Applied bioinformatics for the identification of regulatory elements

By Wyeth W. Wasserman, Albin Sandelin. Nature reviews genetics, 2004

1,268 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development (2004, positive): “The completion of a number of vertebrate genome sequences [5,6,7,8,9], as well as the concurrent development of genomic alignment, visualisation, and analytical bioinformatics tools (for an overview see [10]), has made large”
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update (2007, background): “An important problem with genome-wide scanning with matrix models is the limited information content in a typical matrix, resulting in numerous spurious hits just due to sequence background (1,2).”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, background): “Analysis of TFBSs for a given TF provides models for its specific DNAbinding preferences, which in turn can be used to predict TFBSs in DNA sequences (4).”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, background): “Analysis of TFBSs for a given TF provides models for its specific DNA-binding preferences, which in turn can be used to predict TFBSs in DNA sequences (4).”
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).”
ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs (2014, methodology): “For example, we only know the sequence specificity of less than half (Matys et al. 2003; Wasserman and Sandelin 2004; Pachkov et al. 2007) of the ; 1500 (Vaquerizas et al. 2009) TFs in mammalian genomes.”
Natural similarity measures between position frequency matrices with an application to clustering (2008, background): “Detection of binding sites is a crucial task in deciphering gene regulatory networks (Wasserman and Sandelin, 2004).”
Global mapping of binding sites for Nrf2 identifies novel targets in cell survival response through ChIP-Seq profiling and network analysis (2010, background): “Binding site predictions suffer from high rates of false positives (46), as evident in the 7% of control peaks containing an Nrf2 profile match with a score 0.”
JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles (2013, background): “From a set of known transcription factor binding sites (TFBSs) for a given TF, the binding preference is generally represented in the form of a position weight matrix (PWM) (also called position-specific scoring matrix) derived from a position frequency matrix (PFM).”
Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, methodology): “The use of computational methods to detect the locations of promoters and enhancers has been a key focus of bioinformatics for twenty years (see reviews [6, 7]).”
Promoter features related to tissue specificity as measured by Shannon entropy (2005, background): “Investigators have searched for combinations of transcription-factor-binding sites that confer tissue-specific expression on particular cell types such as muscle [9] or liver [10] in mammals, or in body plan specification in the fruit fly [11,12] (see [13] for a review).”
The Human Transcription Factors. (2018, neutral): “As a result, decoding how gene regulation relates to TF-bindingmotifs and gene sequences remains a major practical challenge; the resulting frustration has been embodied in the term ‘‘futility theorem’’ (Wasserman and Sandelin, 2004).”