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1、毕 业 设 计（论文） 外 文 文 献 翻 译文献、资料中文题目：第二代测序（ NGS）技术 文献、资料英文题目：文献、资料来源：文献、资料发表（出版）日期：院 （部）：专班姓学业：级：名：号：指导教师：翻译日期： 2017.02.14Science has kept changing its form from observational to experimental to data-driven in the field of life science. With the advancement of Next Generation Sequencing (NGS) technology
2、, new findings are coming up with a great amount of responsibilities whereas storing and analysing these data is concern Li 1, Stephens et al. 2. During last decade, the cost of sequencing has reduced heavily by allowing access to more scientists. A simple search in PUBMED can provide the scenario o
3、f exponential growth of the number of reports published using NGS technology. However, the deposition of raw data in the public domain is increasing dramatically outstripping the proper annotation of these data which is still half-cooked or ambiguous. The data scientists have already started facing
4、difficulty in envisaging the scientific standpoint of handling the data deluge. The only solution to sail across this flood of data is to develop efficient and flexible algorithms which can analyse the raw data and extract meaningful information. Already approaches like compressive genomics, cloud c
5、omputing, No SQL, etc. have been coined to deal with the big data issue. Compressive algorithms help in reducing the task of computing on redundancy data by allowing direct computation on the compressed data Loh 3. This approach can also be implemented with tools such as Basic Local Alignment Search
6、 Tool (BLAST) to achieve sublinear analysis. Cloud computing is basically an alternative to the economic and efficiency problems of the common user who always has to think of upgrading his available computational facilities to handle the high-throughput data Zhou 4. Researchers have also started usi
7、ng No SQL to store the data in a more classified way. Unlike the available relational databases (My SQL), No SQL stores data using graphs, objects and many more which provides an user-friendly as well as more informative view to the large-scale data Have 5. Especially graph databases such as Allegro
8、 Graph, Neo4J, etc. are being preferred by bioinformaticians. While it comes to the analysis of massive data, Neural network approaches (Nns) owe their dynamic efficiency towards all types of biological data Chen 6. The underlying principle of Nns is the machine learning approaches which enhance the
9、 algorithms to recognize patterns, classify the data and so many other features. The traditional way of bioinformatics analysis has become obsolete. Systems biology combines the computational tools, statistical and mathematical models along with high-throughput techniques to analyze the core compone
10、nts in a biological systems and bring out the most significant information such as various regulatory networks along with functions of specific regulators like mi RNAs in the network Li et al., 7. The available computational facilities are not enough to handle the big NGS data; however, there should
11、 be more focus on development of powerful algorithms so that the researchers would be able to know where they are heading with their own data。科学在生命科学领域的形式从观察到实验改变为数据驱动。随着下一代测序（NGS）技术的 进步，新的发现提出了大量的责任，而存储和分析这些数据是关注 Li 1，Stephens 等人。 2。 在过去十年中，测序的成本大大降低，允许更多的科学家。在 PUBMED 中进行简单搜索可 以提供使用 NGS 技术发布的报告数量呈指
12、数增长的情况。然而，原始数据在公共领域的沉 积正在显着超过这些仍然半熟或不明确的数据的适当注释。数据科学家已经开始面临困难， 设想处理数据洪水的科学观点。跨越这些数据的唯一解决方案是开发高效灵活的算法，可以 分析原始数据并提取有意义的信息。已经提出了诸如压缩基因组学，云计算，无 SQL 等方 法来处理大数据问题。压缩算法通过允许对压缩数据 Loh 3进行直接计算，有助于减少对 冗余数据的计算任务。该方法还可以使用诸如基本局部比对搜索工具（BLAST）的工具来实现以实现子线性分析。云计算基本上是普通用户的经济和效率问题的替代方案，他总是不得 不考虑升级他的可用计算设施来处理高吞吐量数据。研究人员
13、还开始使用 No SQL 以更分类 的方式存储数据。与可用的关系数据库（My SQL）不同，没有 SQL 存储数据使用图形，对 象等，它提供了用户友好以及更多的信息视图大型数据有5。尤其是图形数据库，如 Allegro Graph，Neo4J 等，被生物信息学家所喜爱。虽然涉及海量数据的分析，神经网络方法（Nns） 欠其对所有类型的生物数据的动态效率陈6。 Nns 的基本原理是机器学习方法，其增强算 法来识别模式，分类数据和许多其他特征。传统的生物信息学分析方法已经过时。系统生物 学结合了计算工具，统计和数学模型以及高通量技术来分析生物系统中的核心组件，并提出 最重要的信息，如各种监管网络以及特定监管机构的功能，如网络中的 mi RNA Li et al al。，7。可用的计算设施不足以处理大的 NGS 数据;然而，应该更加关注强大的算法的开发， 以便研究人员能够知道他们用自己的数据去哪里。