Introduction

Many kinds of biological activity are regulated by the binding of proteins to their cognate substrates. Of particular interest is the sequence-specific binding of transcription factors to DNA, often in regulatory regions just upstream of the transcription start site of a gene. These binding events play a pivotal role in regulating gene expression.

Sequence specificity among closely related binding sites is nearly always incomplete: some variety in the DNA sequence is routinely observed. For this reason, these inexact binding sequence patterns are commonly described as motifs, represented numerically as frequency matrices, and visualized as sequence logos.

Despite their importance in current research, there has been until now, to the best of our knowledge, no single, annotated, comprehensive collection of publicly available motifs. The current package attempts to provide such a collection, offering more than ten thousand annotated matrices from multiple organisms, within the context of the Bioconductor project. The matrices can be filtered and selected on the basis of their metadata, used with other Bioconductor packages (for instance, seqLogo can be used for for visualization) or easily exported for use with standard software and websites such as those provided by the MEME Suite.

Transcription factor binding sites (TFBS) can only be imperfectly predicted from sequence matching of motif to DNA sequence. When using MotifDb, please keep in mind that actual and functional TF binding occurs under the influence of many factors:

• Chromatin accessibility
• An appropriate balance of activating and repressing epigenetic influences
• The presence of the the transcription factor protein in adequate numbers
• “DNA shape” to complement or substitute for DNA sequence match
• Cooperativity with other DNA-binding factors, or co-factor interaction

Still, motif-matching to DNA sequence plays an important role in identifying gene regulatory events.

Quick Start

CTCF is a DNA-binding factor regulating the 3D structure of chromatin¹. What is its binding motif?

The MotifDb query function performs a broad, case-neutral text search through all the metadata (all the annotation) for all of the motifs. It returns a list of matching motifs. More information about the metadata is provided below.

We begin with a simple search which retrieves all motifs annotated with the case-neutral string “ctcf”.

library(MotifDb)
query(MotifDb, "ctcf")

## MotifDb object of length 24
## | Created from downloaded public sources, last update: 2022-Mar-04
## | 24 position frequency matrices from 10 sources:
## |        HOCOMOCOv10:    3
## | HOCOMOCOv11-core-A:    2
## |              HOMER:    3
## |        JASPAR_2014:    2
## |        JASPAR_CORE:    1
## |       SwissRegulon:    2
## |         jaspar2016:    2
## |         jaspar2018:    3
## |         jaspar2022:    5
## |          jolma2013:    1
## | 4 organism/s
## |           Hsapiens:   16
## |      Dmelanogaster:    4
## |          Mmusculus:    1
## |              other:    3
## Hsapiens-SwissRegulon-CTCFL.SwissRegulon 
## Hsapiens-SwissRegulon-CTCF.SwissRegulon 
## Hsapiens-HOCOMOCOv10-CTCFL_HUMAN.H10MO.A 
## Hsapiens-HOCOMOCOv10-CTCF_HUMAN.H10MO.A 
## Mmusculus-HOCOMOCOv10-CTCF_MOUSE.H10MO.A 
## ...
## Dmelanogaster-jaspar2022-CTCF-MA0531.1 
## Hsapiens-jaspar2022-CTCFL-MA1102.2 
## Hsapiens-jaspar2022-CTCF-MA1929.1 
## Hsapiens-jaspar2022-CTCF-MA1930.1 
## Hsapiens-jolma2013-CTCF

Let us refine the search, looking only for human Jaspar 2018, or HOCOMOCO v11 core motifs, category “A”. Eliminate “CTCFL”.

library(MotifDb)
motifs <- query(MotifDb, andStrings=c("CTCF", "hsapiens"),
                orStrings=c("jaspar2018", "hocomocov11-core-A"),
                notStrings="ctcfl")
length(motifs)

## [1] 2

Motifs from different sources sometimes agree and sometimes differ. Analytical methods for comparison exist, of which two are

* Bioconductor package [DiffLogo](https://bioconductor.org/packages/release/bioc/html/DiffLogo.html)
* Meme Suites [Tomtom](http://meme-suite.org/doc/tomtom.html)

The Biostrings function consensusString provides a quick and sometimes adequate comparison. In this case, this reveals that the two motifs are nearly identical:

sapply(motifs, consensusString)

##                Hsapiens-jaspar2018-CTCF-MA0139.1 
##                            "T??CCAC?AGGGGGCGC??" 
## Hsapiens-HOCOMOCOv11-core-A-CTCF_HUMAN.H11MO.0.A 
##                            "?GGCCACCAGGGGGCGC??"

We can also inspect the similarity visually, using the Bioconductor package seqLogo

library(seqLogo)
seqLogo(motifs[[1]])  # Hsapiens-jaspar2018-CTCF-MA0139.1
seqLogo(motifs[[2]])  # Hsapiens-HOCOMOCOv11-core-A-CTCF_HUMAN.H11MO.0.A

Beware of False Precision

Though we cannot offer published, peer-reviewed support for this cautionary warning, we urge you to consider it and its implications.

One is tempted to regard curated motif matrices from respected sources as a reliable guide to TF/DNA binding potential. A common strategy is to match motif against sequence, retaining only matches above a certain threshold fidelity: for instance a minScore for Biostrings::matchPWM, or a p-value or q-value threshold for FIMO.

We explored this topic (unpublished data) using recent high-quality CTCF ChIP-seq and FIMO, for which the default p-value sequence match threshold is 1e-4. This scatterplot shows that high-scoring ChIP-seq hits sometimes occur at binding sites where motif-match scores are low. We therefore suggest that motif-matching is most useful in conjuction with other information, for instance open chromatin from highly-resovled experiments (scATAC-seq), DNAse footprinting, epigenetic markers, and correlated tissue-specific, or cell-type specific gene and TF protein expression.

References