Cheluvaraja S, Meirovitch H. Calculation of the entropy and free energy by the hypothetical scanning Monte Carlo method: Application to peptides. [J Chem Phys. 2005 Feb;122(5):54903]: Applies the hypothetical scanning Monte Carlo approach to calculate the absolute entropy and free energy of peptide chains in vacuum. The authors’ long-term goal is to extend the method to any peptide and apply it to a peptide immersed in a box with explicit water.
Foissac S, Schiex T. Integrating alternative splicing detection into gene prediction. [BMC Bioinformatics 2005, 6:25]: Presents an integrative approach that incorporates alternative splicing detection into ab initio gene prediction. This method relies on the analysis of genomically aligned transcript sequences (ESTs and/or cDNAs), and has been implemented in the dynamic programming algorithm of the graph-based gene finder EuGene. Availability: http://www.inra.fr/bia/T/EuGene/.
Hayamizu T, Mangan M, Corradi J, Kadin J, Ringwald M. The Adult Mouse Anatomical Dictionary: a tool for annotating and integrating data. [Genome Biology 2005, 6:R29]: Describes an ontology to provide standardized nomenclature for anatomical terms in the postnatal mouse. The Adult Mouse Anatomical Dictionary is structured as a directed acyclic graph, and will be used to annotate and integrate different types of data pertinent to anatomy, such as gene expression patterns and phenotype information. Availability: http://www.informatics.jax.org/searches/AMA_form.shtml.
Kato T, Tsuda K, Asai K. Selective integration of multiple biological data for supervised network inference. [Bioinformatics advance access published online Feb. 22, 2005]: Discusses a new kernel-based method for supervised graph inference based on multiple types of biological datasets such as gene expression, phylogenetic profiles, and amino acid sequences. The method assigns a weight to each type of dataset and thereby selects informative ones. Availability: upon request.
Kunin V, Ouzounis C. Clustering the annotation space of proteins. [BMC Bioinformatics 2005, 6:24]: Describes an approach called CLAN that clusters proteins according to both annotation and sequence similarity. The approach is able to cluster the complete SwissProt database within minutes, according to the authors. Availability: http://maine.ebi.ac.uk:8000/cgi-bin/clan/ClanSearch.pl.
Lyman E, Zuckerman D. Simulation of Biomolecules by Resolution Exchange. [ArXiv pre-print archive: http://arXiv.org/abs/q-bio/0502014]: Introduces a simulation algorithm, called “resolution exchange,” that combines local sampling, based upon a detailed, high resolution model, with a more global sampling, afforded by a reduced resolution model.
Moreland J, Gramada A, Buzko O, Zhang Q, Bourne P. The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications. [BMC Bioinformatics 2005, 6:21]: Presents the Molecular Biology Toolkit, a Java-based software toolkit for developing applications for protein analysis and visualization. MBT provides an assortment of core classes with a uniform data model for describing biological structures and automates most common tasks associated with the development of applications in the molecular sciences, according to the authors. Availability: http://mbt.sdsc.edu.
Rouse RJ, Castagnetto J, Niedner RH. PatGen — a consolidated resource for searching genetic patent sequences. [Bioinformatics advance access published online Feb. 22, 2005]: Introduces PatGen, an integrated database containing data from bioinformatic and patent resources. Availability: http://www.patgendb.com.
Sarai A, Siebers J, Selvaraj S, Gromiha MM, Kono H. Integration of bioinformatics and computational biology to understand protein-DNA recognition mechanism. [J Bioinform Comput Biol. 2005 Feb;3(1):169-83]: Describes a knowledge-based approach that derives empirical potential functions for the specific interactions between bases and amino acids, as well as for DNA conformation, from statistical analyses of structural information of protein-DNA complexes. These potentials are used to quantify the specificity of protein-DNA recognition in order to analyze the structure-function characteristics of transcription factors.
Shah S, Huang Y, Xu T, Yuen M, Ling J, Ouellette B. Atlas — a data warehouse for integrative bioinformatics. [BMC Bioinformatics 2005, 6:34]: Introduces a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. Availability: http://bioinformatics.ubc.ca/atlas/.
Shmygelska A, Hoos H. An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. [BMC Bioinformatics 2005, 6:30]: Describes an improvement of previous work using the ant colony optimization (ACO) algorithm to address the non-deterministic NP-hard combinatorial problem of predicting a protein’s conformation from its amino acid sequence. The new algorithm, ACO-HPPF-3, performs better than previous algorithms on sequences whose native conformations do not contain structural nuclei at the ends, but rather in the middle of the sequence, according to the authors.
Slater G, Birney E. Automated generation of heuristics for biological sequence comparison. [BMC Bioinformatics 2005, 6:31]: Describes an approach called bounded sparse dynamic programming, which simplifies the implementation of heuristics for sequence alignment algorithms.
Stamatakis A, Ludwig T, Meier H. RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees. [Bioinformatics 2005 21(4):456-463]: Describes RAxML-III, a program for rapid maximum likelihood-based inference of large evolutionary trees. The software can compute 1,000-taxon trees in less than 24 hours on a PC. According to the authors, RAxML-III performs worse than PHYML and MrBayes on synthetic data, but outperforms both programs on real data. Availability: http://wwwbode.cs.tum.edu/~stamatak/.
Tirosh I, Barkai N. Computational verification of protein-protein interactions by orthologous co-expression. [BMC Bioinformatics 2005, 6:40]: Presents a computational method for verifying protein-protein interactions based on the co-expression of orthologs of interacting partners.
Via A, Zanzoni A, Helmer-Citterich M. Seq2Struct: a resource for establishing sequence-structure links. [Bioinformatics 2005 21(4):551-553]: Presents Seq2Struct, a web resource for the identification of sequence-structure links that contains a collection of annotated links between Swiss-Prot, TrEMBL, PDB, and SCOP database entries. Availability: http://surface.bio.uniroma2.it/seq2struct/.