scholarly article | Q13442814 |
P8978 | DBLP publication ID | journals/bioinformatics/ZuoK14 |
P356 | DOI | 10.1093/BIOINFORMATICS/BTT200 |
P932 | PMC publication ID | 3957067 |
P698 | PubMed publication ID | 23665773 |
P5875 | ResearchGate publication ID | 236691611 |
P50 | author | Sündüz Keleş | Q91236256 |
P2093 | author name string | Chandler Zuo | |
P2860 | cites work | PICS: probabilistic inference for ChIP-seq. | Q51557644 |
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PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls | Q28305773 | ||
Identification of functional elements and regulatory circuits by Drosophila modENCODE | Q29617551 | ||
A Statistical Framework for the Analysis of ChIP-Seq Data | Q31007638 | ||
An integrated software system for analyzing ChIP-chip and ChIP-seq data | Q33382059 | ||
Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks | Q33390488 | ||
A signal-noise model for significance analysis of ChIP-seq with negative control | Q33550016 | ||
ChIP-chip versus ChIP-seq: lessons for experimental design and data analysis. | Q33831056 | ||
ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions | Q33970447 | ||
Heritable individual-specific and allele-specific chromatin signatures in humans | Q34087197 | ||
Variation in transcription factor binding among humans | Q34105133 | ||
Systematic evaluation of factors influencing ChIP-seq fidelity | Q34241267 | ||
Normalization of ChIP-seq data with control | Q34376050 | ||
Genome-scale analysis of escherichia coli FNR reveals complex features of transcription factor binding. | Q34789412 | ||
Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration | Q35451782 | ||
Design and analysis of ChIP-seq experiments for DNA-binding proteins | Q37003887 | ||
P433 | issue | 6 | |
P407 | language of work or name | English | Q1860 |
P921 | main subject | statistics | Q12483 |
P1104 | number of pages | 8 | |
P304 | page(s) | 753-760 | |
P577 | publication date | 2013-05-10 | |
P1433 | published in | Bioinformatics | Q4914910 |
P1476 | title | A statistical framework for power calculations in ChIP-seq experiments | |
P478 | volume | 30 |
Q55379249 | A Hierarchical Framework for State-Space Matrix Inference and Clustering. |
Q31141946 | A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets |
Q90372548 | A chromatin integration labelling method enables epigenomic profiling with lower input |
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Q38775774 | Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation |
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Q35933400 | Systematic evaluation of the impact of ChIP-seq read designs on genome coverage, peak identification, and allele-specific binding detection |
Q30829889 | Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis |
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