The proper handling of customer tickets and main-tenance requests is pivotal for enterprises as it directly impacts customer satisfaction. The ability to rapidly and efficiently react and solve reported issues is in fact a key factor from the customers' perspective, resulting in positive feedback for the company, leading to higher economic and brand-image revenues. The automatic detection of failures from maintenance tickets and support requests can grant faster and more efficient reactions to customers' equipment failures as well as reduced maintenance costs. The analysis of support and maintenance requests is a well-known problem in Natural Language Processing (NLP). State-of-the-art solutions in this field rely on Transformers models, pre-trained on large text corpora, and then fine-tuned on the specific downstream task. However, due to their intrinsic nature, support requests are highly domain-specific and usually similar to short telegraph messages, where the focus is typically encapsulated in short sequences rather than in long dependencies. Hence, ad-hoc methods for pattern recognition might provide comparable performances with respect to Transformers. In this work, two alternative approaches are proposed, based on: Kernel Meth-ods in conjunction with Boost Decision Trees (SpectrumBoost), and Neural Networks for Multiple Representation Learning (DeepMRL). These models have been tested and compared against state-of-the-art models on a real-world set of 131305 maintenance tickets in the Italian language, suggesting that the proposed models outperform Transformers both in the prediction accuracy and in the time and computational resources required for their training.

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis. RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity. AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The recent shift towards high-throughput screening is posing new challenges for the interpretation of experimental results. Here we propose the cleverSuite approach for large-scale characterization of protein groups. DESCRIPTION: The central part of the cleverSuite is the cleverMachine (CM), an algorithm that performs statistics on protein sequences by comparing their physico-chemical propensities. The second element is called cleverClassifier and builds on top of the models generated by the CM to allow classification of new datasets. RESULTS: We applied the cleverSuite to predict secondary structure properties, solubility, chaperone requirements and RNA-binding abilities. Using cross-validation and independent datasets, the cleverSuite reproduces experimental findings with great accuracy and provides models that can be used for future investigations. AVAILABILITY: The intuitive interface for dataset exploration, analysis and prediction is available at http://s.tartaglialab.com/clever_suite.

SUMMARY: Here we introduce catRAPID omics, a server for large-scale calculations of protein-RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition. RESULTS: We developed a web server to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis (custom libraries can be also generated). The catRAPID omics was benchmarked on the recently published RNA interactomes of Serine/arginine-rich splicing factor 1 (SRSF1), Histone-lysine N-methyltransferase EZH2 (EZH2), TAR DNA-binding protein 43 (TDP43) and RNA-binding protein FUS (FUS) as well as on the protein interactomes of U1/U2 small nucleolar RNAs, X inactive specific transcript (Xist) repeat A region (RepA) and Crumbs homolog 3 (CRB3) 3'-untranslated region RNAs. Our predictions are highly significant (P < 0.05) and will help the experimentalist to identify candidates for further validation. AVAILABILITY: catRAPID omics can be freely accessed on the Web at http://s.tartaglialab.com/catrapid/omics. Documentation, tutorial and FAQs are available at http://s.tartaglialab.com/page/catrapid_group.