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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014
摘要
Sets of sequence data used in 系統發育 analysis are often plagued by both random noise and systematic biases. Since the commonly used methods of 系統發育 reconstruction are designed to produce trees it is an important task to evaluate these trees a posteriori. Preferably, however, one would like to assess the suitability of the input data for 系統發育 analysis a priori and, if possible, obtain 資訊 on how to prune the data sets to improve the quality of 系統發育 reconstruction without introducing unwarranted biases. In the last few years several different approaches, algorithms, and software tools have been proposed for this purpose. Here we provide an overview of the state of the art and briefly discuss the most pressing open problems.
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