discussion

08.05.15

This entry was posted in Abstracts discussion and tagged RNA-seq Systems Biology on by

Computational and analytical challenges in single-cell transcriptomics

Oliver Stegle1, Sarah A. Teichmann1,2 and John C. Marioni1,2

1European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. 2Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. Correspondence to J.C.M.  e-mail: marioni@ebi.ac.uk doi:10.1038/nrg3833 Published online 28 January 2015

 

Abstract

The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.

07.22.15

This entry was posted in Abstracts discussion and tagged correlation proportionality Systems Biology WID on by

Proportionality: A Valid Alternative to Correlation for Relative Data

David Lovell
Queensland University of Technology, Brisbane, Australia
Vera Pawlowsky-Glahn
Dept. d’Informàtica, Matemàtica Aplicada i Estadística. U. de Girona, España
Juan José Egozcue
Dept. Applied Mathematics III, U. Politécnica de Catalunya, Barcelona, Spain
Samuel Marguerat
MRC Clinical Sciences Centre, Imperial College London, United Kingdom
Jürg Bähler
Research Department of Genetics, Evolution and Environment, University College London, United Kingdom

Abstract

In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative—or compositional—data, differential expression needs careful interpretation, and correlation—a statistical workhorse for analyzing pairwise relationships—is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic ϕ which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.

12.10.14

This entry was posted in Abstracts discussion and tagged Cancer Genome Atlas DNA methylation ENCODE miRNAs RACER Systems Biology transcription factor on by

Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia

Yue Li, Minggao Liang, Zhaolei Zhang

Abstract

Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML prognostic marker. Together, we provided a novel framework that successfully integrated the TCGA and ENCODE data in revealing AML-specific regulatory program at global level.

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.

10.29.14

This entry was posted in Abstracts discussion and tagged arabidopsis coregulation Matlab Systems Biology time series wigwams on by

Wigwams: identifying gene modules co-regulated across multiple biological conditions

  1. Krzysztof Polanski1,†,
  2. Johanna Rhodes1,†‡,
  3. Claire Hill2,
  4. Peijun Zhang2,
  5. Dafyd J. Jenkins1,
  6. Steven J. Kiddle1,§,
  7. Aleksey Jironkin1,
  8. Jim Beynon1,2,
  9. Vicky Buchanan-Wollaston1,2,
  10. Sascha Ott1 and
  11. Katherine J. Denby1,2,*

+ Author Affiliations


  1. 1Warwick Systems Biology Centre and 2School of Life Sciences, University of Warwick, CV4 7AL, UK
  1. ↵*To whom correspondence should be addressed.
  • Received September 17, 2013.
  • Revision received December 12, 2013.
  • Accepted December 13, 2013.

Abstract

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.

Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions.

Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams.

Contact: k.j.denby@warwick.ac.uk

Supplementary Data: Supplementary data are available at Bioinformatics online.

10.15.14

This entry was posted in Abstracts discussion and tagged Algorithms Graemlin 2.0 network aligner scoring function Systems Biology on by

Automatic Parameter Learning for Multiple Network Alignment

Jason Flannick1, Antal Novak1, Chuong B. Do1, Balaji S. Srinivasan2, and Serafim Batzoglou1
1Department of Computer Science, Stanford University, Stanford, CA 94305, USA
2Department of Statistics, Stanford University, Stanford, CA 94305, USA

Abstract

We developed Græmlin 2.0, a new multiple network aligner with (1) a novel scoring func-
tion; (2) an algorithm that automatically learns the scoring function’s parameters; and (3) an
algorithm that uses the scoring function to globally align multiple networks. Existing alignment
tools use heuristic scoring functions, which must be hand-tuned to a given set of networks and
do not apply to multiple network alignment.
Our scoring function can use arbitrary features of a multiple network alignment, such as
protein deletions, protein duplications, protein mutations, and interaction losses. Our parameter
learning algorithm uses a training set of known network alignments to learn parameters for
our scoring function and thereby automatically adapts it to any set of networks. Our global
alignment algorithm finds approximate multiple network alignments in linear time.
We tested Græmlin 2.0’s accuracy on protein interaction networks from IntAct, DIP, and
the Stanford Network Database. We show that, on each of these datasets, Græmlin 2.0 has
higher sensitivity and specificity than existing network aligners. Græmlin 2.0 is available under
the GNU public license at http://graemlin.stanford.edu.

09.17.14

This entry was posted in Abstracts discussion and tagged DNA methlation epigenetics gene expression gioblastoma PARADIGM Systems Biology on by

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

 

Charles J. Vaske1,†, Stephen C. Benz2,†, J. Zachary Sanborn2, Dent Earl2, Christopher Szeto2, Jingchun Zhu2, David Haussler1,2 and Joshua M. Stuart2,*

+ Author Affiliations

1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA

* To whom correspondence should be addressed.

Abstract

Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines.

Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.

Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway’s activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference.

Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.

Availability:Source code available at http://sbenz.github.com/Paradigm

Contact: jstuart@soe.ucsc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

08.06.2014

This entry was posted in Abstracts discussion and tagged Cdx2 ChIP-seq MultiGPS regulatory proteins Systems Biology on by

An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding

 

Shaun Mahony*, Matthew D. Edwards*, Esteban O. Mazzoni, Richard I. Sherwood, Akshay Kakumanu, Carolyn A. Morrison, Hynek Wichterle, David K. Gifford

*equal contributor

Published: March 27, 2014    DOI: 10.1371/journal.pcbi.1003501

Abstract

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS’s multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.

07.09.2014 and 07.23.14

This entry was posted in Abstracts discussion and tagged DREAM 8 signaling networks signaling proteins Systems Biology time course trajectories on by

Predicting Dynamic Signaling Network Response under Unseen Perturbations

Fan Zhu 1 and Yuanfang Guan 1,2,3,*

1 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

2 Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA

3 Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA

* To whom correspondence should be addressed.

ABSTRACT

Motivation: Predicting trajectories of signaling networks under complex perturbations is one of the most valuable but challenging tasks in systems biology. Signaling networks are involved in most of the biological pathways and modeling their dynamics has wide applications including drug design and treatment outcome prediction.

Results: In this paper, we report a novel model for predicting the cell type-specific time course response of signaling proteins under unseen perturbations. This algorithm achieved the top performance in the 2013 8th Dialogue for Reverse Engineering Assessments and Methods (DREAM 8) sub challenge: time course prediction in breast cancer cell lines. We formulate the trajectory prediction problem into a standard regularization problem; the solution becomes solving this discrete ill-posed problem. This algorithm includes three steps: denoising, estimating regression coefficients and modeling trajectories under unseen perturbations. We further validated the accuracy of this method against simulation and experimental data. Furthermore, this method reduces computational time by magnitudes compared to state-of-the-art methods, allowing genome-wide modeling of signaling pathways and time course trajectories to be carried out in a practical time.

Availability and Implementation: Source code is available at http://guanlab.ccmb.med.umich.edu/DREAM/code.html and as supplementary file online. Contact: gyuanfan@umich.edu

 

06.25.2014

This entry was posted in Abstracts discussion and tagged DNA methylation gene expression Giovanni Montana regression Systems Biology on by

Network-guided regression for detecting associations between DNA methylation and gene expression

 

Zi Wang1, Edward Curry2 and Giovanni Montana1,3,*

1Department of Mathematics, Imperial College London, London SW7 2AZ.

2 Division of Cancer, Imperial College London, Hammersmith Hospital, London, W12 0NN

3 Department of Biomedical Engineering, King’s College London, St Thomas’ Hospital, London SE1 7EH

*To whom correspondence should be addressed. Giovanni Montana, E-mail: giovanni.montana@kcl.ac.uk

 

Abstract 

Motivation: High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. This data could yield discoveries that lead to breakthroughs in the diagnosis and treatment of human disease, but requires statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.

Results: We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to TCGA datasets on primary ovarian tumours.

Availability: R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/~gmontana/