TFpredict: TFpredict
In brengong/ConservationtextmineR:
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
If type = "single"
uses a data.table with a column named Targeting_Factor and a column named Consensus_Sequence to predict what Targeting Factor consensus sequences
are located within a Protein, DNA, or RNA sequence
Requires a character vector containing the Protein, DNA, or RNA sequence of interest.
Requires transcription factor data table with a column labeled "Targeting_Factor" housing the targeting protein of interest and a column labeled
"Consensus_Sequence" housing the targeting protein consensus sequence.
Usage
1
Arguments
Target
If type = "single", a vector containing one protein, DNA, or RNA character string. If type = "multiple", a data.table containing two columns, one labeled "Sequence" and one labeled "gene_symbol".
If type = "multiple_species", a data.table containing four columns, one labeled "Sequence" one labeled "gene_symbol", one labeled "Common_Name", and one labeled "Scientific_Name". If type = "multiple_species_unknown",
a data.table containing three columns, one labeled "Sequence" one labeled "gene_symbol", and one labeled "Scientific_Name".
Targeting_Factor_DT
a data table with two columns labeled: Targeting_Factor and Consensus_Sequence
type
a single character indicating "single", "multiple", "multiple_species", or "multiple_species_unknown"
Details
If type = "multiple"
Uses two data.tables. The first one (Targeting_Factor_DT) houses a column named "Targeting_Factor" and a column named "Consensus_Sequence". The
second one (Target) houses a column named "Sequence" and a column named "gene_symbol". It returns what targeting protein consensus sequences
housed in Targeting_Factor_DT are located within a data table of protein, DNA, RNA sequence housed in Target.
Requires data.table (Targeting_Factor_DT) with a column named "Targeting_Factor" and a column named "Consensus_Sequence" housing the consensus sequence.
Requires data.table (Target) with a columns named "Sequence" and "gene_symbol" housing the protein, DNA, or RNA sequences and designated names to query.
If type = "multiple_species"
Uses two data.tables. The first one (Targeting_Factor_DT) houses a column named "Targeting_Factor" and a column named "Consensus_Sequence". The
second one (Target) houses a column named "Sequence", a column named "gene_symbol", column labeled "Common_Name", and a column labeled "Scientific_Name". It returns what consensus sequences
housed in Targeting_Factor_DT are located within a data table of protein, DNA, opr RNA sequence housed in Target across multiple species.
Requires data.table (Targeting_Factor_DT) with a column named "Targeting_Factor" and a column named "Consensus_Sequence" housing the consensus sequence.
Requires data.table (Target) with a columns named "Sequence", "gene_symbol", "Common_Name", and "Scientific_Name", housing the protein, DNA, or RNA sequences and designated names to query.
If type = "multiple_species_unknown"
Used if the Target data.table does not contain a column labeled "Common_Name".
Uses two data.tables. The first one (Targeting_Factor_DT) houses a column named "Targeting_Factor" and a column named "Consensus_Sequence". The
second one (Target) houses a column named "Sequence", a column named "gene_symbol", and a column labeled "Scientific_Name". It returns what consensus sequences
housed in Targeting_Factor_DT are located within a data.table of protein, DNA, or RNA sequence housed in Target across multiple species.
Requires data.table (Targeting_Factor_DT) with a column named "Targeting_Factor" and a column named "Consensus_Sequence" housing the consensus sequence.
Requires data.table (Target) with a columns named "Sequence", "gene_symbol", and "Scientific_Name", housing the protein, DNA, or RNA sequences and designated names to query.
Value
A data table
Author(s)
Brendan Gongol
Examples
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Targeting_Factor <- c("AMPK", "PKA", "PKC", "MAPK", "CAMKKB", "CAMKI", "CAMKIV", "CKII", "CDK", "SRC", "AKT")
Consensus_Sequence <- c("AGCNVTQ", "PPKLYS", "AAT","AAAAAAAAAAATTGCNVMDEDE", "AA", "ATA", "GTTT", "AAAAAAAAAAAAAAAAAAAAA", "(A|T)..(H|F)", "GCTAAGCTGCGCAATTTTTGTATTTTGT|AGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTG","(A|T)T(A|G)(C|T)(C|T)T(C|T)")
data_table <- data.table(cbind(Targeting_Factor, Consensus_Sequence))
data_table
Prot <- "ACQVAPKLHGEAGCNVTQDWTYMMGVCSTASYAATWEQDEPLWYMAATNGHCATWWAAASSCATAFQTSKLPIIIGHATSDF"
Sequence <- c("ACDFEQAGCNVTQPCTSTSGANDEPHYYASTGFWYKAGCNVTQETCCKLLHAQSWW",
"ACQVAPKLHGEDWTYMMGVCSTASYWEQDEPLWYMNGHCATWWAAASSCTAQTSKLPIIIGHATSDF",
"TGHATSHCTANMKLPYWQEDTGSCANMHGTYYYDEDEDDASQWWWMNNNCGYTEWSDFGCPKKK",
"AAAATSTSTSGGGGGAAACCCCNNNNMMMMPPPPWWWWQHGGTTYYNNCCAA", "AAAAAATTTTTTCCCCCCGGGGGG")
gene_symbol <- c("PABP", "EIF4E", "SREBP", "FOXO", "ABCA1")
Common_Name <- c("Rat", "Mouse", "Human", "Cattle", "Dog")
Scientific_Name <- c("Rattus_norvegicus", "Mus_musculus", "Homo_sapiens", "Bos_taurus", "Canis_familiaris")
proteinTarg <- data.frame(cbind(gene_symbol,Sequence, Common_Name, Scientific_Name))
proteinTarg$Sequence <- as.character(proteinTarg$Sequence)
proteinTarg$gene_symbol <- as.character(proteinTarg$gene_symbol)
proteinTarg$Common_Name <- as.character(proteinTarg$Common_Name)
proteinTarg$Scientific_Name <- as.character(proteinTarg$Scientific_Name)
proteinTarg
TFpredict(Prot, data_table , type = "single")
TFpredict(proteinTarg, data_table, type = "multiple")
TFpredict(proteinTarg, data_table, type = "multiple_species")
TFpredict(proteinTarg, data_table, type = "multiple_species_unknown")
Targeting_Factor <- c("KLF2", "PGC1A", "FOXO1", "NCL", "SREBP", "MYC", "HIF", "NF-KB", "TXNIP", "PPAR")
Consensus_Sequence <- c("AAGCT", "GCGC", "AAT","AAAAAAAAAAAAAAAAAAAAAAAA", "AA", "ATA", "GTTT", "AAAAAAAAAAAAAAAAAAAAA", "(A|T)..(C|G)", "GCTAAGCTGCGCAATTTTTGTATTTTGT|AGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTG")
TX_data_table <- data.table(cbind(Targeting_Factor, Consensus_Sequence))
TX_data_table
chromo_seq <- "AAGCTAAGCTAAGCTGCGCAATTTTTGTATTTTGTTTAAACAGAATCCTCAAGGGAACATCATCCTCAGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTGTTTTTAAAGCT"
Sequence <- c("AAGCTAAGCTAAGCTGCGCAATTTTTGTATTTTGTTTAAACAGAATCCTCAAGGGAACATCATCCTCAGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTGTTTTTAAAGCT",
"CTGTTTCGAGCCTGAATCTCGATCGCTCGCGCTAGACAGCTCGACGCACTTTTCAGCAGGAGCCTG",
"TCAGCAGATAGCGCTCGATACAGCTCGACAGCTCTTGCTGTATTGTGTG",
"TTGCTGTATTGTGTGATCCTCGATACAGGTATTTTCTGAGCCTGATAGCTAGCTTTGCTGTATTGTGTG",
"AAAAAATTTTTTCCCCCCGGGGGG")
gene_symbol <- c("AKT", "PI3K", "SREBP", "FOXO", "ABCA1")
Common_Name <- c("Rat", "Mouse", "Human", "Cattle", "Dog")
Scientific_Name <- c("Rattus_norvegicus", "Mus_musculus", "Homo_sapiens", "Bos_taurus", "Canis_familiaris")
chromo <- data.frame(cbind(Sequence, gene_symbol, Common_Name, Scientific_Name))
chromo$Sequence <- as.character(chromo$Sequence)
chromo$gene_symbol <- as.character(chromo$gene_symbol)
chromo$Common_Name <- as.character(chromo$Common_Name)
chromo$Scientific_Name <- as.character(chromo$Scientific_Name)
chromo
TFpredict(chromo_seq, TX_data_table , type = "single")
TFpredict(chromo, TX_data_table, type = "multiple")
TFpredict(chromo, TX_data_table, type = "multiple_species")
TFpredict(chromo, TX_data_table, type = "multiple_species_unknown")
brengong/ConservationtextmineR documentation built on July 29, 2019, 10:05 a.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
If type = "single" uses a data.table with a column named Targeting_Factor and a column named Consensus_Sequence to predict what Targeting Factor consensus sequences are located within a Protein, DNA, or RNA sequence Requires a character vector containing the Protein, DNA, or RNA sequence of interest. Requires transcription factor data table with a column labeled "Targeting_Factor" housing the targeting protein of interest and a column labeled "Consensus_Sequence" housing the targeting protein consensus sequence.
Usage
1 |
Arguments
Target |
If type = "single", a vector containing one protein, DNA, or RNA character string. If type = "multiple", a data.table containing two columns, one labeled "Sequence" and one labeled "gene_symbol". If type = "multiple_species", a data.table containing four columns, one labeled "Sequence" one labeled "gene_symbol", one labeled "Common_Name", and one labeled "Scientific_Name". If type = "multiple_species_unknown", a data.table containing three columns, one labeled "Sequence" one labeled "gene_symbol", and one labeled "Scientific_Name". |
Targeting_Factor_DT |
a data table with two columns labeled: Targeting_Factor and Consensus_Sequence |
type |
a single character indicating "single", "multiple", "multiple_species", or "multiple_species_unknown" |
Details
If type = "multiple" Uses two data.tables. The first one (Targeting_Factor_DT) houses a column named "Targeting_Factor" and a column named "Consensus_Sequence". The second one (Target) houses a column named "Sequence" and a column named "gene_symbol". It returns what targeting protein consensus sequences housed in Targeting_Factor_DT are located within a data table of protein, DNA, RNA sequence housed in Target. Requires data.table (Targeting_Factor_DT) with a column named "Targeting_Factor" and a column named "Consensus_Sequence" housing the consensus sequence. Requires data.table (Target) with a columns named "Sequence" and "gene_symbol" housing the protein, DNA, or RNA sequences and designated names to query.
If type = "multiple_species" Uses two data.tables. The first one (Targeting_Factor_DT) houses a column named "Targeting_Factor" and a column named "Consensus_Sequence". The second one (Target) houses a column named "Sequence", a column named "gene_symbol", column labeled "Common_Name", and a column labeled "Scientific_Name". It returns what consensus sequences housed in Targeting_Factor_DT are located within a data table of protein, DNA, opr RNA sequence housed in Target across multiple species. Requires data.table (Targeting_Factor_DT) with a column named "Targeting_Factor" and a column named "Consensus_Sequence" housing the consensus sequence. Requires data.table (Target) with a columns named "Sequence", "gene_symbol", "Common_Name", and "Scientific_Name", housing the protein, DNA, or RNA sequences and designated names to query.
If type = "multiple_species_unknown" Used if the Target data.table does not contain a column labeled "Common_Name". Uses two data.tables. The first one (Targeting_Factor_DT) houses a column named "Targeting_Factor" and a column named "Consensus_Sequence". The second one (Target) houses a column named "Sequence", a column named "gene_symbol", and a column labeled "Scientific_Name". It returns what consensus sequences housed in Targeting_Factor_DT are located within a data.table of protein, DNA, or RNA sequence housed in Target across multiple species. Requires data.table (Targeting_Factor_DT) with a column named "Targeting_Factor" and a column named "Consensus_Sequence" housing the consensus sequence. Requires data.table (Target) with a columns named "Sequence", "gene_symbol", and "Scientific_Name", housing the protein, DNA, or RNA sequences and designated names to query.
Value
A data table
Author(s)
Brendan Gongol
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | Targeting_Factor <- c("AMPK", "PKA", "PKC", "MAPK", "CAMKKB", "CAMKI", "CAMKIV", "CKII", "CDK", "SRC", "AKT")
Consensus_Sequence <- c("AGCNVTQ", "PPKLYS", "AAT","AAAAAAAAAAATTGCNVMDEDE", "AA", "ATA", "GTTT", "AAAAAAAAAAAAAAAAAAAAA", "(A|T)..(H|F)", "GCTAAGCTGCGCAATTTTTGTATTTTGT|AGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTG","(A|T)T(A|G)(C|T)(C|T)T(C|T)")
data_table <- data.table(cbind(Targeting_Factor, Consensus_Sequence))
data_table
Prot <- "ACQVAPKLHGEAGCNVTQDWTYMMGVCSTASYAATWEQDEPLWYMAATNGHCATWWAAASSCATAFQTSKLPIIIGHATSDF"
Sequence <- c("ACDFEQAGCNVTQPCTSTSGANDEPHYYASTGFWYKAGCNVTQETCCKLLHAQSWW",
"ACQVAPKLHGEDWTYMMGVCSTASYWEQDEPLWYMNGHCATWWAAASSCTAQTSKLPIIIGHATSDF",
"TGHATSHCTANMKLPYWQEDTGSCANMHGTYYYDEDEDDASQWWWMNNNCGYTEWSDFGCPKKK",
"AAAATSTSTSGGGGGAAACCCCNNNNMMMMPPPPWWWWQHGGTTYYNNCCAA", "AAAAAATTTTTTCCCCCCGGGGGG")
gene_symbol <- c("PABP", "EIF4E", "SREBP", "FOXO", "ABCA1")
Common_Name <- c("Rat", "Mouse", "Human", "Cattle", "Dog")
Scientific_Name <- c("Rattus_norvegicus", "Mus_musculus", "Homo_sapiens", "Bos_taurus", "Canis_familiaris")
proteinTarg <- data.frame(cbind(gene_symbol,Sequence, Common_Name, Scientific_Name))
proteinTarg$Sequence <- as.character(proteinTarg$Sequence)
proteinTarg$gene_symbol <- as.character(proteinTarg$gene_symbol)
proteinTarg$Common_Name <- as.character(proteinTarg$Common_Name)
proteinTarg$Scientific_Name <- as.character(proteinTarg$Scientific_Name)
proteinTarg
TFpredict(Prot, data_table , type = "single")
TFpredict(proteinTarg, data_table, type = "multiple")
TFpredict(proteinTarg, data_table, type = "multiple_species")
TFpredict(proteinTarg, data_table, type = "multiple_species_unknown")
Targeting_Factor <- c("KLF2", "PGC1A", "FOXO1", "NCL", "SREBP", "MYC", "HIF", "NF-KB", "TXNIP", "PPAR")
Consensus_Sequence <- c("AAGCT", "GCGC", "AAT","AAAAAAAAAAAAAAAAAAAAAAAA", "AA", "ATA", "GTTT", "AAAAAAAAAAAAAAAAAAAAA", "(A|T)..(C|G)", "GCTAAGCTGCGCAATTTTTGTATTTTGT|AGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTG")
TX_data_table <- data.table(cbind(Targeting_Factor, Consensus_Sequence))
TX_data_table
chromo_seq <- "AAGCTAAGCTAAGCTGCGCAATTTTTGTATTTTGTTTAAACAGAATCCTCAAGGGAACATCATCCTCAGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTGTTTTTAAAGCT"
Sequence <- c("AAGCTAAGCTAAGCTGCGCAATTTTTGTATTTTGTTTAAACAGAATCCTCAAGGGAACATCATCCTCAGTTCTTTTTGTGTATTAGCTCAGATTTTCCAGCTGTTTTTAAAGCT",
"CTGTTTCGAGCCTGAATCTCGATCGCTCGCGCTAGACAGCTCGACGCACTTTTCAGCAGGAGCCTG",
"TCAGCAGATAGCGCTCGATACAGCTCGACAGCTCTTGCTGTATTGTGTG",
"TTGCTGTATTGTGTGATCCTCGATACAGGTATTTTCTGAGCCTGATAGCTAGCTTTGCTGTATTGTGTG",
"AAAAAATTTTTTCCCCCCGGGGGG")
gene_symbol <- c("AKT", "PI3K", "SREBP", "FOXO", "ABCA1")
Common_Name <- c("Rat", "Mouse", "Human", "Cattle", "Dog")
Scientific_Name <- c("Rattus_norvegicus", "Mus_musculus", "Homo_sapiens", "Bos_taurus", "Canis_familiaris")
chromo <- data.frame(cbind(Sequence, gene_symbol, Common_Name, Scientific_Name))
chromo$Sequence <- as.character(chromo$Sequence)
chromo$gene_symbol <- as.character(chromo$gene_symbol)
chromo$Common_Name <- as.character(chromo$Common_Name)
chromo$Scientific_Name <- as.character(chromo$Scientific_Name)
chromo
TFpredict(chromo_seq, TX_data_table , type = "single")
TFpredict(chromo, TX_data_table, type = "multiple")
TFpredict(chromo, TX_data_table, type = "multiple_species")
TFpredict(chromo, TX_data_table, type = "multiple_species_unknown")
|
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