BaCelLo: a balanced subcellular localization predictor
By Andrea Pierleoni, Pier Luigi Martelli, Piero Fariselli, Rita Casadio. Intelligent Systems in Molecular Biology, 2006
336 citations recorded by the source corpus.
MOTIVATION The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects. RESULTS BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. AVAILABILITY BaCelLo can be accessed at http://www.biocomp.unibo.it/bacello/.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/d6fd210a9e444ca4fa615dbad63618a455300b5f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Molecular Biology
- Henrik Nielsen
- Oliver Kohlbacher
- Sebastian Briesemeister
- Søren Brunak
- G. von Heijne
- Torsten Blum
- Kuo-Chen Chou
- Olof Emanuelsson
Citations in the corpus
- MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, methodology): “More information concerning the creation of the BaCelLo data sets can be found in Pierleoni et al [19] and Casadio et al .”
- Locating proteins in the cell using TargetP, SignalP and related tools (2007, methodology): “75 ) uses the composition of the first and last half of each sequence, whereas BaCelL”