科尔-BWT:带有最大匹配的泛基因种子链接改善了阅读分类.
Nathaniel K Brown1, Vikram S Shivakumar1, Ben Langmead1
1Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
概括
我们介绍了col-BWT,这是一种新的数据结构,可以有效地识别参考序列中的直线匹配. 与现有的压缩索引技术相比,这种方法可以提高读取分类的准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 识别对线匹配的传统链接方法在计算上昂贵,需要超线性时间.
- 现有的压缩全文索引只捕获细粒度的对线性,缺少粗粒度的信息,这些信息对于基于泛基因组的读取分类等应用至关重要.
- 泛基因组分析需要高效地处理大量匹配,而目前的方法很难做到这一点.
研究的目的:
- 开发一种新的数据结构,col-BWT (collinear Burrows-Wheeler Transform),用于有效计算细粒度和粗粒度对线统计数据.
- 为了使准确和快速阅读分类与大型参考集合,如 pangenomes.
- 克服现有的压缩索引方法在捕获粗粒度对线性方面的局限性.
主要方法:
- col-BWT是直接从字符串集合中构建的,绕过了多个序列对齐的需要.
- 它识别了与这些匹配对应的多最大唯一匹配 (多MUM) 和BWT子运行.
- 数据结构高效地标记"道化"子运行与他们的多MUM标识符,使得对线性识别.
主要成果:
- col-BWT为具有 n 长度 BWT 和 r 运行的 d 个序列提供了一个 O ((r + n/d) 空间索引.
- 它根据查询长度在线时间计算细粒度和粗粒度链统计.
- 使用col-BWT的分类准确性明显超过以前的压缩索引方法,并且与基于对齐的方法相匹配.
结论:
- col-BWT是一种高效的数据结构,用于识别对线匹配,在读取分类中提供了显著的改进.
- 它有效地捕捉了粗粒度对线性,这是大规模基因组分析的关键特征.
- 这种方法为传统的链接和基于对齐的方法提供了更快,同样准确的替代方案.
相关概念视频
Maxam-Gilbert Sequencing
12.5K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Challenges of the Maxam-Gilbert Method
The...
12.5K
Evolutionary Relationships through Genome Comparisons
6.8K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.8K
RNA-seq
11.7K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
11.7K
Next-generation Sequencing
97.6K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
97.6K
Sanger Sequencing
772.9K
DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
772.9K


