Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing.
Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing.
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DOI：
10.1186/1471-2164-5-72
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发表时间：
2004-09-28
期刊：
影响因子：
4.4
通讯作者：
Haas SA
中科院分区：
文献类型：
--
作者：
Gupta S;Zink D;Korn B;Vingron M;Haas SA
可变剪接对人类转录组和蛋白质组的复杂性有重大贡献。可变剪接异构体的计算预测通常基于EST序列，这些序列也可用于近似相关转录本的表达模式。然而，EST数据中所代表的组织数量有限，以及不同的cDNA构建方案可能会影响EST揭示组织特异性表达转录本的预测能力。
我们基于EST共有序列的基因组定位（SpliceNest）以及GeneNest数据库中提供的文库注释来预测组织和肿瘤特异性剪接异构体。我们进一步确定潜在的罕见组织特异性转录本，即那些仅由来自标准化文库的EST所代表的转录本。然后通过对40种组织类型进行RT - PCR实验来验证一部分预测的组织和肿瘤特异性异构体。
我们的策略揭示了427个至少有一种组织特异性转录本的基因以及1120个显示肿瘤特异性异构体的基因。虽然我们对计算预测的组织特异性异构体进行的实验评估在确认这些异构体在相应组织中的表达方面成功率较高，但该策略经常无法检测到预期的受限表达模式。利用标准化cDNA文库对假定的低表达转录本进行分析表明，我们检测组织特异性异构体的能力在很大程度上取决于相应转录本的表达水平以及实验方法的敏感性。特别是预测为疾病特异性的剪接异构体往往代表在一组健康组织中表达的转录本，而非新的异构体。
我们建议将可变剪接异构体的计算预测与实验验证相结合，以便有效地描绘出一组准确的组织特异性转录本。
Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. We propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts.
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