Classification of ncRNAs using position and size information in deep sequencing data.
Classification of ncRNAs using position and size information in deep sequencing data.
复制标题
DOI：
10.1093/bioinformatics/btq363
复制
发表时间：
2010-09-15
期刊：
影响因子：
--
通讯作者：
Zimmer R
中科院分区：
文献类型：
--
作者：
Erhard F;Zimmer R
动机：在生命的所有分支中，小的非编码RNA(NcRNAs)在各种细胞功能中发挥着重要作用。有了下一代测序技术，以高通量方式研究ncRNA已经成为可能，通过使用专门的算法，可以在深度测序数据中检测到ncRNA类别，如miRNAs。通常，这种方法针对特定类别的ncRNA。许多方法依赖于RNA二级结构预测，但预测并不总是准确的，并且并不是所有的ncRNA类都具有共同的二级结构。对ncRNA的无偏分类方法对于提高准确性和在测序数据中检测新的ncRNA类别可能是重要的。结果：在这里，我们提出了一种称为Alps(模式矩阵比对评分)的评分系统，该系统仅使用来自深度测序实验的主要信息，即阅读的相对位置和长度，来对ncRNA进行分类。阿尔卑斯不做进一步的假设，例如关于ncRNA类中的共同结构性质，但仍然能够高精度地识别ncRNA类。由于Alps不是为识别特定类别的ncRNA而设计的，因此它可以用于检测新的ncRNA类别，只要这些未知的ncRNA具有深度测序阅读长度和位置的特征模式。我们在公开的深度测序数据上对我们的评分系统进行了评估，结果表明它能够以高灵敏度和高特异性对已知的ncRNA进行分类。可获得性：数据集hESC和Eb的计算模式矩阵可在项目网站http://www.bio.ifi.lmu.de/ALPS.上获得根据作者的请求，可以获得所描述的方法的实现。联系人：florian.erhard@Bio.ifi.lmu.de
Motivation: Small non-coding RNAs (ncRNAs) play important roles in various cellular functions in all clades of life. With next-generation sequencing techniques, it has become possible to study ncRNAs in a high-throughput manner and by using specialized algorithms ncRNA classes such as miRNAs can be detected in deep sequencing data. Typically, such methods are targeted to a certain class of ncRNA. Many methods rely on RNA secondary structure prediction, which is not always accurate and not all ncRNA classes are characterized by a common secondary structure. Unbiased classification methods for ncRNAs could be important to improve accuracy and to detect new ncRNA classes in sequencing data. Results: Here, we present a scoring system called ALPS (alignment of pattern matrices score) that only uses primary information from a deep sequencing experiment, i.e. the relative positions and lengths of reads, to classify ncRNAs. ALPS makes no further assumptions, e.g. about common structural properties in the ncRNA class and is nevertheless able to identify ncRNA classes with high accuracy. Since ALPS is not designed to recognize a certain class of ncRNA, it can be used to detect novel ncRNA classes, as long as these unknown ncRNAs have a characteristic pattern of deep sequencing read lengths and positions. We evaluate our scoring system on publicly available deep sequencing data and show that it is able to classify known ncRNAs with high sensitivity and specificity. Availability: Calculated pattern matrices of the datasets hESC and EB are available at the project web site http://www.bio.ifi.lmu.de/ALPS. An implementation of the described method is available upon request from the authors. Contact: florian.erhard@bio.ifi.lmu.de
登录
查看更多内容
影响因子：
4.3
作者：
Will S;Reiche K;Hofacker IL;Stadler PF;Backofen R
通讯作者：
Backofen R
影响因子：
1.8
作者：
HOFACKER, IL;FONTANA, W;SCHUSTER, P
通讯作者：
SCHUSTER, P
影响因子：
7
作者：
Morin, Ryan D.;O'Connor, Michael D.;Marra, Marco A.
通讯作者：
Marra, Marco A.
影响因子：
10.5
作者：
Babiarz, Joshua E.;Ruby, J. Graham;Blelloch, Robert
通讯作者：
Blelloch, Robert
影响因子：
64.5
作者：
Thompson, Debrah M.;Parker, Roy
通讯作者：
Parker, Roy