Assessing computational tools for the discovery of transcription factor binding sites

By Martin Tompa, Nan Li, Timothy L. Bailey, George M. Church, B. De Moor, Eleazar Eskin, A. Favorov, Martin C. Frith, Yutao Fu, W. James Kent and 15 more. Nature Biotechnology, 2005

1,411 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2de184d273e23749dbb33fceb5c2ac671077d673

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Citations in the corpus

ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data (2012, background): “The major limitation in identifying the transcriptional regulatory relationships of lncRNAs and miRNAs has been the high false-positive rates of predictive algorithms for transcription factor binding sites (TFBSs) (9).”
Non-coding nuclear DNA markers in phylogenetic reconstruction (2009, background): “Determining the presence of transcription factor binding sites also is not a straightforward procedure, since their sequences are highly diverse (Qiu 2003; Rombauts et al. 2003; Pavesi et al. 2004; Wasserman and Sandelin 2004; Tompa et al. 2005).”
Identifying DNA sequences recognized by a transcription factor using a bacterial one-hybrid system (2006, background): “Many other algorithms are also available for motif identificatio”
An improved map of conserved regulatory sites for Saccharomyces cerevisiae (2006, background): “Tompa and co-workers [10], who recently assessed a number of motif discovery programs, demonstrated that these algorithms perform much better on synthetic data than on real data.”
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, methodology): “Most of these methods performed well on yeast or bacterial promoter sequences, but they showed limited performances when applied to mouse or human [96].”
Transcriptional regulatory elements in the human genome. (2006, neutral): “The plethora of programs available can be overwhelming; to this end, the field is becoming more self-critical and finding ways to evaluate and compare the performance of such programs (181).”
The DIURNAL project: DIURNAL and circadian expression profiling, model-based pattern matching, and promoter analysis. (2007, background): “A number of algorithms have been developed to identify known and putative regulatory elements in the promoter sequences of coregulated genes (for review, see Rombauts et al. 2003; Tompa et al. 2005).”
MEME: discovering and analyzing DNA and protein sequence motifs (2006, background): “The problem tends to be worse in eukaryotes than in prokaryotes and yeast because eukaryotic TFBS tend to be shorter and more variable [5].”
NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction (2009, methodology): “We defined a residue-level correlation coefficient (analogous to nucleotide level correlation coefficient [12,13] for DNA motifs) and found that our model yielded a coefficient of 0.36.”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, neutral): “Computational approaches to predict physical interactions can partly circumvent bias (TABLE 1) , but they do not address tissue specificity, and there may be relatively low reproducibility across different methods 58,59 .”
Discovering Motifs in Ranked Lists of DNA Sequences (2007, methodology): “Several excellent reviews narrate the different strategies for motif detection and use quantitative benchmarking to compare their performance [14–18].”
Quantifying similarity between motifs (2007, neutral): “A wide variety of statistical methods have been developed to identify sequence motifs in an unsupervised manner from collections of functionally related sequences [3].”
A survey of DNA motif finding algorithms (2007, methodology): “[57] that biologists should use a few complementary tools in combination rather than relying on a single one and pursue the top few predicted motifs of each rather than the single most significant motif, which also reflects the success of Hu et al .”