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NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data.

BMC genomics

Kim NK, Jayatillake RV, Spouge JL.
PMID: 23706083
BMC Genomics. 2013 May 25;14:349. doi: 10.1186/1471-2164-14-349.

BACKGROUND: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.RESULTS: We propose a rigorous...

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Kim NK, Jayatillake RV, Spouge JL. NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. BMC Genomics. 2013;14:349doi: 10.1186/1471-2164-14-349.
Kim, N. K., Jayatillake, R. V., & Spouge, J. L. (2013). NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. BMC genomics, 14349. https://doi.org/10.1186/1471-2164-14-349
Kim, Nak-Kyeong, et al. "NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data." BMC genomics vol. 14 (2013): 349. doi: https://doi.org/10.1186/1471-2164-14-349
Kim NK, Jayatillake RV, Spouge JL. NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. BMC Genomics. 2013 May 25;14:349. doi: 10.1186/1471-2164-14-349. PMID: 23706083; PMCID: PMC3672025.
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Workflows for microarray data processing in the Kepler environment.

BMC bioinformatics

Stropp T, McPhillips T, Ludäscher B, Bieda M.
PMID: 22594911
BMC Bioinformatics. 2012 May 17;13:102. doi: 10.1186/1471-2105-13-102.

BACKGROUND: Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data...

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Stropp T, McPhillips T, Ludäscher B, et al. Workflows for microarray data processing in the Kepler environment. BMC Bioinformatics. 2012;13:102doi: 10.1186/1471-2105-13-102.
Stropp, T., McPhillips, T., Ludäscher, B., & Bieda, M. (2012). Workflows for microarray data processing in the Kepler environment. BMC bioinformatics, 13102. https://doi.org/10.1186/1471-2105-13-102
Stropp, Thomas, et al. "Workflows for microarray data processing in the Kepler environment." BMC bioinformatics vol. 13 (2012): 102. doi: https://doi.org/10.1186/1471-2105-13-102
Stropp T, McPhillips T, Ludäscher B, Bieda M. Workflows for microarray data processing in the Kepler environment. BMC Bioinformatics. 2012 May 17;13:102. doi: 10.1186/1471-2105-13-102. PMID: 22594911; PMCID: PMC3431220.
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Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE.

BMC genomics

Trinh QM, Jen FY, Zhou Z, Chu KM, Perry MD, Kephart ET, Contrino S, Ruzanov P, Stein LD.
PMID: 23875683
BMC Genomics. 2013 Jul 22;14:494. doi: 10.1186/1471-2164-14-494.

BACKGROUND: Funded by the National Institutes of Health (NIH), the aim of the Model Organism ENCyclopedia of DNA Elements (modENCODE) project is to provide the biological research community with a comprehensive encyclopedia of functional genomic elements for both model...

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Trinh QM, Jen FY, Zhou Z, et al. Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE. BMC Genomics. 2013;14:494doi: 10.1186/1471-2164-14-494.
Trinh, Q. M., Jen, F. Y., Zhou, Z., Chu, K. M., Perry, M. D., Kephart, E. T., Contrino, S., Ruzanov, P., & Stein, L. D. (2013). Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE. BMC genomics, 14494. https://doi.org/10.1186/1471-2164-14-494
Trinh, Quang M, et al. "Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE." BMC genomics vol. 14 (2013): 494. doi: https://doi.org/10.1186/1471-2164-14-494
Trinh QM, Jen FY, Zhou Z, Chu KM, Perry MD, Kephart ET, Contrino S, Ruzanov P, Stein LD. Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE. BMC Genomics. 2013 Jul 22;14:494. doi: 10.1186/1471-2164-14-494. PMID: 23875683; PMCID: PMC3734164.
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Nanobody-based chromatin immunoprecipitation.

Methods in molecular biology (Clifton, N.J.)

Duc TN, Hassanzadeh-Ghassabeh G, Saerens D, Peeters E, Charlier D, Muyldermans S.
PMID: 22886272
Methods Mol Biol. 2012;911:491-505. doi: 10.1007/978-1-61779-968-6_31.

Chromatin immunoprecipitation (ChIP), followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq), is becoming a widely used powerful method for the analysis of the in vivo DNA-protein interactions at genomic scale.The success of ChIP largely depends on the quality...

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Duc TN, Hassanzadeh-Ghassabeh G, Saerens D, et al. Nanobody-based chromatin immunoprecipitation. Methods Mol Biol. 2012;911:491-505doi: 10.1007/978-1-61779-968-6_31.
Duc, T. N., Hassanzadeh-Ghassabeh, G., Saerens, D., Peeters, E., Charlier, D., & Muyldermans, S. (2012). Nanobody-based chromatin immunoprecipitation. Methods in molecular biology (Clifton, N.J.), 911491-505. https://doi.org/10.1007/978-1-61779-968-6_31
Duc, Trong Nguyen, et al. "Nanobody-based chromatin immunoprecipitation." Methods in molecular biology (Clifton, N.J.) vol. 911 (2012): 491-505. doi: https://doi.org/10.1007/978-1-61779-968-6_31
Duc TN, Hassanzadeh-Ghassabeh G, Saerens D, Peeters E, Charlier D, Muyldermans S. Nanobody-based chromatin immunoprecipitation. Methods Mol Biol. 2012;911:491-505. doi: 10.1007/978-1-61779-968-6_31. PMID: 22886272.
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Cheap-seq.

Science translational medicine

Greene CS.
PMID: 28003542
Sci Transl Med. 2016 Dec 21;8(370):370ec203. doi: 10.1126/scitranslmed.aal3701.

No abstract available.

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Greene CS. Cheap-seq. Sci Transl Med. 2016;8(370):370ec203doi: 10.1126/scitranslmed.aal3701.
Greene, C. S. (2016). Cheap-seq. Science translational medicine, 8(370), 370ec203. https://doi.org/10.1126/scitranslmed.aal3701
Greene, Casey S. "Cheap-seq." Science translational medicine vol. 8,370 (2016): 370ec203. doi: https://doi.org/10.1126/scitranslmed.aal3701
Greene CS. Cheap-seq. Sci Transl Med. 2016 Dec 21;8(370):370ec203. doi: 10.1126/scitranslmed.aal3701. PMID: 28003542.
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LS-GKM: a new gkm-SVM for large-scale datasets.

Bioinformatics (Oxford, England)

Lee D.
PMID: 27153584
Bioinformatics. 2016 Jul 15;32(14):2196-8. doi: 10.1093/bioinformatics/btw142. Epub 2016 Mar 15.

UNLABELLED: gkm-SVM is a sequence-based method for predicting and detecting the regulatory vocabulary encoded in functional DNA elements, and is a commonly used tool for studying gene regulatory mechanisms. Here we introduce new software, LS-GKM, which removes several limitations...

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Lee D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics. 2016;32(14):2196-8doi: 10.1093/bioinformatics/btw142.
Lee, D. (2016). LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics (Oxford, England), 32(14), 2196-8. https://doi.org/10.1093/bioinformatics/btw142
Lee, Dongwon. "LS-GKM: a new gkm-SVM for large-scale datasets." Bioinformatics (Oxford, England) vol. 32,14 (2016): 2196-8. doi: https://doi.org/10.1093/bioinformatics/btw142
Lee D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics. 2016 Jul 15;32(14):2196-8. doi: 10.1093/bioinformatics/btw142. Epub 2016 Mar 15. PMID: 27153584; PMCID: PMC4937189.
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Analyzing ChIP-chip data using bioconductor.

PLoS computational biology

Toedling J, Huber W.
PMID: 19043553
PLoS Comput Biol. 2008 Nov;4(11):e1000227. doi: 10.1371/journal.pcbi.1000227. Epub 2008 Nov 28.

No abstract available.

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Toedling J, Huber W. Analyzing ChIP-chip data using bioconductor. PLoS Comput Biol. 2008;4(11):e1000227doi: 10.1371/journal.pcbi.1000227.
Toedling, J., & Huber, W. (2008). Analyzing ChIP-chip data using bioconductor. PLoS computational biology, 4(11), e1000227. https://doi.org/10.1371/journal.pcbi.1000227
Toedling, Joern, and Huber, Wolfgang. "Analyzing ChIP-chip data using bioconductor." PLoS computational biology vol. 4,11 (2008): e1000227. doi: https://doi.org/10.1371/journal.pcbi.1000227
Toedling J, Huber W. Analyzing ChIP-chip data using bioconductor. PLoS Comput Biol. 2008 Nov;4(11):e1000227. doi: 10.1371/journal.pcbi.1000227. Epub 2008 Nov 28. PMID: 19043553; PMCID: PMC2582686.
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jChIP: a graphical environment for exploratory ChIP-Seq data analysis.

BMC research notes

Chojnowski K, Goryca K, Rubel T, Mikula M.
PMID: 25260876
BMC Res Notes. 2014 Sep 26;7:676. doi: 10.1186/1756-0500-7-676.

BACKGROUND: Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-Seq) provides a powerful tool for discovering protein-DNA interactions. Still, the computational analysis of the great amount of ChIP-Seq data generated, involving mapping of raw data to reference genome, has been a...

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Chojnowski K, Goryca K, Rubel T, et al. jChIP: a graphical environment for exploratory ChIP-Seq data analysis. BMC Res Notes. 2014;7:676doi: 10.1186/1756-0500-7-676.
Chojnowski, K., Goryca, K., Rubel, T., & Mikula, M. (2014). jChIP: a graphical environment for exploratory ChIP-Seq data analysis. BMC research notes, 7676. https://doi.org/10.1186/1756-0500-7-676
Chojnowski, Krzysztof, et al. "jChIP: a graphical environment for exploratory ChIP-Seq data analysis." BMC research notes vol. 7 (2014): 676. doi: https://doi.org/10.1186/1756-0500-7-676
Chojnowski K, Goryca K, Rubel T, Mikula M. jChIP: a graphical environment for exploratory ChIP-Seq data analysis. BMC Res Notes. 2014 Sep 26;7:676. doi: 10.1186/1756-0500-7-676. PMID: 25260876; PMCID: PMC4189168.
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intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data.

BMC genomics

Cao Y, Kitanovski S, Hoffmann D.
PMID: 33372591
BMC Genomics. 2020 Dec 29;21:802. doi: 10.1186/s12864-020-07205-6.

BACKGROUND: RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can...

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Cao Y, Kitanovski S, Hoffmann D. intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data. BMC Genomics. 2020;21:802doi: 10.1186/s12864-020-07205-6.
Cao, Y., Kitanovski, S., & Hoffmann, D. (2020). intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data. BMC genomics, 21802. https://doi.org/10.1186/s12864-020-07205-6
Cao, Yingying, et al. "intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data." BMC genomics vol. 21 (2020): 802. doi: https://doi.org/10.1186/s12864-020-07205-6
Cao Y, Kitanovski S, Hoffmann D. intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data. BMC Genomics. 2020 Dec 29;21:802. doi: 10.1186/s12864-020-07205-6. PMID: 33372591; PMCID: PMC7771091.
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The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data.

BMC genomics

Ambrosini G, Dreos R, Kumar S, Bucher P.
PMID: 27863463
BMC Genomics. 2016 Nov 18;17(1):938. doi: 10.1186/s12864-016-3288-8.

BACKGROUND: ChIP-seq and related high-throughput chromatin profilig assays generate ever increasing volumes of highly valuable biological data. To make sense out of it, biologists need versatile, efficient and user-friendly tools for access, visualization and itegrative analysis of such data.RESULTS:...

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Ambrosini G, Dreos R, Kumar S, et al. The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data. BMC Genomics. 2016;17(1):938doi: 10.1186/s12864-016-3288-8.
Ambrosini, G., Dreos, R., Kumar, S., & Bucher, P. (2016). The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data. BMC genomics, 17(1), 938. https://doi.org/10.1186/s12864-016-3288-8
Ambrosini, Giovanna, et al. "The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data." BMC genomics vol. 17,1 (2016): 938. doi: https://doi.org/10.1186/s12864-016-3288-8
Ambrosini G, Dreos R, Kumar S, Bucher P. The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data. BMC Genomics. 2016 Nov 18;17(1):938. doi: 10.1186/s12864-016-3288-8. PMID: 27863463; PMCID: PMC5116162.
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RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data.

Computer methods and programs in biomedicine

Rubio-Camarillo M, López-Fernández H, Gómez-López G, Carro Á, Fernández JM, Torre CF, Fdez-Riverola F, Glez-Peña D.
PMID: 27886717
Comput Methods Programs Biomed. 2017 Jan;138:73-81. doi: 10.1016/j.cmpb.2016.10.008. Epub 2016 Oct 26.

BACKGROUND AND OBJECTIVE: To facilitate routine analysis and to improve the reproducibility of the results, next-generation sequencing (NGS) analysis requires intuitive, efficient and integrated data processing pipelines.METHODS: We have selected well-established software to construct a suite of automated and...

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Rubio-Camarillo M, López-Fernández H, Gómez-López G, et al. RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data. Comput Methods Programs Biomed. 2016;138:73-81doi: 10.1016/j.cmpb.2016.10.008.
Rubio-Camarillo, M., López-Fernández, H., Gómez-López, G., Carro, �. �., Fernández, J. M., Torre, C. F., Fdez-Riverola, F., & Glez-Peña, D. (2017). RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data. Computer methods and programs in biomedicine, 13873-81. https://doi.org/10.1016/j.cmpb.2016.10.008
Rubio-Camarillo, Miriam, et al. "RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data." Computer methods and programs in biomedicine vol. 138 (2017): 73-81. doi: https://doi.org/10.1016/j.cmpb.2016.10.008
Rubio-Camarillo M, López-Fernández H, Gómez-López G, Carro Á, Fernández JM, Torre CF, Fdez-Riverola F, Glez-Peña D. RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data. Comput Methods Programs Biomed. 2017 Jan;138:73-81. doi: 10.1016/j.cmpb.2016.10.008. Epub 2016 Oct 26. PMID: 27886717.
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A statistical framework for power calculations in ChIP-seq experiments.

Bioinformatics (Oxford, England)

Zuo C, Keleş S.
PMID: 23665773
Bioinformatics. 2014 Mar 15;30(6):753-60. doi: 10.1093/bioinformatics/btt200. Epub 2013 May 10.

MOTIVATION: ChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Although the availability of basic analysis tools for ChIP-seq data is rapidly increasing, there has not been much progress on the related...

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Zuo C, Keleş S. A statistical framework for power calculations in ChIP-seq experiments. Bioinformatics. 2013;30(6):753-60doi: 10.1093/bioinformatics/btt200.
Zuo, C., & Keleş, S. (2014). A statistical framework for power calculations in ChIP-seq experiments. Bioinformatics (Oxford, England), 30(6), 753-60. https://doi.org/10.1093/bioinformatics/btt200
Zuo, Chandler, and Keleş, Sündüz. "A statistical framework for power calculations in ChIP-seq experiments." Bioinformatics (Oxford, England) vol. 30,6 (2014): 753-60. doi: https://doi.org/10.1093/bioinformatics/btt200
Zuo C, Keleş S. A statistical framework for power calculations in ChIP-seq experiments. Bioinformatics. 2014 Mar 15;30(6):753-60. doi: 10.1093/bioinformatics/btt200. Epub 2013 May 10. PMID: 23665773; PMCID: PMC3957067.
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Showing 1 to 12 of 20 entries