Perl编程入门:生物信息学实践指南

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" Beginning Perl for Bioinformatics 是一本由 James Tisdall 撰写的书籍,旨在帮助生物学背景且编程经验有限的读者学习如何使用Perl语言进行生物信息学分析。该书首次出版于2001年,由O'Reilly出版社发行,共384页,ISBN为0-596-00080-4。书中通过解决特定问题或问题类别,使读者在完成阅读后能掌握Perl基础,拥有解析BLAST和GenBank数据等任务的程序,并具备进一步进行高级生物信息学编程的能力。 本书首先介绍了什么是生物信息学,以及为何生物学家需要学习编程。接着,它阐述了生物学和计算机科学的结合,特别是DNA和蛋白质的组织结构,以及“in silico”(计算生物学)的概念和计算能力的局限性。在开始Perl编程部分,书中强调了Perl的学习曲线相对平缓且具有多种优势,包括如何在个人计算机上安装Perl、运行Perl程序、选择文本编辑器以及获取帮助的方法。 此外,书中探讨了编程的艺术,提到每个人都有自己的编程风格,并介绍了一些基本的编程原则和技巧。这包括变量、数据类型、流程控制(如条件语句和循环)、函数的使用,以及错误处理等概念。书中还涵盖了正则表达式,这对于处理生物信息学中的序列数据至关重要,因为它们可以高效地匹配和操作复杂的模式。 随着深入,作者会引导读者处理更复杂的任务,如解析常见的生物信息学文件格式,如FASTA和GenBank,以及如何利用Perl模块来简化工作。书中可能还会讨论到BLAST(Basic Local Alignment Search Tool)结果的解析,这对于比较基因和蛋白质序列极其重要。 在面向对象编程方面,读者将了解如何使用Perl的面向对象特性来构建可重用和模块化的代码,这对于创建复杂生物信息学应用程序非常关键。最后,书中可能还包括一些实际案例研究和练习,以巩固所学知识并鼓励读者应用到实际项目中。 "Beginning Perl for Bioinformatics"是一本实用的教程,适合生物学学生和研究人员,旨在帮助他们掌握编程技能,特别是在生物信息学领域,以便更有效地分析和解释大量的生物数据。"

精简下面表达:Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

2023-02-27 上传