Biologie Computationnelle et Quantitative, UPMC and CNRS UMR7238, Paris, France and Mathématique Informatique et Génome, INRA UR1077, Jouy-en-Josas, France.
Bioinformatics. 2014 May 15;30(10):1409-16. doi: 10.1093/bioinformatics/btu042. Epub 2014 Jan 27.
The most common RNA-Seq strategy consists of random shearing, amplification and high-throughput sequencing of the RNA fraction. Methods to analyze transcription level variations along the genome from the read count profiles generated by the RNA-Seq protocol are needed.
We developed a statistical approach to estimate the local transcription levels and to identify transcript borders. This transcriptional landscape reconstruction relies on a state-space model to describe transcription level variations in terms of abrupt shifts and more progressive drifts. A new emission model is introduced to capture not only the read count variance inside a transcript but also its short-range autocorrelation and the fraction of positions with zero counts. The estimation relies on a particle Gibbs algorithm whose running time makes it more suited to microbial genomes. The approach outperformed read-overlapping strategies on synthetic and real microbial datasets.
A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/.
Supplementary data are available at Bioinformatics online.
最常见的 RNA-Seq 策略包括随机剪接、扩增和 RNA 片段的高通量测序。需要从 RNA-Seq 方案生成的读取计数谱中分析基因组上转录水平变化的方法。
我们开发了一种统计方法来估计局部转录水平并识别转录边界。这种转录景观重建依赖于状态空间模型，根据突然的变化和更渐进的漂移来描述转录水平的变化。引入了一种新的发射模型来捕获不仅在一个转录本内的读取计数方差，而且还捕获其短程自相关和零计数位置的分数。估计依赖于粒子 Gibbs 算法，其运行时间使其更适合微生物基因组。该方法在合成和真实微生物数据集上优于读取重叠策略。
名为 Parseq 的程序可在以下网址获得：http://www.lgm.upmc.fr/parseq/。
补充资料可在生物信息学在线获得。
