Monthly Archives: October 2014

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.