NSF Postdoctoral Fellowship in Biology FY 2017: Improving RNA-Seq Analysis through Graph-based Analysis and Computational Indexing

这是NSF生物学博士后研究奖学金，在该计划下，扩大了生物学中代表性不足的群体的参与。这位名叫努里·特蕾莎·皮尔斯的研究员正在进行研究和接受培训，以增加生物学中代表性不足的群体的参与。这位科学家的导师是加州大学戴维斯分校的C.泰特斯·布朗。该项目的目标是改进RNA测序分析，同时举办教学研讨会，以增加生物信息学领域的可及性。海洋微生物多样性仍然很难使用经典的生态学技术进行研究，但现在通过对海水样本进行下一代测序是可行的。随着测序技术产量的增加，迫切需要能够在使用最小处理能力快速处理大量数据的同时实现高精度的工具。这位研究员正在通过开发分析测序数据集的改进方法来解决这一问题，同时增加参考数据的可及性和实用性。访问这些新的数据库和分析技术将使生物学家能够利用测序数据来解决更大的问题，并有可能彻底改变我们对海洋群落多样性和功能的理解。除了这些科学目标，该研究员正在进行数据科学培训和推广，让广泛的学生接触生物信息学研究，帮助建立一个更公平、多样化和包容性的学院。这位研究员正在教授基本编码，这是招募下一代海洋生物学家的关键技能，对他们来说，生物信息学将是必不可少的工具。该项目利用两种计算技术，即De Bruijn图以及通过序列布卢姆树和最小散列进行索引，以改进RNA-Seq分析。该项目的目标1是通过开发De Bruijn图而不是共识转录组来改进全基因组RNA-Seq(转录组)分析，作为注释和表达的参考。海洋微生物真核转录组测序项目数据的计算实验正在用于评估改进情况。该研究员还使用上述索引技术来创建现有参考数据的简化表示，以便于利用新数据快速计算查询序列相似性(目标2)和表达相似性(目标3)。在整个项目中，这位研究员正在接受软件开发、项目管理、开放科学实践和赠款写作方面的培训，以提高她的技术和学术技能。此外，该研究员正在通过开发教材和一系列编码研讨会，培训一批不同的学生学习数据密集型生物学。

This is an NSF Postdoctoral Research Fellowship in Biology, under the program Broadening Participation of Groups Under-represented in Biology. The fellow, Nuri Theresa Pierce, is conducting research and receiving training that is increasing the participation of groups underrepresented in biology. The fellow is being mentored by C. Titus Brown at the University of California-Davis. The goals of this project are to improve RNA sequencing analysis while teaching workshops to increase accessibility into the field of bioinformatics. Marine microbial diversity remains difficult to study using classic ecological techniques, but is now feasible via next generation sequencing of seawater samples. With increased output of sequencing technologies, there is an urgent need for tools that achieve high accuracy while processing large amounts of data quickly using minimal processing power. This fellow is addressing this issue by developing improved methods for analyzing sequencing datasets while increasing the accessibility and utility of reference data. Access to these new databases and analysis techniques will allow biologists to use sequencing data to tackle larger problems and has the potential to revolutionize our understanding of the diversity and function of ocean communities. Beyond these scientific goals, the fellow is conducting data science training and outreach to expose a wide array of students to bioinformatics research, to help build a more equitable, diverse, and inclusive academe. The fellow is teaching basic coding, a skill that is critical in recruiting the next generation of marine biologists, for whom bioinformatics will be an essential tool.This project leverages two computational techniques, de bruijn graphs and indexing via Sequence Bloom Trees and Minimum Hashing, to improve RNA-Seq analysis. Aim 1 of the project is to improve genome-wide RNA-Seq (transcriptome) analysis by developing the de bruijn graph, rather than the consensus transcriptome, as the reference for annotation and expression. Computational experiments with Marine Microbial Eukaryotic Transcriptome Sequencing Project data are being used to assess improvements. The fellow is also using the indexing techniques described above to create a reduced representation of existing reference data to facilitate rapid computational querying for sequence similarity (Aim 2) and expression similarity (Aim 3) with new data. Throughout the project, the fellow is receiving training in software development, project management, open science practices, and grant writing to improve her technical and academic skillsets. In addition, the fellow is training a diverse set of students in data intensive biology through development of teaching materials and a series of coding workshops.