An algorithm for finding signals of unknown length in DNA sequences

By Giulio Pavesi, Giancarlo Mauri, Graziano Pesole. Intelligent Systems in Molecular Biology, 2001

387 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/df04ac14526dce6f2037ca96f6b2b5ce1c6f5452

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Citations in the corpus

A survey of DNA motif finding algorithms (2007, background): “[34] and Eskin and Pevzner [35] respectively, are also based on the suffix tree and its variant.”
High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints (2012, methodology): “4.2 [21], POSMO v2 [24], HMS v0.”
Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes (2004, methodology): “However, as demonstrated in (2,12), the exhaustive search formotifs can be significantly accelerated if the input sequences are preprocessed and organized in a suitable indexing structure, such as a suffix tree (13), especially when their lengths fall within theusual rangeofTFBSs.”
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps (2015, methodology): “[33, 34]), and methods like Weeder and MITRA organized efficient searches for enriched k-mers and composite k-mer patterns, respectively, using traversal of suffix tree or retrieval tree data structures to count occurrences of k-mer occurrences with inexact matches [35, 36].”
Finding composite regulatory patterns in DNA sequences (2002, background): “Sagot (1998) and Pavesi et al. (2001) resurrected the Waterman et al. (1984) idea with a new approach that eliminates its excessive memory requirements.”
Global transcriptome analysis reveals circadian regulation of key pathways in plant growth and development (2008, methodology): “Analysis of circadian clock regulatory elements We employed four different strategies to identify potential motifs of interest: a trio of established motif discovery tools (stand-alone versions of AlignACE v2004 [88,89], Weeder v1.2 [ 90 ,91], and MotifSampler v3.2 [92,93]) and an exhaustive in sili”
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, neutral): “The string-based approaches rely on the detection of statistically over-represented words (oligonucleotides or spaced motifs) compared to a given background [83–88].”
Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. (2008, methodology): “This indicates that Oct4 is pivotal in stabilizing the nucleoprotein complex and estab- Matrices predicted by the de novo motif-discovery algorithm Weeder. lishes a hierarchy of regulatory interactions between Oct4, STAT3, and Smad1.”
Genome-wide prediction of transcription factor binding sites using an integrated model (2010, result): “…the motifs identified by MEME [46], all position specific frequency matrices (PSFMs) (Figure S8 and Table S1 in Additional file 2) were similar to those documented in the TRANSFAC database [22] and those reported in [32] by running the motif finding algorithms Weeder [47] or NestedMICA [48].”
High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions (2010, methodology): “Weeder performs an exhaustive search for the most overrepresented k-mer patterns for a given specified size k.”
Mismatch String Kernels for SVM Protein Classification (2002, methodology): “We use a mismatch tree data structure (similar to a trie or suffix tree [16, 17]) to represent the feature space (the set of all -mers) and perform a lexical traversal of all -mers occurring in the sample dataset match with up to of mismatches; the entire kernel matrix”