Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination
Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023001204 |
| _version_ | 1797384156966027264 |
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| author | Siyuan Wu Ulf Schmitz |
| author_facet | Siyuan Wu Ulf Schmitz |
| author_sort | Siyuan Wu |
| collection | DOAJ |
| description | Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level).In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies. |
| first_indexed | 2024-03-08T21:31:25Z |
| format | Article |
| id | doaj.art-96bccdeaf980456c9e062a0923f7f841 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:25Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-96bccdeaf980456c9e062a0923f7f8412023-12-21T07:31:15ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012123732380Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determinationSiyuan Wu0Ulf Schmitz1Department of Molecular & Cell Biology, James Cook University, Townsville 4811, Queensland, Australia; Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns 4870, Queensland, Australia; School of Mathematics, Monash University, Melbourne 3800, Victoria, AustraliaDepartment of Molecular & Cell Biology, James Cook University, Townsville 4811, Queensland, Australia; Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns 4870, Queensland, Australia; Corresponding author at: Department of Molecular & Cell Biology, James Cook University, Townsville 4811, Queensland, Australia.Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level).In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.http://www.sciencedirect.com/science/article/pii/S2001037023001204Mathematical modellingRNA velocityAlternative splicingIsoform expressionPesudotemopral trajectory inferenceTranscriptome diversity |
| spellingShingle | Siyuan Wu Ulf Schmitz Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination Computational and Structural Biotechnology Journal Mathematical modelling RNA velocity Alternative splicing Isoform expression Pesudotemopral trajectory inference Transcriptome diversity |
| title | Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination |
| title_full | Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination |
| title_fullStr | Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination |
| title_full_unstemmed | Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination |
| title_short | Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination |
| title_sort | single cell and long read sequencing to enhance modelling of splicing and cell fate determination |
| topic | Mathematical modelling RNA velocity Alternative splicing Isoform expression Pesudotemopral trajectory inference Transcriptome diversity |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023001204 |
| work_keys_str_mv | AT siyuanwu singlecellandlongreadsequencingtoenhancemodellingofsplicingandcellfatedetermination AT ulfschmitz singlecellandlongreadsequencingtoenhancemodellingofsplicingandcellfatedetermination |