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# motifcounter - R package for analysing TFBSs in DNA sequences. [![Travis-CI Build Status](]( [![Coverage Status](]( This software package grew out of the work that I did to obtain my PhD. If it is of help for your analysis, please cite ``` @Manual{, title = {motifcounter: R package for analysing TFBSs in DNA sequences}, author = {Wolfgang Kopp}, year = {2017}, doi = {10.18129/B9.bioc.motifcounter} } ``` A detailed description of the compound Poisson model is available in ``` @article{improvedcompound, title={An improved compound Poisson model for the number of motif hits in DNA sequences}, author={Kopp, Wolfgang and Vingron, Martin}, journal={Bioinformatics}, pages={btx539}, year={2017}, publisher={Oxford University Press} } ``` ## Usage ``` # Estimate a background model on a set of sequences bg <- readBackground(sequences, order) # Normalize a given PFM new_motif <- normalizeMotif(motif) # Evaluate the scores along a given sequence scores <- scoreSequence(sequence, motif, bg) # Evaluate the motif hits along a given sequence hits <- motifHits(sequence, motif, bg) # Evaluate the average score profile score_profile <- scoreProfile(sequences, motif, bg) # Evaluate the average motif hit profile hit_profile <- motifHitProfile(sequences, motif, bg) # Compute the motif hit enrichment enrichment <- motifEnrichment(sequences, motif, bg) ``` ## Hallmarks of `motifcounter` The `motifcounter` package facilitates the analysis of transcription factor binding sites (TFBSs) in DNA sequences. It can be used to scan a set of DNA sequences for known motifs (e.g. from TRANSFAC or JASPAR) in order to determine the positions and enrichment of TFBSs in the sequences. Therefore, an analysis with `motifcounter` requires as input 1. a position frequency matrix (PFM) which represents the TF affinity towards the DNA 2. a background model, which is estimated from a given DNA sequence and which serves as a reference for the statistical analysis. 3. a desired false positive level, for identifying putative TFBSs in DNA sequences. For example, a reasonable choice would be to choose a false positive level such that only one in 1000 positions are called TFBSs falsely. 4. a given DNA sequence, which is subject to the TFBS analysis. The package aims to improve motif hit enrichment analysis. To this end, the package offers a number of features: 1. `motifcounter` supports the use of **higher-order Markov models** to account for the sequence composition in unbound DNA segments. This improves the reliability of the enrichment analysis, because higher-order sequence features occur commonly in natural DNA sequences (e.g. CpG islands). 2. The package automatically accounts for **self-overlapping** motif structures<sup><a href="#fn1" id="ref1">1</a></sup>. This aspect is important for reducing the false positives obtained from the enrichment test, which is prevalent for repeat-like and palindromic motifs. `motifcounter` not only determines self-overlapping motif hit occurrences on a single DNA strand, but (by default) also with respect to the reverse strand. ### Enrichment model `motifcounter` implements two analytic approximations of the *distribution of the number of motif hits* in random DNA sequences that can optionally be used for the enrichment test: 1. A *compound Poisson approximation* 2. A *combinatorial approximation* Both approximations yield highly accurate results for stringent false positive levels. Moreover, if you intend to analyse long DNA sequences or a large set of individual sequences (total sequence length >10kb), we recommend to use the *compound Poisson approximation*. On the other hand, we recommend the *combinatorial approximation* if a relaxed false positive level is prefered to identify TFBSs. ## Installation An easy way to install `motifcounter` is by facilitating the `devtools` R package. ```R #install.packages("devtools") library(devtools) install_github("wkopp/motifcounter", build_vignettes=TRUE) ``` Alternatively, the package can also be cloned or downloaded from this github-rep, built via `R CMD build` and installed via the `R CMD INSTALL` command. ## Getting started The `motifcounter` package contains a tutorial that illustrates: 1. how to determine position- and strand-specific TF motif binding sites, 2. how to analyse the profile of motif hit occurrences across a set of aligned sequences, and 3. how to test for motif enrichment in a given set of sequences. The tutorial can be found in the package-vignette: ```R library(motifcounter) vignette("motifcounter") ``` ## Acknowledgements Thanks to [matthuska]( for reviewing and commenting on the package. <hr></hr> <sup id="fn1">1: Self-overlapping motifs induce **clumps of motif hits** (that is, mutually overlapping motif hits) when a DNA sequence is scanned for hits. As a consequence of **motif clumping**, the distribution of the number of motif hits, and thus, the enrichment test are affected.<a href="#ref1" title="Jump back to footnote 1 in the text.">↩</a></sup>