MSLP: mRNA subcellular localization predictor based on machine learning techniques
<h3>Background</h3><p dir="ltr">Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are l...
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2023
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| _version_ | 1864513526909894656 |
|---|---|
| author | Saleh Musleh (15279190) |
| author2 | Mohammad Tariqul Islam (7854059) Rizwan Qureshi (15279193) Nehad M. Alajez (7397276) Tanvir Alam (638619) |
| author2_role | author author author author |
| author_facet | Saleh Musleh (15279190) Mohammad Tariqul Islam (7854059) Rizwan Qureshi (15279193) Nehad M. Alajez (7397276) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saleh Musleh (15279190) Mohammad Tariqul Islam (7854059) Rizwan Qureshi (15279193) Nehad M. Alajez (7397276) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2023-03-22T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12859-023-05232-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MSLP_mRNA_subcellular_localization_predictor_based_on_machine_learning_techniques/25250080 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Biochemistry and cell biology Mathematical sciences Applied mathematics RNA mRNA Machine learning Sequence analysis Localization prediction Subcellular localization |
| dc.title.none.fl_str_mv | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.</p><h3>Methods</h3><p dir="ltr">In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs.</p><h3>Results</h3><p dir="ltr">Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach.</p><h3>Availability</h3><p dir="ltr">We have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12859-023-05232-0" target="_blank">https://dx.doi.org/10.1186/s12859-023-05232-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_502e5b08c806cb18bd0ade64fc497764 |
| identifier_str_mv | 10.1186/s12859-023-05232-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25250080 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MSLP: mRNA subcellular localization predictor based on machine learning techniquesSaleh Musleh (15279190)Mohammad Tariqul Islam (7854059)Rizwan Qureshi (15279193)Nehad M. Alajez (7397276)Tanvir Alam (638619)Biological sciencesBiochemistry and cell biologyMathematical sciencesApplied mathematicsRNAmRNAMachine learningSequence analysisLocalization predictionSubcellular localization<h3>Background</h3><p dir="ltr">Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.</p><h3>Methods</h3><p dir="ltr">In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs.</p><h3>Results</h3><p dir="ltr">Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach.</p><h3>Availability</h3><p dir="ltr">We have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Bioinformatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12859-023-05232-0" target="_blank">https://dx.doi.org/10.1186/s12859-023-05232-0</a></p>2023-03-22T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12859-023-05232-0https://figshare.com/articles/journal_contribution/MSLP_mRNA_subcellular_localization_predictor_based_on_machine_learning_techniques/25250080CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252500802023-03-22T03:00:00Z |
| spellingShingle | MSLP: mRNA subcellular localization predictor based on machine learning techniques Saleh Musleh (15279190) Biological sciences Biochemistry and cell biology Mathematical sciences Applied mathematics RNA mRNA Machine learning Sequence analysis Localization prediction Subcellular localization |
| status_str | publishedVersion |
| title | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| title_full | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| title_fullStr | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| title_full_unstemmed | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| title_short | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| title_sort | MSLP: mRNA subcellular localization predictor based on machine learning techniques |
| topic | Biological sciences Biochemistry and cell biology Mathematical sciences Applied mathematics RNA mRNA Machine learning Sequence analysis Localization prediction Subcellular localization |