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Biological Sequence Analysis

生物序列分析

Retrieve, annotate, and compare biological sequences from NCBI, Ensembl, and UniProt. Covers nucleotide search, sequence fetching, gene summaries, ortholog discovery, and protein sequence extraction.
从NCBI、Ensembl和UniProt检索、注释并对比生物序列。涵盖核苷酸搜索、序列获取、基因摘要、直系同源基因发现以及蛋白质序列提取。

When to Use

使用场景

  • "Get the mRNA sequence for BRCA1"
  • "Search NCBI for E. coli K-12 complete genome"
  • "Find orthologs of TP53 across species"
  • "Fetch the protein sequence for UniProt P04637"
  • "Get the CDS sequence for Ensembl transcript ENST00000269305"
  • "获取BRCA1的mRNA序列"
  • "在NCBI中搜索大肠杆菌K-12完整基因组"
  • "查找跨物种的TP53直系同源基因"
  • "获取UniProt登录号P04637对应的蛋白质序列"
  • "获取Ensembl转录本ENST00000269305的CDS序列"

Workflow

工作流程

Input -> Phase 1: Gene ID resolution -> Phase 2: Nucleotide retrieval
      -> Phase 3: Protein sequences -> Phase 4: Orthologs -> Output
输入 -> 阶段1：基因ID解析 -> 阶段2：核苷酸检索
      -> 阶段3：蛋白质序列获取 -> 阶段4：直系同源基因查找 -> 输出

Phase 1: Gene Identification and Summary

阶段1：基因识别与摘要

NCBIGene_search: 
term
(string REQUIRED, format 
"TP53[Symbol] AND Homo sapiens[Organism]"
), 
retmax
(int, default 10). Returns 
{status, data: {esearchresult: {idlist: ["7157"]}}}
.
NCBIGene_get_summary: 
id
(string REQUIRED, e.g., "7157"). Returns 
{status, data: {result: {"7157": {name, description, summary, chromosome, maplocation, genomicinfo, mim}}}}
. Result is keyed by gene ID string.
NCBIDatasets_get_gene_by_symbol: 
symbol
(string REQUIRED, e.g., "BRCA1"), 
taxon
(string, e.g., "human"). Returns gene ID, description, location, cross-references.
NCBIDatasets_get_gene: 
gene_id
(string REQUIRED, e.g., "7157"). Returns comprehensive gene info.
NCBIGene_search：
term
（字符串类型，必填，格式为
"TP53[Symbol] AND Homo sapiens[Organism]"
），
retmax
（整数类型，默认值为10）。返回结果为
{status, data: {esearchresult: {idlist: ["7157"]}}}
。
NCBIGene_get_summary：
id
（字符串类型，必填，例如"7157"）。返回结果为
{status, data: {result: {"7157": {name, description, summary, chromosome, maplocation, genomicinfo, mim}}}}
。结果以基因ID字符串作为键。
NCBIDatasets_get_gene_by_symbol：
symbol
（字符串类型，必填，例如"BRCA1"），
taxon
（字符串类型，例如"human"）。返回基因ID、描述、位置、交叉引用信息。
NCBIDatasets_get_gene：
gene_id
（字符串类型，必填，例如"7157"）。返回全面的基因信息。

Phase 2: Nucleotide Sequence Search and Retrieval

阶段2：核苷酸序列搜索与检索

NCBI_search_nucleotide: 
query
(free-form), 
organism
(string), 
gene
(string), 
strain
(string), 
keywords
(string), 
seq_type
("complete_genome"/"mRNA"/"refseq"), 
limit
(int, default 20). Returns 
{status, data: {uids: [...], accessions: [...]}}
.
NCBI_fetch_accessions: 
uids
(array REQUIRED, e.g., ["545778205"]). Returns 
{status, data: ["U00096.3"], count: 1}
.
NCBI_get_sequence: 
accession
(string REQUIRED, e.g., "NM_007294"), 
format
("fasta"/"gb"/"embl"). Returns 
{status, data: "FASTA string...", accession, format, length}
.
EnsemblSeq_get_region_sequence: 
region
(string REQUIRED, "chr:start-end", e.g., "17:7668421-7668520"), 
species
(default "homo_sapiens"). Returns 
{status, data: {sequence, sequence_length}}
.
ensembl_get_sequence: 
id
(string REQUIRED, Ensembl ID), 
type
("genomic"/"cds"/"cdna"/"protein"), 
multiple_sequences
(bool). Returns sequence data.
Gotchas:
  • NCBI_search_nucleotide returns UIDs, not accessions. Use NCBI_fetch_accessions to convert.
  • NCBI_fetch_accessions requires 
    uids
    (NOT 
    accessions
    ).
  • ensembl_get_sequence with gene ID (ENSG) + type != "genomic" requires 
    multiple_sequences=true
    . Use transcript IDs (ENST) for specific sequences.
NCBI_search_nucleotide：
query
（自由格式），
organism
（字符串类型），
gene
（字符串类型），
strain
（字符串类型），
keywords
（字符串类型），
seq_type
（可选值为"complete_genome"/"mRNA"/"refseq"），
limit
（整数类型，默认值为20）。返回结果为
{status, data: {uids: [...], accessions: [...]}}
。
NCBI_fetch_accessions：
uids
（数组类型，必填，例如["545778205"]）。返回结果为
{status, data: ["U00096.3"], count: 1}
。
NCBI_get_sequence：
accession
（字符串类型，必填，例如"NM_007294"），
format
（可选值为"fasta"/"gb"/"embl"）。返回结果为
{status, data: "FASTA格式字符串...", accession, format, length}
。
EnsemblSeq_get_region_sequence：
region
（字符串类型，必填，格式为"chr:start-end"，例如"17:7668421-7668520"），
species
（默认值为"homo_sapiens"）。返回结果为
{status, data: {sequence, sequence_length}}
。
ensembl_get_sequence：
id
（字符串类型，必填，Ensembl ID），
type
（可选值为"genomic"/"cds"/"cdna"/"protein"），
multiple_sequences
（布尔类型）。返回序列数据。
注意事项:
  • NCBI_search_nucleotide返回的是UID而非登录号，需使用NCBI_fetch_accessions进行转换。
  • NCBI_fetch_accessions需要传入
    uids
    参数（而非
    accessions
    ）。
  • 使用ensembl_get_sequence时，若传入基因ID（ENSG）且
    type
    不等于"genomic"，需设置
    multiple_sequences=true
    。如需特定序列，请使用转录本ID（ENST）。

Recipe: Get mRNA for a human gene

示例：获取人类基因的mRNA序列

  1. NCBI_search_nucleotide(organism="Homo sapiens", gene="BRCA1", seq_type="mRNA", limit=5)
  2. NCBI_fetch_accessions(uids=[first_uid])
    -> accession
  3. NCBI_get_sequence(accession="NM_007294", format="fasta")
  1. 调用
    NCBI_search_nucleotide(organism="Homo sapiens", gene="BRCA1", seq_type="mRNA", limit=5)
  2. 调用
    NCBI_fetch_accessions(uids=[首个UID])
    -> 获取登录号
  3. 调用
    NCBI_get_sequence(accession="NM_007294", format="fasta")

Phase 3: Protein Sequence Retrieval

阶段3：蛋白质序列检索

UniProt_get_sequence_by_accession: 
accession
(string REQUIRED, e.g., "P04637"). Returns 
{result: "MEEPQSDP..."}
. Note: response key is 
result
, NOT 
data
.
EnsemblSeq_get_id_sequence: 
ensembl_id
(string REQUIRED, e.g., "ENSP00000269305"), 
type
("protein"/"cdna"/"cds"). Returns 
{status, data: {ensembl_id, molecule, sequence, sequence_length}}
.
UniProt_get_entry_by_accession: 
accession
(string REQUIRED). Full protein annotation.
Gotchas:
  • UniProt_get_sequence_by_accession returns 
    {result: "..."}
    , not 
    {status, data}
    .
  • For Ensembl protein seqs, use ENSP IDs. For cDNA/CDS, use ENST IDs.
  • To find UniProt accession from gene: use NCBIDatasets_get_gene_by_symbol (has cross-refs).
UniProt_get_sequence_by_accession：
accession
（字符串类型，必填，例如"P04637"）。返回结果为
{result: "MEEPQSDP..."}
。注意：响应的键为
result
，而非
data
。
EnsemblSeq_get_id_sequence：
ensembl_id
（字符串类型，必填，例如"ENSP00000269305"），
type
（可选值为"protein"/"cdna"/"cds"）。返回结果为
{status, data: {ensembl_id, molecule, sequence, sequence_length}}
。
UniProt_get_entry_by_accession：
accession
（字符串类型，必填）。返回完整的蛋白质注释信息。
注意事项:
  • UniProt_get_sequence_by_accession返回的格式为
    {result: "..."}
    ，而非
    {status, data}
    。
  • 获取Ensembl蛋白质序列时，请使用ENSP ID；获取cDNA/CDS序列时，请使用ENST ID。
  • 若需通过基因名称查找UniProt登录号：使用NCBIDatasets_get_gene_by_symbol（包含交叉引用信息）。

Phase 4: Ortholog and Comparative Analysis

阶段4：直系同源基因与对比分析

NCBIDatasets_get_orthologs: 
gene_id
(string REQUIRED, NCBI Gene ID e.g., "7157"), 
page_size
(int, default 20, max 100). Returns 
{status, data: [{gene_id, symbol, description, taxname, common_name, chromosomes}]}
.
NCBIProtein_get_summary: 
id
(string REQUIRED, GI number or accession). Returns protein title, organism, length.
Gotcha: NCBIDatasets_get_orthologs requires NCBI Gene ID (numeric string), not gene symbol or Ensembl ID. Resolve via Phase 1 first.
NCBIDatasets_get_orthologs：
gene_id
（字符串类型，必填，NCBI基因ID，例如"7157"），
page_size
（整数类型，默认值为20，最大值为100）。返回结果为
{status, data: [{gene_id, symbol, description, taxname, common_name, chromosomes}]}
。
NCBIProtein_get_summary：
id
（字符串类型，必填，GI编号或登录号）。返回蛋白质标题、所属物种、长度信息。
注意事项：NCBIDatasets_get_orthologs需要传入NCBI基因ID（数字字符串），而非基因符号或Ensembl ID。请先通过阶段1解析获取。

Recipe: Compare orthologs

示例：对比直系同源基因

  1. NCBIGene_search(term="TP53[Symbol] AND Homo sapiens[Organism]")
    -> "7157"
  2. NCBIDatasets_get_orthologs(gene_id="7157", page_size=10)
    -> mouse Trp53, rat Tp53, etc.
  1. 调用
    NCBIGene_search(term="TP53[Symbol] AND Homo sapiens[Organism]")
    -> 获取"7157"
  2. 调用
    NCBIDatasets_get_orthologs(gene_id="7157", page_size=10)
    -> 获取小鼠Trp53、大鼠Tp53等直系同源基因

Phase 5: Domain Architecture and Homology

阶段5：结构域架构与同源性

InterPro_get_entries_for_protein: 
accession
(UniProt ID). Returns InterPro domain/family/superfamily entries with positions.
Pfam_get_protein_annotations: 
accession
(UniProt ID). Returns Pfam domain hits with exact residue coordinates and E-values.
BLAST_protein_search: 
sequence
(amino acid string), 
database
(default "swissprot"), 
limit
. Returns homologs with alignment scores, identity, E-values.
EnsemblCompara_get_orthologues: 
gene
(gene symbol, e.g., "CFTR"), 
species
(e.g., "human"). User-friendly alternative to NCBIDatasets_get_orthologs — accepts gene symbols directly.
InterPro_get_entries_for_protein：
accession
（UniProt ID）。返回带有位置信息的InterPro结构域/家族/超家族条目。
Pfam_get_protein_annotations：
accession
（UniProt ID）。返回带有精确残基坐标和E值的Pfam结构域匹配结果。
BLAST_protein_search：
sequence
（氨基酸字符串），
database
（默认值为"swissprot"），
limit
。返回带有比对得分、一致性和E值的同源序列。
EnsemblCompara_get_orthologues：
gene
（基因符号，例如"CFTR"），
species
（例如"human"）。是NCBIDatasets_get_orthologs的用户友好替代工具——可直接接受基因符号作为参数。

Phase 6: Variant and Clinical Context

阶段6：变异与临床背景

EnsemblVEP_annotate_hgvs: 
hgvs_notation
(e.g., "NM_000492.4:c.1521_1523del"). Returns consequence, protein impact, genomic coordinates.
ClinVar_search_variants: 
gene
(gene symbol). Returns variant count and IDs for clinical significance lookup.
PubMed_search_articles: 
query
, 
limit
. Literature context for gene/variant findings.
EnsemblVEP_annotate_hgvs：
hgvs_notation
（例如"NM_000492.4:c.1521_1523del"）。返回变异后果、蛋白质影响、基因组坐标信息。
ClinVar_search_variants：
gene
（基因符号）。返回临床意义查询所需的变异数量和ID。
PubMed_search_articles：
query
，
limit
。为基因/变异研究结果提供文献背景。

Tool Parameter Quick Reference

工具参数速查

ToolCorrect ParamCommon Mistake
NCBIGene_search
term
(with [Symbol] syntax)
query
or 
gene
NCBIGene_get_summary
id
(string)		Integer type
NCBI_fetch_accessions
uids
(array)
accessions
NCBI_get_sequence
accession
(string)		Passing UID
NCBIDatasets_get_orthologs
gene_id
(string)		Gene symbol
EnsemblSeq_get_id_sequence
ensembl_id
id
ensembl_get_sequence
id
+ 
multiple_sequences
Omitting multiple_sequences for gene+CDS
UniProt_get_sequence_by_accession
accession
Response is 
result
not 
data
工具正确参数常见错误
NCBIGene_search
term
（使用[Symbol]语法）		使用
query
或
gene
参数
NCBIGene_get_summary
id
（字符串类型）		使用整数类型
NCBI_fetch_accessions
uids
（数组类型）		使用
accessions
参数
NCBI_get_sequence
accession
（字符串类型）		传入UID
NCBIDatasets_get_orthologs
gene_id
（字符串类型）		传入基因符号
EnsemblSeq_get_id_sequence
ensembl_id
使用
id
参数
ensembl_get_sequence
id
+ 
multiple_sequences
对于基因+CDS类型，省略multiple_sequences参数
UniProt_get_sequence_by_accession
accession
误将响应中的
result
当作
data

Fallbacks

备选方案

  • Gene not found -> try NCBIDatasets_get_gene_by_symbol with explicit taxon
  • No accessions from search -> broaden query (remove strain/seq_type filters)
  • Ensembl error for gene+CDS -> use transcript ID (ENST) or set multiple_sequences=true
  • UniProt accession unknown -> NCBIDatasets_get_gene or UniProt_search for cross-refs
  • Ortholog search empty -> verify gene_id is numeric NCBI Gene ID
  • 未找到基因 -> 尝试使用NCBIDatasets_get_gene_by_symbol并指定明确的分类单元
  • 搜索未返回登录号 -> 扩大查询范围（移除菌株/序列类型筛选条件）
  • Ensembl基因+CDS查询出错 -> 使用转录本ID（ENST）或设置multiple_sequences=true
  • 未知UniProt登录号 -> 使用NCBIDatasets_get_gene或UniProt_search获取交叉引用信息
  • 直系同源基因搜索无结果 -> 验证gene_id是否为数字型NCBI基因ID

Sequence Analysis Reasoning (CRITICAL)

序列分析推理规则（重要）

LOOK UP DON'T GUESS -- always fetch sequences, coordinates, and domain boundaries from databases. Do not reconstruct them from memory.
查资料而非猜测——序列、坐标和结构域边界务必从数据库获取，切勿凭记忆重构。

When to Use Which Tool

工具选择指南

Question TypeTool ChoiceWhy
"Find similar sequences"BLAST_protein_searchHomology search against databases; returns E-values and identity
"What domains does this protein have?"InterPro_get_entries_for_protein or Pfam_get_protein_annotationsDomain architecture with exact residue coordinates
"Get the sequence of gene X"NCBI_search_nucleotide -> NCBI_get_sequenceNucleotide retrieval by gene name
"Compare orthologs"NCBIDatasets_get_orthologs or EnsemblCompara_get_orthologuesCross-species gene comparison
"What is the protein impact of variant X?"EnsemblVEP_annotate_hgvsConsequence prediction with protein coordinates
"Align two sequences"BLAST (pairwise)Quick pairwise comparison with scoring
问题类型工具选择原因
"查找相似序列"BLAST_protein_search针对数据库进行同源性搜索；返回E值和一致性
"该蛋白质包含哪些结构域？"InterPro_get_entries_for_protein或Pfam_get_protein_annotations提供带有精确残基坐标的结构域架构
"获取基因X的序列"NCBI_search_nucleotide -> NCBI_get_sequence通过基因名称检索核苷酸序列
"对比直系同源基因"NCBIDatasets_get_orthologs或EnsemblCompara_get_orthologues跨物种基因对比
"变异X对蛋白质有何影响？"EnsemblVEP_annotate_hgvs预测变异后果并提供蛋白质坐标
"比对两条序列"BLAST（两两比对）快速两两比对并给出评分

Reading Frame Selection Strategy

阅读框选择策略

When translating a DNA sequence to protein:
  1. Do NOT guess the reading frame -- preferred: use 
    DNA_translate_reading_frames
    tool; fallback: 
    translate_dna.py
    which tries all 3 frames automatically
  2. The correct frame is the one with the LONGEST open reading frame (no premature stops)
  3. If the sequence starts with ATG, frame 1 is likely correct -- but verify
  4. If all 3 frames have early stop codons, the sequence may be: (a) non-coding, (b) reversed, or (c) contains sequencing errors. Try reverse complement first.
将DNA序列翻译为蛋白质时：
  1. 切勿猜测阅读框——首选：使用
    DNA_translate_reading_frames
    工具；备选：使用
    translate_dna.py
    脚本自动尝试全部3种阅读框
  2. 正确的阅读框是拥有最长开放阅读框（无提前终止密码子）的那个
  3. 若序列以ATG开头，阅读框1大概率正确——但需验证
  4. 若3种阅读框均存在提前终止密码子，该序列可能是：(a) 非编码序列，(b) 反向序列，或(c) 包含测序错误。优先尝试反向互补序列。

Protein Domain Interpretation

蛋白质结构域解读

When asked about protein function or structure:
  1. Get domain architecture first: 
    InterPro_get_entries_for_protein
    returns all annotated domains with positions
  2. Domain families indicate function: Kinase domain = phosphorylation activity; SH2 domain = phosphotyrosine binding; zinc finger = DNA binding
  3. Variants in conserved domains are more likely pathogenic than those in linker regions
  4. LOOK UP domain boundaries from the database -- do not estimate positions from memory
当被问及蛋白质功能或结构时：
  1. 先获取结构域架构：
    InterPro_get_entries_for_protein
    返回所有带位置信息的注释结构域
  2. 结构域家族指示功能：激酶结构域=磷酸化活性；SH2结构域=磷酸酪氨酸结合；锌指结构域=DNA结合
  3. 保守结构域中的变异比连接区的变异更可能致病
  4. 查资料获取结构域边界——切勿凭记忆估算位置

Reasoning for Protein Feature Questions

蛋白质特征问题推理规则

When asked "how many X residues in region Y of protein Z":
  1. Identify the correct protein — Gene names are ambiguous. GABAA has many subunits (GABRA1, GABRB2, GABRR1...). Read the question carefully for the specific subunit. Use 
    proteins_api_search
    with gene name + "human" to find the right accession.
  2. Find the region boundaries — Use 
    proteins_api_get_features
    with the accession to get annotated domains (TRANSMEM, DOMAIN, REGION). Don't guess positions — get them from the database.
  3. Count residues in the region — Fetch the sequence, extract the region, count. WRITE Python code for this — don't try to count manually.
    • Residue Counting Strategy: 
      python3 skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py --type count_region --accession P24046 --start 318 --end 440 --residue C
    • For residue counting questions, ALWAYS use the script or 
      sequence[start:end].count('C')
      . Do NOT estimate or count from memory.
  4. Account for multimers — READ THE QUESTION for "homomeric", "pentamer", "tetramer", "dimer". If the question asks about a homomeric receptor (e.g., "homomeric GABAAρ1"), every subunit is identical. Count the residues in ONE subunit, then multiply:
    • Homomeric pentamer (most ligand-gated ion channels like GABAA ρ1): × 5
    • Homotetramer (many ion channels): × 4
    • Homodimer: × 2 If the question says "in the TM3-TM4 linker domains" (plural), it means across all subunits in the complex.
当被问及“蛋白质Z的Y区域中有多少个X残基”时：
  1. 确定正确的蛋白质——基因名称存在歧义。例如GABAA有多个亚基（GABRA1、GABRB2、GABRR1...）。仔细阅读问题以确定具体亚基。使用
    proteins_api_search
    工具，传入基因名称+"human"以找到正确的登录号。
  2. 查找区域边界——使用
    proteins_api_get_features
    工具，传入登录号以获取注释的结构域（TRANSMEM、DOMAIN、REGION）。切勿猜测位置——从数据库获取。
  3. 统计区域内的残基数量——获取序列，提取目标区域，统计数量。编写Python代码完成此操作——切勿手动统计。
    • 残基统计策略：
      python3 skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py --type count_region --accession P24046 --start 318 --end 440 --residue C
    • 对于残基统计问题，务必使用脚本或
      sequence[start:end].count('C')
      。切勿估算或凭记忆统计。
  4. 考虑多聚体情况——仔细阅读问题中的“同源多聚体”“五聚体”“四聚体”“二聚体”等表述。若问题询问同源多聚体受体（例如“同源多聚体GABAAρ1”），每个亚基均相同。统计单个亚基中的残基数量，再乘以对应倍数：
    • 同源五聚体（大多数配体门控离子通道如GABAA ρ1）：× 5
    • 同源四聚体（许多离子通道）：× 4
    • 同源二聚体：× 2 若问题提及“TM3-TM4连接区（复数）”，指的是复合物中所有亚基的该区域。

Bundled Computation Scripts

内置计算脚本

Never manually count residues, compute GC%, or write reverse-complement logic inline. Run these scripts instead — they are tested and handle edge cases.
切勿手动统计残基、计算GC含量或编写反向互补逻辑。请运行以下脚本——它们经过测试，可处理边缘情况。

biology_facts.py — Biology reference lookup

biology_facts.py —— 生物学参考资料查询

Script: 
skills/tooluniverse-sequence-analysis/scripts/biology_facts.py
Use this script to look up commonly-confused biology facts instead of relying on memory. It covers receptor types, ion channel stoichiometry, neurotransmitters, immune cell markers, and gene naming confusions.
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type receptor --name "GABAA"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type ion_channel --name "NMDA"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type gene_confusion --name "GABRA1"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type receptor  # list all entries
Types: 
receptor
(stoichiometry, pharmacology), 
ion_channel
(subunit arrangement), 
neurotransmitter
(synthesis, receptors), 
immune_cell
(markers, lineage), 
gene_confusion
(commonly mixed-up genes like GABRA1 vs GABRR1).
Mandatory use: any question about receptor type/stoichiometry, immune cell markers, or gene name disambiguation.
脚本路径：
skills/tooluniverse-sequence-analysis/scripts/biology_facts.py
使用此脚本查询易混淆的生物学事实，而非依赖记忆。涵盖受体类型、离子通道化学计量、神经递质、免疫细胞标志物以及基因命名混淆等内容。
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type receptor --name "GABAA"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type ion_channel --name "NMDA"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type gene_confusion --name "GABRA1"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type receptor  # 列出所有条目
类型选项：
receptor
（化学计量、药理学）、
ion_channel
（亚基排列）、
neurotransmitter
（合成、受体）、
immune_cell
（标志物、谱系）、
gene_confusion
（易混淆基因如GABRA1与GABRR1）。
强制使用场景：任何关于受体类型/化学计量、免疫细胞标志物或基因名称歧义的问题。

amino_acids.py — Codon table, amino acid properties, wobble pairing

amino_acids.py —— 密码子表、氨基酸性质、摇摆配对

Script: 
skills/tooluniverse-sequence-analysis/scripts/amino_acids.py
Use this script for any question about the genetic code, codon degeneracy, amino acid chemistry, codon usage bias, or tRNA wobble pairing. All outputs are JSON.
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type codon_table
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --name "Cysteine"
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --code C
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --code TRP
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid            # list all 20
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type count_codons --sequence "ATGCCCAAATTT..."
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "GAU"
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "IAU"
Modes:
--type
What it returnsKey fields
codon_table
All 64 codons grouped by amino aciddegeneracy, codons, human codon usage %, stop codon names, degeneracy distribution (1/2/3/4/6)
amino_acid
Properties of one or all amino acidsname, one_letter, three_letter, mw_da, pKa_side_chain, polarity, charge_ph7, hydrophobicity_index (Kyte-Doolittle), backbone_pKa, codons, degeneracy, rare_codons_le15pct
count_codons
Codon frequency analysis for a DNA sequencecodon_counts with AA annotation and human usage freq, amino_acid_composition, rare_codons_present
wobble
Codons recognised by a given anticodonrecognised_codons (RNA+DNA form, AA), synonymous_only, wobble rule explanation
When to use (mandatory):
  • Any question about how many codons encode a given amino acid (degeneracy)
  • Any question about rare vs. common codons for protein expression optimisation
  • Any question about tRNA anticodon recognition / wobble base pairing
  • Any question about amino acid physical-chemical properties (MW, pKa, hydrophobicity, polarity, charge)
  • Any question about the names of stop codons (Amber/Ochre/Opal)
  • Before manually stating codon degeneracy — verify with 
    codon_table
Wobble rules: I pairs U/C/A (3 codons); G pairs U/C; U pairs A/G; C pairs G only; A pairs U only (rare). Use 
--type wobble --anticodon "GAU"
to verify.
Amino acid lookup: accepts full name (
--name "Cysteine"
), 1-letter (
--code C
), or 3-letter (
--code CYS
).
脚本路径：
skills/tooluniverse-sequence-analysis/scripts/amino_acids.py
使用此脚本查询遗传密码、密码子简并性、氨基酸化学性质、密码子使用偏好或tRNA摇摆配对相关问题。所有输出均为JSON格式。
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type codon_table
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --name "Cysteine"
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --code C
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --code TRP
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid            # 列出全部20种氨基酸
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type count_codons --sequence "ATGCCCAAATTT..."
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "GAU"
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "IAU"
模式说明:
--type
返回内容关键字段
codon_table
按氨基酸分组的全部64种密码子简并性、密码子、人类密码子使用百分比、终止密码子名称、简并性分布（1/2/3/4/6）
amino_acid
单个或全部氨基酸的性质名称、单字母缩写、三字母缩写、分子量（道尔顿）、侧链pKa、极性、pH7时的电荷、疏水性指数（Kyte-Doolittle）、主链pKa、密码子、简并性、稀有密码子（占比≤15%）
count_codons
DNA序列的密码子频率分析带氨基酸注释和人类使用频率的密码子计数、氨基酸组成、存在的稀有密码子
wobble
给定反密码子可识别的密码子可识别的密码子（RNA+DNA形式、对应氨基酸）、仅同义密码子、摇摆规则说明
强制使用场景:
  • 任何关于编码特定氨基酸的密码子数量（简并性）的问题
  • 任何关于蛋白质表达优化中稀有/常见密码子的问题
  • 任何关于tRNA反密码子识别/摇摆碱基配对的问题
  • 任何关于氨基酸物理化学性质（分子量、pKa、疏水性、极性、电荷）的问题
  • 任何关于终止密码子名称（琥珀/赭石/乳白）的问题
  • 手动陈述密码子简并性之前——务必使用
    codon_table
    验证
摇摆规则：I（次黄嘌呤）可与U/C/A配对（3种密码子）；G可与C/U配对；U可与A/G配对；C仅可与G配对；A仅可与U配对（罕见）。使用
--type wobble --anticodon "GAU"
进行验证。
氨基酸查询：接受全名（
--name "Cysteine"
）、单字母缩写（
--code C
）或三字母缩写（
--code CYS
）。

Codon-Anticodon Matching Reasoning (CRITICAL for tRNA problems)

密码子-反密码子配对推理规则（tRNA问题必看）

When solving "which codons does this tRNA recognize" or "which tRNA reads this codon":
  1. Anticodon is written 3'->5' but conventionally listed 5'->3'. The FIRST position of the anticodon (5' end) is the WOBBLE position and pairs with the THIRD position of the codon (3' end).
  2. Anticodon-codon pairing is ANTIPARALLEL: anticodon 5'-X-Y-Z-3' pairs with codon 3'-X'-Y'-Z'-5' (i.e., codon 5'-Z'-Y'-X'-3').
  3. Wobble position rules (anticodon 5' base -> codon 3' base it can pair with):
    • C -> G only (1 codon)
    • A -> U only (1 codon; rare in bacteria, common in mitochondria)
    • U -> A or G (2 codons)
    • G -> C or U (2 codons)
    • I (inosine, deaminated A) -> U, C, or A (3 codons)
  4. Minimum tRNA set: Because I reads 3 bases and G/U each read 2, a 4-codon family (e.g., GCN = Ala) needs only 2 tRNAs: one with I at wobble position (reads 3 of 4 codons) and one with C or U at wobble (reads the remaining 1-2).
  5. ALWAYS use the script: 
    python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "IAU"
    to verify rather than reasoning from memory.
解决“该tRNA可识别哪些密码子”或“哪个tRNA读取该密码子”问题时：
  1. 反密码子以3'->5'方向书写，但通常以5'->3'列出。反密码子的第一个位置（5'端）是摇摆位，与密码子的第三个位置（3'端）配对。
  2. 反密码子-密码子配对是反向平行的：反密码子5'-X-Y-Z-3'与密码子3'-X'-Y'-Z'-5'配对（即密码子5'-Z'-Y'-X'-3'）。
  3. 摇摆位规则（反密码子5'碱基 -> 可配对的密码子3'碱基）：
    • C -> 仅G（1种密码子）
    • A -> 仅U（1种密码子；在细菌中罕见，在线粒体中常见）
    • U -> A或G（2种密码子）
    • G -> C或U（2种密码子）
    • I（次黄嘌呤，脱氨基A）-> U、C或A（3种密码子）
  4. 最小tRNA集合：由于I可识别3种碱基，G/U各可识别2种，因此4密码子家族（例如GCN=丙氨酸）仅需2种tRNA：一种摇摆位为I（识别4种密码子中的3种），另一种摇摆位为C或U（识别剩余1-2种）。
  5. 务必使用脚本验证：运行
    python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "IAU"
    ，而非凭记忆推理。

translate_dna.py — DNA to protein translation

translate_dna.py —— DNA转蛋白质翻译

Preferred: use 
DNA_translate_reading_frames
tool (via MCP/SDK) with 
sequence
parameter. Fallback: run 
translate_dna.py
directly.
python3 skills/tooluniverse-sequence-analysis/scripts/translate_dna.py "ATGCCC..."
Tries all 3 reading frames, picks longest ORF automatically.
首选：使用
DNA_translate_reading_frames
工具（通过MCP/SDK），传入
sequence
参数。备选：直接运行
translate_dna.py
脚本。
python3 skills/tooluniverse-sequence-analysis/scripts/translate_dna.py "ATGCCC..."
自动尝试全部3种阅读框，自动选择最长的开放阅读框。

sequence_tools.py — Residue counting, GC content, reverse complement, stats

sequence_tools.py —— 残基统计、GC含量、反向互补、序列统计

Script: 
skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py
Preferred: Use ToolUniverse tools (via MCP/SDK) instead of the script:
  • Sequence_count_residues
    tool -- Count residues in a sequence or region. Fallback: 
    sequence_tools.py --type count_residues
    or 
    --type count_region
  • Sequence_gc_content
    tool -- GC% of DNA. Fallback: 
    sequence_tools.py --type gc_content
  • Sequence_reverse_complement
    tool -- DNA reverse complement. Fallback: 
    sequence_tools.py --type reverse_complement
  • Sequence_stats
    tool -- Auto-detect type, length, MW. Fallback: 
    sequence_tools.py --type stats
Fallback script modes (use 
--type
):
  • count_residues
    : Count residue in full sequence. 
    --sequence "ACDE..." --residue C
  • count_region
    : Count in region (1-based inclusive). 
    --sequence "MAC..." --start 5 --end 20 --residue C
    OR 
    --accession P24046 --start 318 --end 440 --residue C
    (fetches from UniProt live)
  • gc_content
    : GC% of DNA. 
    --sequence "ATGCGATCG"
  • reverse_complement
    : DNA reverse complement. 
    --sequence "ATGCGATCG"
  • stats
    : Auto-detect DNA/RNA/Protein, compute length, MW for protein. 
    --sequence "ATGCG..."
ALWAYS use 
count_region --accession
when the user gives a UniProt accession + region -- do not count manually.
脚本路径：
skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py
首选：使用ToolUniverse工具（通过MCP/SDK）替代脚本：
  • Sequence_count_residues
    工具——统计序列或区域内的残基数量。备选：
    sequence_tools.py --type count_residues
    或
    --type count_region
  • Sequence_gc_content
    工具——计算DNA的GC含量。备选：
    sequence_tools.py --type gc_content
  • Sequence_reverse_complement
    工具——生成DNA反向互补序列。备选：
    sequence_tools.py --type reverse_complement
  • Sequence_stats
    工具——自动检测序列类型、长度、蛋白质分子量。备选：
    sequence_tools.py --type stats
备选脚本模式（使用
--type
指定）：
  • count_residues
    ：统计全序列中的残基数量。
    --sequence "ACDE..." --residue C
  • count_region
    ：统计指定区域内的残基数量（1-based包含性）。
    --sequence "MAC..." --start 5 --end 20 --residue C
    或 
    --accession P24046 --start 318 --end 440 --residue C
    （从UniProt实时获取序列）
  • gc_content
    ：计算DNA的GC含量。
    --sequence "ATGCGATCG"
  • reverse_complement
    ：生成DNA反向互补序列。
    --sequence "ATGCGATCG"
  • stats
    ：自动检测DNA/RNA/蛋白质类型，计算长度、蛋白质分子量。
    --sequence "ATGCG..."
当用户提供UniProt登录号+区域时，务必使用
count_region --accession
——切勿手动统计。

Interpretation Framework

解读框架

Sequence Quality Assessment

序列质量评估

IndicatorHigh QualityAcceptableCaution
RefSeq statusNM_/NP_ (curated)XM_/XP_ (predicted)No RefSeq (GenBank only)
Sequence versionLatest version (.N)Previous versionRemoved/replaced
AnnotationReviewed (UniProt Swiss-Prot)Unreviewed (TrEMBL)No annotation
Gene symbolHGNC approvedAlias/synonymLocus tag only
指标高质量可接受需注意
RefSeq状态NM_/NP_（已审核）XM_/XP_（预测）无RefSeq（仅GenBank）
序列版本最新版本（.N）旧版本已移除/替换
注释状态已审核（UniProt Swiss-Prot）未审核（TrEMBL）无注释
基因符号HGNC批准别名/同义词仅基因座标签

Synthesis Questions

综合问题分析

  1. Is this the correct sequence? (verify organism, gene symbol, isoform)
  2. Is it the canonical isoform? (RefSeq MANE Select or UniProt canonical)
  3. How well-annotated is it? (SwissProt > TrEMBL > GenBank predicted)
  4. Are there known variants? (ClinVar pathogenic variants in this sequence)
  1. 这是正确的序列吗？（验证物种、基因符号、同工型）
  2. 这是标准同工型吗？（RefSeq MANE Select或UniProt标准型）
  3. 注释完善程度如何？（SwissProt > TrEMBL > GenBank预测序列）
  4. 是否存在已知变异？（该序列中是否有ClinVar致病性变异）

Answer Formatting (CRITICAL)

回答格式要求（重要）

TRIM YOUR ANSWER: If the question asks "what protein", answer with JUST the protein name. Do not add parenthetical abbreviations, descriptions, or qualifications. Example: answer "Glucose-6-phosphate 1-dehydrogenase", NOT "Glucose-6-phosphate 1-dehydrogenase (G6PD, EC 1.1.1.49)". When identifying a protein from a sequence, use BLAST/UniProt and report the top hit name exactly as it appears in the database — no embellishment.
精简回答：若问题询问“是什么蛋白质”，仅回答蛋白质名称。请勿添加括号缩写、描述或限定语。示例：回答“葡萄糖-6-磷酸脱氢酶”，而非“葡萄糖-6-磷酸脱氢酶（G6PD, EC 1.1.1.49）”。通过序列识别蛋白质时，使用BLAST/UniProt并准确报告数据库中的顶级匹配名称——无需额外修饰。

Peptide & Foldamer Structure

肽类与折叠体结构

  • Alpha-peptide helices: alpha-helix (3.6 res/turn, i->i+4 H-bonds), 3_10-helix (3 res/turn, i->i+3), pi-helix (4.4 res/turn, i->i+5).
  • Beta-peptide helices: named by H-bond ring size. 14-helix (i->i+2, 14-membered rings), 12-helix, 10-helix, 8-helix.
  • Beta-amino acid ring size determines helix type: 4-membered cyclic constraint -> 10-helix; 5-membered (e.g., ACPC) -> 12-helix; 6-membered (e.g., ACHC) -> 14-helix. Acyclic beta3-residues default to 14-helix.
  • Mixed alpha/beta foldamers (1:1 alternation): form 11-helix (i->i+3, 11-atom rings) or 14/15-helix (i->i+4, alternating 14- and 15-atom rings). Longer sequences prefer the 14/15-helix.
  • Key rule: the number in the helix name = number of atoms in the hydrogen-bonded ring.
  • Cyclic beta-amino acids (ACPC, ACHC) constrain backbone torsion angles, favoring specific helix types over acyclic residues.
  • α-肽螺旋：α-螺旋（每圈3.6个残基，i->i+4氢键）、3₁₀-螺旋（每圈3个残基，i->i+3氢键）、π-螺旋（每圈4.4个残基，i->i+5氢键）。
  • β-肽螺旋：以氢键环的大小命名。14-螺旋（i->i+2，14元环）、12-螺旋、10-螺旋、8-螺旋。
  • β-氨基酸环大小决定螺旋类型：4元环约束 -> 10-螺旋；5元环（如ACPC）-> 12-螺旋；6元环（如ACHC）-> 14-螺旋。无环β3-残基默认形成14-螺旋。
  • α/β混合折叠体（1:1交替）：形成11-螺旋（i->i+3，11原子环）或14/15-螺旋（i->i+4，交替形成14和15原子环）。较长序列倾向于形成14/15-螺旋。
  • 关键规则：螺旋名称中的数字 = 氢键环中的原子数量。
  • 环状β-氨基酸（ACPC、ACHC）约束主链扭转角，相较于无环残基更易形成特定螺旋类型。

Limitations

局限性

  • ensembl_get_sequence gene IDs + non-genomic type need 
    multiple_sequences=true
  • NCBIDatasets_get_orthologs requires NCBI Gene ID (not symbol); UniProt returns canonical isoform only
  • 使用ensembl_get_sequence时，基因ID+非基因组类型需设置
    multiple_sequences=true
  • NCBIDatasets_get_orthologs需要NCBI基因ID（而非基因符号）；UniProt仅返回标准同工型