regex result tables
Experimental structure/metadata	pdb	PDB entry ID plus resource	raw PDB/mmCIF text or JSON
Predicted protein structure	alphafold	amino-acid sequence(s) or FASTA	PDB and PAE JSON files in a folder

Detailed signatures, schemas, and CLI mappings are in references/api-reference.md. Recipes are in references/workflows.md. Always consult references/troubleshooting.md when validation or a remote/local dependency fails.

Input discipline

1. A Python string can be a literal sequence or a path. For BLAST/BLAT, a .fa or .txt path is parsed and only its first sequence is submitted. MUSCLE and DIAMOND accept a FASTA path or sequence/list input; AlphaFold accepts a FASTA/text path or sequence/list. Use ordinary FASTA headers beginning with > and put sequence characters on following lines.
2. gget uppercases remote-search sequences. Its default type detection treats a string containing only A, T, G, C, and N as nucleotide; otherwise it may classify valid amino-acid letters as protein. A protein made only of DNA letters is ambiguous: set BLAST program/database or BLAT seqtype explicitly.
3. Do not pass a gene symbol, Ensembl ID, PDB ID, or UniProt accession as a sequence. Resolve identifiers first with the appropriate route, except for elm(..., uniprot=True) and pdb.
4. Validate alphabet, length, header/sequence pairing, and intended query/reference orientation before starting a remote request or local subprocess. A malformed FASTA raises from the shared parser rather than producing a trustworthy result.

Fast Python starts

import gget

hits = gget.blast("MKWMFKEDHSLEHRCVESAKIRAKY", database="nr", limit=10)
locations = gget.blat("ATGCTGAATTTATGCTGAATTTATGCTGAATTT", assembly="human")
gget.muscle(["MSSSSWLLLSLVAVTAAQST", "MSSSSWLLLSLVEVTAAQST"], out="aligned.afa")
aln = gget.diamond("MPEPTIDE", reference="reference.fa")
ortho_df, regex_df = gget.elm("LIAQSIGQASFV")
entry = gget.pdb("4ACQ", resource="entry")

Python defaults are generally DataFrames for tabular tools; use json=True where supported. muscle and alphafold return None and communicate through files/stdout. pdb returns text for pdb/mmcif and JSON-like objects for metadata resources. Save behavior is tool-specific; do not assume every save or out argument names the same kind of path.

Fast CLI starts

gget blast "MKWMFKEDHSLEHRCVESAKIRAKY" --database nr --limit 10
gget blat "ATGCTGAATTTATGCTGAATTTATGCTGAATTT" --assembly human
gget muscle sequences.fa --out aligned.afa
gget diamond query.fa -ref reference.fa -o diamond-results
gget setup elm
gget elm LIAQSIGQASFV -o elm-results
gget pdb 4ACQ -r mmcif -o 4ACQ.cif
# Only after installing and checking the optional runtime:
gget alphafold protein.fa -o prediction

On the CLI, tabular commands print CSV by default; --csv selects JSON output where implemented. --quiet suppresses progress. gget diamond uses a positional query followed by required -ref/--reference; keep those positional arguments before the option to avoid argparse consuming a reference-looking sequence as another query.

Remote search/location

• BLAST: choose blastn/blastp/translated mode and a compatible database when auto-detection is not unambiguous. limit controls returned descriptions/hits and expect is the cutoff. NCBI's service rules matter: do not submit more often than once per 10 seconds or poll one RID more often than once per minute; schedule batches of more than 50 searches in the documented off-hours.
• BLAT: choose DNA, protein, translated%20RNA, or translated%20DNA; aliases map human→hg38, mouse→mm39, and zebrafinch→taeGut2. The implementation truncates input over 8,000 characters. An unrecognized assembly can be served as UCSC's default genome, so inspect the returned genome column.

Local analysis and structure

• MUSCLE uses PPP by default and super5=True for larger inputs (the docs suggest a few hundred sequences) to reduce time/memory. Set out="...afa" to persist aligned FASTA; with no out, gget creates and removes a temporary alignment after printing a colored view.
• DIAMOND is a local protein search. Normal mode is protein query against protein reference (blastp); translated=True is nucleotide query against amino-acid reference (blastx). The diamond_db and out lifecycle, including a source-level database-path quirk, is documented in the API and troubleshooting references.
• ELM requires the four local files installed by gget setup elm. It returns (ortholog_df, regex_df): the first contains experimentally validated motif information found through DIAMOND orthologs, and the second contains direct regex matches. expand=True adds validation protein/organism/reference columns to the regex table.
• PDB resource pdb first tries legacy downloads and falls back to mmCIF for unavailable large structures. Use resource="mmcif" explicitly when downstream software accepts it; inspect the actual returned text and saved extension.
• AlphaFold validates sequences before downloading MSA databases and running Jackhmmer/model inference. A monomer is capped at 2,500 residues, each sequence at 3,400, and total input at 3,400; the implementation warns above 3,000 and can exhaust memory/disk.

Output semantics

• A DataFrame is convenient for filtering and column selection; choose json=True for JSON-compatible records, but remember that tuple-returning ELM has two separate tables.
• save=True on Python remote tools writes fixed filenames in the current directory, while out on DIAMOND/ELM/AlphaFold is a folder and out on MUSCLE/PDB is normally a file path.
• A None return can mean no match, an upstream error, or an optional runtime branch; read the log and validate inputs before treating it as biological absence.
• Structure text is not JSON: preserve the format reported by PDB and use a parser appropriate for PDB or mmCIF.
• Alignment files should be checked for headers, sequence count, and equal aligned lengths before downstream use.

Completion checklist

• Confirm sequence type and query/reference orientation.
• Record exact tool, database/assembly/resource, sensitivity, limits, and output path.
• For remote calls, retain the returned table/object and note no-match or throttling messages.
• For local calls, check the binary version/permissions and verify an expected output file is non-empty and parseable.
• For AlphaFold, verify selected_prediction.pdb and predicted_aligned_error.json in out; do not infer biological confidence beyond the supplied pLDDT/PAE values.