Transcription factor binding site (TFBS) analysis with the “TFBSTools” package (original) (raw)
Contents
- 1 Introduction
- 2 S4 classes in TFBSTools
- 3 Database interfaces for JASPAR2014 database
- 4 PFM, PWM and ICM methods
- 5 TFFM methods
- 6 Scan sequence and alignments with PWM pattern
- 7 Use de novo motif discovery software
- 8 Session info
- 9 References
- Appendix
Introduction
Eukaryotic regulatory regions are characterized based a set of discovered transcription factor binding sites (TFBSs), which can be represented as sequence patterns with various degree of degeneracy.
This TFBSTools package is designed to be a compuational framework for TFBSs analysis. Based on the famous perl module TFBS (Lenhard and Wasserman 2002), we extended the class definitions and enhanced implementations in an interactive environment. So far this package contains a set of integrated R S4 style classes, tools, JASPAR database interface functions. Most approaches can be described in three sequential phases. First, a pattern is generated for a set of target sequences known to be bound by a specific transcription factor. Second, a set of DNA sequences are analyzed to determine the locations of sequences consistent with the described binding pattern. Finally, in advanced cases, predictive statistical models of regulatory regions are constructed based on mutiple occurrences of the detected patterns.
Since JASPAR2016, the next generation of transcription factor binding site, TFFM (Mathelier and Wasserman 2013), was introduced into JASPAR for the first time. Now TFBSTools also supports the manipulation of TFFM. TFFM is based on hidden Markov Model (HMM). The biggest advantage of TFFM over basic PWM is that it can model position interdependence within TFBSs and variable motif length. A novel graphical representation of the TFFM motifs that captures the position interdependence is also introduced. For more details regarding TFFM, please refer to http://cisreg.cmmt.ubc.ca/TFFM/doc/.
TFBSTools aims to support all these functionalities in the environment R, except the external motif finding software, such as MEME (Bailey and Elkan 1994).
Database interfaces for JASPAR2014 database
This section will demonstrate how to operate on the JASPAR 2014 database. JASPAR is a collection of transcription factor DNA-binding preferences, modeled as matrices. These can be converted into PWMs, used for scanning genomic sequences. JASPAR is the only database with this scope where the data can be used with no restrictions (open-source). A Bioconducto experiment data package _JASPAR2014_is provided with each release of JASPAR.
Search JASPAR2014 database
This search function fetches matrix data for all matrices in the database matching criteria defined by the named arguments and returns a PFMatrixList object. For more search criterias, please see the help page for getMatrixSet.
suppressMessages(library(JASPAR2014))
opts <- list()
opts[["species"]] <- 9606
opts[["name"]] <- "RUNX1"
opts[["type"]] <- "SELEX"
opts[["all_versions"]] <- TRUE
PFMatrixList <- getMatrixSet(JASPAR2014, opts)
PFMatrixList
#> PFMatrixList of length 1
#> names(1): MA0002.1
opts2 <- list()
opts2[["type"]] <- "SELEX"
PFMatrixList2 <- getMatrixSet(JASPAR2014, opts2)
PFMatrixList2
#> PFMatrixList of length 111
#> names(111): MA0004.1 MA0006.1 MA0008.1 MA0009.1 ... MA0588.1 MA0589.1 MA0590.1Store, delete and initialize JASPAR2014 database
We also provide some functions to initialize an empty JASPAR2014 style database, store new PFMatrix or PFMatrixList into it, or delete some records based on ID. The backend of the database is SQLite.
db <- "myMatrixDb.sqlite"
initializeJASPARDB(db, version="2014")
#> [1] "Success"
data("MA0043")
storeMatrix(db, MA0043)
#> [1] "Success"
deleteMatrixHavingID(db,"MA0043.1")
file.remove(db)
#> [1] TRUEPFM, PWM and ICM methods
This section will give an introduction of matrix operations, including conversion from PFM to PWM and ICM, profile matrices comparison, dynamic random profile generation.
PFM to PWM
The method toPWM can convert PFM to PWM (Wasserman and Sandelin 2004). Optional parameters include type, pseudocounts, bg. The implementation in this package is a bit different from that in Biostrings.
First of all, toPWM allows the input matrix to have different column sums, which means the count matrix can have an unequal number of sequences contributing to each column. This scenario is rare, but exists in JASPAR SELEX data.
Second, we can specify customized pseudocounts.pseudocounts is necessary for correcting the small number of counts or eliminating the zero values before log transformation. In TFBS perl module, the square root of the number of sequences contributing to each column. However, it has been shown to too harsh (Nishida, Frith, and Nakai 2009). Hence, a default value of 0.8 is used. Of course, it can be changed to other customized value or even different values for each column.
pwm <- toPWM(pfm, pseudocounts=0.8)
pwm
#> An object of class PWMatrix
#> ID: MA0004.1
#> Name: Arnt
#> Matrix Class: Zipper-Type
#> strand: +
#> Pseudocounts: 0.8
#> Tags:
#> $family
#> [1] "Helix-Loop-Helix"
#>
#> $species
#> [1] "10090"
#>
#> $tax_group
#> [1] "vertebrates"
#>
#> $medline
#> [1] "7592839"
#>
#> $type
#> [1] "SELEX"
#>
#> $ACC
#> [1] "P53762"
#>
#> $pazar_tf_id
#> [1] "TF0000003"
#>
#> $TFBSshape_ID
#> [1] "11"
#>
#> $TFencyclopedia_ID
#> [1] "580"
#>
#> Background:
#> A C G T
#> 0.25 0.25 0.25 0.25
#> Matrix:
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> A -0.3081223 1.884523 -4.700440 -4.700440 -4.700440 -4.700440
#> C 1.6394103 -4.700440 1.957772 -4.700440 -4.700440 -4.700440
#> G -4.7004397 -2.115477 -4.700440 1.957772 -4.700440 1.957772
#> T -4.7004397 -4.700440 -4.700440 -4.700440 1.957772 -4.700440PFM to ICM
The method toICM can convert PFM to ICM (Schneider et al. 1986). Besides the similar pseudocounts, bg, you can also choose to do the schneider correction.
The information content matrix has a column sum between 0 (no base preference) and 2 (only 1 base used). Usually this information is used to plot sequence log.
How a PFM is converted to ICM: we have the PFM matrix \(x\), base backrgound frequency \(bg\), \(pseudocounts\) for correction.
\[Z[j] = \sum_{i=1}^{4} x[i,j]\]
\[p[i,j] = {(x[i,j] + bg[i] \times pseudocounts[j]) \over (Z[j] + \sum_{i}bg[i] \times pseudocounts[j]}\]
\[D[j] = \log_2{4} + \sum_{i=1}^{4} p[i,j]*\log{p[i,j]}\]
\[ICM[i,j] = p[i,j] \times D[j]\]
icm <- toICM(pfm, pseudocounts=0.8, schneider=TRUE)
icm
#> An object of class ICMatrix
#> ID: MA0004.1
#> Name: Arnt
#> Matrix Class: Zipper-Type
#> strand: +
#> Pseudocounts: 0.8
#> Schneider correction: TRUE
#> Tags:
#> $family
#> [1] "Helix-Loop-Helix"
#>
#> $species
#> [1] "10090"
#>
#> $tax_group
#> [1] "vertebrates"
#>
#> $medline
#> [1] "7592839"
#>
#> $type
#> [1] "SELEX"
#>
#> $ACC
#> [1] "P53762"
#>
#> $pazar_tf_id
#> [1] "TF0000003"
#>
#> $TFBSshape_ID
#> [1] "11"
#>
#> $TFencyclopedia_ID
#> [1] "580"
#>
#> Background:
#> A C G T
#> 0.25 0.25 0.25 0.25
#> Matrix:
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> A 0.203985791 1.30439694 0.01588159 0.01588159 0.01588159 0.01588159
#> C 0.786802337 0.01358747 1.60404024 0.01588159 0.01588159 0.01588159
#> G 0.009713609 0.08152481 0.01588159 1.60404024 0.01588159 1.60404024
#> T 0.009713609 0.01358747 0.01588159 0.01588159 1.60404024 0.01588159To plot the sequence logo, we use the package seqlogo. In sequence logo, each position gives the information content obtained for each nucleotide. The higher of the letter corresponding to a nucleotide, the larger information content and higher probability of getting that nucleotide at that position.
seqLogo(icm)Align PFM to a custom matrix or IUPAC string
In some cases, it is beneficial to assess similarity of existing profile matrices, such as JASPAR, to a newly discovered matrix (as with using BLAST for sequence data comparison when using Genbank).
TFBSTools provides tools for comparing pairs of PFMs, or a PFM with IUPAC string, using a modified Needleman-Wunsch algorithm(Sandelin et al. 2003).
## one to one comparison
data(MA0003.2)
data(MA0004.1)
pfmSubject <- MA0003.2
pfmQuery <- MA0004.1
PFMSimilarity(pfmSubject, pfmQuery)
#> score relScore
#> 7.294736 60.789466
## one to several comparsion
PFMSimilarity(pfmList, pfmQuery)
#> $pfm1
#> score relScore
#> 12 100
#>
#> $pfm2
#> score relScore
#> 12 100
## align IUPAC string
IUPACString <- "ACGTMRWSYKVHDBN"
PFMSimilarity(pfmList, IUPACString)
#> $pfm1
#> score relScore
#> 8.81500 73.45833
#>
#> $pfm2
#> score relScore
#> 8.81500 73.45833PWM similarity
To measure the similarity of two PWM matrix in three measurements:normalised Euclidean distance, _Pearson correlation_and Kullback Leibler divergence (Linhart, Halperin, and Shamir 2008). Given two PWMs in probability type, \(P^1\) and \(P^2\), where \(l\) is the length.\(P^j_{i,b}\) is the values in column \(i\) with base \(b\) in PWM \(j\). The normalised Euclidean distance is computed in
\[ D(P^1, P^2) = {1 \over {\sqrt{2}l}} \cdot \sum_{i=1}^{l} \sqrt{\sum_{b \in {\{A,C,G,T\}}} (P_{i,b}^1-P_{i,b}^2)^2}\]
This distance is between 0 (perfect identity) and 1 (complete dis-similarity).
The pearson correlation coefficient is computed in
\[ r(P^1, P^2) = {1 \over l} \cdot \sum_{i=1}^l {\sum_{b \in \{A,C,G,T\}} (P_{i,b}^1 - 0.25)(P_{i,b}^2-0.25) \over \sqrt{\sum_{b \in \{A,C,G,T\}} (P_{i,b}^1 - 0.25)^2 \cdot \sum_{b \in \{A,C,G,T\}} (P_{i,b}^2 - 0.25)^2}}\]
The Kullback-Leibler divergence is computed in
\[KL(P^1, P^2) = {1 \over {2l}} \cdot \sum_{i=1}^l \sum_{b \in \{A,C,G,T\}} (P_{i,b}^1\log{ P_{i,b}^1 \over P_{i,b}^2}+ P_{i,b}^2\log{P_{i,b}^2 \over {P_{i,b}^1}})\]
data(MA0003.2)
data(MA0004.1)
pwm1 <- toPWM(MA0003.2, type="prob")
pwm2 <- toPWM(MA0004.1, type="prob")
PWMSimilarity(pwm1, pwm2, method="Euclidean")
#> [1] 0.5134956
PWMSimilarity(pwm1, pwm2, method="Pearson")
#> [1] 0.2828507
PWMSimilarity(pwm1, pwm2, method="KL")
#> [1] 2.385866Dynamic random profile generation
In this section, we will demonstrate the capability of random profile matrices generation with matrix permutation and probabilitis sampling. In many computational/simulation studies, it is particularly desired to have a set of random matrices. Some cases includes the estimation of distance between putative TFBS and transcription start site, the evaluation of comparison between matrices (Bryne et al. 2008). These random matrices are expected to have same statistical properties with the selcted profiles, such as nucleotide content or information content.
The permutation method is relatively easy. It simply shuffles the columns either constrainted in each matrix, or columns almong all selected matrices. The probabilistic sampling is more complicated and can be done in two steps:
- A Dirichlet multinomial mixture model is trained on all available matrices in JASPAR.
- Random columns are sampled from the posterior distribution of the trained Dirichlet model based on selected profiles.
## Matrice permutation
permuteMatrix(pfmQuery)
#> An object of class PFMatrix
#> ID: MA0004.1
#> Name: Arnt
#> Matrix Class: Zipper-Type
#> strand: +
#> Tags:
#> $comment
#> [1] "-"
#>
#> $family
#> [1] "Helix-Loop-Helix"
#>
#> $medline
#> [1] "7592839"
#>
#> $pazar_tf_id
#> [1] "TF0000003"
#>
#> $tax_group
#> [1] "vertebrates"
#>
#> $tfbs_shape_id
#> [1] "11"
#>
#> $tfe_id
#> [1] "580"
#>
#> $type
#> [1] "SELEX"
#>
#> $collection
#> [1] "CORE"
#>
#> $species
#> [1] "10090"
#>
#> $acc
#> [1] "P53762"
#>
#> Background:
#> A C G T
#> 0.25 0.25 0.25 0.25
#> Matrix:
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> A 0 19 0 0 4 0
#> C 0 0 0 20 16 0
#> G 20 1 0 0 0 20
#> T 0 0 20 0 0 0
permuteMatrix(pfmList, type="intra")
#> PFMatrixList of length 2
#> names(2): pfm1 pfm2
permuteMatrix(pfmList, type="inter")
#> PFMatrixList of length 2
#> names(2): pfm1 pfm2## Dirichlet model training
data(MA0003.2)
data(MA0004.1)
pfmList <- PFMatrixList(pfm1=MA0003.2, pfm2=MA0004.1, use.names=TRUE)
dmmParameters <- dmmEM(pfmList, K=6, alg="C")
## Matrice sampling from trained Dirichlet model
pwmSampled <- rPWMDmm(MA0003.2, dmmParameters$alpha0, dmmParameters$pmix,
N=1, W=6)TFFM methods
The graphical representation of TFFM
Basic PWMs can be graphically represented by the sequence logos shown above. A novel graphical representation of TFFM is requied for taking the dinucleotide dependence into account.
For the upper part of the sequence logo, we represent the nucleotide probabilities at position \(p\) for each possible nucleotide at position \(p-1\). Hence, each column represents a position within a TFBS and each row the nucleotide probabilities found at that position. Each row assumes a specific nucleotide has been emitted by the previous hidden state. The intersection between a column corresponding to position \(p\) and row corresponding to nucleotide \(n\) gives the probabilities of getting each nucleotide at position \(p\) if \(n\) has been seen at position \(p-1\). The opacity to represent the sequence logo is proportional to the probablity of possible row to be used by the TFFM.
## sequence logo for First-order TFFM
seqLogo(tffmFirst) ## sequence logo for detailed TFFM
seqLogo(tffmDetail)Scan sequence and alignments with PWM pattern
searchSeq
searchSeq scans a nucleotide sequence with the pattern represented in the PWM. The strand argument controls which strand of the sequence will be searched. When it is _*_, both strands will be scanned.
A SiteSet object will be returned which can be exported into GFF3 or GFF2 format. Empirical p-values for the match scores can be calculated by an exact method from TFMPvalue or the distribution of sampled scores.
library(Biostrings)
data(MA0003.2)
data(MA0004.1)
pwmList <- PWMatrixList(MA0003.2=toPWM(MA0003.2), MA0004.1=toPWM(MA0004.1),
use.names=TRUE)
subject <- DNAString("GAATTCTCTCTTGTTGTAGTCTCTTGACAAAATG")
siteset <- searchSeq(pwm, subject, seqname="seq1", min.score="60%", strand="*")
sitesetList <- searchSeq(pwmList, subject, seqname="seq1",
min.score="60%", strand="*")
## generate gff2 or gff3 style output
head(writeGFF3(siteset))
#> seqname source feature start end score strand frame
#> 1 seq1 TFBS TFBS 8 13 -1.888154 + .
#> 2 seq1 TFBS TFBS 21 26 -1.888154 + .
#> 3 seq1 TFBS TFBS 29 34 -3.908935 + .
#> 4 seq1 TFBS TFBS 8 13 -1.961403 - .
#> 5 seq1 TFBS TFBS 10 15 -3.908935 - .
#> 6 seq1 TFBS TFBS 21 26 -1.961403 - .
#> attributes
#> 1 TF=Arnt;class=Zipper-Type;sequence=CTCTTG
#> 2 TF=Arnt;class=Zipper-Type;sequence=CTCTTG
#> 3 TF=Arnt;class=Zipper-Type;sequence=AAAATG
#> 4 TF=Arnt;class=Zipper-Type;sequence=CAAGAG
#> 5 TF=Arnt;class=Zipper-Type;sequence=AACAAG
#> 6 TF=Arnt;class=Zipper-Type;sequence=CAAGAG
head(writeGFF3(sitesetList))
#> seqname source feature start end score strand frame
#> MA0003.2 seq1 TFBS TFBS 18 32 -16.437682 - .
#> MA0004.1.1 seq1 TFBS TFBS 8 13 -1.888154 + .
#> MA0004.1.2 seq1 TFBS TFBS 21 26 -1.888154 + .
#> MA0004.1.3 seq1 TFBS TFBS 29 34 -3.908935 + .
#> MA0004.1.4 seq1 TFBS TFBS 8 13 -1.961403 - .
#> MA0004.1.5 seq1 TFBS TFBS 10 15 -3.908935 - .
#> attributes
#> MA0003.2 TF=TFAP2A;class=Zipper-Type;sequence=TTTTGTCAAGAGACT
#> MA0004.1.1 TF=Arnt;class=Zipper-Type;sequence=CTCTTG
#> MA0004.1.2 TF=Arnt;class=Zipper-Type;sequence=CTCTTG
#> MA0004.1.3 TF=Arnt;class=Zipper-Type;sequence=AAAATG
#> MA0004.1.4 TF=Arnt;class=Zipper-Type;sequence=CAAGAG
#> MA0004.1.5 TF=Arnt;class=Zipper-Type;sequence=AACAAG
head(writeGFF2(siteset))
#> seqname source feature start end score strand frame
#> 1 seq1 TFBS TFBS 8 13 -1.888154 + .
#> 2 seq1 TFBS TFBS 21 26 -1.888154 + .
#> 3 seq1 TFBS TFBS 29 34 -3.908935 + .
#> 4 seq1 TFBS TFBS 8 13 -1.961403 - .
#> 5 seq1 TFBS TFBS 10 15 -3.908935 - .
#> 6 seq1 TFBS TFBS 21 26 -1.961403 - .
#> attributes
#> 1 TF "Arnt"; class "Zipper-Type"; sequence "CTCTTG"
#> 2 TF "Arnt"; class "Zipper-Type"; sequence "CTCTTG"
#> 3 TF "Arnt"; class "Zipper-Type"; sequence "AAAATG"
#> 4 TF "Arnt"; class "Zipper-Type"; sequence "CAAGAG"
#> 5 TF "Arnt"; class "Zipper-Type"; sequence "AACAAG"
#> 6 TF "Arnt"; class "Zipper-Type"; sequence "CAAGAG"
## get the relative scores
relScore(siteset)
#> [1] 0.6652185 0.6652185 0.6141340 0.6633668 0.6141340 0.6633668
relScore(sitesetList)
#> $MA0003.2
#> [1] 0.6196884
#>
#> $MA0004.1
#> [1] 0.6652185 0.6652185 0.6141340 0.6633668 0.6141340 0.6633668
## calculate the empirical p-values of the scores
pvalues(siteset, type="TFMPvalue")
#> [1] 0.02734375 0.02734375 0.04638672 0.04052734 0.04638672 0.04052734
pvalues(siteset, type="sampling")
#> [1] 0.0296 0.0296 0.0617 0.0418 0.0617 0.0418searchAln
searchAln scans a pairwise alignment with the pattern represented by the PWM. It reports only those hits that are present in equivalent positions of both sequences and exceed a specified threshold score in both, AND are found in regions of the alignment above the specified.
library(Biostrings)
data(MA0003.2)
pwm <- toPWM(MA0003.2)
aln1 <- DNAString("ACTTCACCAGCTCCCTGGCGGTAAGTTGATC---AAAGG---AAACGCAAAGTTTTCAAG")
aln2 <- DNAString("GTTTCACTACTTCCTTTCGGGTAAGTAAATATATAAATATATAAAAATATAATTTTCATC")
sitePairSet <- searchAln(pwm, aln1, aln2, seqname1="seq1", seqname2="seq2",
min.score="50%", cutoff=0.5,
strand="*", type="any")
## generate gff style output
head(writeGFF3(sitePairSet))
#> seqname source feature start end score strand frame
#> 1 seq1 TFBS TFBS 6 20 -9.515444 + .
#> 2 seq1 TFBS TFBS 7 21 -13.348617 + .
#> 3 seq1 TFBS TFBS 8 22 -13.182322 + .
#> 4 seq1 TFBS TFBS 9 23 -3.729917 + .
#> 5 seq1 TFBS TFBS 10 24 -7.677850 + .
#> 6 seq1 TFBS TFBS 14 28 -20.774619 + .
#> attributes
#> 1 TF=TFAP2A;class=Zipper-Type;sequence=ACCAGCTCCCTGGCG
#> 2 TF=TFAP2A;class=Zipper-Type;sequence=CCAGCTCCCTGGCGG
#> 3 TF=TFAP2A;class=Zipper-Type;sequence=CAGCTCCCTGGCGGT
#> 4 TF=TFAP2A;class=Zipper-Type;sequence=AGCTCCCTGGCGGTA
#> 5 TF=TFAP2A;class=Zipper-Type;sequence=GCTCCCTGGCGGTAA
#> 6 TF=TFAP2A;class=Zipper-Type;sequence=CCTGGCGGTAAGTTG
head(writeGFF2(sitePairSet))
#> seqname source feature start end score strand frame
#> 1 seq1 TFBS TFBS 6 20 -9.515444 + .
#> 2 seq1 TFBS TFBS 7 21 -13.348617 + .
#> 3 seq1 TFBS TFBS 8 22 -13.182322 + .
#> 4 seq1 TFBS TFBS 9 23 -3.729917 + .
#> 5 seq1 TFBS TFBS 10 24 -7.677850 + .
#> 6 seq1 TFBS TFBS 14 28 -20.774619 + .
#> attributes
#> 1 TF "TFAP2A"; class "Zipper-Type"; sequence "ACCAGCTCCCTGGCG"
#> 2 TF "TFAP2A"; class "Zipper-Type"; sequence "CCAGCTCCCTGGCGG"
#> 3 TF "TFAP2A"; class "Zipper-Type"; sequence "CAGCTCCCTGGCGGT"
#> 4 TF "TFAP2A"; class "Zipper-Type"; sequence "AGCTCCCTGGCGGTA"
#> 5 TF "TFAP2A"; class "Zipper-Type"; sequence "GCTCCCTGGCGGTAA"
#> 6 TF "TFAP2A"; class "Zipper-Type"; sequence "CCTGGCGGTAAGTTG"
## search the Axt alignment
# library(CNEr)
# axtFilesHg19DanRer7 <- file.path(system.file("extdata", package="TFBSTools"),
# "hg19.danRer7.net.axt")
# axtHg19DanRer7 <- readAxt(axtFilesHg19DanRer7)
# sitePairSet <- searchAln(pwm, axtHg19DanRer7, min.score="80%",
# windowSize=51L, cutoff=0.7, strand="*",
# type="any", conservation=NULL, mc.cores=1)
# GRangesTFBS <- toGRangesList(sitePairSet, axtHg19DanRer7)
# GRangesTFBS$targetTFBS
# GRangesTFBS$queryTFBSsearchPairBSgenome
searchPairBSgenome is designed to do the genome-wise phylogenetic footprinting. Given two BSgenome, a chain file for liftover from one genome to another,searchPairBSgenome identifies the putative transcription factor binding sites which are conserved in both genomes.
library(rtracklayer)
library(JASPAR2014)
library(BSgenome.Hsapiens.UCSC.hg19)
library(BSgenome.Mmusculus.UCSC.mm10)
pfm <- getMatrixByID(JASPAR2014, ID="MA0004.1")
pwm <- toPWM(pfm)
chain <- import.chain("Downloads/hg19ToMm10.over.chain")
sitePairSet <- searchPairBSgenome(pwm, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10,
chr1="chr1", chr2="chr1",
min.score="90%", strand="+", chain=chain)Use de novo motif discovery software
In this section, we will introduce wrapper functions for external motif discovery programs. So far, MEME is supported. ## MEME runMEME takes a DNAStringSet or a set of characters as input, and returns a MotifSet object.
motifSet <- runMEME(file.path(system.file("extdata",
package="TFBSTools"), "crp0.s"),
binary="meme",
arguments=list("-nmotifs"=3)
)
## Get the sites sequences and surrounding sequences
sitesSeq(motifSet, type="all")
## Get the sites sequences only
sitesSeq(motifSet, type="none")
consensusMatrix(motifSet)Session info
Here is the output of sessionInfo() on the system on which this document was compiled:
#> R version 4.5.0 RC (2025-04-04 r88126)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] JASPAR2014_1.43.0 Biostrings_2.76.0 GenomeInfoDb_1.44.0
#> [4] XVector_0.48.0 IRanges_2.42.0 S4Vectors_0.46.0
#> [7] BiocGenerics_0.54.0 generics_0.1.3 TFBSTools_1.46.0
#> [10] BiocStyle_2.36.0
#>
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Bailey, T L, and C Elkan. 1994. “Fitting a Mixture Model by Expectation Maximization to Discover Motifs in Biopolymers.” Proc Int Conf Intell Syst Mol Biol 2: 28–36.
Bryne, Jan Christian, Eivind Valen, Man-Hung Eric Tang, Troels Marstrand, Ole Winther, Isabelle da Piedade, Anders Krogh, Boris Lenhard, and Albin Sandelin. 2008. “JASPAR, the Open Access Database of Transcription Factor-Binding Profiles: New Content and Tools in the 2008 Update.” Nucleic Acids Res. 36 (Database issue): D102–106. https://doi.org/10.1093/nar/gkm955.
Lenhard, Boris, and Wyeth W Wasserman. 2002. “TFBS: Computational Framework for Transcription Factor Binding Site Analysis.” Bioinformatics 18 (8): 1135–6.
Linhart, Chaim, Yonit Halperin, and Ron Shamir. 2008. “Transcription Factor and microRNA Motif Discovery: The Amadeus Platform and a Compendium of Metazoan Target Sets.” Genome Res. 18 (7): 1180–9. https://doi.org/10.1101/gr.076117.108.
Mathelier, Anthony, and Wyeth W Wasserman. 2013. “The Next Generation of Transcription Factor Binding Site Prediction.” PLoS Comput. Biol. 9 (9): e1003214. https://doi.org/10.1371/journal.pcbi.1003214.
Nishida, Keishin, Martin C Frith, and Kenta Nakai. 2009. “Pseudocounts for Transcription Factor Binding Sites.” Nucleic Acids Res. 37 (3): 939–44. https://doi.org/10.1093/nar/gkn1019.
Sandelin, Albin, Annette Höglund, Boris Lenhard, and Wyeth W Wasserman. 2003. “Integrated Analysis of Yeast Regulatory Sequences for Biologically Linked Clusters of Genes.” Funct. Integr. Genomics 3 (3): 125–34. https://doi.org/10.1007/s10142-003-0086-6.
Schneider, T D, G D Stormo, L Gold, and A Ehrenfeucht. 1986. “Information Content of Binding Sites on Nucleotide Sequences.” J. Mol. Biol. 188 (3): 415–31.
Wasserman, Wyeth W, and Albin Sandelin. 2004. “Applied bioinformatics for the identification of regulatory elements.” Nature Publishing Group 5 (4): 276–87.