PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes
نویسندگان
چکیده
MOTIVATION PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. RESULTS We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. AVAILABILITY http://www.psort.org/psortb (download open source software or use the web interface). CONTACT [email protected] SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
منابع مشابه
PSORTdb—an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea
The subcellular localization (SCL) of a microbial protein provides clues about its function, its suitability as a drug, vaccine or diagnostic target and aids experimental design. The first version of PSORTdb provided a valuable resource comprising a data set of proteins of known SCL (ePSORTdb) as well as pre-computed SCL predictions for proteomes derived from complete bacterial genomes (cPSORTd...
متن کاملPSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis
MOTIVATION PSORTb v.1.1 is the most precise bacterial localization prediction tool available. However, the program's predictive coverage and recall are low and the method is only applicable to Gram-negative bacteria. The goals of the present work are as follows: increase PSORTb's coverage while maintaining the existing precision level, expand it to include Gram-positive bacteria and then carry ...
متن کاملTitle: PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis Running head: PSORTb Localization Prediction
Motivation: PSORTb v.1.1 is the most precise bacterial localization prediction tool available. However the program’s predictive coverage and recall are low and the method is only applicable to Gram-negative bacteria. The goals of the present work were: increase PSORTb’s coverage while maintaining the existing precision level, expand it to include Gram-positive bacteria, and then carry out a com...
متن کاملEvidence for an association between Wnt-independent -catenin intracellular localization and ovarian apoptotic events in normal and PCO-induced rat ovary
The association of secreted frizzled related protein type 4 (Sfrp4) as an antagonist of Wnt mole-cules in apoptotic events has been reported previously. Moreover, its increased expression has been reported in the ovary of women with polycystic ovary (PCO). We have demonstrated in-creased Sfrp4 in PCO-induced rat ovary related to an increased number of apoptotic follicles showing nuclear ?cateni...
متن کاملPSORTdb: a protein subcellular localization database for bacteria
Information about bacterial subcellular localization (SCL) is important for protein function prediction and identification of suitable drug/vaccine/diagnostic targets. PSORTdb (http://db.psort.org/) is a web-accessible database of SCL for bacteria that contains both information determined through laboratory experimentation and computational predictions. The dataset of experimentally verified in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره 26 شماره
صفحات -
تاریخ انتشار 2010