MOTIVATION: Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved. RESULTS: In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated. AVAILABILITY AND IMPLEMENTATION: The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Bulked segregant analysis combined with next generation sequencing has proven to be a simple and efficient approach for fast mapping of quantitative trait loci (QTLs). However, how to estimate the proportion of phenotypic variance explained by a QTL (or termed QTL heritability) in such pooled QTL mapping is an unsolved problem. Results: In this paper, we propose a method called PQHE to estimate QTL heritability using pooled sequencing data obtained under different experimental designs. Simulation studies indicated that our method is correct and feasible. Four practical examples from rice and yeast are demonstrated, each representing a different situation. Availability and implementation: The R scripts of our method are open source under GPLv3 license at http://genetics.fafu.edu.cn/PQHE or https://github.com/biotangweiqi/PQHE. The R scripts require the R package rootSolve. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Text copying is easy to do but not easy to be detected. Copy detection is a delicate ground. It determines whether a document is copied from others or not. It helps to verify and to detect paper redistribution, thesis plagiarism or copyright violation. A novel method of Chinese text copy detection design and implementation is discussed in this paper, based on theory of mathematical expression of Chinese characters. It can implement the functions include characteristic reconstruction, registration and detection. Experimental results show that the system has the advantages on detecting accuracy and high speed.