Automatic Parameter Learning for Multiple Local Network Alignment

Flannick, Jason; Novak, Antal F.; Chuong, B.; Srinivasan, Balaji; Batzoglou, Serafim

doi:10.1089/cmb.2009.0099

Cited by 69 publications

(63 citation statements)

References 40 publications

(30 reference statements)

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“…Local network alignments attempt to align small subsets of nodes between multiple networks, whereas global network alignments attempt to find the best alignment of all nodes in one network with another (Singh et al, 2008). Various heuristics exist for global alignment of two or more networks, and typically these methods first use homology to prioritize the alignment of nodes and then incorporate a measure of topology to refine alignments (Hu et al, 2005;Flannick et al, 2009;Kalaev et al, 2009;Liao et al, 2009;Zaslavskiy et al, 2009;Chindelevitch et al, 2010). Some methods strictly use topology to guide alignments (Kuchaiev et al, 2010), given that network motifs are often conserved in functionally related systems Shen-Orr et al, 2002).…”

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confidence: 99%

Gene Coexpression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice

2011

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One major objective for plant biology is the discovery of molecular subsystems underlying complex traits. The use of genetic and genomic resources combined in a systems genetics approach offers a means for approaching this goal. This study describes a maize (Zea mays) gene coexpression network built from publicly available expression arrays. The maize network consisted of 2,071 loci that were divided into 34 distinct modules that contained 1,928 enriched functional annotation terms and 35 cofunctional gene clusters. Of note, 391 maize genes of unknown function were found to be coexpressed within modules along with genes of known function. A global network alignment was made between this maize network and a previously described rice (Oryza sativa) coexpression network. The IsoRankN tool was used, which incorporates both gene homology and network topology for the alignment. A total of 1,173 aligned loci were detected between the two grass networks, which condensed into 154 conserved subgraphs that preserved 4,758 coexpression edges in rice and 6,105 coexpression edges in maize. This study provides an early view into maize coexpression space and provides an initial network-based framework for the translation of functional genomic and genetic information between these two vital agricultural species.The combination of genomics, genetics, and systemslevel computational methods provides a powerful approach toward insight into complex biological systems. Of particular significance is the discovery of genetic interactions that lead to desirable agricultural and economic traits in the Poaceae family (grasses). The Poaceae includes valuable crops such as rice (Oryza sativa), maize (Zea mays), wheat (Triticum spp.), and sugarcane (Saccharum officinarum), which are globally some of the most agriculturally and economically important crops (FAOSTAT, 2007). Understanding complex interactions underlying agronomic traits within these species, therefore, is of great significance, in particular to help with crop improvements to meet the challenges of plant and human health but also for basic understanding of complex biological systems.In addition to their pivotal role in agriculture, grasses offer a powerful model system in that their genomes are closely conserved and functional genomic knowledge gained in one species can be hypothesized to occur in another syntenic region (translational functional genomics; Paterson et al., 2009). In cases of grass species with poorly resolved, polyploid genomes such as sugarcane, where genomic resources are not as far progressed as in other grasses (e.g. rice, sorghum [Sorghum bicolor], maize, etc.), translational functional genomics methods may be the most cost-effective strategy for crop improvement as well as for unraveling the functional consequences of polyploidy. Additionally, crops rich in genetically mapped loci deposited in sites like Gramene (Jaiswal, 2011) provide a rich source of systems genetic hypotheses that could in principle accelerate the translation of interacting gene sets ass...

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confidence: 99%

Gene Coexpression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice

2011

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“…The main disadvantage of this approach is that it involves to estimate many parameters. Recently, a supervised, automated parameter learner was proposed to lessen the burden of parameter tuning (Flannick et al, 2009). Another phylogeny-guided local network alignment was proposed by Kalaev et al (2008).…”

Section: Multiple Protein Network Alignmentmentioning

confidence: 99%

A Survey on Evolutionary Analysis in PPI Networks

Jancura¹,

Marchiori²

2012

Protein-Protein Interactions - Computational and Experimental Tools

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“…Grmlin 2.0 [7], developed by Flannick et al, includes a new scoring function and a parameter learning process for multiple network alignments under the given training aligned network samples. The scoring function takes account of the cost of protein deletions, protein duplications, protein mutations, and interaction losses as features for the learning.…”

Section: Related Workmentioning

confidence: 99%

Fixed-Parameter Tractable Combinatorial Algorithms for Metabolic Networks Alignments

Cheng

Wei

Zelikovsky

et al. 2010

2010 IEEE International Conference on Data Mining Workshops

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The accumulation of high-throughput genomic and proteomic data allows for the reconstruction of the increasingly large and complex metabolic networks. In order to analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks. But even finding similar networks is computationally challenging.Based on the property of gene duplication and function sharing in biological network, we have formulated the network alignment problem which asks the optimal vertex-tovertex mapping allowing path contraction, vertex deletion, and vertex insertions. In this paper we present fixed parameter tractable combinatorial algorithms, which take into account the enzymes' functions and the similarity of arbitrary network topologies such as trees and arbitrary graphs with allowing the different types of vertex deletions. The proposed algorithms are fixed parameter tractable in the liner or square of the size of feedback vertex set respectively for the case of disallowing or allowing the deletions.We have developed the web service tool MetNetAligner which aligns metabolic networks. We evaluated our results by the randomized P-Value computation. In the computation, we followed two standard randomization procedures and further developed two other random graph generators which keep the more stringent and consistent topology constraints. By comparing their distribution of the significant alignment pairs, we observed that the more stringent constraints in the topology the random graph generator has, the more pairs of significant alignments there exist.We also performed pairwise mapping of all pathways for four organisms and found a set of statistically significant pathway similarities. We have applied the network alignment to identifying pathway holes which are resulted by inconsistency and missing enzymes.MetNetAligner is available at http:// alla.cs.gsu.edu:8080/MinePW/pages/gmapping/GMMain.html Two random graph generations and the list of identified pathway holes are available online.

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Automatic Parameter Learning for Multiple Local Network Alignment

Cited by 69 publications

References 40 publications

Gene Coexpression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice

Gene Coexpression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice

A Survey on Evolutionary Analysis in PPI Networks

Fixed-Parameter Tractable Combinatorial Algorithms for Metabolic Networks Alignments

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