LGL: Creating a Map of Protein Function with an Algorithm for Visualizing Very Large Biological Networks

Adai, Alex; Date, Shailesh V.; Wieland, Shannon C.; Marcotte, Edward M.

doi:10.1016/j.jmb.2004.04.047

Cited by 183 publications

(138 citation statements)

References 40 publications

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“…Among them, the most famous is based on improvements of force directed methods ( [15,16,2]). Another approach consists in representing the graph as a matrix and using linear algebra techniques ( [18,27]).…”

Section: Related Workmentioning

confidence: 99%

“…The LGL algorithm presented in [2] was first designed to visualize a protein map and can therefore handle weighted edges. The filtration in that algorithm is made by first computing a minimum spanning tree of the graph using the well-known Kruskal's algorithm [23] and then rooting that tree on the graph center.…”

Section: Force Directed Based Approachmentioning

confidence: 99%

“…Therefore, the technique of [3] consists in detecting the biconnected components of the graph. Then, each biconnected component is drawn using a modified version of LGL [2] and the biconnected components tree is drawn using an area aware version of RINGS [25]. Finally, an edge crossing reduction step is performed.…”

Section: Topologies Detection Based Approachmentioning

confidence: 99%

“…We make a distinction between the top level quotient graph and the other levels of the hierarchy. As the top level quotient graph can be large, we use modified version of LGL algorithm [2] while we use either a circular drawing algorithm or and the force directed algorithm described in [14] to draw clusters. All these algorithms are modified to take into account the sizes of the nodes.…”

Section: Bottom-up Drawing Algorithmmentioning

confidence: 99%

“…This extension of LGL [2] reduce the number of node-node overlaps by modifing the size of the grid cells used in LGL. It is possible to completely avoid node-node overlap if the size of each cell is enought large.…”

Section: Top Level Drawingmentioning

confidence: 99%

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Large Quasi-Tree Drawing: A Neighborhood Based Approach

Bourqui

2009

2009 13th International Conference Information Visualisation

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Section: Related Workmentioning

confidence: 99%

Section: Force Directed Based Approachmentioning

confidence: 99%

Section: Topologies Detection Based Approachmentioning

confidence: 99%

Section: Bottom-up Drawing Algorithmmentioning

confidence: 99%

Section: Top Level Drawingmentioning

confidence: 99%

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Large Quasi-Tree Drawing: A Neighborhood Based Approach

Bourqui

2009

2009 13th International Conference Information Visualisation

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Structure Comparison and Alignment

Bourne

Shindyalov

2003

Structural Bioinformatics

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Since the 1970s, after the seminal work of Rossmann and Argos (1978) comparing binding sites of known enzyme structures, the comparison and alignment of protein structures has come to be a fundamental and widely used task in computational structure biology. Three main steps are needed for comparing two protein structures: first, the detection of their common similarities; second, the alignment of the structures based on such similarities; and third, a statistical measure of the similarity. Considering the first two steps, structure comparison refers to the analysis of the similarities and differences between two or more structures, and structure alignment refers to establishing which amino acid residues are equivalent between them. The majority of commonly used methods do a reasonably good job in recognizing obvious similarities between protein structures. However, the alignment of two or more structures is a more difficult task, and its accuracy may depend on the method or program used as well as what the user is trying to accomplish, which will be discussed subsequently. All programs that are briefly described in this chapter perform both steps and are commonly known as protein structure alignment methods.It is also important to immediately clear up any confusion between structure alignment and structure superposition since such terms are often interchanged in the literature. As mentioned above, structure alignment tries to identify the equivalences between pairs of amino acid residues from the structures to superpose, while structure superposition requires the previous knowledge of such equivalences. Thus, structure superposition tries to solve the simpler geometrical task of minimizing the distance between already known equivalent residues of the superimposed structures by finding a transformation that produces either the lowest root-mean-square deviation (RMSD) or the maximal equivalences within an

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