Department of Computer Science and Engineering, University of California, San Diego, CA, USA.
Bioinformatics. 2011 Jul 1;27(13):i137-41. doi: 10.1093/bioinformatics/btr208.
The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open.
In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data.
http://www.cs.toronto.edu/~pashadag.
高通量测序（HTS）平台的不断改进已经开始带来无数新的应用。因此，测序读段的纠错仍然是一个重要的问题。尽管有几个工具在处理读段采样接近均匀的数据集时表现出色，但从极不均匀的数据集（如单细胞测序）中纠正读段的问题仍然存在。
在本文中，我们开发了一种无需任何均匀性假设的纠错方法 Hammer。Hammer 基于汉明图和测序错误的简单概率模型的组合。它是一种简单且适应性强的算法，在非均匀的单细胞数据上优于其他工具，同时在正常的多细胞数据上取得可比的结果。
http://www.cs.toronto.edu/~pashadag.
