SignalP 4.0: discriminating signal peptides from transmembrane regions
TL;DR: SignalP 4.0 was the best signal-peptide predictor for all three organism types but was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal- peptide correlation when there are no transmembrane proteins present.
Abstract: We benchmarked SignalP 4.0 against SignalP 3.0 and ten other signal peptide prediction algorithms (Fig. 1). We compared prediction performance using the Matthews correlation coefficient16, for which each sequence was counted as a true or false positive or negative. To test SignalP 4.0 performance, we did not use data that had been used in training the networks or selecting the optimal architecture, and the test data did not contain homologs to the training and optimization data (Supplementary Methods). The test set for SignalP 3.0 was also independent of the training set because we removed sequences used to construct SignalP 3.0 and their homologs from the benchmark data. For other algorithms more recent than SignalP 3.0, the benchmark data may include data used to train the methods, possibly leading to slight overestimations of their performance. Our results show that SignalP 4.0 was the best signal-peptide predictor for all three organism types (Fig. 1). This comes at a price, however, because SignalP 4.0 was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal-peptide correlation when there are no transmembrane proteins present (Supplementary Results). An ideal method would have the best SignalP 4.0: discriminating signal peptides from transmembrane regions
Citations
More filters
TL;DR: Prokka is introduced, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer, and produces standards-compliant output files for further analysis or viewing in genome browsers.
Abstract: UNLABELLED: The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. AVAILABILITY AND IMPLEMENTATION: Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/.
10,432 citations
TL;DR: In this paper, a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level.
Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.
9,745 citations
TL;DR: A new Java-based architecture for the widely used protein function prediction software package InterProScan is described, resulting in a flexible and stable system that is able to use both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis.
Abstract: Motivation: Robust, large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterise many millions of sequences. Here we describe a new Java-based architecture for the widely-used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete re-implementation of the software framework, resulting in a flexible and stable system that is able to utilise both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the (open) source code is hosted at Google Code. Availability: InterProScan is distributed via FTP at ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/ and the source code is available from http://code.google.com/p/interproscan/. Contact: http://www.ebi.ac.uk/support or interhelp@ebi.ac.uk
5,434 citations
Cites background from "SignalP 4.0: discriminating signal ..."
...…SMART (Letunic et al., 2012), PIRSF (Wu et al., 2004), Panther (Mi et al., 2012), HAMAP (Pedruzzi et al., 2012), Prosite (Sigrist et al., 2012), ProDom (Bru et al., 2005), PRINTS (Attwood et al., 2012), CATHGene3D (Lees et al., 2012) and SUPERFAMILY (De Lima Morais et al., 2011)] are more…...
[...]
TL;DR: A deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs is presented.
Abstract: Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
2,732 citations
TL;DR: A subcellular map of the human proteome is presented to facilitate functional exploration of individual proteins and their role in human biology and disease and integrated into existing network models of protein-protein interactions for increased accuracy.
Abstract: Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map ...
1,878 citations
Additional excerpts
...0 (53), Phobius (54), and SPOCTOPUS (55)....
[...]
References
More filters
01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion
17,604 citations
TL;DR: Improvements of the currently most popular method for prediction of classically secreted proteins, SignalP, which consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated.
6,492 citations
TL;DR: A new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence that performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets.
Abstract: We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server.
5,480 citations
01 Jan 1997
TL;DR: In this paper, a new method for the identification of in performance compared with the weight matrix method signal peptides and their cleavage sites based on neural (Arrigo et al., 1991; Ladunga et al, 1991; Schneider and networks trained on separate sets of prokaryotic and eukaryotic sequence.
Abstract: applicable prediction methods with significant improvements We have developed a new method for the identification of in performance compared with the weight matrix method signal peptides and their cleavage sites based on neural (Arrigo et al., 1991; Ladunga et al., 1991; Schneider and networks trained on separate sets of prokaryotic and Wrede, 1993). eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be Materials and methods applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal- The data were taken from SWISS-PROT version 29 (Bairoch anchor sequences is also possible, though with lower preci- and Boeckmann, 1994). The data sets were divided into sion. Predictions can be made on a publicly available prokaryotic and eukaryotic entries and the prokaryotic data sets WWW server.
5,191 citations
TL;DR: Although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.
4,522 citations