QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    How many pairs of positively correlated genes are both non-essential? If Expression_Corr > 0, it means the expression correlation is positive

    【Evidence】
    If Expression_Corr > 0, it means the expression correlation is positive

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T2.GeneID2) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr > 0 AND T1.Essential = 'Non-Essential'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    If a pair of genes is positively correlated, what is the possibility of it being composed of two genes both with over 10 chromosomes? Positively correlated means Expression_Corr > 0; Possibility = count(the pairs of genes with both chromosomes over 20) / count(pairs of genes that are positively correlated)

    【Evidence】
    Positively correlated means Expression_Corr > 0; Possibility = count(the pairs of genes with both chromosomes over 20) / count(pairs of genes that are positively correlated)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(T1.Chromosome > 10 AND T3.Chromosome > 10, 1, 0)) AS REAL) * 100 / COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 INNER JOIN Genes AS T3 ON T3.GeneID = T2.GeneID2 WHERE T2.Expression_Corr > 0
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    For the pairs of genes both from the class ATPases, what is the average expression correlation score?

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT AVG(T2.Expression_Corr) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Class = 'ATPases'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    Lists all genes by identifier number located in the cytoplasm and whose function is metabolism.

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT GeneID FROM Genes WHERE Localization = 'cytoplasm' AND Function = 'METABOLISM'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    How many different genes do we have if we add those located in the plasma and in the nucleus?

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(GeneID) FROM Classification WHERE Localization IN ('plasma', 'nucleus')
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    What kind of expression correlation occurs in physical type interacting gene pairs and what percentage of these are negatively correlated? If the Expression_Corr value is negative then it's negatively correlated. Percentage of Negative Correlation = count(negative Expression_Corr physical type) / count(Expression_Corr physical type) * 100%

    【Evidence】
    If the Expression_Corr value is negative then it's negatively correlated. Percentage of Negative Correlation = count(negative Expression_Corr physical type) / count(Expression_Corr physical type) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT Expression_Corr FROM Interactions WHERE Type = 'Physical' UNION ALL SELECT CAST(SUM(Expression_Corr < 0) AS REAL) * 100 / COUNT(*) FROM Interactions WHERE Type = 'Physical'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    What percentage of genes located in the cytoskeleton are of unknown class? And of these, how many are not conditional phenotypes? Percentage = count(genes located in the cytoskeleton unknown class) / count(genes located in the cytoskeleton) * 100%

    【Evidence】
    Percentage = count(genes located in the cytoskeleton unknown class) / count(genes located in the cytoskeleton) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(Localization = 'cytoskeleton' AND Phenotype = 'Conditional phenotypes') , CAST(SUM(Localization = 'cytoskeleton') AS REAL) * 100 / COUNT(GeneID) FROM Genes;
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    What type of interactions occurs in genes whose function is cellular transport and transport medicine and are classified as non-essential?

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.Type FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Function = 'TRANSCRIPTION' AND T1.Essential = 'Non-Essential'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    List all genes whose interaction is with genes located in the nucleus in which it is positively correlated. If the Expression_Corr value is positive then it's positively correlated

    【Evidence】
    If the Expression_Corr value is positive then it's positively correlated

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.GeneID FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr > 0 AND T1.Localization = 'nucleus'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    Taking all the essential genes of the transcription factors class located in the nucleus as a reference, how many of them carry out a genetic-type interaction with another gene? List them.

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.GeneID1 FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Localization = 'nucleus' AND T1.Class = 'Transcription factors' AND T1.Essential = 'Essential' AND T2.Expression_Corr != 0
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    Of all the nonessential genes that are not of the motorprotein class and whose phenotype is cell cycle defects, how many do not have a physical type of interaction?

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Type != 'Physical' AND T1.Phenotype = 'Cell cycle defects' AND T1.Class != 'Motorproteins' AND T1.Essential = 'Non-Essential'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    Of the genes whose phenotype and motif are nucleic acid metabolism defects, PS00107, what percentage perform positive interaction with another gene? Percentage of no interaction = [count(nucleic acid metabolism, PS00107, no interaction) / count(nucleic acid metabolism, PS00107) * 100%

    【Evidence】
    Percentage of no interaction = [count(nucleic acid metabolism, PS00107, no interaction) / count(nucleic acid metabolism, PS00107) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(T2.Expression_Corr > 0, 1, 0)) AS REAL) * 100 / COUNT(T2.GeneID1) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T1.Phenotype = 'Nucleic acid metabolism defects' AND T1.Motif = 'PS00107'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 genes
【Schema】
# Table: Interactions
[
(GeneID1:TEXT, Primary Key, Examples: [G234064, G234065, G234073]),
(GeneID2:TEXT, Primary Key, Examples: [G234126, G235065, G234371]),
(Type:TEXT, Examples: [Genetic-Physical, Genetic, Physical]),
(Expression_Corr:REAL, Examples: [0.914095071, 0.751584888, 0.823773738])
]
# Table: Classification
[
(GeneID:TEXT, Primary Key, Examples: [G234064, G234065, G234070]),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
# Table: Genes
[
(GeneID:TEXT, Examples: [G234064, G234065, G234070]),
(Essential:TEXT, Examples: [Essential, Non-Essential, ?]),
(Class:TEXT, Examples: [GTP/GDP-exchange factors (GEFs)]),
(Complex:TEXT, Examples: [Translation complexes]),
(Phenotype:TEXT, Examples: [?]),
(Motif:TEXT, Examples: [PS00824, PS00825, ?]),
(Chromosome:INTEGER, Examples: [1, 2, 3]),
(Function:TEXT),
(Localization:TEXT, Examples: [cytoplasm, cytoskeleton, nucleus])
]
【Foreign keys】
Interactions.GeneID1=Classification.GeneID
Interactions.GeneID2=Classification.GeneID
Genes.GeneID=Classification.GeneID

    【Question】
    Which negatively correlated, genetically interacting genes are non-essential? What percentage do they represent with respect to those that are essential? If the Expression_Corr value is negative then it's negatively correlated; Percentage of Essensity = [count(negatively correlated, genetical interaction, non-essential) / count(negatively correlated, genetical interaction, non-essential+negatively correlated, genetical interaction, essential)] * 100%

    【Evidence】
    If the Expression_Corr value is negative then it's negatively correlated; Percentage of Essensity = [count(negatively correlated, genetical interaction, non-essential) / count(negatively correlated, genetical interaction, non-essential+negatively correlated, genetical interaction, essential)] * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(COUNT(T1.GeneID) AS REAL) * 100 / ( SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 ) FROM Genes AS T1 INNER JOIN Interactions AS T2 ON T1.GeneID = T2.GeneID1 WHERE T2.Expression_Corr < 0 AND T1.Essential = 'Non-Essential'