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ZINBA 将局部协变量与 DNA 测序数据相结合,以识别广泛和狭窄的富集区域,即使在扩增的基因组区域内也是如此。

ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions.

机构信息

Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

出版信息

Genome Biol. 2011 Jul 25;12(7):R67. doi: 10.1186/gb-2011-12-7-r67.

Abstract

ZINBA (Zero-Inflated Negative Binomial Algorithm) identifies genomic regions enriched in a variety of ChIP-seq and related next-generation sequencing experiments (DNA-seq), calling both broad and narrow modes of enrichment across a range of signal-to-noise ratios. ZINBA models and accounts for factors that co-vary with background or experimental signal, such as G/C content, and identifies enrichment in genomes with complex local copy number variations. ZINBA provides a single unified framework for analyzing DNA-seq experiments in challenging genomic contexts.

摘要

ZINBA(零膨胀负二项式算法)可识别 ChIP-seq 和相关下一代测序实验(DNA-seq)中丰富的基因组区域,在各种信噪比范围内调用广泛和狭窄的富集模式。ZINBA 模型考虑了与背景或实验信号相关的因素,如 G/C 含量,并识别了具有复杂局部拷贝数变异的基因组中的富集。ZINBA 为分析具有挑战性基因组背景的 DNA-seq 实验提供了单一统一的框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8146/3218829/624a27910970/gb-2011-12-7-r67-1.jpg

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