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发出DNA的特性

尽管许多研究人员习惯于以视觉观看DNA，但在四月，研究员马克·D·坦普尔（Mark D Temple）通过将代码转换为音符来识别突变的新方法。将DNA代码转换为音乐的超声算法很可能代表了一种全新的寻找突变的方式，原本会错过。这些算法使用不同的声音表示DNA特征，例如，绑定位点，限制性核酸内切核酸酶站点和SNP都以自己独特的方式突出显示。

由DNA制作的歌曲可能很快就不会在图表上登上图表，但可能会导致探索序列和发现否则会忽略的功能的新方法。

ImageJ2：下一代

ImageJ has long been an immensely popular bioinformatics tool used in a wide variety of fields in both the biological and physical sciences. A huge, engaged community has developed to support and develop the software over the years but there comes a time when such successful software needs to be updated to be ready for the trials ahead. Curtis T. Rueden and his colleagues published ImageJ2 in November ready to meet the challenges posed by increasingly complex new datasets and to ensure that the software is capable of adapting to the future needs of the community.

该新软件已从头开始重新构建，并在原始ImageJ上扩展。它旨在继续支持社区，同时确保它可以自由发展和发展，并探索新的途径，以使用其他现有的图像处理工具。在他们热情的社区的支持和反馈下，没有理由认为ImageJ2不会继续越来越强大。

dna的dna

近年来，改进技术和技术使研究人员能够组装大量的基因组。第一个真核基因组于2000年出版，从那时起，基因组的组装和发表速度已大大提高。但是，以这样的速度也必须谨慎。我们如何确定靶DNA不会被外国DNA污染？2017年12月，Janna L. Fierst和Duncan A. Murdock使用机器学习提出了一种新颖的方法，以帮助确保从头组装的基因组序列不含不必要的外部序列。

保证无菌DNA样品是现代测序工作中的主要挑战。外国DNA可以从微生物群，内共生体或实验室工具或试剂中获取。为了确保我们可以从测序数据中得出扎实的结论，我们必须确保数据就是我们认为的。在组装序列中已经发现了许多污染错误。现有的减少污染错误的方法确实存在，但很容易在去除序列或仅消除许多潜在序列方面过于激进。这项研究中证明的机器学习方法（使用决策树）表明，在维持目标DNA的同时取消外国序列的可能性很大。

可视化微生物暗物质

ICOVE是一种软件工具，而不是可以可视化基因组片段“箱”，可以重新组装以创建草稿微生物基因组

High-throughput sequencing has really come to the fore in recent years allowing for further exploration of so-called “microbial dark matter”: microbial communities which have proved resistant to attempts to cultivate them. Analyses of these communities have created large numbers of genomics fragments that need to be grouped together, or binned, in order to reassemble draft microbial genomes.

ICOVE是一种软件工具，而不是可以可视化基因组片段“箱”，可以重新组装以创建草稿微生物基因组。

ICoVeR(互动Contig-bin验证和再保险finement tool) is a new interactive visualisation software tool than can visualise these bins and perform further clustering as necessary. Its open design also means that it is easily updated with new algorithms and solutions to improve performance.

生物信息学：必不可少的，却隐藏在明显的视线中？

最后，我们重点介绍了Bartett的一些信件et alexploring the relationship between bioinformatics and life sciences. Bioinformatics is seen as increasingly important in life sciences but the work is often not given due credit.

Bioinformatics is multidisciplinary in nature – it can be considered a service, a collection of tools and/or methods and a field of study in its own right. And it has become integral to life sciences, complimenting the work of the wet lab. Having studied the bioinformatics community, and the work bioinformaticians perform, Bartlett and colleagues discuss whether bioinformatics is now a victim of “black boxing” – that it has become so successful that the focus has switched from the process itself, for example the creation of an algorithm or software, to only the results of that process.