Fast, Sensitive Discovery of Conserved Genome-Wide Motifs

Abstract

Regulatory sites that control gene expression are essential to the proper functioning of cells, and identifying them is critical for modeling regulatory networks. We have developed Magma (Multiple Aligner of Genomic Multiple Alignments), a software tool for multiple species, multiple gene motif discovery. Magma identifies putative regulatory sites that are conserved across multiple species and occur near multiple genes throughout a reference genome. Magma takes as input multiple alignments that can include gaps. It uses efficient clustering methods that make it about 70 times faster than PhyloNet, a previous program for this task, with slightly greater sensitivity. We ran Magma on all non-coding DNA conserved between Caenorhabditis elegans and five additional species, about 70 Mbp in total, in < 4 h. We obtained 2,309 motifs with lengths of 6-20 bp, each occurring at least 10 times throughout the genome, which collectively covered about 566 kbp of the genomes, approximately 0.8% of the input. Predicted sites occurred in all types of non-coding sequence but were especially enriched in the promoter regions. Comparisons to several experimental datasets show that Magma motifs correspond to a variety of known regulatory motifs.

Innate Host Defense Requires TFEB-Mediated Transcription of Cytoprotective and Antimicrobial Genes

“…To identify such factor(s), we examined which transcription factor binding sites were over-represented in the promoters of S. aureus- induced C. elegans genes. Using the software MAGMA (Ihuegbu et al, 2012), we analyzed the transcription start site (TSS)-proximal upstream 2 kb, and detected over-representation of the E-box DNA motif (CACGTG, P<0.001) (Fig. 1A).…”

“…249 genes were identified whose probe intensity were higher than expected by chance ( 15, p < 0.01). In parallel, we discovered a catalog of 2,309 potentially functional DNA motifs with MAGMA using its default parameters (Ihuegbu et al, 2012). MAGMA uses C. elegans as the reference genome and compares segments of the genome that are conserved across five other nematode species, to identify motifs within the reference genome that occur in intergenic, intronic, and 5′ and 3′ untranslated regions of annotated genes (Ihuegbu et al, 2012).…”

“…In parallel, we discovered a catalog of 2,309 potentially functional DNA motifs with MAGMA using its default parameters (Ihuegbu et al, 2012). MAGMA uses C. elegans as the reference genome and compares segments of the genome that are conserved across five other nematode species, to identify motifs within the reference genome that occur in intergenic, intronic, and 5′ and 3′ untranslated regions of annotated genes (Ihuegbu et al, 2012). To narrow the search space, conserved sites for each motif (the “exemplar sites”) were associated with nearby genes using PeakAnalyzer (Salmon-Divon et al, 2010), defining 664 motifs that were associated with at least 20 genes and were present within 2 kb of their translation start sites.…”

Genome-wide discovery of active regulatory elements and transcription factor footprints in Caenorhabditis elegans using DNase-seq

“…We also identified novel motifs in noncoding DHSs for which there were no known functions. Some of these matched conserved DNA motifs found by two prior studies (Elemento and Tavazoie 2005;Ihuegbu et al 2012). We performed Gene Ontology (GO) and anatomy enrichment analysis on genes associated with these motifs to predict potential function ( Supplemental Tables S4, S5).…”

Mining Association Rules that Incorporate Transcription Factor Binding Sites and Gene Expression Patterns in C. elegans

“…Because the genome is relatively compact, regulatory sequences are often contained within relatively short non-coding promoter regions, which are often close to the regulated gene (The C. elegans Sequencing Consortium, 1998). A number of groups have used both computational and biological techniques to elicit TFBSs and regulatory networks (Arda and Walhout, 2010); (Bigelow et al, 2004); (Hobert et al, 2010); (Ihuegbu et al, 2012); (Newburger and Bulyk, 2009); (Reece-Hoyes et al, 2005). Much information related to cell type specific gene expression has been elucidated (Bamps and Hope, 2008); (Hunt-Newbury et al, 2007), and collected in curated databases (Hope Laboratory Expression Pattern Database); (WormBase).…”