NHGRI/DIR Bioinformatics and Scientific Programming Core

NHGRI生物信息学和科学编程核心通过提供生物信息学和计算分析方面的专业知识和帮助，积极支持NHGRI研究人员正在进行的研究。核心促进获得专业的软件和硬件，开发通用的软件解决方案，可以解决基因组研究中的各种问题，开发数据库解决方案，用于有效存档和检索实验和临床数据，向基因组社区传播新的软件和数据库解决方案。并为NHGRI研究人员和受训人员提供生物信息学方面的教育机会。 2012-2013年完成的科学项目包括评估FANCA和FANCC中非Alu驱动的断裂点缺失侧翼序列中的共享基序;分析RNA-seq数据以检测由于RRP 1B敲低导致的基因表达和剪接的全球变化;设计和实施临床基因组数据库（CGD），这是一个可搜索的基于Web的数据库，包含已知遗传原因的所有疾病;通过将实验室产生的基因型与COSMIC中的基因型合并，（Catalogue of Somatic Mutations in Cancer）;质量控制和随后从HGVS格式的cDNA和蛋白质突变中检索基因组坐标;通过转移性黑素瘤样品的外显子组测序对与黑素瘤有关的一系列基因进行途径分析;预测Itk缺陷小鼠的胸腺细胞中的基因调控区;整理多中心调查数据，研究葡萄糖脑苷脂酶突变与路易体痴呆之间的关联;分析斑马鱼TILLING项目的序列痕迹以检测突变 正在进行的科学项目包括使用NextGen序列数据注释Mnemiopsis基因组;通过RNA-seq检测早期发育过程中的基因和同种型表达变化;分析Mnemiopsis中的人类疾病基因直系同源物;分析序列痕迹以检测肿瘤样本中推定致癌基因的突变;开发人类畸形术语工具，使临床医生能够建立和维护可下载的患者状况电子表格;脊椎动物、无脊椎动物和植物基因组中大外显子的表征;乳腺癌信息核心（BIC）的持续更新和改进;开发生物信息学管道，使用Illumina序列标签绘制斑马鱼逆转录病毒整合位点，并识别ENSEMBL注释的基因;开发网站和数据库以搜索整合位点;分析RNA-seq数据以研究Fanconia贫血患者中的选择性剪接;分析随时间推移选择的组织类型中的选择性剪接基因;鉴定一组500个甲基化标记，其分类不同类型的肿瘤;通过ChIP-seq鉴定RRP 1B的DNA结合位点;确定RRP 1B敲除对基因表达的影响;开发定制的SQL数据库，用于存储和计算犬基因型、表型、序列、变异、样本数据和谱系数据的大量记录;分析ChIP-seq数据以鉴定犬膀胱肿瘤细胞系中特异性组蛋白修饰的基因组位置;基因组引导的、从头开始的和从头开始的转录物组装用于RNA-seq数据; ChIP-seq的分析以研究小鼠中增强子位点处的Sox 10转录因子结合，并将Sox 10结合与EP 300和H3 K4 Me 1结合相关联;将转录因子映射和注释到实验和预测的转录因子结合位点;开发基于网络的调查，研究女性对医生与患者谈论体重的技术的感受（体重管理相互作用研究）;鉴定共变突变和途径以分类肿瘤亚型;测量胸腺细胞在四种不同刺激和四种不同突变下的基因表达变化;执行多物种BLAST以估计蛋白质编码基因的跨物种序列相似性;软件流水线，以基于访客与史密森尼基因组展览互动时的SMS文本响应来创建词云; SBRB PI与史密森尼基因组展览联合开发的八项调查的Web界面和数据收集的开发;以及沉默突变对TCGA RNA-seq数据剪接的影响的调查。

The NHGRI Bioinformatics and Scientific Programming Core actively supports the research being performed by NHGRI investigators by providing expertise and assistance in bioinformatics and computational analysis. The Core facilitates access to specialized software and hardware, develops generalized software solutions that can address a variety of questions in genomic research, develops database solutions for the efficient archiving and retrieval of experimental and clinical data, disseminates new software and database solutions to the genome community at-large, collaborates with NHGRI researchers on computationally-intensive projects, and provides educational opportunities in bioinformatics to NHGRI Investigators and trainees. Scientific projects completed in 2012-2013 include the valuation of shared motifs in sequences flanking non-Alu driven break point deletions in FANCA and FANCC; the analysis of RNA-seq data to detect global changes in gene expression and splicing due to RRP1B knockdown; the design and implementation of the Clinical Genomic Database (CGD), a searchable, Web-based database of all conditions with known genetic causes; the creation of a comprehensive list of mutations by merging lab-generated genotypes with those in COSMIC (Catalogue of Somatic Mutations in Cancer); quality control and subsequent retrieval of genomic coordinates from HGVS-formatted cDNA and protein mutations; pathway analysis of a list of genes implicated in melanoma by exome sequencing of metastatic melanoma samples; prediction of gene regulatory regions in thymocytes of Itk deficient mice; collation of multi-center survey data to study association between glucocerebrosidase mutations and Lewy Body dementia; and analysis of sequence traces to detection mutations for the zebrafish TILLING project Ongoing scientific projects include the annotation of the Mnemiopsis genome using NextGen sequence data; the detection of gene and isoform expression changes during early development by RNA-seq; the analysis of human disease gene orthologs in Mnemiopsis; the analysis of sequence traces to detect mutations in putative oncogenes in tumor samples; development of a human malformation terminology tool to allow clinicians to build and maintain downloadable spreadsheets of patient conditions; the characterization of large exons in vertebrate, invertebrate, and plant genomes; ongoing updates and improvements to the Breast Cancer Information Core (BIC); the development of a bioinformatic pipeline to map zebrafish retroviral integration sites using Illumina sequence tags and to identify integrations occurring within ENSEMBL-annotated genes; development of a Web site and database to search for integration sites; the analysis of RNA-seq data to investigate alternative splicing in Fanconia Anemia patients; the analysis of alternatively spliced genes in select tissue types over time; the identification of a set of 500 methylation markers that classifies different types of tumors; the identification of DNA binding sites of RRP1B by ChIP-seq; determination of the effects of RRP1B knockdown on gene expression; the development of a customized SQL database for storing and computing on large numbers of records for canine genotypes, phenotypes, sequences, variations, sample data, and pedigree data; the analysis of ChIP-seq data to identify the genomic locations of specific histone modifications in dog bladder tumor cell lines; genome-guided, ab initio, and de novo transcript assembly for RNA-seq data; the analysis of ChIP-seq to investigate Sox10 transcription factor binding at enhancer sites in mouse, and correlating Sox10 binding with EP300 and H3K4Me1 binding; the mapping and annotation of transcription factors to experimental and predicted transcription factor binding sites; the development of a Web-based survey studying how women feel about the techniques doctors use to talk with patients about their weight (Weight Management Interaction Study); identification of co-varying mutations and pathways to classify subtypes of tumors; measurement of gene expression changes in thymocytes over four different stimulations and four different mutations; biomarker selection of targets from RNAi screens; performing multi-species BLAST to estimate cross-species sequence similarity of protein-coding genes; software pipeline to create word cloud based on SMS text responses from visitors as they interact with the Smithsonian genome exhibit; development of a Web interface and data collection for eight surveys developed by SBRB PIs in conjunction with the Smithsonian genome exhibit; and investigation of the effect of silent mutations on splicing from TCGA RNA-seq data.