Department of Computer Science, 1873 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1873, USA.
Genome Biol. 2012 Jan 31;13(1):R4. doi: 10.1186/gb-2012-13-1-r4.
We propose a method for predicting splice graphs that enhances curated gene models using evidence from RNA-Seq and EST alignments. Results obtained using RNA-Seq experiments in Arabidopsis thaliana show that predictions made by our SpliceGrapher method are more consistent with current gene models than predictions made by TAU and Cufflinks. Furthermore, analysis of plant and human data indicates that the machine learning approach used by SpliceGrapher is useful for discriminating between real and spurious splice sites, and can improve the reliability of detection of alternative splicing. SpliceGrapher is available for download at http://SpliceGrapher.sf.net.
我们提出了一种预测剪接图谱的方法，该方法使用 RNA-Seq 和 EST 比对的证据来增强已注释的基因模型。在拟南芥中使用 RNA-Seq 实验获得的结果表明，与 TAU 和 Cufflinks 相比，我们的 SpliceGrapher 方法做出的预测与当前的基因模型更为一致。此外，对植物和人类数据的分析表明，SpliceGrapher 所使用的机器学习方法可用于区分真实和虚假的剪接位点，并能提高对可变剪接检测的可靠性。SpliceGrapher 可在 http://SpliceGrapher.sf.net 下载。
