compendiumdb: an R package for retrieval and storage of functional genomics data.

UNLABELLED: Currently, the Gene Expression Omnibus (GEO) contains public data of over 1 million samples from more than 40 000 microarray-based functional genomics experiments. This provides a rich source of information for novel biological discoveries. However, unlocking this potential often requires retrieving and storing a large number of expression profiles from a wide range of different studies and platforms. The compendiumdb R package provides an environment for downloading functional genomics data from GEO, parsing the information into a local or remote database and interacting with the database using dedicated R functions, thus enabling seamless integration with other tools available in R/Bioconductor. AVAILABILITY AND IMPLEMENTATION: The compendiumdb package is written in R, MySQL and Perl. Source code and binaries are available from CRAN (http://cran.r-project.org/web/packages/compendiumdb/) for all major platforms (Linux, MS Windows and OS X) under the GPLv3 license. CONTACT: p.d.moerland@amc.uva.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets.

BACKGROUND: Two-dimensional differential gel electrophoresis (2D-DIGE) provides a powerful technique to separate proteins on their isoelectric point and apparent molecular mass and quantify changes in protein expression. Abundantly available proteins in spots can be identified using mass spectrometry-based approaches. However, identification is often not possible for low-abundant proteins. RESULTS: We present a novel computational approach to prioritize candidate proteins for unidentified spots. Our approach exploits noisy information on the isoelectric point and apparent molecular mass of a protein spot in combination with functional similarities of candidate proteins to already identified proteins to select and rank candidates. We evaluated our method on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. Using leave-one-out cross-validation, we show that the true-positive rate for the top-5 ranked proteins is 43.8%. CONCLUSIONS: Our approach shows good performance on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. We expect our method to be highly useful in (re-)mining other 2D-DIGE experiments in which especially the low-abundant protein spots remain to be identified.

CyclinPred: a SVM-based method for predicting cyclin protein sequences.

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server--CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods.