Morton Taj, Wong Weng-Keen, Megraw Molly
Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA.
Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA, Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.
Bioinformatics. 2015 Dec 1;31(23):3725-32. doi: 10.1093/bioinformatics/btv464. Epub 2015 Aug 8.
The computational identification of gene transcription start sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are arranged in different spatial patterns along the chromosome.
In this work, we present the Transcription Initiation Pattern Recognizer (TIPR), a sequence-based machine learning model that identifies TSSs with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize. TIPR predicts not only the locations of TSSs but also the expected spatial initiation pattern each TSS will form along the chromosome-a novel capability for TSS prediction algorithms. As spatial initiation patterns are associated with spatiotemporal expression patterns and gene function, this capability has the potential to improve gene annotations and our understanding of the regulation of transcription initiation. The high nucleotide resolution of this model locates TSSs within 10 nucleotides or less on average.
Model source code is made available online at http://megraw.cgrb.oregonstate.edu/software/TIPR/.
megrawm@science.oregonstate.edu.
Supplementary data are available at Bioinformatics online.
通过计算识别基因转录起始位点(TSS),无需进行昂贵的实验就能深入了解基因的调控和功能,特别是对于注释不完整的生物体。高分辨率通用TSS预测仍然是一个具有挑战性的问题,在识别和区分沿染色体以不同空间模式排列的TSS方面,近期进展甚微。
在这项工作中,我们提出了转录起始模式识别器(TIPR),这是一种基于序列的机器学习模型,它能够高精度、高分辨率地识别基因组中多种空间分布模式下的TSS,包括以前难以表征的广泛分布的TSS模式。TIPR不仅能预测TSS的位置,还能预测每个TSS沿染色体将形成的预期空间起始模式,这是TSS预测算法的一项新能力。由于空间起始模式与时空表达模式和基因功能相关,这项能力有可能改善基因注释,并增进我们对转录起始调控的理解。该模型的高核苷酸分辨率平均能将TSS定位在10个核苷酸或更少的范围内。
模型源代码可在http://megraw.cgrb.oregonstate.edu/software/TIPR/在线获取。
megrawm@science.oregonstate.edu。
补充数据可在《生物信息学》在线获取。