Extensive Variation in Chromatin States Across Humans

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Model-based Analysis of ChIP-Seq (MACS)

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An integrated encyclopedia of DNA elements in the human genome

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Differential expression analysis for sequence count data

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Fast and accurate long-read alignment with Burrows-Wheeler transform.

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TopHat: discovering splice junctions with RNA-Seq

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Mathematical model for studying genetic variation in terms of restriction endonucleases.

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Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

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A map of human genome variation from population-scale sequencing.

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An integrated map of genetic variation from 1,092 human genomes

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De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets

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Phylogenetic Modeling of Regulatory Element Turnover Based on Epigenomic Data

Dukler, N., Huang, Y.-F., & Siepel, A. C. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa073 · Cited by 18

Probing instructions for expression regulation in gene nucleotide compositions

Bessière, C., Taha, M., Petitprez, F., Vandel, J., Marin, J., Bréhélin, L., Lèbre, S., & Lecellier, C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005921 · Cited by 16

Sodium bicarbonate cotransporter NBCe2 gene variants increase sodium and bicarbonate transport in human renal proximal tubule cells

Gildea, J. J., Xu, P., Kemp, B. A., Carlson, J. M., Tran, H. T. D., Wang, D. B., Langouët-Astrié, C. J., McGrath, H. E., Carey, R. M., José, P. A., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0189464 · Cited by 15

Path from schizophrenia genomics to biology: gene regulation and perturbation in neurons derived from induced pluripotent stem cells and genome editing

Duan, J. (2015) · Neuroscience Bulletin · doi:10.1007/s12264-014-1488-2 · Cited by 14

Human Enhancers Are Fragile and Prone to Deactivating Mutations

Li, S., & Ovcharenko, I. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv118 · Cited by 14

Genetic Determinants of Epigenetic Patterns: Providing Insight into Disease

Cazaly, E., Charlesworth, J., Dickinson, J. L., & Holloway, A. F. (2015) · Molecular Medicine · doi:10.2119/molmed.2015.00001 · Cited by 14

Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci

Buchkovich, M. L., Eklund, K., Duan, Q., Li, Y., Mohlke, K. L., & Furey, T. S. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0117-x · Cited by 15

Transcription factor expression is the main determinant of variability in gene co‐activity

van Duin, L., Krautz, R., Rennie, S., & Andersson, R. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211392 · Cited by 11

A Long-Distance Chromatin Affair

Denker, A., & de Laat, W. L. (2015) · Cell · doi:10.1016/j.cell.2015.08.022 · Cited by 13

Rules Governing the Mechanism of Epigenetic Reprogramming Memory

Luu, P., Daniela Gerovska, D., Schöler, H. R., & Araúzo-Bravo, M. J. (2018) · Epigenomics · doi:10.2217/epi-2017-0098 · Cited by 13

Gene expression models based on transcription factor binding events confer insight into functional cis -regulatory variants

Shi, W., Fornés, O., & Wasserman, W. W. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty992 · Cited by 14

Comparative epigenomics: defining and utilizing epigenomic variations across species, time‐course, and individuals

Shu Dong, X. I. A. O., Cao, X., & Zhong, S. (2014) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1274 · Cited by 13

Assessment of imprinting- and genetic variation-dependent monoallelic expression using reciprocal allele descendants between human family trios

Chuang, T.-J., Tseng, Y.-H., Chen, C.-Y., & Wang, Y.-D. (2017) · Scientific Reports · doi:10.1038/s41598-017-07514-z · Cited by 11

Genes with differential expression across ancestries are enriched in ancestry-specific disease effects likely due to gene-by-environment interactions

Wang, J., Zhang, Z., Lu, Z., Mancuso, N., & Gazal, S. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.07.021 · Cited by 16

Quantitative Evaluation and Optimization of Co-drugging to Improve Anti-HIV Latency Therapy

Wong, V. C., Fong, L. E., Adams, N. M., Xue, Q., Dey, S. S., & Miller‐Jensen, K. (2014) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-014-0336-9 · Cited by 11

Recently Evolved Enhancers Emerge with High Interindividual Variability and Less Frequently Associate with Disease

Castelijns, B., Baak, M. L., Geeven, G., Vermunt, M. W., Wiggers, C. R. M., Timpanaro, I. S., Kondova, I. I., de Laat, W. L., & Creyghton, M. P. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107799 · Cited by 11

Defective chromatin architectures in embryonic stem cells derived from somatic cell nuclear transfer impair their differentiation potentials

Wu, D., Li, X., Sun, Q.-R., Dou, C., Xu, T., He, H., Luo, H., Fu, H., Bu, G., Luo, B., et al. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-04384-2 · Cited by 10

Fine Mapping and Candidate Gene Search of Quantitative Trait Loci for Growth and Obesity Using Mouse Intersubspecific Subcongenic Intercrosses and Exome Sequencing

Ishikawa, A., & Okuno, S.-I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113233 · Cited by 10

Mining the topography and dynamics of the 4D Nucleome to identify novel CNS drug pathways

Higgins, G. A., Allyn-Feuer, A., Georgoff, P. E., Nikolian, V. C., Alam, H. B., & Athey, B. D. (2017) · Methods · doi:10.1016/j.ymeth.2017.03.012 · Cited by 10

Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data

Jung, S., Angarica, V. E., Andrade-Navarro, M. A., Buckley, N. J., & del Sol, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-04929-6 · Cited by 9

DNA Methylation in Honey Bees and the Unresolved Questions in Insect Methylomics

Wedd, L., Kucharski, R., & Maleszka, R. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-11454-0_7 · Cited by 10

Adaptive Evolution and Epigenetics

Kronholm, I. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-805388-1.00028-6 · Cited by 11

Loss of KMT2C reprograms the epigenomic landscape in hPSCs resulting in NODAL overexpression and a failure of hemogenic endothelium specification

Maurya, S. S., Yang, W., Tamai, M., Zhang, Q., Erdmann-Gilmore, P., Bystry, A., Rodrigues, F. M., Valentine, M. C., Wong, W., Townsend, R., et al. (2021) · Epigenetics · doi:10.1080/15592294.2021.1954780 · Cited by 10

Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer

Li, X., Pan, X., Zhou, H., Wang, P., Gao, Y., Shang, S., Guo, S., Sun, J., Xiong, Z., Ning, S., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab401 · Cited by 10

The emerging landscape of in vitro and in vivo epigenetic allelic effects

Gregg, C. J. (2017) · F1000Research · doi:10.12688/f1000research.11491.1 · Cited by 10

Mucosal RNA and protein expression as the next frontier in IBS: abnormal function despite morphologically intact small intestinal mucosa

Rodiño‐Janeiro, B. K., Pardo‐Camacho, C., Santos, J., & Martínez, C. (2019) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00186.2018 · Cited by 10

Long-read transcriptomics of a diverse human cohort reveals ancestry bias in gene annotation

Clavell-Revelles, P., Reese, F., Sala, S. C., Degalez, F., Arnan, C., Oliveros, W., Palumbo, E., Perteghella, T., Guigó, R., & Melé, M. (2025) · Nature Communications · doi:10.1038/s41467-025-66096-x · Cited by 9

Latexin and hematopoiesis

Zhang, C., & Liang, Y. (2018) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000428 · Cited by 10

PCRMS: a database of predicted cis-regulatory modules and constituent transcription factor binding sites in genomes

Ni, P., & Su, Z. (2022) · Database · doi:10.1093/database/baac024 · Cited by 7

From genetic variation to phenotype via chromatin

Stower, H. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3622 · Cited by 9

Role of NSD1 in H2O2-induced GSTM3 suppression

Chu, G., Li, Y., Dong, X., Liu, J., & Zhao, Y. (2014) · Cellular Signalling · doi:10.1016/j.cellsig.2014.08.026 · Cited by 9

Review Polyploidy and epigenetic events in the evolution of Anura

Beçak, M. L. (2014) · Genetics and Molecular Research · doi:10.4238/2014.august.7.15 · Cited by 9

Functional Variants Identified Efficiently through an Integrated Transcriptome and Epigenome Analysis

Meng, F., Yuan, G., Zhu, X., Zhou, Y., Wang, D., & Guo, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-21024-6 · Cited by 9

Chronic lymphocytic leukemia (CLL) risk is mediated by multiple enhancer variants within CLL risk loci

Yan, H., Tian, S., Kleinstern, G., Wang, Z., Lee, J., Boddicker, N. J., Cerhan, J. R., Kay, N. E., Braggio, E., & Slager, S. L. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa165 · Cited by 10

Enhancer heterogeneity in acute lymphoblastic leukemia drives differential gene expression in patients

Smith, A., Denny, N., Chahrour, C., Sharp, K., Arachi, M., Dopico-Fernandez, A. M., Elliott, N., Harman, J. R., Jackson, T., Geng, H., et al. (2025) · Blood · doi:10.1182/blood.2024028019 · Cited by 8

Genetic factors underlying discordance in chromatin accessibility between monozygotic twins

Kim, K., Ban, H., Seo, J., Lee, K., Yavartanoo, M., Kim, S. C., Park, K., Cho, S. B., & Choi, J. K. (2014) · Genome biology · doi:10.1186/gb-2014-15-5-r72 · Cited by 8

Development of a novel pink-eyed dilution mouse model showing progressive darkening of the eyes and coat hair with aging

Ishikawa, A., Sugiyama, M., Hondo, E., Kinoshita, K., & Yamagishi, Y. (2015) · EXPERIMENTAL ANIMALS · doi:10.1538/expanim.14-0101 · Cited by 6

Intersection of genetics and epigenetics in monozygotic twin genomes

Kim, K., Lee, K., Bang, H., Kim, J. Y., & Choi, J. K. (2015) · Methods · doi:10.1016/j.ymeth.2015.10.020 · Cited by 9

HTR1B gene variants associate with the susceptibility of Raynauds’ phenomenon in workers exposed hand-arm vibration

Chen, Q., Li, L., Xiao, B., Lin, H., Yang, A., Li, H., Tang, S., & Huang, H. (2016) · Clinical Hemorheology and Microcirculation · doi:10.3233/ch-152021 · Cited by 8

Characterizing batch effects and binding site-specific variability in ChIP-seq data

Teng, M., Du, D., Chen, D., & Irizarry, R. A. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab098 · Cited by 8

A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome

Ni, P., Wilson, D., & Su, Z. (2022) · BMC Genomics · doi:10.1186/s12864-022-08933-7 · Cited by 7

Identification of factors associated with duplicate rate in ChIP-seq data

Tian, S.-L., Peng, S.-X., Kalmbach, M. T., Gaonkar, K. S., Bhagwate, A., Ding, W., Eckel-Passow, J., Yan, H., & Slager, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214723 · Cited by 5

Heterogeneous chromatin mobility derived from chromatin states is a determinant of genome organisation in S. cerevisiaePreprint

Sewitz, S. A., Fahmi, Z., Aljebali, L., Bancroft, J., Brustolini, O. J. B., Saad, H., Goiffon, I., Várnai, C., Wingett, S., Wong, H., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/106344 · Cited by 9

Genetic variation within transcriptional regulatory elements and its implications for human disease

Rada-Iglesias, A. (2014) · Biological Chemistry · doi:10.1515/hsz-2014-0109 · Cited by 7

QTL Analysis Beyond eQTLs

Wen, J., Nodzak, C., & Shi, X. M. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_14 · Cited by 7

Q-Nuc: a bioinformatics pipeline for the quantitative analysis of nucleosomal profiles

Wang, Y., Sun, Q., Liang, J., Li, H., Czajkowsky, D. M., & Shao, Z. (2019) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-019-00354-7 · Cited by 7

Populational landscape of INDELs affecting transcription factor-binding sites in humans

Ribeiro-dos-Santos, A. M., da Silva, V. L., de Souza, J. E. S., & SOUZA, S. J. D. (2015) · BMC Genomics · doi:10.1186/s12864-015-1744-5 · Cited by 7

Targeted next generation sequencing of the entire vitamin D receptor gene reveals polymorphisms correlated with vitamin D deficiency among older Filipino women with and without fragility fracture

Zumaraga, M. P., Julius, M. P., Recto, J. M., Abrahan, L. L., Azurin, E., Tanchoco, C. C., Jimeno, C., & Palmes-Saloma, C. P. (2016) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2016.12.003 · Cited by 7

Methods for fine-mapping with chromatin and expression data

Roytman, M., Kichaev, G., Gusev, A., & Paşaniuc, B. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007240 · Cited by 6

From Schizophrenia Genetics to Disease Biology: Harnessing New Concepts and Technologies

Duan, J., Sanders, A. R., & Gejman, P. V. (2019) · Journal of Psychiatry and Brain Science · doi:10.20900/jpbs.20190014 · Cited by 8

The regulation of methylation on the Z chromosome and the identification of multiple novel Male Hyper-Methylated regions in the chicken

Höglund, A., Henriksen, R., Churcher, A. M., Guerrero‐Bosagna, C., Barrio, Á. M., Johnsson, M., Jensen, P., & Wright, D. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1010719 · Cited by 6

Redundant enhancers and causal variants in the TCF7L2 gene

Ruiz-Narváez, E. (2014) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.17 · Cited by 6

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Chen, H., Tu, S., Yuan, C., Tian, F., Zhang, Y., Sun, Y., & Shao, Z. (2022) · Genome biology · doi:10.1186/s13059-022-02627-9 · Cited by 6

Cis-regulatory variation: significance in biomedicine and evolution

Friedensohn, S., & Sawarkar, R. (2014) · Cell and Tissue Research · doi:10.1007/s00441-014-1855-3 · Cited by 6

Mechanism of allele specific assembly and disruption of master regulator transcription factor complexes of NF- K Bp50, NF- K Bp65 and HIF1a on a non-coding FAS SNP

Awah, C. U., Tamm, S., Hedtfeld, S., Steinemann, D., Tümmler, B., Tsiavaliaris, G., & Stanke, F. (2016) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2016.09.002 · Cited by 6

Spatial Chromatin Architecture Alteration by Structural Variations in Human Genomes at Population ScalePreprint

Sadowski, M., Kraft, A., Szałaj, P., Wlasnowolski, M., Tang, Z., Ruan, Y., & Plewczyński, D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/266981 · Cited by 6

Common DNA sequence variation influences 3-dimensional conformation of the human genomePreprint

Gorkin, D. U., Qiu, Y., Hu, M., Fletez‐Brant, K., Liu, T., Schmitt, A. D., Noor, A., Chiou, J., Gaulton, K. J., Sebat, J. L., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/592741 · Cited by 7

Epigenetic variation impacts ancestry-associated differences in the transcriptional response to influenza infectionPreprint

Aracena, K. A., Lin, Y., Luo, K., Pacis, A., Gona, S., Mu, Z., Yotova, V., Sindeaux, R., Pramatarova, A., Simon, M.-M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.10.491413 · Cited by 6

Adaptive evolution and epigenetics

Kronholm, I. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91909-8.00014-1 · Cited by 5

Transcriptional Dynamics at Brain Enhancers: from Functional Specialization to Neurodegeneration

Vermunt, M. W., & Creyghton, M. P. (2016) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-016-0689-7 · Cited by 5

Differentially methylated regions within lung cancer risk loci are enriched in deregulated enhancers

Laplana, M., Bieg, M., Faltus, C., Melnik, S., Bogatyrova, O., Gu, Z., Muley, T., Meister, M. A., Dienemann, H., Herpel, E., et al. (2021) · Epigenetics · doi:10.1080/15592294.2021.1878723 · Cited by 5

Recursive Convolutional Neural Networks for Epigenomics

Symeonidi, A., Nicolaou, A., Johannes, F. M., & Christlein, V. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412272 · Cited by 5

Genetic determinants of chromatin accessibility in T cell activation across humansPreprint

Gate, R. E., Cheng, C. S., Aiden, A. P., Siba, A., Tabaka, M., Lituiev, D., Machol, I., Gordon, M. G., Subramaniam, M., Shamim, M., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/090241 · Cited by 7

Multi-omics analysis reveals signatures of selection and loci associated with complex traits in pigsPreprint

Yi, G., Liu, L., Yao, Y., Liu, Y., Li, J., Yang, Y., Fang, L., Mo, D., Zhang, L., Liu, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.19.558553 · Cited by 5

Ancestry-specific regulatory and disease architectures are likely due to cell-type-specific gene-by-environment interactionsPreprint

Wang, J., & Gazal, S. (2023) · medRxiv · doi:10.1101/2023.10.20.23297214 · Cited by 5

SNP-Mediated Binding of TBX1 to the Enhancer Element of IL-10 Reduces the Risk of Behçet’s Disease

Tan, H., Su, G., Tan, X., Qin, Y., Chen, L., Yuan, G., Kijlstra, A., & Yang, P. (2021) · Epigenomics · doi:10.2217/epi-2021-0215 · Cited by 4

Epigenetics and Control of RNAs

Maatz, H., van Heesch, S., Kreuchwig, F., Faber, A. B., Adami, E., Hübner, N., & Heinig, M. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6427-7_9 · Cited by 4

In Vitro Modeling of Complex Neurological Diseases

Soldner, F., & Jaenisch, R. (2017) · Research and perspectives in neurosciences · doi:10.1007/978-3-319-60192-2_1 · Cited by 5

dcHiC: differential compartment analysis of Hi-C datasetsPreprint

Chakraborty, A., Wang, J., & Ay, F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.02.429297 · Cited by 4

The epipliancy journey: Tumor initiation at the mercy of identity crisis and epigenetic drift

Benhassoun, R., Morel, A., Jacquot, V., Puisieux, A., & Ouzounova, M. (2025) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2025.189307 · Cited by 4

The Importance of Context: Uncovering Species- and Tissue-Specific Effects of Genetic Risk Variants for Type 2 Diabetes

Thomsen, S. K., McCarthy, M. I., & Gloyn, A. L. (2016) · Frontiers in Endocrinology · doi:10.3389/fendo.2016.00112 · Cited by 3

Improved cohesin HiChIP protocol and bioinformatic analysis for robust detection of chromatin loops and stripes

Buka, K., Parteka-Tojek, Z., Agarwal, A., Denkiewicz, M., Korsak, S., Chiliński, M., Banecki, K. H., & Plewczyński, D. (2025) · Communications Biology · doi:10.1038/s42003-025-07847-w · Cited by 4

Comparative Analysis of Genome and Epigenome in Closely Related Medaka Species Identifies Conserved Sequence Preferences for DNA Hypomethylated Domains

Uno, A., Nakamura, R., Tsukahara, T., Qü, W., Sugano, S., Suzuki, Y., Morishita, S., & Takeda, H. (2016) · ZOOLOGICAL SCIENCE · doi:10.2108/zs160030 · Cited by 3

Cell-type-specific epigenomic variations associated with BRCA1 mutation in pre-cancer human breast tissues

Hsieh, Y.-P., Naler, L. B., Ma, S., & Lu, C. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqac006 · Cited by 3

Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans

Triqueneaux, G., Burny, C., Symmons, O., Janczarski, S., Gruffat, H., & Yvert, G. (2020) · Communications Biology · doi:10.1038/s42003-020-1075-1 · Cited by 3

Spectacle: Faster and more accurate chromatin state annotation using spectral learningPreprint

Song, J., & Chen, K. C. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/002725 · Cited by 3

SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data

Arnold, C., Bhat, P., & Zaugg, J. B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw127 · Cited by 2

From Identification to Function: Current Strategies to Prioritise and Follow-Up GWAS Results

Berlanga‐Taylor, A. J. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7868-7_15 · Cited by 2

Genetic Contexts Characterize Dynamic Histone Modification Patterns Among Cell Types

Lin, Y., Li, Y., Zhu, X., Huang, Y., Li, M., & Li, M. (2019) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-019-00338-7 · Cited by 2

EpiSAFARI: sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

Harmanci, A. O., Harmanci, A. O., Swaminathan, J., & Gopalakrishnan, V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz702 · Cited by 2

Histone acetylome-wide association study of tuberculosisPreprint

del Rosario, R. C. H., Poschmann, J., Kumar, P., Cheng, C. Y., Ong, S. T., Hajan, H. S., Kumar, D., Marzuki, M., Lu, X., Lee, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/644112 · Cited by 2

Trans control of cardiac mRNA translation in a protein length-dependent fashionPreprint

Witte, F., Ruiz-Orera, J., Mattioli, C. C., Blachut, S., Adami, E., Schulz, J., Schneider-Lunitz, V., Hummel, O., Patone, G., Mücke, M., et al. (2020) · bioRxiv · doi:10.1101/2020.06.05.133298 · Cited by 2

Best practices for ChIP-seq and its data analysis

Hou, H., Hudson, M., & Liang, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-817218-6.00019-x · Cited by 1

Genetic variation drives differences in obesity-related gene regulation

Kaikkonen, M. U. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.02.009 · Cited by 1

Genome-wide identification and analysis of recurring patterns of epigenetic variation across individuals

Zou, J., Maciejewski, E., & Ernst, J. (2025) · Communications Biology · doi:10.1038/s42003-025-08179-5 · Cited by 1

Rare regulatory mutations disrupt mesenchymal molecular programs driving endocardial cushion formation in bicuspid aortic valve

Zhigulev, A., Buyan, A., Lázár, E., Gryzunov, N., Lång, K., Mauron, R., Nozdrin, V., Spalinskas, R., Pradhananga, S., Sjögren, M. P., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71758-5 · Cited by 1

Quantitative Comparison of ChIP-Seq Data Sets Using MAnorm

Shao, Z., & Zhang, Y. (2015) · Translational bioinformatics · doi:10.1007/978-94-017-9927-0_4 · Cited by 1

Variation in genes involved in epigenetic processes offers insights into tropically adapted cattle diversity

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Differences in Histone Modifications Between Individuals

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