SUMMARY: Here we present APTANI2, an expanded and optimized version of APTANI, a computational tool for selecting target-specific aptamers from high-throughput-Systematic Evolution of Ligands by Exponential Enrichment data through sequence-structure analysis. As compared to its original implementation, APTANI2 ranks aptamers and identifies relevant structural motifs through the calculation of a score that combines frequency and structural stability of each secondary structure predicted in any aptamer sequence. In addition, APTANI2 comprises modules for a deeper investigation of sequence motifs and secondary structures, a graphical user interface that enhances its usability, and coding solutions that improve performances. AVAILABILITY AND IMPLEMENTATION: Source code, documentation and example command lines can be downloaded from http://aptani.unimore.it. APTANI2 is implemented in Python 3.4, released under the GNU GPL3.0 License, and compatible with Linux, Mac OS and the MS Windows subsystem for Linux. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

The integration of large-scale genomic and pharmacological data from cancer cell lines promises to be effective in the discovery of new genetic markers of drug sensitivity and of clinically relevant anticancer compounds. The Mutations and Drugs Portal (MDP, http://mdp.unimore.it) is a web accessible database that combines the cell-based NCI60 pharmacological screening with genomic data extracted from the Cancer Cell Line Encyclopedia and the NCI60 DTP projects. MDP currently contains drug sensitivity data for more than 50,800 compounds, describing response to drugs across 115 cancer cell lines. To identify genomic features associated to drug response, cell line drug sensitivity data are integrated with large genomic datasets, including information on somatic mutations and transcriptional data. MDP can be queried for drugs active in cancer cell lines carrying mutations or transcriptional alterations in specific cancer genes and signaling pathways or for genetic and transcriptional profiles associated to sensitivity or resistance to a given compound. Results are presented through graphical representations with links to related data and are fully downloadable. MDP provides a user-friendly web resource to perform in-silico high-throughput screenings of thousand of compounds and facilitate the discovery of associations between genomic portraits and drug responses.

MOTIVATION: Aptamers are synthetic nucleic acid molecules that can bind biological targets in virtue of both their sequence and three-dimensional structure. Aptamers are selected using SELEX, Systematic Evolution of Ligands by EXponential enrichment, a technique that exploits aptamer-target binding affinity. The SELEX procedure, coupled with high-throughput sequencing (HT-SELEX), creates billions of random sequences capable of binding different epitopes on specific targets. Since this technique produces enormous amounts of data, computational analysis represents a critical step to screen and select the most biologically relevant sequences. RESULTS: Here, we present APTANI, a computational tool to identify target-specific aptamers from HT-SELEX data and secondary structure information. APTANI builds on AptaMotif algorithm, originally implemented to analyze SELEX data; extends the applicability of AptaMotif to HT-SELEX data and introduces new functionalities, as the possibility to identify binding motifs, to cluster aptamer families or to compare output results from different HT-SELEX cycles. Tabular and graphical representations facilitate the downstream biological interpretation of results. AVAILABILITY AND IMPLEMENTATION: APTANI is available at http://aptani.unimore.it. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.