[1]ZHANG Li,MA Yue,WU Dongyang.Estimation of transcription variant expression level based on multi-condition multi-sample RNA-Seq data[J].CAAI Transactions on Intelligent Systems,2021,16(6):1126-1135.[doi:10.11992/tis.202101028]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 6
Page number:
1126-1135
Column:
学术论文—人工智能基础
Public date:
2021-11-05
- Title:
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Estimation of transcription variant expression level based on multi-condition multi-sample RNA-Seq data
- Author(s):
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ZHANG Li1; MA Yue2; WU Dongyang1
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1. College of Information Science and Technology, Nanjing Forest University, Nanjing 210016, China;
2. College of Integrated Chinese and Western Medicine, Jiangsu Health Vocational College, Nanjing 210018, China
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- Keywords:
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RNA-Seq; multi-condition; multi-sample; isoform; expression estimation; sparsity; read bias; data noise
- CLC:
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TP391
- DOI:
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10.11992/tis.202101028
- Abstract:
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When analyzing multi-condition multi-sample RNA-sequencing (MCMS RNA-Seq) data, the existing methods for estimating transcription variant expression levels ignore the high similarity between read distribution samples. Thus, this study proposes a method for estimating transcription variant expression levels based on MCMS-Seq data. A joint bias estimation model was developed to extract read distribution similarity between samples, considering the influence of both global and local biases on read distribution at the same time. In addition, two regularization items, ${{{L_2}} / {{L_1}}}$ and ${L_1}$ sparse constraints, were added to reflect sparsity characteristics between genes and transcription variants and to eliminate the influence of technical errors and data noise. This method allows a more accurate estimation of transcription variant expression levels based on MCMS-Seq data and provides more meaningful biological interpretations.