Monthly Archives: November 2014

11.26.14

This entry was posted in Uncategorized on by

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation

  1. Tarmo Äijö1,*,
  2. Vincent Butty2,
  3. Zhi Chen3,
  4. Verna Salo3,
  5. Subhash Tripathi3,
  6. Christopher B. Burge2,
  7. Riitta Lahesmaa3 and
  8. Harri Lähdesmäki1,3,*

+Author Affiliations


  1. 1Department of Information and Computer Science, Aalto University, FI-00076 Aalto, Finland, 2Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA and 3Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
  1. ↵*To whom correspondence should be addressed

Abstract

Motivation: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species’ landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed.

Results: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T–cell activation (Th0). To quantify Th17–specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment–specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint–wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected.

Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/

Contact: tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fi

Supplementary information: Supplementary data are available atBioinformatics online.

11.12.14

This entry was posted in Abstracts discussion and tagged DNase histone modifications Markov model Systems Biology transcription factor on by

Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications

  1. Eduardo G. Gusmao1,*,
  2. Christoph Dieterich2,
  3. Martin Zenke3,4 and
  4. Ivan G. Costa1,5,6,*

+Author Affiliations


  1. 1IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, 2Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, 3Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, 4Helmholtz Institute for Biomedical Engineering, 52074, 5Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and 6Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil
  1. ↵*To whom correspondence should be addressed
  • Received October 28, 2013.
  • Revision received June 27, 2014.
  • Accepted July 25, 2014.

Abstract

Motivation: The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction.

Results: We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such asde novo motif finding.

Availability and implementation: Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm.

Contact: ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de

Supplementary information: Supplementary data are available atBioinformatics online.