1. Computational and Systems Biology
  2. Genetics and Genomics

A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution

  1. Xu Zhou
  2. Alexander W Blocker
  3. Edoardo M Airoldi  Is a corresponding author
  4. Erin K O'Shea  Is a corresponding author
  1. Yale School of Medicine, United States
  2. Harvard University, United States
  3. Howard Hughes Medical Institute, Harvard University, United States
https://doi.org/10.7554/eLife.16970
Share this article
Cite this article
  1. Xu Zhou
  2. Alexander W Blocker
  3. Edoardo M Airoldi
  4. Erin K O'Shea
(2016)
A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution
eLife 5:e16970.
https://doi.org/10.7554/eLife.16970
  2. Download BibTeX
  3. Download .RIS

Abstract

Understanding chromatin function requires knowing the precise location of nucleosomes. MNase-seq methods have been widely applied to characterize nucleosome organization in vivo, but generally lack the accuracy to determine the precise nucleosome positions. Here we develop a computational approach leveraging digestion variability to determine nucleosome positions at base-pair resolution from MNase-seq data. We generate a variability template as a simple error model for how MNase digestion affects mapping of individual nucleosomes. Applied to both yeast and human cells, this analysis reveals that alternatively positioned nucleosomes are prevalent and create significant heterogeneity in a cell population. We show that the periodic occurrences of dinucleotide sequences relative to nucleosome dyads can be directly determined from genome-wide nucleosome positions from MNase-seq. Alternatively positioned nucleosomes near transcription start sites likely represent different states of promoter nucleosomes during transcription initiation. Our method can be applied to map nucleosome positions in diverse organisms at base-pair resolution.

Data availability

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Xu Zhou

    Yale School of Medicine, New Haven, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1692-6823

  2. Alexander W Blocker

    Department of Statistics, Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.

  3. Edoardo M Airoldi

    Department of Statistics, Harvard University, Cambridge, United States
    For correspondence
    airoldi@fas.harvard.edu
    Competing interests
    No competing interests declared.

  4. Erin K O'Shea

    Faculty of Arts and Sciences Center for Systems Biology, Howard Hughes Medical Institute, Harvard University, Cambridge, United States
    For correspondence
    osheae@hhmi.org
    Competing interests
    Erin K O'Shea, President at the Howard Hughes Medical Institute, one of the three founding funders of eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2649-1018

Funding

Howard Hughes Medical Institute

  • Xu Zhou
  • Erin K O'Shea

National Institute of General Medical Sciences (GM-096193)

  • Alexander W Blocker
  • Edoardo M Airoldi

Alfred P. Sloan Foundation

  • Alexander W Blocker
  • Edoardo M Airoldi

Jane Coffin Childs Memorial Fund for Medical Research

  • Xu Zhou

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2016, Zhou et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,489
    views
  • 875
    downloads
  • 21
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Xu Zhou
  2. Alexander W Blocker
  3. Edoardo M Airoldi
  4. Erin K O'Shea
(2016)
A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution
eLife 5:e16970.
https://doi.org/10.7554/eLife.16970

Share this article

https://doi.org/10.7554/eLife.16970

Categories and tags

Research organisms

Further reading

    1. Computational and Systems Biology

    Efficient coding in biophysically realistic excitatory-inhibitory spiking networks

    Veronika Koren, Simone Blanco Malerba ... Stefano Panzeri
    Research Article

    The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

    1. Computational and Systems Biology

    Evaluation of information flows in the RAS-MAPK system using transfer entropy measurements

    Nobuhisa Umeki, Yoshiyuki Kabashima, Yasushi Sako
    Research Article

    The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Discovering root causal genes with high-throughput perturbations

    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.