{"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/dynamic-read-mapping-and-online-consensus","title":"Dynamic read mapping and online consensus calling for better variant detection","arxiv_id":"1605.09070","date":"2016-05-29","proceeding":null,"authors":[],"abstract":"Variant detection from high-throughput sequencing data is an essential step\nin identification of alleles involved in complex diseases and cancer. To deal\nwith these massive data, elaborated sequence analysis pipelines are employed. A\ncore component of such pipelines is a read mapping module whose accuracy\nstrongly affects the quality of resulting variant calls.\n  We propose a dynamic read mapping approach that significantly improves read\nalignment accuracy. The general idea of dynamic mapping is to continuously\nupdate the reference sequence on the basis of previously computed read\nalignments. Even though this concept already appeared in the literature, we\nbelieve that our work provides the first comprehensive analysis of this\napproach.\n  To evaluate the benefit of dynamic mapping, we developed a software pipeline\n(http://github.com/karel-brinda/dymas) that mimics different dynamic mapping\nscenarios. The pipeline was applied to compare dynamic mapping with the\nconventional static mapping and, on the other hand, with the so-called\niterative referencing - a computationally expensive procedure computing an\noptimal modification of the reference that maximizes the overall quality of all\nalignments. We conclude that in all alternatives, dynamic mapping results in a\nmuch better accuracy than static mapping, approaching the accuracy of iterative\nreferencing.\n  To correct the reference sequence in the course of dynamic mapping, we\ndeveloped an online consensus caller named OCOCO\n(http://github.com/karel-brinda/ococo). OCOCO is the first consensus caller\ncapable to process input reads in the online fashion.\n  Finally, we provide conclusions about the feasibility of dynamic mapping and\ndiscuss main obstacles that have to be overcome to implement it. We also review\na wide range of possible applications of dynamic mapping with a special\nemphasis on variant detection.","url_abs":"http://arxiv.org/abs/1605.09070v1","url_pdf":"http://arxiv.org/pdf/1605.09070v1.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":"dynamic-read-mapping-and-online-consensus","repo_url":"https://github.com/karel-brinda/dymas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"dynamic-read-mapping-and-online-consensus","repo_url":"https://github.com/karel-brinda/ococo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}