Results 31 to 40 of about 459,773 (306)

Results of ChIP-Seq and DNase-Seq

, 2023
Results of ChIP-Seq and DNase-Seq for Cavendish banana ...
Xiuxiu Li (13880603)
core   +1 more source

Computational methodology for ChIP‐seq analysis [PDF]

Quantitative Biology, 2013
Chromatin immunoprecipitation coupled with massive parallel sequencing (ChIP‐seq) is a powerful technology to identify the genome‐wide locations of DNA binding proteins such as transcription factors or modified histones. As more and more experimental laboratories are adopting ChIP‐seq to unravel the transcriptional and epigenetic regulatory mechanisms,
Hyunjin Shin, Tao Liu 0022, Xikun Duan, Yong Zhang 0006, Xiaole Shirley Liu   +4 more
openaire   +2 more sources

Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique

Frontiers in Cellular and Infection Microbiology, 2023
IntroductionVarious sequencing based approaches are used to identify and characterize the activities of cis-regulatory elements in a genome-wide fashion.
Ronald J. Nowling, Kimani Njoya, John G. Peters, Michelle M. Riehle   +3 more
doaj   +1 more source

PICS: Probabilistic Inference for ChIP-seq [PDF]

Biometrics, 2010
ChIP-seq, which combines chromatin immunoprecipitation with massively parallel short-read sequencing, can profile in vivo genome-wide transcription factor-DNA association with higher sensitivity, specificity and spatial resolution than ChIP-chip. While it presents new opportunities for research, ChIP-seq poses new challenges for statistical analysis ...
Zhang, Xuekui, Robertson, Gordon, Krzywinski, Martin, Ning, Kaida, Droit, Arnaud, Jones, Steven, Gottardo, Raphael   +6 more
openaire   +4 more sources

CSA: a web service for the complete process of ChIP-Seq analysis

BMC Bioinformatics, 2019
Background Chromatin immunoprecipitation sequencing (ChIP-seq) is a technology that combines chromatin immunoprecipitation (ChIP) with next generation of sequencing technology (NGS) to analyze protein interactions with DNA.
Min Li, Li Tang, Fang-Xiang Wu, Yi Pan, Jianxin Wang   +4 more
doaj   +1 more source

Imputation for transcription factor binding predictions based on deep learning. [PDF]

PLoS Computational Biology, 2017
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard ...
Qian Qin, Jianxing Feng
doaj   +1 more source

ChIP-Seq of HNRNPL in proliferating keratinocytes.

, 2021
List of the genes bound by HNRNPL including the coordinates where the peaks were found. ChIP-Seq, chromatin immunoprecipitation sequencing; HNRNPL, heterogeneous nuclear ribonucleoprotein L. (XLSX)
George L. Sen (6847421), Jingting Li (3284010), Varun Bansal (5802104), Yifang Chen (318398), Manisha Tiwari (3697003)   +4 more
core   +1 more source

Impact of genome assembly status on ChIP-Seq and ChIP-PET data mapping

BMC Research Notes, 2009
Background ChIP-Seq and ChIP-PET can potentially be used with any genome for genome wide profiling of protein-DNA interaction sites. Unfortunately, it is probable that most genome assemblies will never reach the quality of the human genome assembly ...
Sachs Laurent, Buisine Nicolas
doaj   +1 more source

lifei176/ChIP-seq-ATAC-seq: ATAC-seq and ChIP-seq

, 2021
This is first ...
lifei176
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ChIP-Seq analysis of the recruitment of Nurr1 and histone modifiers to the HIV provirus.

, 2022
Histograms show numbers of sequence reads on the Y axis along the length of the reporter HIV-1 pro-viral genome on the X axis. A, Aligned reporter genome. B, Nurr1 (Santa Cruz Biotech, Cat #sc-81345). C, CoREST (Cell Signaling, Cat #14567.
David Alvarez-Carbonell (3721597), Konstantin Leskov (90606), Jonathan Karn (220557), Saba Valadkhan (11442560), Kien Nguyen (1484146), Fengchun Ye (164906), Yoelvis Garcia-Mesa (3721603), Sheetal Sreeram (13015208)   +7 more
core   +1 more source