In vivo enhancer analysis of human conserved non-coding sequences

By Len A. Pennacchio, N. Ahituv, Alan M. Moses, Shyam Prabhakar, Marcelo Araujo da Nóbrega, Malak Shoukry, Simon Minovitsky, Inna Dubchak, Amy Holt, K. Lewis and 9 more. Nature, 2006

1,219 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eee57a35ccb97b0719c5f0d900875ee6fcad6fab

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Citations in the corpus

Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation. (2008, background): “2004), VISTA ID290 (Pennacchio et al. 2006), and m1561 (Zerucha et al.”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “Among those sequences, the non-coding DNA functionality determination is especially challenging, since over 98% of human genome are non-coding DNA and it is very difficult to perform biological experiment to investigate functionality of every non-coding DNA sequence chunk [113].”
GeneHancer: genome-wide integration of enhancers and target genes in GeneCards (2017, background): “The most direct approaches involve enhancer reporter assays, directed, for example, at non-coding DNA segments that show high interspecies sequence conservation (14, 15).”
The Genetic Signatures of Noncoding RNAs (2009, background): “These sequences range from promoter regions that have no recognizable sequence similarity yet direct orthologous patterns of gene expression between fishes and mammals [76] to highly conserved non-genic elements [77,78], and ‘‘ultraconserved’’ sequences that have remained essentially unchanged over ”
High-Throughput Genomic Data in Systematics and Phylogenetics (2013, background): “UCEs are thought to be involved in gene regulation and development (e.g., Woolfe et al. 2005, Pennacchio et al. 2006) and to experience strong purifying selection (e.g., Katzman et al. 2007).”
Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data (2011, neutral): “These include many examples of constraint-pre-dicted enhancers with crucial roles in embryonic development 64,77 and more general correlations between non-coding molecular functionality and evolutionary constraint 19,78 .”
A Mammalian enhancer trap resource for discovering and manipulating neuronal cell types (2016, background): “Cell-type-specific patterns of gene expression are thought to reflect interactions between regulatory sequences within the proximal promoter, and at other far more distal sites (Nathanson et al., 2009; Pennacchio et al., 2006).”
Identification and characterization of enhancers controlling the inflammatory gene expression program in macrophages. (2010, background): “…approaches to enhancer mapping successfully led to the identification of many functional enhancers, which included several ultraconserved elements (Pennacchio et al., 2006; Visel et al., 2008), namely short extragenic regions identical between the human and rodent genomes (Bejerano et al., 2004;…”
Genome-scale functional characterization of Drosophila developmental enhancers in vivo (2014, neutral): “Thissuggeststhatourunder- standingofregulatorysequenceswillbenefitfromtheongoingfunc-tionalcharacterizationofenhancers 4–7 .”
Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription (2013, result): “These findings are consistent with quantitatively important roles of enhancers in signal-dependent transcriptional responses, as well as evolutionary conservation of enhancer elements (Ghisletti et al., 2010; Pennacchio et al., 2006; Woolfe et al., 2005).”
The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016, methodology): “Comparative genomic methods have been applied by several groups (Lindblad-Tohet al., 2011;Margulies et al., 2007; Pennacchio et al., 2006;Pollard et al., 2006;Prabhakar et al., 2006;Siepel et al., 2005) to identify putative conserved CREs or regulatory RNAs, a disproportionate number of which are pr”
PEDLA: predicting enhancers with a deep learning-based algorithmic framework (2016, methodology): “These approaches involve mapping the binding sites of enhancer indicative transcription factors (TFs) (26-29) or cofactors (30-32) by ChIP-Seq, identifying open chromatin with techniques such as DNase-Seq (33-35), and interrogating covalent histone modifications by ChIP-Seq (7,12,36,37).”