FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

High-Resolution Profiling of Histone Methylations in the Human Genome

Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845

Automated generation of heuristics for biological sequence comparison

Slater, G., & Birney, E. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-31 · Cited by 2,959

Genome-Wide Mapping of in Vivo Protein-DNA Interactions

Johnson, D. S., Mortazavi, A., Myers, R. M., & Wold, B. (2007) · Science · doi:10.1126/science.1141319 · Cited by 2,927

Dynamic Regulation of Nucleosome Positioning in the Human Genome

Schones, D. E., Cui, K., Cuddapah, S., Roh, T., Barski, A., Wang, Z., Wei, G., & Zhao, K. (2008) · Cell · doi:10.1016/j.cell.2008.02.022 · Cited by 1,473

Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing

Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., Zeng, T., Euskirchen, G., Bernier, B., Varhol, R., Delaney, A., et al. (2007) · Nature Methods · doi:10.1038/nmeth1068 · Cited by 1,454

Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells

Morin, R. D., O’Connor, M. D., Griffith, M., Kuchenbauer, F., Delaney, A., Prabhu, A.-L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., et al. (2008) · Genome Research · doi:10.1101/gr.7179508 · Cited by 1,110

Genomic mapping by fingerprinting random clones: A mathematical analysis

Lander, E. S., & Waterman, M. S. (1988) · Genomics · doi:10.1016/0888-7543(88)90007-9 · Cited by 942

Ultradeep Bisulfite Sequencing Analysis of DNA Methylation Patterns in Multiple Gene Promoters by 454 Sequencing

Taylor, K. H., Kramer, R. S., Davis, J. W., Guo, J., Duff, D. J., Xu, D. H., Caldwell, C. W., & Shi, H. (2007) · Cancer Research · doi:10.1158/0008-5472.can-07-1016 · Cited by 258

Alternate pathways for Bcl6‐mediated regulation of B cell to plasma cell differentiation

Alinikula, J., Nera, K. ‐., Junttila, S., & Lassila, O. (2011) · European Journal of Immunology · doi:10.1002/eji.201141553 · Cited by 63

A survey of motif finding Web tools for detecting binding site motifs in ChIP-Seq data

Tran, N. T. L., & Huang, C.-H. (2014) · Biology Direct · doi:10.1186/1745-6150-9-4 · Cited by 69

The next generation: Using new sequencing technologies to analyse gene regulation

Cullum, R., Alder, O., & Hoodless, P. A. (2010) · Respirology · doi:10.1111/j.1440-1843.2010.01899.x · Cited by 61

Effects of Oestrogen on MicroRNA Expression in Hormone-Responsive Breast Cancer Cells

Ferraro, L., Ravo, M., Nassa, G., Tarallo, R., Filippo, M. R. D., Giurato, G., Cirillo, F., Stellato, C., Silvestro, S., Cantarella, C., et al. (2012) · Hormones and Cancer · doi:10.1007/s12672-012-0102-1 · Cited by 59

Short- and long-term effects of 56Fe irradiation on cognition and hippocampal DNA methylation and gene expression

Impey, S., Jopson, T. D., Pelz, C., Tafessu, A., Fareh, F., Zuloaga, D. G., Marzulla, T., Riparip, L., Stewart, B., Rosi, S., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-3110-7 · Cited by 57

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment

Worsley-Hunt, R., Mathelier, A., del Peso, L., & Wasserman, W. W. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-472 · Cited by 56

HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data

Ashoor, H., Hérault, A., Kamoun, A., Radvanyi, F., Bajić, V. B., Barillot, E., & Boeva, V. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt524 · Cited by 63

Shape-based peak identification for ChIP-Seq

Hower, V., Evans, S. N., & Pachter, L. S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-15 · Cited by 54

Genome-wide in silico prediction of gene expression

McLeay, R. C., Lesluyes, T., Cuéllar-Partida, G., & Bailey, T. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts529 · Cited by 51

QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions

Liu, B., Yi, J., Aishwarya, S. V., Lan, X., Ma, Y., Huang, T. H. ‐., Leone, G. W., & Jin, V. X. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s8-s3 · Cited by 54

Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts

Szalkowski, A. M., & Schmid, C. D. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq068 · Cited by 50

Genome-wide Runx2 occupancy in prostate cancer cells suggests a role in regulating secretion

Little, G. H., Noushmehr, H., Baniwal, S. K., Berman, B. P., Coetzee, G. A., & Frenkel, B. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1219 · Cited by 50

Discovering homotypic binding events at high spatial resolution

Guo, Y., Papachristoudis, G., Altshuler, R. C., Gerber, G. K., Jaakkola, T., Gifford, D. K., & Mahony, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq590 · Cited by 48

High throughput RNA sequencing of a hybrid maize and its parents shows different mechanisms responsive to nitrogen limitation

Bi, Y., Meyer, A., Downs, G. S., Shi, X., El‐Kereamy, A., LUKENS, L. N., & Rothstein, S. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-77 · Cited by 48

A compendium of genome-wide hematopoietic transcription factor maps supports the identification of gene regulatory control mechanisms

Hannah, R., Joshi, A., Wilson, N. K., Kinston, S., & Göttgens, B. (2011) · Experimental Hematology · doi:10.1016/j.exphem.2011.02.009 · Cited by 47

Nucleosome positioning and histone modifications define relationships between regulatory elements and nearby gene expression in breast epithelial cells

Rhie, S. K., Hazelett, D. J., Coetzee, S. G., Yan, C., Noushmehr, H., & Coetzee, G. A. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-331 · Cited by 47

Protein–DNA binding in high-resolution

Mahony, S., & Pugh, B. F. (2015) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409238.2015.1051505 · Cited by 48

Native chromatin immunoprecipitation (N-ChIP) and ChIP-Seq of Schistosoma mansoni: Critical experimental parameters

Cosseau, C., Azzi, A., Smith, K., Freitag, M., Mitta, G., & Grunau, C. (2009) · Molecular and Biochemical Parasitology · doi:10.1016/j.molbiopara.2009.02.015 · Cited by 49

Transcriptome-wide microRNA and target dynamics in the fat body during the gonadotrophic cycle of Aedes aegypti

Zhang, X., Aksoy, E., Girke, T., Raikhel, A. S., & Karginov, F. V. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1701474114 · Cited by 47

Genome-wide identification of DNA–protein interactions using chromatin immunoprecipitation coupled with flow cell sequencing

Hoffman, B. G., & Jones, S. J. M. (2009) · Journal of Endocrinology · doi:10.1677/joe-08-0526 · Cited by 48

An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data

Liu, B., Yang, J., Li, Y., McDermaid, A., & Ma, Q. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx026 · Cited by 42

Statistical Analyses of Next Generation Sequence Data: A Partial Overview

Datta, S., Datta, S., Kim, S., Chakraborty, S., & Gill, R. S. (2010) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.1000138 · Cited by 41

Proton irradiation induces persistent and tissue-specific DNA methylation changes in the left ventricle and hippocampus

Impey, S., Pelz, C., Tafessu, A., Marzulla, T., Turker, M. S., & Raber, J. (2016) · BMC Genomics · doi:10.1186/s12864-016-2581-x · Cited by 41

ChAsE: chromatin analysis and exploration tool

Younesy, H., Nielsen, C. B., Lorincz, M. C., Jones, S. J. M., Karimi, M. M., & Möller, T. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw382 · Cited by 41

TNFα signalling primes chromatin for NF-κB binding and induces rapid and widespread nucleosome repositioning

Diermeier, S. D., Kolovos, P., Heizinger, L., Schwartz, U., Georgomanolis, T., Zirkel, A., Wedemann, G., Grosveld, F. G., Knoch, T., Merkl, R., et al. (2014) · Genome biology · doi:10.1186/s13059-014-0536-6 · Cited by 37

Combining multiple ChIP-seq peak detection systems using combinatorial fusion

Schweikert, C., Brown, S. M., Tang, Z., Smith, P. R., & Hsu, D. F. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s8-s12 · Cited by 39

Systematic study of human long intergenic non-coding RNAs and their impact on cancer

Sun, L. D., Luo, H., Liao, Q., Bu, D., Zhao, G., Liu, C., Liu, Y., & Zhao, Y. C. (2013) · Science China Life Sciences · doi:10.1007/s11427-013-4460-x · Cited by 38

Nebula—a web-server for advanced ChIP-seq data analysis

Boeva, V., Lermine, A., Barette, C., Guillouf, C., & Barillot, E. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts463 · Cited by 38

Efficiently identifying genome-wide changes with next-generation sequencing data

Huang, W., Umbach, D. M., Jordan, N. V., Abell, A. N., Johnson, G. L., & Li, L. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr592 · Cited by 37

SOX9 modulates the expression of key transcription factors required for heart valve development

Garside, V. C., Cullum, R., Alder, O., Lu, D. Y., Vander Werff, R., Bilenky, M., Zhao, Y., Jones, S. J. M., Marra, M. A., Underhill, T. M., et al. (2015) · Development · doi:10.1242/dev.125252 · Cited by 65

Transcriptome-wide identification of RNA binding sites by CLIP-seq

Murigneux, V., Saulière, J., Crollius, H. R., & Hir, H. L. (2013) · Methods · doi:10.1016/j.ymeth.2013.03.022 · Cited by 39

Spark: A navigational paradigm for genomic data explorationPreprint

Nielsen, C. B., Younesy, H., O’Geen, H., Xu, X., Jackson, A. R., Milosavljevic, A., Wang, T., Costello, J., Hirst, M., Farnham, P., et al. (2012) · Genome Research · doi:10.1101/gr.140665.112 · Cited by 35

Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data

Bao, Y., Vinciotti, V., Wit, E. C., & Hoen, P. A. C. ’. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-169 · Cited by 34

NF-E2 p45 Is Important for Establishing Normal Function of Platelets

Fujita, R., Takayama-Tsujimoto, M., Satoh, H., Gutiérrez, L., Aburatani, H., Fujii, S., Sarai, A., Bresnick, E. H., Yamamoto, M. P., & Motohashi, H. (2013) · Molecular and Cellular Biology · doi:10.1128/mcb.01274-12 · Cited by 32

Retrotransposons as a major source of epigenetic variations in the mammalian genome

Ekram, M. B., Kang, K., Kim, H., & Kim, J. (2012) · Epigenetics · doi:10.4161/epi.19462 · Cited by 31

An effective approach for identification of in vivo protein-DNA binding sites from paired-end ChIP-Seq data

Wang, C., Xu, J., Zhang, D., Wilson, Z. A., & Zhang, D. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-81 · Cited by 33

Bi-directional and shared epigenomic signatures following proton and 56Fe irradiation

Impey, S., Jopson, T. D., Pelz, C., Tafessu, A., Fareh, F., Zuloaga, D. G., Marzulla, T., Riparip, L., Stewart, B., Rosi, S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-09191-4 · Cited by 30

Genome-Wide Analysis of Transcriptional Reprogramming in Mouse Models of Acute Myeloid Leukaemia

Bonadies, N., Foster, S. D., Chan, W.-I., Kvinlaug, B. T., Spensberger, D., Dawson, M. A., Spooncer, E., Whetton, A. D., Bannister, A. J., Huntly, B. J. P., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016330 · Cited by 30

Computational methodology for ChIP‐seq analysis

Shin, H., Liu, T., Duan, X., Zhang, Y., & Liu, X. S. (2013) · Quantitative Biology · doi:10.1007/s40484-013-0006-2 · Cited by 29

Genome-Wide Anaplasma phagocytophilum AnkA-DNA Interactions Are Enriched in Intergenic Regions and Gene Promoters and Correlate with Infection-Induced Differential Gene Expression

Dumler, J. S., Sinclair, S. H. G., Pappas-Brown, V., & Shetty, A. C. (2016) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2016.00097 · Cited by 29

Spi-1/PU.1 activates transcription through clustered DNA occupancy in erythroleukemia

Ridinger-Saison, M., Boeva, V., Rimmelé, P., Kulakovskiy, I., Gallais, I., Levavasseur, B., Paccard, C., Legoix-Né, P., Morlé, F., Nicolas, A., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks659 · Cited by 28

A Global Clustering Algorithm to Identify Long Intergenic Non-Coding RNA - with Applications in Mouse Macrophages

Garmire, L. X., Garmire, D. G., Huang, W. J. M., Yao, J., Glass, C. K., & Subramaniam, S. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024051 · Cited by 27

Construction of a T cell receptor signaling range for spontaneous development of autoimmune disease

Tanaka, A., Maeda, S., Nomura, T., Llamas-Covarrubias, M. A., Tanaka, S., Lin, J., Lim, E. L., Morikawa, H., Kitagawa, Y., Akizuki, S., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20220386 · Cited by 28

Whole-genome in-silico subtractive hybridization (WISH) - using massive sequencing for the identification of unique and repetitive sex-specific sequences: the example of Schistosoma mansoni

Portela, J., Grunau, C., Cosseau, C., Beltran‐Bech, S., Dantec, C., Parrinello, H., & Boissier, J. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-387 · Cited by 28

Quantitative analysis of ChIP-seq data uncovers dynamic and sustained H3K4me3 and H3K27me3 modulation in cancer cells under hypoxia

Adriaens, M. E., Prickaerts, P., Chan‐Seng‐Yue, M., van den Beucken, T., Dahlmans, V. E. H., Eijssen, L., Beck, T., Wouters, B. G., Voncken, J. W., & Evelo, C. T. (2016) · Epigenetics & Chromatin · doi:10.1186/s13072-016-0090-4 · Cited by 27

Harnessing virtual machines to simplify next-generation DNA sequencing analysis

Nocq, J., Celton, M., Gendron, P., Lemieux, S., & Wilhelm, B. T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt352 · Cited by 27

Estimation of CpG coverage in whole methylome next-generation sequencing studies

van den Oord, E. J. C. G., Bukszár, J., Rudolf, G., Nerella, S., McClay, J. L., Xie, L. Y., & Åberg, K. A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-50 · Cited by 26

AREM: Aligning Short Reads from ChIP-Sequencing by Expectation Maximization

Newkirk, D. A., Biesinger, J., Chon, A., Yokomori, K., & Xie, X. S. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2011.0185 · Cited by 25

BIND&MODIFY: a long-range method for single-molecule mapping of chromatin modifications in eukaryotes

Weng, Z., Ruan, F., Chen, W., Chen, Z., Xie, Y., Luo, M., Xie, Z., Zhang, C., Wang, J., Sun, Y., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02896-y · Cited by 24

ChIP-Seq

Lefrançois, P., Zheng, W., & Snyder, M. P. (2010) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(10)70004-5 · Cited by 26

The Analysis of ChIP-Seq Data

Ma, W., & Wong, W. H. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385075-1.00003-2 · Cited by 26

A regression analysis of gene expression in ES cells reveals two gene classes that are significantly different in epigenetic patterns

Park, S., & Nakai, K. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s1-s50 · Cited by 23

Mapping Protein–DNA Interactions Using ChIP-Sequencing

Massie, C. E., & Mills, I. G. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-376-9_11 · Cited by 24

Identification of CELF1 RNA targets by CLIP-seq in human HeLa cells

Tonquèze, O. L., Gschloessl, B., Legagneux, V., Paillard, L., & Audic, Y. (2016) · Genomics Data · doi:10.1016/j.gdata.2016.04.009 · Cited by 22

An Overview of the Computational Analyses and Discovery of Transcription Factor Binding Sites

Ladunga, I. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-854-6_1 · Cited by 20

bPeaks: a bioinformatics tool to detect transcription factor binding sites from ChIPseq data in yeasts and other organisms with small genomes

Merhej, J., Frigo, A., Crom, S. L., Camadro, J., Devaux, F., & Lelandais, G. (2014) · Yeast · doi:10.1002/yea.3031 · Cited by 20

Chromosome wide analysis of CUGBP1 binding sites identifies the tetraspanin CD9 mRNA as a target for CUGBP1-mediated down-regulation

Tonquèze, O. L., Gschloessl, B., Namanda-Vanderbeken, A., Legagneux, V., Paillard, L., & Audic, Y. (2010) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2010.03.020 · Cited by 19

A fully Bayesian hidden Ising model for ChIP-seq data analysis

Mo, Q. (2011) · Biostatistics · doi:10.1093/biostatistics/kxr029 · Cited by 19

Characterising ChIP-seq binding patterns by model-based peak shape deconvolution

Mendoza-Parra, M. A., Nowicka, M., Gool, W. V., & Gronemeyer, H. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-834 · Cited by 19

Cardiovascular Transcriptomics and Epigenomics Using Next-Generation Sequencing

Wu, P.-Y., Chandramohan, R., Phan, J. H., Mahle, W. T., Gaynor, J. W., Maher, K., & Wang, M. D. (2014) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000129 · Cited by 18

ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data

Wu, S., Wang, J., Zhao, W., Pounds, S. B., & Cheng, C. (2010) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-7-18 · Cited by 18

Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data

Ghosh, D., & Qin, Z. S. (2010) · Genes · doi:10.3390/genes1020317 · Cited by 18

Identifying peaks in *-seq data using shape information

Strino, F., & Lappé, M. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1042-5 · Cited by 18

A Review on Bioinformatics Enrichment Analysis Tools Towards Functional Analysis of High Throughput Gene Set Data

Jing, L. S., Shah, F. F. M., Mohamad, M. S., Moorthy, K., Deris, S., Zakaria, Z., & Napis, S. B. (2015) · Current Proteomics · doi:10.2174/157016461201150506200927 · Cited by 18

Multiple testing of local maxima for detection of peaks in ChIP-Seq data

Schwartzman, A., Jaffe, A. E., Gavrilov, Y., & Meyer, C. A. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas594 · Cited by 17

DNA Sequence Motif

Kulakovskiy, I. V., & Makeev, V. J. (2013) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-411637-5.00005-6 · Cited by 17

Processing and analyzing ChIP-seq data: from short reads to regulatory interactions

Leleu, M., Lefebvre, G. C., & Rougemont, J. (2010) · Briefings in Functional Genomics · doi:10.1093/bfgp/elq022 · Cited by 16

Cancer omics: From regulatory networks to clinical outcomes

Tang, B., Hsu, P.-Y., Huang, T. H. ‐., & Jin, V. X. (2012) · Cancer Letters · doi:10.1016/j.canlet.2012.11.033 · Cited by 15

Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data

Kruczyk, M., Umer, H. M., Enroth, S., & Komorowski, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-280 · Cited by 14

iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data

Yu, C., Qi, X., Lin, Y., Li, Y., & Shen, B. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103605 · Cited by 14

LOcating Non-Unique matched Tags (LONUT) to Improve the Detection of the Enriched Regions for ChIP-seq Data

Wang, R., Hsu, H.-K., Blattler, A., Wang, Y., Lan, X., Wang, Y., Hsu, P.-Y., Leu, Y., Huang, T. H. ‐., Farnham, P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067788 · Cited by 13

Dissecting Regulatory Networks in Host-Pathogen Interaction Using ChIP-on-chip Technology

Sala, C., Grainger, D. C., & Cole, S. T. (2009) · Cell Host & Microbe · doi:10.1016/j.chom.2009.04.007 · Cited by 14

Interpreting and Visualizing ChIP-seq Data with the seqMINER Software

Ye, T., Ravens, S., Krebs, A. R., & Tora, L. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0512-6_8 · Cited by 13

NGSView: an extensible open source editor for next-generation sequencing data

Arner, E., Hayashizaki, Y., & Daub, C. O. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp611 · Cited by 13

Twist1 Transcriptional Targets in the Developing Atrio-Ventricular Canal of the Mouse

Vrljicak, P., Cullum, R. L., Xu, E. E., Chang, A. C. Y., Wederell, E. D., Bilenky, M., Jones, S. J. M., Marra, M. A., Karsan, A., & Hoodless, P. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040815 · Cited by 12

PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information

Wu, H., & Ji, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089694 · Cited by 11

HMCan-diff: a method to detect changes in histone modifications in cells with different genetic characteristics

Ashoor, H., Louis‐Brennetot, C., Janoueix‐Lerosey, I., Bajić, V. B., & Boeva, V. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1319 · Cited by 12

WaveSeq: A Novel Data-Driven Method of Detecting Histone Modification Enrichments Using Wavelets

Mitra, A., & Song, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045486 · Cited by 11

Analysis of the p53/microRNA Network in Cancer

Kaller, M., Hünten, S., Siemens, H., & Hermeking, H. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-08356-3_7 · Cited by 12

WACS: improving ChIP-seq peak calling by optimally weighting controls

Awdeh, A., Turcotte, M., & Perkins, T. J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-020-03927-2 · Cited by 10

Uncovering Transcription Factor Modules Using One- and Three-dimensional Analyses

Lan, X., Farnham, P., & Jin, V. X. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.r111.309229 · Cited by 10

RECAP reveals the true statistical significance of ChIP-seq peak calls

Chitpin, J. G., Awdeh, A., & Perkins, T. J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz150 · Cited by 10

Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling

Stanton, K., Jin, J., Lederman, R. R., Weissman, S. M., & Kluger, Y. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx799 · Cited by 10

iMotifs: an integrated sequence motif visualization and analysis environment

Piipari, M., Down, T. A., Saini, H. K., Enright, A. J., & Hubbard, T. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq026 · Cited by 10

Epigenome sequencing comes of age in development, differentiation and disease mechanism research

Zhao, Q. C., & Zhang, Y. (2011) · Epigenomics · doi:10.2217/epi.10.78 · Cited by 9

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

Kornacker, K., Rye, M. B., Håndstad, T., & Drabløs, F. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-176 · Cited by 9

Identification and Functional Annotation of Genome-Wide ER-Regulated Genes in Breast Cancer Based on ChIP-Seq Data

Ding, M., Wang, H., Chen, J., Shen, B., & Zhong-hua, X. (2012) · Computational and Mathematical Methods in Medicine · doi:10.1155/2012/568950 · Cited by 9

Long-range single-molecule mapping of chromatin modification in eukaryotesPreprint

Weng, Z., Ruan, F., Chen, W., Xie, Z., Xie, Y., Zhang, C., Chen, Z., Wang, J., Sun, Y., Fang, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.08.451578 · Cited by 6

Computational Epigenetics

Wei, L. K., & Au, A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-805388-1.00012-2 · Cited by 9

Next-Generation Sequencing

Herzyk, P. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386882-4.00008-6 · Cited by 9

Weakly Positioned Nucleosomes Enhance the Transcriptional Competency of Chromatin

Belch, Y., Yang, J., Liu, Y., Malkaram, S. A., Liu, R., Riethoven, J.-J. M., & Ladunga, I. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012984 · Cited by 8

Genome-Wide Analysis of c-MYC-Regulated mRNAs and miRNAs, and c-MYC DNA Binding by Next-Generation Sequencing

Jackstadt, R., Menssen, A., & Hermeking, H. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-429-6_11 · Cited by 8

Towards an understanding of the epigenetics of schistosomes: a comparative epigenomic study

Lepesant, J. M. J., Grunau, C., & Cosseau, C. (2011) · Memórias do Instituto Oswaldo Cruz · doi:10.1590/s0074-02762011000700007 · Cited by 8

dCaP: detecting differential binding events in multiple conditions and proteins

Chen, K.-B., Hardison, R. C., & Zhang, Y. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s9-s12 · Cited by 8

Computational Analysis of ChIP-seq Data

Ji, H. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-854-6_9 · Cited by 7

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

Wang, X., & Zhang, X. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-s2-s3 · Cited by 7

GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge

Carvalho, A. M., & Oliveira, A. L. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-13 · Cited by 6

Improved ChIP-chip analysis by a mixture model approach

Sun, W., Buck, M. J., Patel, M., & Davis, I. J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-173 · Cited by 6

Optimized detection of transcription factor-binding sites in ChIP-seq experiments

Elo, L. L., Kallio, A., Laajala, T. D., Hawkins, R. D., Korpelainen, E., & Aittokallio, T. A. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr839 · Cited by 6

Epigenomics: Sequencing the Methylome

Hirst, M. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-281-0_3 · Cited by 6

Sole-Search: an integrated analysis program for peak detection and functional annotation using ChIP-seq data

Blahnik, K. R., Dou, L., O’Geen, H., McPhillips, T., Xu, X., Cao, A. R., Iyengar, S., Nicolet, C. M., Ludäscher, B., Korf, I. F., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1012 · Cited by 117

W-ChIPeaks: a comprehensive web application tool for processing ChIP-chip and ChIP-seq data

Lan, X., Bonneville, R., Apostolos, J., Wu, W., & Jin, V. X. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq669 · Cited by 45

HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data

Qin, Z. S., Yu, J., Shen, J., Maher, C. A., Hu, M., Sundaram, S. K., Yu, J., & Chinnaiyan, A. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-369 · Cited by 104

read-enriched regions in ChIP-Seq data Open Access

Qin, Z. S., Yu, J., Shen, J., Maher, C. A., Hu, M., Sundaram, S. K., Yu, J., & Chinnaiyan, A. M. (2013)

PeakAnalyzer: Genome-wide annotation of chromatin binding and modification loci

Salmon‐Divon, M., Dvinge, H., Koort, K., & Bertone, P. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-415 · Cited by 244