We are analyzing https://link.springer.com/article/10.1186/1471-2105-12-139.

Title:
PeakRanger: A cloud-enabled peak caller for ChIP-seq data | BMC Bioinformatics
Description:
Background Chromatin immunoprecipitation (ChIP), coupled with massively parallel short-read sequencing (seq) is used to probe chromatin dynamics. Although there are many algorithms to call peaks from ChIP-seq datasets, most are tuned either to handle punctate sites, such as transcriptional factor binding sites, or broad regions, such as histone modification marks; few can do both. Other algorithms are limited in their configurability, performance on large data sets, and ability to distinguish closely-spaced peaks. Results In this paper, we introduce PeakRanger, a peak caller software package that works equally well on punctate and broad sites, can resolve closely-spaced peaks, has excellent performance, and is easily customized. In addition, PeakRanger can be run in a parallel cloud computing environment to obtain extremely high performance on very large data sets. We present a series of benchmarks to evaluate PeakRanger against 10 other peak callers, and demonstrate the performance of PeakRanger on both real and synthetic data sets. We also present real world usages of PeakRanger, including peak-calling in the modENCODE project. Conclusions Compared to other peak callers tested, PeakRanger offers improved resolution in distinguishing extremely closely-spaced peaks. PeakRanger has above-average spatial accuracy in terms of identifying the precise location of binding events. PeakRanger also has excellent sensitivity and specificity in all benchmarks evaluated. In addition, PeakRanger offers significant improvements in run time when running on a single processor system, and very marked improvements when allowed to take advantage of the MapReduce parallel environment offered by a cloud computing resource. PeakRanger can be downloaded at the official site of modENCODE project: http://www.modencode.org/software/ranger/
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Keywords {🔍}

peak, data, peakranger, pubmed, article, google, chipseq, binding, scholar, peaks, figure, regions, sites, central, callers, cas, sets, analysis, software, file, number, caller, enriched, size, original, algorithms, genome, authors, performance, calling, summits, resolution, set, algorithm, test, bioinformatics, full, histone, cloud, computing, benchmarks, additional, site, broad, false, region, chromatin, sensitivity, reads, parallel,

Topics {✒️}

related subjects cold spring harbor vivo protein-dna interactions article download pdf inter-peak spacing varied modern multi-core processors true-negative binding sites undergo post-call processing resolve closely-spaced peaks chip-seq experiments relative semi-synthetic datasets consisting interpret chip-seq data cloud-enabled peak caller biological binding sites pre-computed genome tables evaluating inter-peak resolution chip-seq data analysis hard-coded file paths high-throughput sequence tags target binding site real chip-seq datasets real-world data sets full size image chip-seq production environment full access biological research settings average twin-peak separation chip-seq data sets increasing inter-peak separation chip-exo peak-callers target binding sites lincoln stein article feng clustering approach independent chip-seq datasets distinguish closely-spaced peaks semi-synthetic data set massively parallel sequencing chip-seq peak detection cloud parallel computing defining read-enriched regions primary download site depleted central nucleosomes support multi-thread mode increasing inter-peak distance specific data formats synthetic binding site regular single-processor version cloud computing[31] offers central nucleosome

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WebPage:
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         description:Chromatin immunoprecipitation (ChIP), coupled with massively parallel short-read sequencing (seq) is used to probe chromatin dynamics. Although there are many algorithms to call peaks from ChIP-seq datasets, most are tuned either to handle punctate sites, such as transcriptional factor binding sites, or broad regions, such as histone modification marks; few can do both. Other algorithms are limited in their configurability, performance on large data sets, and ability to distinguish closely-spaced peaks. In this paper, we introduce PeakRanger, a peak caller software package that works equally well on punctate and broad sites, can resolve closely-spaced peaks, has excellent performance, and is easily customized. In addition, PeakRanger can be run in a parallel cloud computing environment to obtain extremely high performance on very large data sets. We present a series of benchmarks to evaluate PeakRanger against 10 other peak callers, and demonstrate the performance of PeakRanger on both real and synthetic data sets. We also present real world usages of PeakRanger, including peak-calling in the modENCODE project. Compared to other peak callers tested, PeakRanger offers improved resolution in distinguishing extremely closely-spaced peaks. PeakRanger has above-average spatial accuracy in terms of identifying the precise location of binding events. PeakRanger also has excellent sensitivity and specificity in all benchmarks evaluated. In addition, PeakRanger offers significant improvements in run time when running on a single processor system, and very marked improvements when allowed to take advantage of the MapReduce parallel environment offered by a cloud computing resource. PeakRanger can be downloaded at the official site of modENCODE project: http://www.modencode.org/software/ranger/
         datePublished:2011-05-09T00:00:00Z
         dateModified:2011-05-09T00:00:00Z
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            Enrich Region
            Peak Calling
            Target Binding Site
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      headline:PeakRanger: A cloud-enabled peak caller for ChIP-seq data
      description:Chromatin immunoprecipitation (ChIP), coupled with massively parallel short-read sequencing (seq) is used to probe chromatin dynamics. Although there are many algorithms to call peaks from ChIP-seq datasets, most are tuned either to handle punctate sites, such as transcriptional factor binding sites, or broad regions, such as histone modification marks; few can do both. Other algorithms are limited in their configurability, performance on large data sets, and ability to distinguish closely-spaced peaks. In this paper, we introduce PeakRanger, a peak caller software package that works equally well on punctate and broad sites, can resolve closely-spaced peaks, has excellent performance, and is easily customized. In addition, PeakRanger can be run in a parallel cloud computing environment to obtain extremely high performance on very large data sets. We present a series of benchmarks to evaluate PeakRanger against 10 other peak callers, and demonstrate the performance of PeakRanger on both real and synthetic data sets. We also present real world usages of PeakRanger, including peak-calling in the modENCODE project. Compared to other peak callers tested, PeakRanger offers improved resolution in distinguishing extremely closely-spaced peaks. PeakRanger has above-average spatial accuracy in terms of identifying the precise location of binding events. PeakRanger also has excellent sensitivity and specificity in all benchmarks evaluated. In addition, PeakRanger offers significant improvements in run time when running on a single processor system, and very marked improvements when allowed to take advantage of the MapReduce parallel environment offered by a cloud computing resource. PeakRanger can be downloaded at the official site of modENCODE project: http://www.modencode.org/software/ranger/
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         Target Binding Site
         Histone Modification Mark
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         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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      name:Cold Spring Harbor Laboratory, Cold Spring Harbor, USA
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