Chamrad D, et al. Interpretation of mass spectrometry data for high-throughput proteomics. [Anal Bioanal Chem. 2003 Jul 5 (epub ahead of print)]: Describes software component of the ProteinScape database for high-throughput protein mass fingerprinting identification that relies on automated calibration, peak rejection, and the use of a “metasearch” approach that employs various search engines.
Chiang J, Yu, H. MeKE: discovering the functions of gene products from biomedical literature via sentence alignment. [Bioinformatics 2003 Jul 22;19(11) 1417-1422]: Ontology-based text-mining system to extract knowledge about the functions of gene products from the biomedical literature. Available at http://ismp.csie.ncku.edu.tw/~yuhc/meke/.
Ding C. Unsupervised feature selection via two-way ordering in gene expression analysis [Bioinformatics 2003 Jul 22;19(11) 1259-1266]: An unsupervised method for selecting relevant genes based on their similarity information that relies on a two-way ordering mechanism to discard irrelevant genes. Available at http://www.nersc.gov/~cding/2way.
Eckart JD, Sobral BW. A Life Scientist’s Gateway to Distributed Data Management and Computing: The PathPort/ToolBus Framework. [OMICS. 2003 Spring;7(1):79-88]: A client-server framework for bioinformatics data management.
Enright A, Kunin V, Ouzounis C. Protein families and TRIBES in genome sequence space. [Nucleic Acids Research, 2003, Vol. 31, No. 15 4632-4638]: Database of protein family information, including annotations, protein sequence alignments, and phylogenetic distributions describing 311,257 proteins from 83 completely sequenced genomes. Available at http://www.ebi.ac.uk/research/cgg/tribes/.
Grasso C, Quist M, Ke K, Lee C. POAVIZ: a Partial Order Multiple Sequence Alignment Visualizer. [Bioinformatics. 2003 Jul 22;19(11):1446-8]: Visualization tool for multiple sequence alignment. Available at http://www.bioinformatics.ucla.edu/poa.
Hoon S, et al. Biopipe: A Flexible Framework for Protocol-Based Bioinformatics Analysis. [Genome Res. 2003 Jul 17 (epub ahead of print)]. Framework for integrating and coordinating bioinformatics analysis methods over a compute farm.
Janssen P, et al. COmplete GENome Tracking (COGENT): a flexible data environment for computational genomics [Bioinformatics 2003 19: 1451-1452]: Database of fully sequenced and published genomes to ensure reproducibility of results in computational genomics. Available at http://maine.ebi.ac.uk:8000/services/cogent/.
Kinoshita K, Furui J, Nakamura H. Identification of protein functions from a molecular surface database, eF-site. [Struct Funct Genomics. 2002;2(1):9-22]: A database of molecular surfaces of proteins calculated using atom coordinates that includes estimated biochemical functions and hydrophobicity information. Available at: http://pi.protein.osaka-u. ac.jp/eF-site/.
Lai E, Tomancak P, Williams R, Rubin G. Computational identification of Drosophila microRNA genes. [Genome Biology 2003 4(7):R42]: Describes a program called “miRseeker” that analyzes euchromatic sequences of Drosophila melanogaster and D. pseudoobscura for conserved sequences that display a pattern of nucleotide divergence characteristic of known microRNAs. Available at http://toy.lbl.gov:9050/cgi-bin/miRseeker.pl.
Malde K, Coward E, Jonassen I. Fast sequence clustering using a suffix array algorithm. [Bioinformatics 2003 Jul 22;19(11) 1221-1226]: EST clustering algorithm based on suffix arrays. Available at http://www.ii.uib.no/~ketil/bio/.
Quon GT, Gordon P, Sensen CW. 4D bioinformatics: a new look at the ribosome as an example. [IUBMB Life. 2003 Apr-May;55(4-5):279-83.] Adaption of the Java Molecular Viewer for virtual reality display of molecular structures. Available at http://cave.ucalgary.ca.
Raychaudhuri S, Chang J, Imam F, Altman B. The computational analysis of scientific literature to define and recognize gene expression clusters. [Nucleic Acids Research, 2003, Vol. 31, No. 15 4553-4560]: Computational method that uses the peer-reviewed literature in the automatic analysis of gene expression data sets by first applying hierarchical clustering to the data set and then using text from abstracts about genes to resolve hierarchical cluster boundaries and recognize those clusters that are most functionally coherent.
Shah SP, et al. GeneComber: combining outputs of gene prediction programs for improved results. [Bioinformatics 2003 Jul 1;19(10):1296-7]: Software that automates ab initio gene prediction by running Genscan and HMMgene on input DNA sequence and integrating the output. Available at: http://bioinformatics.ubc.ca/genecomber.