CatRAPID signature: identification of ribonucleoproteins and RNA-binding regions

Motivation: Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets

Radiation therapy during the coronavirus disease 2019 (covid-19) pandemic in Italy: a view of the nation's young oncologists

status: publishe

AIRO Breast Cancer Group Best Clinical Practice 2022 Update

Introduction: Breast cancer is the most common tumor in women and represents the leading cause of cancer death. Radiation therapy plays a key-role in the treatment of all breast cancer stages. Therefore, the adoption of evidence-based treatments is warranted, to ensure equity of access and standardization of care in clinical practice.Method: This national document on the highest evidence-based available data was developed and endorsed by the Italian Association of Radiation and Clinical Oncology (AIRO) Breast Cancer Group.We analyzed literature data regarding breast radiation therapy, using the SIGN (Scottish Intercollegiate Guidelines Network) methodology (www.sign.ac.uk). Updated findings from the literature were examined, including the highest levels of evidence (meta-analyses, randomized trials, and international guidelines) with a significant impact on clinical practice. The document deals with the role of radiation therapy in the treatment of primary breast cancer, local relapse, and metastatic disease, with focus on diagnosis, staging, local and systemic therapies, and follow up. Information is given on indications, techniques, total doses, and fractionations.Results: An extensive literature review from 2013 to 2021 was performed. The work was organized according to a general index of different topics and most chapters included individual questions and, when possible, synoptic and summary tables. Indications for radiation therapy in breast cancer were examined and integrated with other oncological treatments. A total of 50 questions were analyzed and answered.Four large areas of interest were investigated: (1) general strategy (multidisciplinary approach, contraindications, preliminary assessments, staging and management of patients with electronic devices); (2) systemic therapy (primary, adjuvant, in metastatic setting); (3) clinical aspects (invasive, non-invasive and micro-invasive carcinoma; particular situations such as young and elderly patients, breast cancer in males and cancer during pregnancy; follow up with possible acute and late toxicities; loco-regional relapse and metastatic disease); (4) technical aspects (radiation after conservative surgery or mastectomy, indications for boost, lymph node radiotherapy and partial breast irradiation).Appendixes about tumor bed boost and breast and lymph nodes contouring were implemented, including a dedicated web application. The scientific work was reviewed and validated by an expert group of breast cancer key-opinion leaders.Conclusions: Optimal breast cancer management requires a multidisciplinary approach sharing therapeutic strategies with the other involved specialists and the patient, within a coordinated and dedicated clinical path. In recent years, the high-level quality radiation therapy has shown a significant impact on local control and survival of breast cancer patients. Therefore, it is necessary to offer and guarantee accurate treatments according to the best standards of evidence-based medicine

Protein-dependent prediction of messenger RNA binding using Support Vector Machines

RNA-binding proteins interact specifically with RNA strands to regulate important cellular processes. Knowing the binding partners of a protein is a crucial issue in biology and it is essential to understand the protein function and its involvement in diseases. The identification of the interactions is currently resolvable only through in vivo and in vitro experiments which may not detect all binding partners. Computational methods which capture the protein-dependent nature of the binding phenomena could help to predict, in silico, the binding and could be resistant against experimental biases. This thesis addresses the creation of models based on support vector machines and trained on experimental data. The goal is the identification of RNAs which bind specifically to a regulatory protein. Starting from a case study, done with protein CELF1, we extend our approach and propose three methods to predict whether an RNA strand can be bound by a particular RNA-binding protein. The methods use support vector machines and different features based on the sequence (method Oli), the motif score (method OliMo) and the secondary structure (method OliMoSS). We apply them to different experimentally-derived datasets and compare the predictions with two methods: RNAcontext and RPISeq. Oli outperforms OliMoSS and RPISeq affirming our protein specific prediction and suggesting that oligo frequencies are good discriminative features. Oli and RNAcontext are the most competitive methods in terms of AUC. A Precision-Recall analysis reveals a better performance for Oli. On a second experimental dataset, where negative binding information is available, Oli outperforms RNAcontext with a precision of 0.73 vs. 0.59. Our experiments show that features based on primary sequence information are highly discriminative to predict the binding between protein and RNA. Sequence motifs can improve the prediction only for some RNA-binding proteins. Finally, we can conclude that experimental data on RNA-binding can be effectively used to train protein-specific models for in silico predictions

Identification of Regulatory Binding Sites on mRNA Using in Vivo Derived Informations and SVMs

International audienc

A computational approach for the discovery of protein-RNA networks

Protein–RNA interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of genes to host defense against pathogens. In this chapter we present the computational approach catRAPID to predict protein–RNA interactions and discuss how it could be used to find trends in ribonucleoprotein networks. We envisage that the combination of computational and experimental approaches will be crucial to unravel the role of coding and noncoding RNAs in protein networks

Discovery of protein-RNA networks

Coding and non-coding RNAs associate with proteins to perform important functions in the cell. Protein-RNA complexes are essential components of the ribosomal and spliceosomal machinery; they are involved in epigenetic regulation and form non-membrane-bound aggregates known as granules. Despite the functional importance of ribonucleoprotein interactions, the precise mechanisms of macromolecular recognition are still poorly understood. Here, we present the latest developments in experimental and computational investigation of protein-RNA interactions. We compare performances of different algorithms and discuss how predictive models allow the large-scale investigation of ribonucleoprotein associations. Specifically, we focus on approaches to decipher mechanisms regulating the activity of transcripts in protein networks. Finally, the catRAPID omics express method is introduced for the analysis of protein-RNA expression networks

A computational approach for the discovery of protein-RNA networks

Protein–RNA interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of genes to host defense against pathogens. In this chapter we present the computational approach catRAPID to predict protein–RNA interactions and discuss how it could be used to find trends in ribonucleoprotein networks. We envisage that the combination of computational and experimental approaches will be crucial to unravel the role of coding and noncoding RNAs in protein networks

catRAPID signature: identification of ribonucleoproteins and RNA-binding regions

MOTIVATION: Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets. AVAILABILITY AND IMPLEMENTATION: catRAPID signature can be accessed at http://s.tartaglialab.com/new_submission/signatureThe research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement nº RIBOMYLOME_309545. We acknowledge support of the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208 and FEDER funds (European Regional Development Fund) under the project number BFU2014-55054-P. R. Delli Ponti is supported by the MINECO’s pre-doctoral grant Severo Ochoa 2013–2017 (SVP-2014-068402)

Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets

BACKGROUND: Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. RESULTS: We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. CONCLUSIONS: In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal . Each of the pages contains links to the corresponding documentation and tutorial.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P). We also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013–2017’ (SEV-2012-0208). PK and RDP are recipients of “La Caixa” and “Severo Ochoa” studentships, respectivel