Xiaobei Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaobei Zhao has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Albin Sandelin
University of Copenhagen
2 shared publications - Brian John Parker
John Curtin School of Medical Research School of Computing John Curtin School of Medical Research Australian National University Australian National University Australian National University
1 shared publication - Eivind Valen
Department of Biosciences, University of Oslo, Oslo, Norway
1 shared publication - Jiayu Wen
The John Curtin School of Medical Research, The Australian National University, Canberra, Australia
1 shared publication - Mette Jørgensen
PERSIMUNE Centre of Excellence
1 shared publication - Raivo Kolde
Institute of Computer Science, University of Tartu, Tartu, Estonia
1 shared publication
Latest Publications
- Open Access
- 17 February 2012
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 28 (8) , 1164-1165
GMD: measuring the distance between histograms with applications on high-throughput sequencing reads
Summary:GMD (generalized minimum distance of distributions) is an R package to assess the similarity between spatial distributions of read-based sequencing data such as ChIP-seq and RNA-seq. GMD calculates the optimal distance between pairs of normalized signal distributions, optionally sliding one distribution over the other to ‘align’ the distributions. GMD also provides graphical and downstream clustering tools. Availability: The R package GMD source code is available at http://cran.r-project.org/web/packages/GMD/ under GPL license Contact:xiaobei@binf.ku.dk; mailto:albin@binf.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online.
- 17 February 2012
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 28 (8) , 1164-1165
Open AccessGMD: measuring the distance between histograms with applications on high-throughput sequencing reads
Summary:GMD (generalized minimum distance of distributions) is an R package to assess the similarity between spatial distributions of read-based sequencing data such as ChIP-seq and RNA-seq. GMD calculates the optimal distance between pairs of normalized signal distributions, optionally sliding one distribution over the other to ‘align’ the distributions. GMD also provides graphical and downstream clustering tools. Availability: The R package GMD source code is available at http://cran.r-project.org/web/packages/GMD/ under GPL license Contact:xiaobei@binf.ku.dk; mailto:albin@binf.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online.
- Open Access
- 3 November 2011
- Published by Springer Nature in BMC Genomics
- Vol. 12 (1) , 544
Prediction of RNA Polymerase II recruitment, elongation and stalling from histone modification data
Background: Initiation and elongation of RNA polymerase II (RNAPII) transcription is regulated by both DNA sequence and chromatin signals. Recent breakthroughs make it possible to measure the chromatin state and activity of core promoters genome-wide, but dedicated computational strategies are needed to progress from descriptive annotation of data to quantitative, predictive models. Results: Here, we describe a computational framework which with high accuracy can predict the locations of core promoters, the amount of recruited RNAPII at the promoter, the amount of elongating RNAPII in the gene body, the mRNA production originating from the promoter and finally also the stalling characteristics of RNAPII by considering both quantitative and spatial features of histone modifications around the transcription start site (TSS). As the model framework can also pinpoint the signals that are the most influential for prediction, it can be used to infer underlying regulatory biology. For example, we show that the H3K4 di- and tri- methylation signals are strongly predictive for promoter location while the acetylation marks H3K9 and H3K27 are highly important in estimating the promoter usage. All of these four marks are found to be necessary for recruitment of RNAPII but not sufficient for the elongation. We also show that the spatial distributions of histone marks are almost as predictive as the signal strength and that a set of histone marks immediately downstream of the TSS is highly predictive of RNAPII stalling. Conclusions: In this study we introduce a general framework to accurately predict the level of RNAPII recruitment, elongation, stalling and mRNA expression from chromatin signals. The versatility of the method also makes it ideally suited to investigate other genomic data.
- 3 November 2011
- Published by Springer Nature in BMC Genomics
- Vol. 12 (1) , 544
Open AccessPrediction of RNA Polymerase II recruitment, elongation and stalling from histone modification data
Background: Initiation and elongation of RNA polymerase II (RNAPII) transcription is regulated by both DNA sequence and chromatin signals. Recent breakthroughs make it possible to measure the chromatin state and activity of core promoters genome-wide, but dedicated computational strategies are needed to progress from descriptive annotation of data to quantitative, predictive models. Results: Here, we describe a computational framework which with high accuracy can predict the locations of core promoters, the amount of recruited RNAPII at the promoter, the amount of elongating RNAPII in the gene body, the mRNA production originating from the promoter and finally also the stalling characteristics of RNAPII by considering both quantitative and spatial features of histone modifications around the transcription start site (TSS). As the model framework can also pinpoint the signals that are the most influential for prediction, it can be used to infer underlying regulatory biology. For example, we show that the H3K4 di- and tri- methylation signals are strongly predictive for promoter location while the acetylation marks H3K9 and H3K27 are highly important in estimating the promoter usage. All of these four marks are found to be necessary for recruitment of RNAPII but not sufficient for the elongation. We also show that the spatial distributions of histone marks are almost as predictive as the signal strength and that a set of histone marks immediately downstream of the TSS is highly predictive of RNAPII stalling. Conclusions: In this study we introduce a general framework to accurately predict the level of RNAPII recruitment, elongation, stalling and mRNA expression from chromatin signals. The versatility of the method also makes it ideally suited to investigate other genomic data.