Regulatory Sequence Analysis Tools

By Jacques van Helden. Nucleic Acids Res., 2003

697 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ffe93c36533989d0b78ea6ef7e6a7c57cb34eb1a

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In Molecular Biology

Citations in the corpus

Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering (2002, methodology): “Genes whose promoters contained significant matches to the identified matrices were identified using Patser on the RSA tools website [68,69].”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, methodology): “Epigenomics-ChIP seq Removal of PCR duplicates Samtools [107] Peak calling MACS2 [141], SICER2 [142], SPP [143] Peak filtering Bedtools [144] Enrichment quality control ChipQC [145], Phantompeakqualtools [146] Enrichment comparison diffBind [147], MAnorm [148], MMDiff [149] Motif analysis MemeCHiP [”
The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae (2005, methodology): “To support the analysis of the promoter sequences in the yeast genome, a set of software tools is provided by Regulatory Sequences Analysis Tools (RSAT) (5).”
RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond (2015, background): “coli K-12 MG1655 uid57779 genes, where upstream regions are defined as −400 bp upstream to 50 bp downstream from the start codon (16,17).”
HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis (2017, methodology): “Major motif analysis software (14,45) include methods for motif finding, i.”
Three Arabidopsis SnRK2 protein kinases, SRK2D/SnRK2.2, SRK2E/SnRK2.6/OST1 and SRK2I/SnRK2.3, involved in ABA signaling are essential for the control of seed development and dormancy. (2009, methodology): “…promoter elements were determined as described by Wohlbach et al. (2008) using the oligo-analysis program of the RSA Tools ( http://rsat.ulb.ac.be/rsat/ , van Helden 2003 ), with pre-defi ned background frequencies as implemented on the website. a P -values and E -values were computed as defi…”
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “All kernels are converted to sequence logos and aligned with RSAT (Medina-Rivera et al., 2015).”