Big Data Analytics in RNA-sequencing
As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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The Korean Society for Clinical Laboratory Science
2023-12-01
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| Series: | Korean Journal of Clinical Laboratory Science |
| Subjects: | |
| Online Access: | http://www.kjcls.org/journal/view.html?doi=10.15324/kjcls.2023.55.4.235 |
| _version_ | 1827391038103224320 |
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| author | Sung-Hun WOO Byung Chul JUNG |
| author_facet | Sung-Hun WOO Byung Chul JUNG |
| author_sort | Sung-Hun WOO |
| collection | DOAJ |
| description | As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in bioinformatic tools. On the other hand, it is difficult to interpret the complex data underlying the biological meaning without a general understanding of the types of RNA-seq and bioinformatic approaches. In this regard, this paper discusses the two main sections of RNA-seq. First, two major variants of RNA-seq are described and compared with the standard RNA-seq. This provides insights into which RNA-seq method is most appropriate for their research. Second, the most widely used RNA-seq data analyses are discussed: (1) exploratory data analysis and (2) pathway enrichment analysis. This paper introduces the most widely used exploratory data analysis for RNA-seq, such as principal component analysis, heatmap, and volcano plot, which can provide the overall trends in the dataset. The pathway enrichment analysis section introduces three generations of pathway enrichment analysis and how they generate enriched pathways with the RNA-seq dataset. |
| first_indexed | 2024-03-08T17:03:48Z |
| format | Article |
| id | doaj.art-446316ffad574a4b89c293d80d42e175 |
| institution | Directory Open Access Journal |
| issn | 1738-3544 |
| language | English |
| last_indexed | 2024-03-08T17:03:48Z |
| publishDate | 2023-12-01 |
| publisher | The Korean Society for Clinical Laboratory Science |
| record_format | Article |
| series | Korean Journal of Clinical Laboratory Science |
| spelling | doaj.art-446316ffad574a4b89c293d80d42e1752024-01-04T06:45:02ZengThe Korean Society for Clinical Laboratory ScienceKorean Journal of Clinical Laboratory Science1738-35442023-12-0155423524310.15324/kjcls.2023.55.4.235kjcls.2023.55.4.235Big Data Analytics in RNA-sequencingSung-Hun WOO0Byung Chul JUNG1Department of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, KoreaDepartment of Nutritional Sciences and Toxicology, University of California, Berkeley, California, USAAs next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in bioinformatic tools. On the other hand, it is difficult to interpret the complex data underlying the biological meaning without a general understanding of the types of RNA-seq and bioinformatic approaches. In this regard, this paper discusses the two main sections of RNA-seq. First, two major variants of RNA-seq are described and compared with the standard RNA-seq. This provides insights into which RNA-seq method is most appropriate for their research. Second, the most widely used RNA-seq data analyses are discussed: (1) exploratory data analysis and (2) pathway enrichment analysis. This paper introduces the most widely used exploratory data analysis for RNA-seq, such as principal component analysis, heatmap, and volcano plot, which can provide the overall trends in the dataset. The pathway enrichment analysis section introduces three generations of pathway enrichment analysis and how they generate enriched pathways with the RNA-seq dataset.http://www.kjcls.org/journal/view.html?doi=10.15324/kjcls.2023.55.4.235big datacomputational biologysequence analysisrna |
| spellingShingle | Sung-Hun WOO Byung Chul JUNG Big Data Analytics in RNA-sequencing Korean Journal of Clinical Laboratory Science big data computational biology sequence analysis rna |
| title | Big Data Analytics in RNA-sequencing |
| title_full | Big Data Analytics in RNA-sequencing |
| title_fullStr | Big Data Analytics in RNA-sequencing |
| title_full_unstemmed | Big Data Analytics in RNA-sequencing |
| title_short | Big Data Analytics in RNA-sequencing |
| title_sort | big data analytics in rna sequencing |
| topic | big data computational biology sequence analysis rna |
| url | http://www.kjcls.org/journal/view.html?doi=10.15324/kjcls.2023.55.4.235 |
| work_keys_str_mv | AT sunghunwoo bigdataanalyticsinrnasequencing AT byungchuljung bigdataanalyticsinrnasequencing |