Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1)

Plant Signaling and Behavior

Volumn 7, Issue 2, 2012, Pages 263-268

  Lingner, Thomas ^a   Kataya, Amr R A ^b   Reumann, Sigrun ^b  

^a Department of Bioinformatics   (Germany)

^b UNIVERSITY OF STAVANGER   (Norway)

Author keywords

Genome screens; Machine learning methods; Peroxisome; Statistics; Targeting

Indexed keywords

ARABIDOPSIS; FUNGI; MAMMALIA;

ARABIDOPSIS PROTEIN; CELL RECEPTOR; PEPTIDE; PEROXISOMAL TARGETING SIGNAL 2 RECEPTOR; PLANT DNA;

ALGORITHM; ARABIDOPSIS; ARTICLE; CYTOSOL; DNA SEQUENCE; EXPRESSED SEQUENCE TAG; GENETIC DATABASE; GENETICS; NORMAL DISTRIBUTION; NUCLEOTIDE SEQUENCE; PEROXISOME; PLANT GENOME; VALIDATION STUDY;

ALGORITHMS; ARABIDOPSIS; ARABIDOPSIS PROTEINS; BASE SEQUENCE; CYTOSOL; DATABASES, GENETIC; DNA, PLANT; EXPRESSED SEQUENCE TAGS; GENOME, PLANT; NORMAL DISTRIBUTION; PEPTIDES; PEROXISOMES; RECEPTORS, CYTOPLASMIC AND NUCLEAR; SEQUENCE ANALYSIS, DNA;

EID: 84861005642     PISSN: 15592316     EISSN: 15592324     Source Type: Journal    
DOI: 10.4161/psb.18720     Document Type: Article

References (11)

1. Lingner T, Kataya AR, Antonicelli GE, Benichou A, Nilssen K, Chen XY, et al. Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses. Plant Cell 2011; 23:1556-72; PMID:21487095; http://dx.doi.org/10.1105/tpc.111. 084095
2. Schneider G, Fechner U. Advances in the prediction of protein targeting signals. Proteomics 2004; 4:1571-80; PMID:15174127; http://dx.doi.org/10.1002/pmic. 200300786
3. Mitschke J, Fuss J, Blum T, Höglund A, Reski R, Kohlbacher O, et al. Prediction of dual protein targeting to plant organelles. New Phytol 2009; 183:224-35; PMID:19368670; http://dx.doi.org/10.1111/j.1469-8137.2009.02832.x
4. Nair R, Rost B. Protein subcellular localization prediction using artificial intelligence technology. Methods Mol Biol 2008; 484:435-63; PMID:18592195; http://dx.doi.org/10.1007/978-1-59745-398-1_27
5. Mintz-Oron S, Aharoni A, Ruppin E, Shlomi T. Network-based prediction of metabolic enzymes' subcellular localization. Bioinformatics 2009; 25: i247-52; PMID:19477995; http://dx.doi.org/10.1093/ bioinformatics/btp209
6. Emanuelsson O, Elofsson A, von Heijne G, Cristóbal S. In silico prediction of the peroxisomal proteome in fungi, plants and animals. J Mol Biol 2003; 330:443-56; PMID:12823981; http://dx.doi.org/10.1016/ S0022-2836(03)00553-9
7. Neuberger G, Maurer-Stroh S, Eisenhaber B, Hartig A, Eisenhaber F. Prediction of peroxisomal targeting signal 1 containing proteins from amino acid sequence. J Mol Biol 2003; 328:581-92; PMID:12706718; http://dx.doi.org/10.1016/S0022-2836(03)00319-X
8. Neuberger G, Maurer-Stroh S, Eisenhaber B, Hartig A, Eisenhaber F. Motif refinement of the peroxisomal targeting signal 1 and evaluation of taxon-specific differences. J Mol Biol 2003; 328:567-79; PMID: 12706717; http://dx.doi.org/10.1016/S0022-2836(03)00318-8
9. Reumann S. Specification of the peroxisome targeting signals type 1 and type 2 of plant peroxisomes by bioinformatics analyses. Plant Physiol 2004; 135: 783-800; PMID:15208424; http://dx.doi.org/10.1104/pp.103.035584
10. Bodén M, Hawkins J. Prediction of subcellular localization using sequence-biased recurrent networks. Bioinformatics 2005; 21:2279-86; PMID:15746276; http://dx.doi.org/10.1093/bioinformatics/bti372
11. Hawkins J, Mahony D, Maetschke S, Wakabayashi M, Teasdale RD, Bodén M. Identifying novel peroxisomal proteins. Proteins 2007; 69:606-16; PMID:17636571; http://dx.doi.org/10.1002/prot.21420