Quantifying similarity between motifs

By Shobhit Gupta, John A. Stamatoyannopoulos, Timothy L. Bailey, William Stafford Noble. Genome Biology, 2007

1,930 citations recorded by the source corpus.

A common question within the context of de novo motif discovery is whether a newly discovered, putative motif resembles any previously discovered motif in an existing database. To answer this question, we define a statistical measure of motif-motif similarity, and we describe an algorithm, called Tomtom, for searching a database of motifs with a given query motif. Experimental simulations demonstrate the accuracy of Tomtom's E values and its effectiveness in finding similar motifs.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/474afa1564971a8e6e7c4bf4de43522f175a0ff1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

MEME Suite: tools for motif discovery and searching (2009, methodology): “TOMTOM (4) quantifies the similarity between two motifs, and can be used to search a database of known motifs for matches to motifs found by MEME.”
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences (2015, methodology): “Then, we aligned these motifs to known motifs using the TOMTOM algorithm (26).”
STREME: Accurate and versatile sequence motif discovery (2020, methodology): “We evaluate the accuracy of motif discovery algorithms by comparing their predicted motifs to the known motif for the transcription factor us-ing the Tomtom motif comparison algorithm [4].”
Impacts of the ubiquitous factor Zelda on Bicoid-dependent DNA binding and transcription in Drosophila (2014, methodology): “Identified motifs were compared with the All Drosophila Motif Database using TOMTOM (Gupta et al. 2007).”
PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants (2015, neutral): “0 by using the Tomtom program (25).”
DeepEnhancer: Predicting enhancers by convolutional neural networks (2016, methodology): “In order to compute the similarity of our motifs, we use a tool called TOMTOM with predefined statistical measure of motif-motif similarity [32], [33].”
Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors (2012, methodology): “We compared the discovered motifs with annotated motifs in the JASPAR (Bryne et al. 2008) and TRANSFAC (Matys et al. 2003) databases using TOMTOM (Gupta et al. 2007).”
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, methodology): “[43,44], and motif-motif edges as defined by motif similarity (using Tomtom [45]).”
Exploiting sequence-based features for predicting enhancer–promoter interactions (2017, methodology): “Motifs with signiﬁcantly similar PWMs were connected in the graph, using a threshold of E -value of 10 − 3 in measuring the similarity conﬁdence with results from TomTom.”
A ChIP-seq defined genome-wide map of vitamin D receptor binding: associations with disease and evolution. (2010, methodology): “We found three other motifs that showed similarity (P < 10 5 using TOMTOM (Gupta et al. 2007) to the DR3 motif.”
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features (2014, methodology): “The 10-mers used for creating the 1st PWM were then removed from the list, and the process was repeated on the remaining predictive k-mers, to find up to three PWMs. Lastly, we matched our PWMs to the previously identified PWMs [18] using TOMTOM [36] software.”
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome (2020, methodology): “The final motif files were converted into Weblogo format and compared with JASPAR2018 validated motifs using TOMTOM command-line version (30).”
The MEME Suite (2015, methodology): “The web-based version of the MEME Suite includes 13 tools (1,2,3,4,5,6,7,8,9,10,11,12,13) for performing motif discovery, motif enrichment analysis, motif scanning and motif–motif comparisons (Figure 1).”