Limits...
PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond.

Bible PW, Kanno Y, Wei L, Brooks SR, O'Shea JJ, Morasso MI, Loganantharaj R, Sun HW - PLoS ONE (2015)

Bottom Line: Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research.We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis.To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Skin Biology, Intramural Research Program, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, Maryland, United States of America.

ABSTRACT
Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST's functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work.

No MeSH data available.


Signal Distribution of Key Transcription Factors in Super Enhancers and Typical Enhancers.Quantitative applications of PAPST compared peak signals within super-enhancers (SEs) to those within typical enhancers (TEs) of five factors showing significantly stronger signal in SEs. Red: peak signal distribution within super-enhancers. Green: peak signal distribution within typical enhancers. Peak signals are expressed as normalized read counts. All comparisons are significant with p < 1e-10 (Welch's t-test).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4430287&req=5

pone.0127285.g003: Signal Distribution of Key Transcription Factors in Super Enhancers and Typical Enhancers.Quantitative applications of PAPST compared peak signals within super-enhancers (SEs) to those within typical enhancers (TEs) of five factors showing significantly stronger signal in SEs. Red: peak signal distribution within super-enhancers. Green: peak signal distribution within typical enhancers. Peak signals are expressed as normalized read counts. All comparisons are significant with p < 1e-10 (Welch's t-test).

Mentions: PAPST can generate quantitative data for extended co-localization analysis. ESC super enhancers have been shown to have higher levels of active enhancer epigenetic mark H3K27Ac and binding of key TFs as compared to typical enhancers [16]. We used PAPST to perform total tags based peak signal normalization of ChIP-Seq peaks for Oct4, Sox2, Nanog, Mediator (Med1), and H3K27Ac. Next PAPST was used to generate normalized read signals for these factor’s peaks in super enhancer regions and in typical enhancer regions respectively. The comparative results are shown in Fig 3, which indicate significantly higher levels of these key factors in the super enhancer associated peaks than those associated with the typical enhancers (p-values are: Oct4 2.26E-33, Sox2 1.96E-30, Nanog 1.30E-19, H3K27Ac 1.78E-32, and Med1 4.17E-12 using Welch's t-test). We also used PAPST to quickly generate the co-localization data showing a significantly higher percentage of super enhancers are occupied by H3K27Ac and Med1 compared to typical enhancers (Fig 4). In these rapid applications of PAPST (see the Performance and Usability section above for the timings), co-localized peaks are not only easily identified, but they can also be investigated quantitatively.


PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond.

Bible PW, Kanno Y, Wei L, Brooks SR, O'Shea JJ, Morasso MI, Loganantharaj R, Sun HW - PLoS ONE (2015)

Signal Distribution of Key Transcription Factors in Super Enhancers and Typical Enhancers.Quantitative applications of PAPST compared peak signals within super-enhancers (SEs) to those within typical enhancers (TEs) of five factors showing significantly stronger signal in SEs. Red: peak signal distribution within super-enhancers. Green: peak signal distribution within typical enhancers. Peak signals are expressed as normalized read counts. All comparisons are significant with p < 1e-10 (Welch's t-test).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4430287&req=5

pone.0127285.g003: Signal Distribution of Key Transcription Factors in Super Enhancers and Typical Enhancers.Quantitative applications of PAPST compared peak signals within super-enhancers (SEs) to those within typical enhancers (TEs) of five factors showing significantly stronger signal in SEs. Red: peak signal distribution within super-enhancers. Green: peak signal distribution within typical enhancers. Peak signals are expressed as normalized read counts. All comparisons are significant with p < 1e-10 (Welch's t-test).
Mentions: PAPST can generate quantitative data for extended co-localization analysis. ESC super enhancers have been shown to have higher levels of active enhancer epigenetic mark H3K27Ac and binding of key TFs as compared to typical enhancers [16]. We used PAPST to perform total tags based peak signal normalization of ChIP-Seq peaks for Oct4, Sox2, Nanog, Mediator (Med1), and H3K27Ac. Next PAPST was used to generate normalized read signals for these factor’s peaks in super enhancer regions and in typical enhancer regions respectively. The comparative results are shown in Fig 3, which indicate significantly higher levels of these key factors in the super enhancer associated peaks than those associated with the typical enhancers (p-values are: Oct4 2.26E-33, Sox2 1.96E-30, Nanog 1.30E-19, H3K27Ac 1.78E-32, and Med1 4.17E-12 using Welch's t-test). We also used PAPST to quickly generate the co-localization data showing a significantly higher percentage of super enhancers are occupied by H3K27Ac and Med1 compared to typical enhancers (Fig 4). In these rapid applications of PAPST (see the Performance and Usability section above for the timings), co-localized peaks are not only easily identified, but they can also be investigated quantitatively.

Bottom Line: Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research.We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis.To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Skin Biology, Intramural Research Program, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, Maryland, United States of America.

ABSTRACT
Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST's functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work.

No MeSH data available.