Cross-species analysis of enhancer logic using deep learning
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L. Zon | S. Aerts | É. Cadieu | P. Karras | J. Marine | S. Makhzami | V. Christiaens | Gert Hulselmans | J. Wouters | G. Ghanem | I. I. Taskiran | D. Mauduit | Liesbeth Minnoye | Maurizio Fazio | Linde Van Aerschot | M. Seltenhammer | A. Primot | E. van Rooijen | G. Egidy | Panagiotis Karras | Gert J. Hulselmans | Ellen van Rooijen
[1] J. R. Fresco,et al. Nucleotide Sequence , 2020, Definitions.
[2] R. Tjian,et al. The promoter-specific transcription factor Sp1 binds to upstream sequences in the SV40 early promoter , 1983, Cell.
[3] B. Crombrugghe,et al. Role of the CCAAT-binding protein CBF/NF-Y in transcription. , 1998, Trends in biochemical sciences.
[4] Kelvin H. Lee,et al. Genomic analysis. , 2000, Current opinion in biotechnology.
[5] T. Graves,et al. Surveying Saccharomyces genomes to identify functional elements by comparative DNA sequence analysis. , 2001, Genome research.
[6] E. V. van Donselaar,et al. The Melanocytic Protein Melan‐A/MART‐1 Has a Subcellular Localization Distinct from Typical Melanosomal Proteins , 2002, Traffic.
[7] A. Clark,et al. Evolution of transcription factor binding sites in Mammalian gene regulatory regions: conservation and turnover. , 2002, Molecular biology and evolution.
[8] Martin C. Frith,et al. Cluster-Buster: finding dense clusters of motifs in DNA sequences , 2003, Nucleic Acids Res..
[9] G. Egidy,et al. Establishment and characterization of a normal melanocyte cell line derived from pig skin. , 2003, Pigment cell research.
[10] Bart De Moor,et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis , 2005, Bioinform..
[11] D. Haussler,et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. , 2005, Genome research.
[12] Lothar Reichel,et al. Augmented Implicitly Restarted Lanczos Bidiagonalization Methods , 2005, SIAM J. Sci. Comput..
[13] William Stafford Noble,et al. Quantifying similarity between motifs , 2007, Genome Biology.
[14] D. Schadendorf,et al. Metastatic potential of melanomas defined by specific gene expression profiles with no BRAF signature. , 2006, Pigment cell research.
[15] E. Ukkonen,et al. Genome-wide Prediction of Mammalian Enhancers Based on Analysis of Transcription-Factor Binding Affinity , 2006, Cell.
[16] Colin N. Dewey,et al. Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures , 2007, Nature.
[17] X. Sastre-Garau,et al. Transcription analysis in the MeLiM swine model identifies RACK1 as a potential marker of malignancy for human melanocytic proliferation , 2008, Molecular Cancer.
[18] M. Grabherr,et al. A cis-acting regulatory mutation causes premature hair graying and susceptibility to melanoma in the horse , 2008, Nature Genetics.
[19] S. Barolo,et al. Reverse-engineering a transcriptional enhancer: a case study in Drosophila. , 2008, Tissue Engineering. Part A.
[20] F. Rambow,et al. Identification of differentially expressed genes in spontaneously regressing melanoma using the MeLiM Swine Model , 2008, Pigment cell & melanoma research.
[21] R. Dummer,et al. In vivo switching of human melanoma cells between proliferative and invasive states. , 2008, Cancer research.
[22] L. Zon,et al. Transparent adult zebrafish as a tool for in vivo transplantation analysis. , 2008, Cell stem cell.
[23] M. Levine,et al. Shadow Enhancers as a Source of Evolutionary Novelty , 2008, Science.
[24] Irene K. Moore,et al. The DNA-encoded nucleosome organization of a eukaryotic genome , 2009, Nature.
[25] R. DePinho,et al. BRafV600E cooperates with Pten silencing to elicit metastatic melanoma , 2009, Nature Genetics.
[26] Gonçalo R. Abecasis,et al. The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..
[27] Mikael Bodén,et al. MEME Suite: tools for motif discovery and searching , 2009, Nucleic Acids Res..
[28] Pavel Tomancak,et al. An alignment-free method to identify candidate orthologous enhancers in multiple Drosophila genomes , 2010, Bioinform..
[29] K. Pollard,et al. Detection of nonneutral substitution rates on mammalian phylogenies. , 2010, Genome research.
[30] Cory Y. McLean,et al. GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.
[31] C. Glass,et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. , 2010, Molecular cell.
[32] Aaron R. Quinlan,et al. BIOINFORMATICS APPLICATIONS NOTE , 2022 .
[33] G. Crawford,et al. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. , 2010, Cold Spring Harbor protocols.
[34] R. Young,et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state , 2010, Proceedings of the National Academy of Sciences.
[35] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[36] David A. Orlando,et al. The histone methyltransferase SETDB1 is recurrently amplified in melanoma and accelerates its onset , 2011, Nature.
[37] Albert J. Vilella,et al. A high-resolution map of human evolutionary constraint using 29 mammals , 2011, Nature.
[38] Helge G. Roider,et al. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs , 2011, Nature Protocols.
[39] Charles Y. Lin,et al. DHODH modulates transcriptional elongation in the neural crest and melanoma , 2011, Nature.
[40] J. Carroll,et al. Pioneer transcription factors: establishing competence for gene expression. , 2011, Genes & development.
[41] L. Andersson,et al. Identification of a melanocyte‐specific, microphthalmia‐associated transcription factor‐dependent regulatory element in the intronic duplication causing hair greying and melanoma in horses , 2012, Pigment cell & melanoma research.
[42] S. Aerts,et al. i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules , 2012, Nucleic acids research.
[43] Steven L Salzberg,et al. Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.
[44] Hubing Shi,et al. MDM4 is a key therapeutic target in cutaneous melanoma , 2012, Nature Medicine.
[45] J. van Helden,et al. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets , 2011, Nucleic acids research.
[46] D. Bernstein,et al. SERPINE1 expression discriminates site‐specific metastasis in human melanoma , 2012, Experimental dermatology.
[47] Mary Goldman,et al. The UCSC Genome Browser database: extensions and updates 2011 , 2011, Nucleic Acids Res..
[48] Mary Goldman,et al. The UCSC Genome Browser database: extensions and updates 2013 , 2012, Nucleic Acids Res..
[49] Thomas R. Gingeras,et al. STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..
[50] L. Andersson,et al. Constitutive activation of the ERK pathway in melanoma and skin melanocytes in Grey horses , 2014, BMC Cancer.
[51] L. Andersson,et al. Establishment and characterization of a primary and a metastatic melanoma cell line from Grey horses , 2013, In Vitro Cellular & Developmental Biology - Animal.
[52] David Haussler,et al. The UCSC genome browser and associated tools , 2012, Briefings Bioinform..
[53] Robert Gentleman,et al. Software for Computing and Annotating Genomic Ranges , 2013, PLoS Comput. Biol..
[54] Howard Y. Chang,et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.
[55] Michael D. Wilson,et al. Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways , 2014, eLife.
[56] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[57] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[58] A. Stark,et al. Transcriptional enhancers: from properties to genome-wide predictions , 2014, Nature Reviews Genetics.
[59] Tatsunori B. Hashimoto,et al. Discovery of non-directional and directional pioneer transcription factors by modeling DNase profile magnitude and shape , 2014, Nature Biotechnology.
[60] C. Berking,et al. SOX10 promotes melanoma cell invasion by regulating melanoma inhibitory activity. , 2014, The Journal of investigative dermatology.
[61] Wei Shi,et al. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..
[62] Stein Aerts,et al. iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections , 2014, PLoS Comput. Biol..
[63] Stein Aerts,et al. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly , 2015, Nucleic Acids Res..
[64] S. Aerts,et al. Transcription factor MITF and remodeller BRG1 define chromatin organisation at regulatory elements in melanoma cells , 2015, eLife.
[65] Manolis Kellis,et al. Deep learning for regulatory genomics , 2015, Nature Biotechnology.
[66] F. Gage,et al. Enhancer Divergence and cis-Regulatory Evolution in the Human and Chimp Neural Crest , 2015, Cell.
[67] S. Aerts,et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state , 2015, Nature Communications.
[68] J. T. Erichsen,et al. Enhancer Evolution across 20 Mammalian Species , 2015, Cell.
[69] A. McCallion,et al. Genomic analysis reveals distinct mechanisms and functional classes of SOX10-regulated genes in melanocytes. , 2015, Human molecular genetics.
[70] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[71] L. Zon,et al. A Quantitative System for Studying Metastasis Using Transparent Zebrafish. , 2015, Cancer research.
[72] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[73] B. Bastian,et al. From melanocytes to melanomas , 2016, Nature Reviews Cancer.
[74] David R. Kelley,et al. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.
[75] K. Kaestner,et al. The Pioneer Transcription Factor FoxA Maintains an Accessible Nucleosome Configuration at Enhancers for Tissue-Specific Gene Activation. , 2016, Molecular cell.
[76] G. Wagner,et al. The origin and evolution of cell types , 2016, Nature Reviews Genetics.
[77] Fidel Ramírez,et al. deepTools2: a next generation web server for deep-sequencing data analysis , 2016, Nucleic Acids Res..
[78] J. Wysocka,et al. Ever-Changing Landscapes: Transcriptional Enhancers in Development and Evolution , 2016, Cell.
[79] Alicia N. Schep,et al. Nfib Promotes Metastasis through a Widespread Increase in Chromatin Accessibility , 2016, Cell.
[80] R. Young,et al. A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation , 2016, Science.
[81] C. Ivan,et al. NFAT1 Directly Regulates IL8 and MMP3 to Promote Melanoma Tumor Growth and Metastasis. , 2016, Cancer research.
[82] Xiaohui S. Xie,et al. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences , 2015, bioRxiv.
[83] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[84] R. White,et al. Generation and analysis of zebrafish melanoma models. , 2016, Methods in cell biology.
[85] D. Adams,et al. Cross‐species models of human melanoma , 2015, The Journal of pathology.
[86] Ning Chen,et al. DeepEnhancer: Predicting enhancers by convolutional neural networks , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[87] M. Askarian-Amiri,et al. Signaling Pathways in Melanogenesis , 2016, International journal of molecular sciences.
[88] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[89] D. Fisher,et al. The master role of microphthalmia-associated transcription factor in melanocyte and melanoma biology. , 2017, Laboratory investigation; a journal of technical methods and pathology.
[90] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[91] R. Jauch,et al. Molecular basis for the genome engagement by Sox proteins. , 2017, Seminars in cell & developmental biology.
[92] E. Bernstein,et al. Harnessing BET Inhibitor Sensitivity Reveals AMIGO2 as a Melanoma Survival Gene. , 2017, Molecular cell.
[93] O. Stegle,et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2016, Genome Biology.
[94] Nicholas A. Sinnott-Armstrong,et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues , 2017, Nature Methods.
[95] William Stafford Noble,et al. Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture , 2017, bioRxiv.
[96] W. Pavan,et al. TFAP2 paralogs regulate melanocyte differentiation in parallel with MITF , 2017, PLoS genetics.
[97] L. Zon,et al. From fish bowl to bedside: The power of zebrafish to unravel melanoma pathogenesis and discover new therapeutics , 2017, Pigment cell & melanoma research.
[98] Alexandra E. Fish,et al. Prediction of gene regulatory enhancers across species reveals evolutionarily conserved sequence properties , 2018, PLoS Comput. Biol..
[99] John M. Gaspar,et al. Improved peak-calling with MACS2 , 2018, bioRxiv.
[100] X. Thuru,et al. Isolation and characterization of two canine melanoma cell lines: new models for comparative oncology , 2018, BMC Cancer.
[101] Avanti Shrikumar,et al. Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.4.2.2 , 2019 .
[102] W. E,et al. DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants , 2018, Nucleic acids research.
[103] William Stafford Noble,et al. Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture , 2017, bioRxiv.
[104] Sharon R Grossman,et al. Positional specificity of different transcription factor classes within enhancers , 2018, Proceedings of the National Academy of Sciences.
[105] S. Aerts,et al. The transcription factor Grainyhead primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes , 2018, Nature Genetics.
[106] S. Aerts,et al. Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning , 2019, bioRxiv.
[107] Jun Cheng,et al. The Kipoi repository accelerates community exchange and reuse of predictive models for genomics , 2019, Nature Biotechnology.
[108] Avanti Shrikumar,et al. Base-resolution models of transcription factor binding reveal soft motif syntax , 2019, Nature Genetics.
[109] J. Marine,et al. Melanoma plasticity and phenotypic diversity: therapeutic barriers and opportunities , 2019, Genes & development.
[110] T. Nakagawa,et al. Transcriptome analysis of dog oral melanoma and its oncogenic analogy with human melanoma , 2019, Oncology reports.
[111] Sandy L. Klemm,et al. Chromatin accessibility and the regulatory epigenome , 2019, Nature Reviews Genetics.
[112] Simon C. Potter,et al. The EMBL-EBI search and sequence analysis tools APIs in 2019 , 2019, Nucleic Acids Res..
[113] Beth K. Martin,et al. Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution , 2019, Nature Communications.
[114] K. Lindblad-Toh,et al. Genome-Wide Analysis of Long Non-Coding RNA Profiles in Canine Oral Melanomas , 2019, Genes.
[115] C. André,et al. Canine Melanomas as Models for Human Melanomas: Clinical, Histological, and Genetic Comparison , 2019, Genes.
[116] Stein Aerts,et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data , 2019, Nature Methods.
[117] Fabian J Theis,et al. Deep learning: new computational modelling techniques for genomics , 2019, Nature Reviews Genetics.
[118] S. Johnsen,et al. Perturbing Enhancer Activity in Cancer Therapy , 2019, Cancers.
[119] Jacob M. Schreiber,et al. A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens , 2019, Cell.
[120] S. Aerts,et al. Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma , 2020, Nature Cell Biology.
[121] Hugh Chen,et al. From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.
[122] C. Dienemann,et al. Nucleosome-bound SOX2 and SOX11 structures elucidate pioneer factor function , 2020, Nature.