ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis
Alternative splicing (AS) is a fundamental mechanism that regulates gene expressionin both physiological and pathological processes. This article introduces ASTK, a software package covering upstream and downstream analysis of AS. Initially, ASTK offers a module to perform enrichment analysis at bot...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202300594 |
| _version_ | 1797197603289432064 |
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| author | Shenghui Huang Jiangshuang He Lei Yu Jun Guo Shangying Jiang Zhaoxia Sun Linghui Cheng Xing Chen Xiang Ji Yi Zhang |
| author_facet | Shenghui Huang Jiangshuang He Lei Yu Jun Guo Shangying Jiang Zhaoxia Sun Linghui Cheng Xing Chen Xiang Ji Yi Zhang |
| author_sort | Shenghui Huang |
| collection | DOAJ |
| description | Alternative splicing (AS) is a fundamental mechanism that regulates gene expressionin both physiological and pathological processes. This article introduces ASTK, a software package covering upstream and downstream analysis of AS. Initially, ASTK offers a module to perform enrichment analysis at both the gene‐ and exon‐level to incorporate various impacts by different spliced events on a single gene. We further cluster AS genes and alternative exons into three groups based on spliced exon sizes (micro‐, mid‐, and macro‐), which are preferentially associated with distinct biological pathways. A major challenge in the field has been decoding the regulatory codes of splicing. ASTK adeptly extracts both sequence features and epigenetic marks associated with AS events. Through the application of machine learning algorithms, we identified pivotal features influencing the inclusion levels of most AS types. Notably, the splice site strength is a primary determinant for the inclusion levels in alternative 3’/5’ splice sites (A3/A5). For the alternative first exon and skipping exon classes, a combination of sequence and epigenetic features collaboratively dictate exon inclusion/exclusion. Our findings underscore ASTK's capability to enhance the functional understanding of AS events and shed light on the intricacies of splicing regulation. |
| first_indexed | 2024-04-24T06:46:35Z |
| format | Article |
| id | doaj.art-6409eba008de4d1e8cd2651b11015380 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-24T06:46:35Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-6409eba008de4d1e8cd2651b110153802024-04-22T18:07:16ZengWileyAdvanced Intelligent Systems2640-45672024-04-0164n/an/a10.1002/aisy.202300594ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing AnalysisShenghui Huang0Jiangshuang He1Lei Yu2Jun Guo3Shangying Jiang4Zhaoxia Sun5Linghui Cheng6Xing Chen7Xiang Ji8Yi Zhang9Zhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaZhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaZhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaZhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaZhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaZhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaScientific Research Center Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaSchool of Informatics University of Edinburgh Edinburgh EH8 9AB UKDepartment of Mathematics School of Science & Engineering Tulane University New Orleans 70118 LA USAZhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory Medicine Ministry of Education, China School of Laboratory Medicine and Life Science Wenzhou Medical University Wenzhou Zhejiang Province 325035 ChinaAlternative splicing (AS) is a fundamental mechanism that regulates gene expressionin both physiological and pathological processes. This article introduces ASTK, a software package covering upstream and downstream analysis of AS. Initially, ASTK offers a module to perform enrichment analysis at both the gene‐ and exon‐level to incorporate various impacts by different spliced events on a single gene. We further cluster AS genes and alternative exons into three groups based on spliced exon sizes (micro‐, mid‐, and macro‐), which are preferentially associated with distinct biological pathways. A major challenge in the field has been decoding the regulatory codes of splicing. ASTK adeptly extracts both sequence features and epigenetic marks associated with AS events. Through the application of machine learning algorithms, we identified pivotal features influencing the inclusion levels of most AS types. Notably, the splice site strength is a primary determinant for the inclusion levels in alternative 3’/5’ splice sites (A3/A5). For the alternative first exon and skipping exon classes, a combination of sequence and epigenetic features collaboratively dictate exon inclusion/exclusion. Our findings underscore ASTK's capability to enhance the functional understanding of AS events and shed light on the intricacies of splicing regulation.https://doi.org/10.1002/aisy.202300594alternative splicingepigenetic marksfunctional enrichmentmachine learningsequence featuressplicing codes |
| spellingShingle | Shenghui Huang Jiangshuang He Lei Yu Jun Guo Shangying Jiang Zhaoxia Sun Linghui Cheng Xing Chen Xiang Ji Yi Zhang ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis Advanced Intelligent Systems alternative splicing epigenetic marks functional enrichment machine learning sequence features splicing codes |
| title | ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis |
| title_full | ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis |
| title_fullStr | ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis |
| title_full_unstemmed | ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis |
| title_short | ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis |
| title_sort | astk a machine learning based integrative software for alternative splicing analysis |
| topic | alternative splicing epigenetic marks functional enrichment machine learning sequence features splicing codes |
| url | https://doi.org/10.1002/aisy.202300594 |
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