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The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/bioinformatics-sannio\/ncrna-deep\" xlink:type=\"simple\">https:\/\/github.com\/bioinformatics-sannio\/ncrna-deep<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008415","type":"journal-article","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T13:43:35Z","timestamp":1605102215000},"page":"e1008415","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep learning predicts short non-coding RNA functions from only raw sequence data"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3411-6752","authenticated-orcid":true,"given":"Teresa Maria Rosaria","family":"Noviello","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5995-5077","authenticated-orcid":true,"given":"Francesco","family":"Ceccarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4702-6617","authenticated-orcid":true,"given":"Michele","family":"Ceccarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8342-3487","authenticated-orcid":true,"given":"Luigi","family":"Cerulo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"12","key":"pcbi.1008415.ref001","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1038\/nrg3074","article-title":"Non-coding RNAs in human disease","volume":"12","author":"M Esteller","year":"2011","journal-title":"Nature Reviews Genetics"},{"issue":"suppl_1","key":"pcbi.1008415.ref002","doi-asserted-by":"crossref","first-page":"R17","DOI":"10.1093\/hmg\/ddl046","article-title":"Non-coding RNA","volume":"15","author":"JS Mattick","year":"2006","journal-title":"Human molecular genetics"},{"issue":"D1","key":"pcbi.1008415.ref003","doi-asserted-by":"crossref","first-page":"D335","DOI":"10.1093\/nar\/gkx1038","article-title":"Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families","volume":"46","author":"I Kalvari","year":"2017","journal-title":"Nucleic acids research"},{"issue":"9","key":"pcbi.1008415.ref004","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1101\/gr.247239.118","article-title":"Decrypting noncoding RNA interactions, structures, and functional networks","volume":"29","author":"M Fabbri","year":"2019","journal-title":"Genome Res"},{"issue":"9","key":"pcbi.1008415.ref005","doi-asserted-by":"crossref","first-page":"e66","DOI":"10.1093\/nar\/gkp206","article-title":"Identification and classification of ncRNA molecules using graph properties","volume":"37","author":"L Childs","year":"2009","journal-title":"Nucleic acids research"},{"issue":"22","key":"pcbi.1008415.ref006","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1093\/bioinformatics\/btt509","article-title":"Infernal 1.1: 100-fold faster RNA homology searches","volume":"29","author":"EP Nawrocki","year":"2013","journal-title":"Bioinformatics"},{"issue":"17","key":"pcbi.1008415.ref007","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1093\/bioinformatics\/btx295","article-title":"An efficient graph kernel method for non-coding RNA functional prediction","volume":"33","author":"N Navarin","year":"2017","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1008415.ref008","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13040-017-0148-2","article-title":"nRC: non-coding RNA Classifier based on structural features","volume":"10","author":"A Fiannaca","year":"2017","journal-title":"BioData mining"},{"key":"pcbi.1008415.ref009","unstructured":"Rossi E, Monti F, Bronstein MM, Li\u00f2 P. ncRNA Classification with Graph Convolutional Networks. 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