In silico prediction of splice-altering single nucleotide variants in the human genome.
In silico prediction of splice-altering single nucleotide variants in the human genome.
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DOI：
10.1093/nar/gku1206
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发表时间：
2014-12-16
影响因子：
14.9
通讯作者：
Liu X
中科院分区：
文献类型：
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作者：
Jian X;Boerwinkle E;Liu X
已经开发了计算机模拟工具来预测可能对前体mRNA剪接产生影响的变体。这些工具应用于基础研究和临床实践的主要限制是难以解释输出。大多数工具仅预测给定DNA序列的潜在剪接位点，而不测量由变体引起的剪接信号变化。另一个限制是缺乏对这些工具的大规模评价研究。我们使用受试者操作特征分析比较了8种计算机模拟工具对剪接共有区（scSNV）内的2959个单核苷酸变体的影响。位置权重矩阵模型和MaxEntScan优于其他方法。两个集成学习方法，自适应助推和随机森林，被用来构建模型，利用个人的方法。这两个模型都进一步提高了预测能力，并输出了可直接解释的预测分数。我们将我们的整体评分应用于来自癌症体细胞突变目录数据库的scSNV。分析显示，预测的剪接改变scSNV富含复发性scSNV和已知的癌症基因。我们预先计算了人类基因组中所有潜在scSNV的总体得分，为鉴定从大规模测序研究中发现的剪接改变scSNV提供了全基因组水平的资源。
In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
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影响因子：
14.9
作者：
Cartegni, L;Wang, JH;Krainer, AR
通讯作者：
Krainer, AR
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
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影响因子：
14.9
作者：
Flicek P;Amode MR;Barrell D;Beal K;Billis K;Brent S;Carvalho-Silva D;Clapham P;Coates G;Fitzgerald S;Gil L;Girón CG;Gordon L;Hourlier T;Hunt S;Johnson N;Juettemann T;Kähäri AK;Keenan S;Kulesha E;Martin FJ;Maurel T;McLaren WM;Murphy DN;Nag R;Overduin B;Pignatelli M;Pritchard B;Pritchard E;Riat HS;Ruffier M;Sheppard D;Taylor K;Thormann A;Trevanion SJ;Vullo A;Wilder SP;Wilson M;Zadissa A;Aken BL;Birney E;Cunningham F;Harrow J;Herrero J;Hubbard TJ;Kinsella R;Muffato M;Parker A;Spudich G;Yates A;Zerbino DR;Searle SM
通讯作者：
Searle SM
DOI：
10.1038/nrc1299
发表时间：
2004-03
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
通讯作者：
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DOI：
10.1038/gim.2013.176
发表时间：
2014-07
期刊：
Genetics in medicine : official journal of the American College of Medical Genetics
影响因子：
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作者：
通讯作者：
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