{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-the-latent-transcriptome","title":"Towards the Latent Transcriptome","arxiv_id":"1810.03442","date":"2018-10-08","proceeding":null,"authors":["Assya Trofimov","Francis Dutil","Claude Perreault","Sebastien Lemieux","Yoshua Bengio","Joseph Paul Cohen"],"abstract":"In this work we propose a method to compute continuous embeddings for kmers\nfrom raw RNA-seq data, without the need for alignment to a reference genome.\nThe approach uses an RNN to transform kmers of the RNA-seq reads into a 2\ndimensional representation that is used to predict abundance of each kmer. We\nreport that our model captures information of both DNA sequence similarity as\nwell as DNA sequence abundance in the embedding latent space, that we call the\nLatent Transcriptome. We confirm the quality of these vectors by comparing them\nto known gene sub-structures and report that the latent space recovers exon\ninformation from raw RNA-Seq data from acute myeloid leukemia patients.\nFurthermore we show that this latent space allows the detection of genomic\nabnormalities such as translocations as well as patient-specific mutations,\nmaking this representation space both useful for visualization as well as\nanalysis.","url_abs":"http://arxiv.org/abs/1810.03442v2","url_pdf":"http://arxiv.org/pdf/1810.03442v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-the-latent-transcriptome","repo_url":"https://github.com/TrofimovAssya/TheLatentTranscriptome","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}