Novel algorithms for accurate DNA base-calling
The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2013
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/25052 |
| الوسوم: |
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| _version_ | 1864513441983627264 |
|---|---|
| author | Mohammed, Omniyah Gul |
| author2 | Assaleh, Khaled Husseini, Ghaleb Majdalawieh, Amin |
| author2_role | author author author |
| author_facet | Mohammed, Omniyah Gul Assaleh, Khaled Husseini, Ghaleb Majdalawieh, Amin |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed, Omniyah Gul Assaleh, Khaled Husseini, Ghaleb Majdalawieh, Amin |
| dc.date.none.fl_str_mv | 2013 2022-10-25T08:53:33Z 2022-10-25T08:53:33Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Mohammed, O. , Assaleh, K. , Husseini, G. , Majdalawieh, A. and Woodward, S. (2013) Novel algorithms for accurate DNA base-calling. Journal of Biomedical Science and Engineering, 6, 165-174. doi: 10.4236/jbise.2013.62020. 1937-688X http://hdl.handle.net/11073/25052 10.4236/jbise.2013.62020 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Scientific Research Publishing |
| dc.relation.none.fl_str_mv | https://doi.org/10.4236/jbise.2013.62020 |
| dc.subject.none.fl_str_mv | Artificial Neural Network (ANN) Base-Calling Electropherogram Polynomial Classifier (PC) Sequencing |
| dc.title.none.fl_str_mv | Novel algorithms for accurate DNA base-calling |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the existing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demonstrate the potential of the proposed models for efficient and accurate DNA base-calling. |
| format | article |
| id | aus_68031e379bda02b3f5533c61e736709a |
| identifier_str_mv | Mohammed, O. , Assaleh, K. , Husseini, G. , Majdalawieh, A. and Woodward, S. (2013) Novel algorithms for accurate DNA base-calling. Journal of Biomedical Science and Engineering, 6, 165-174. doi: 10.4236/jbise.2013.62020. 1937-688X 10.4236/jbise.2013.62020 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25052 |
| publishDate | 2013 |
| publisher.none.fl_str_mv | Scientific Research Publishing |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Novel algorithms for accurate DNA base-callingMohammed, Omniyah GulAssaleh, KhaledHusseini, GhalebMajdalawieh, AminArtificial Neural Network (ANN)Base-CallingElectropherogramPolynomial Classifier (PC)SequencingThe ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the existing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demonstrate the potential of the proposed models for efficient and accurate DNA base-calling.Scientific Research Publishing2022-10-25T08:53:33Z2022-10-25T08:53:33Z2013Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMohammed, O. , Assaleh, K. , Husseini, G. , Majdalawieh, A. and Woodward, S. (2013) Novel algorithms for accurate DNA base-calling. Journal of Biomedical Science and Engineering, 6, 165-174. doi: 10.4236/jbise.2013.62020.1937-688Xhttp://hdl.handle.net/11073/2505210.4236/jbise.2013.62020en_UShttps://doi.org/10.4236/jbise.2013.62020oai:repository.aus.edu:11073/250522024-08-22T12:08:25Z |
| spellingShingle | Novel algorithms for accurate DNA base-calling Mohammed, Omniyah Gul Artificial Neural Network (ANN) Base-Calling Electropherogram Polynomial Classifier (PC) Sequencing |
| status_str | publishedVersion |
| title | Novel algorithms for accurate DNA base-calling |
| title_full | Novel algorithms for accurate DNA base-calling |
| title_fullStr | Novel algorithms for accurate DNA base-calling |
| title_full_unstemmed | Novel algorithms for accurate DNA base-calling |
| title_short | Novel algorithms for accurate DNA base-calling |
| title_sort | Novel algorithms for accurate DNA base-calling |
| topic | Artificial Neural Network (ANN) Base-Calling Electropherogram Polynomial Classifier (PC) Sequencing |
| url | http://hdl.handle.net/11073/25052 |