2017英特尔ISEF获奖作品摘要 计算生物与生物信息学 Intel Finalist Abstract Computational Biology and Bioinformatics

获奖作品基本信息

年份	2017
学科	计算生物与生物信息学 Computational Biology and Bioinformatics
国家/州	United States of America

获奖作品名称

Identification of Natural Selection in Mayetiola destructor SNP Markers from Sequencing Data

获奖作品摘要

This research presents a computational pipeline for single nucleotide polymorphism (SNP) analysis. Due to the scientific potential of the complex and multifaceted information provided by SNP data, it is being generated at an unprecedented speed. However, traditional analysis of SNPs is lacking in both efficiency and conclusiveness. This study creates a computational tool consisting of a linear series of steps - a pipeline - that streamlines the processes of both SNP analysis and gene ontology retrieval. Using principal component analysis, Mahalanobis test statistics, False Discovery Rate control, and functional network creation, the pipeline takes thousands of SNPs as input for analysis and then reports structured information for visualization of relationships and generation of targets for further study. Consequently, adaptations and natural selection can be connected to specific genes. An application of the pipeline (as demonstrated in this study), is the analysis of 7039 Mayetiola destructor (Hessian fly) SNPs to identify gene pathways which lead to differences in fly virulence. The data encompasses three different biotypes and 288 total flies. The output of the pipeline includes the identified significant SNP markers, the gene surrounding each identified SNP, any functional pathways of the genes, functional networks of gene-pathway relationships, and insight into how certain functional pathways are related to differences in phenotype. This tool makes gene identification using SNP data an efficient, automatic process and helps to pinpoint targets for further experimental investigation. Wide application of this tool can drastically accelerate discovery of novel genes using SNP data.

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ISEF 简介

英特尔国际科学与工程大奖赛，简称 "ISEF"，由美国 Society for Science and the Public（科学和公共服务协会）主办，英特尔公司冠名赞助，是全球规模最大、等级最高的中学生的科研科创赛事。ISEF 的学术活动学科包括了所有数学、自然科学、工程的全部领域和部分社会科学。ISEF 素有全球青少年科学学术活动的“世界杯”之美誉，旨在鼓励学生团队协作，开拓创新，长期专一深入地研究自己感兴趣的课题。

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英特尔 ISEF 学术活动详细介绍

英特尔 ISEF 全程指导方案

· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·

学科简介：计算生物与生物信息学 Computational Biology and Bioinformatics

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling (MOD): Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD): The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology (EVO): A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience (NEU): A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology (PHA): A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics (GEN): The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other (OTH): Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.