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Bioinformatics Tool-Related Papers of Note, January 2007

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Bern M, Cai Y, Goldberg D. Lookup Peaks: A Hybrid of de Novo Sequencing and Database Search for Protein Identification by Tandem Mass Spectrometry. [Anal Chem. 2007 Jan 23 (e-pub ahead of print)]: Introduces a technique for peptide and protein identification in tandem mass spectrometry followed by database searching using programs such as Sequest or Mascot. The authors describe a hybrid method, implemented in a program called ByOnic, that uses a small amount of de novo analysis to identify “lookup peaks” that can then be used to extract candidate peptides from the database.
 

 
Chen HM, Liu BF, Huang HL, Hwang SF, Ho SY. SODOCK: Swarm optimization for highly flexible protein-ligand docking. [J Comput Chem. 2007 Jan 30;28(2):612-23]: Describes the optimization algorithm SODOCK, which is based on particle swarm optimization for solving flexible protein-ligand docking problems. SODOCK adopts the environment and energy function of AutoDock 3.05, but, according to the authors, SODOCK is superior to AutoDock’s Lamarckian genetic algorithm in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands.
 

 
Donaldson I, Göttgens B. CoMoDis: composite motif discovery in mammalian genomes.  [Nucleic Acids Research 2007 35(1):e1]: Introduces Composite Motif Discovery (CoMoDis), which streamlines computational identification of novel regulatory modules starting from a single seed motif, a binding site conserved across mammalian species. Availability: http://hscl.cimr.cam.ac.uk/CoMoDis_portal.html
 

 
Gille C, Hoffmann S, Holzhuetter H. METANNOGEN: compiling features of biochemical reactions needed for the reconstruction of metabolic networks. [BMC Systems Biology 2007, 1:5]: Describes MetAnnoGen, a program for reconstructing metabolic networks. It uses the Kegg database of biochemical reactions as a primary information source from which biochemical reactions relevant to the considered network can be selected, edited, and stored in a separate database. Reactions not contained in Kegg can be entered manually. Availability: http://3d-alignment.eu/metannogen/.
 

 
Hu G, Shen S, Ruan J. SGA: A grammar-based alignment algorithm. [Comput Methods Programs Biomed. 2007 Jan 29 (e-pub ahead of print)]: Outlines a new sequence alignment method based on an existing method called super pairwise alignment (SPA). The new method, called super genome alignment (SGA), uses Yang-Keiffer coding theory and results in a grammar-based algorithm. According to the authors, SGA is “significantly faster by at least an order of magnitude” than BlastZ, “and suffers on average only about 1 percent loss of the similarity of alignment.”
 

 
Klamt S, Saez-Rodriguez J, Lindquist JA, Simeoni L, Gilles ED. Structural and functional analysis of cellular networks with CellNetAnalyzer. [BMC Systems Biology 2007, 1:2]: Describes CellNetAnalyzer, a Matlab toolbox for structural analysis of metabolic, signaling, and regulatory networks. CellNetAnalyzer includes methods for characterizing functional states, for detecting functional dependencies, for identifying intervention strategies, and for predicting the effects of perturbations. Availability: http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html/.
 

 
Koczyk G, Wyrwicz LS, Rychlewski L. LigProf: A simple tool for in silico prediction of ligand-binding sites.  [J Mol Model. 2007 Jan 3 (e-pub ahead of print)]: Introduces LigProf, a program that predicts potential ligands that bind to a protein, as well as critical residues that stabilize ligands. Availability: http://www.cropnet.pl/ligprof.
 

 
Ofran Y, Rost B. ISIS: interaction sites identified from sequence.  [Bioinformatics 2007 23(2):e13-e16]: Introduces a machine learning-based method that identifies interacting protein residues from sequence alone. The method combines predicted structural features with evolutionary information. According to the authors, the strongest predictions of the method reached over 90 percent accuracy in a cross-validation experiment.
 

 
Prulj N. Biological network comparison using graphlet degree distribution.  [Bioinformatics 2007 23(2):e177-e183]: Describes an approach for comparing biological networks that is based on a new systematic measure of a network's local structure, which imposes a large number of similarity constraints on networks being compared. The new measure of network local structure consists of 73 graphlet degree distributions of graphlets with two to five nodes, but is extendible to a greater number of graphlets, if necessary. The authors combine the 73 graphlet degree distributions into a network “agreement” measure that is between 0 and 1, where 1 means that networks have identical distributions and 0 means that they are far apart. Availability: Upon request ([email protected]).
 

 
Rebholz-Schuhmann D, Kirsch H, Arregui M, Gaudan S, Riethoven M, Stoehr P. EBIMed — text crunching to gather facts for proteins from Medline.  [Bioinformatics 2007 23(2):e237-e244]: Describes EBIMed, a service that combines document retrieval with co-occurrence-based analysis of Medline abstracts. EBIMed retrieves abstracts from Medline and filters for sentences that contain biomedical terminology maintained in public bioinformatics resources. The extracted sentences and terminology are used to generate an overview table on proteins, Gene Ontology annotations, drugs, and species used in the same biological context. Availability: http://www.ebi.ac.uk/Rebholz-srv/ebimed 
 

 
Saito A, Nagasaki M, Oyama M, et al. AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction.  [BMC Bioinformatics 2007, 8:15]: Describes an algorithm that enables relative quantitation at the proteome level by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. The algorithm, implemented in a software tool named AYUMS, automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. Availability: http://www.csml.org/ayums/.

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