Generating a full-length reference transcriptome for human protein-coding genes

生成人类蛋白质编码基因的全长参考转录组

• 批准号: 10331602
• 负责人: David E. Hill
• 金额: $ 75.76万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-22 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10331602
• 关键词: Alternative Splicing; Apoptotic; Automated Annotation; BCL2L1 gene; Base Sequence; Biology; Catalogs; Cells; Clinical; Code; Collaborations; Collection; Complement; Complementary DNA; Complex; Computer software; DNA sequencing; Data; Data Analyses; Data Set; Databases; Deposition; Development; Dimensions; Disease; Exhibits; Expressed Sequence Tags; Genes; Genetic Transcription; Genome; Genomics; Goals; Human; Human Genome; International; Investigation; Knowledge; Length; Literature; Manuals; Messenger RNA; Methods; Modeling; Nature; Open Reading Frames; Paper; Patients; Post-Translational Protein Processing; Process; Protein Isoforms; Proteins; Proteome; RNA Splicing; Resources; Sensitivity and Specificity; Specificity; Stimulus; Structure; Supervision; Technology; Time; Tissues; Transcript; Translations; analysis pipeline; base; cell type; data archive; experimental study; follow-up; genetic disorder diagnosis; genetic testing; genome-wide; insight; molecular sequence database; response; transcriptome; transcriptome sequencing; transcriptomics; whole genome

Abstract Elucidating the coding potential of the genome has benefited from accurate genome sequences and extensive transcriptome sequencing to allow detailed models for protein-coding sequences (CDSs) or open reading frames (ORFs). Although at least one reliable full-length transcript model has been assigned for every protein-coding gene, the majority of alternative isoforms remains uncharacterized due to i) vast differences of expression levels between isoforms expressed from common genes, and ii) the difficulty of obtaining full-length (FL) transcript sequences. Furthermore, there remains a large discrepancy between the total number of transcripts in annotation databases and the number for which there is an annotated FL transcript with experimental evidence. The spectrum of encoded transcripts comprises a vast but finite “isoform-space” with multiple dimensions: i) genes, ii) tissues and cell types, iii) development and time iv) disease, and v) response to stimuli. Just as expression levels vary across cells and tissues, so can the relative abundance of alternatively spliced transcripts. Full, functional understanding of the human genome will not be possible without empirical knowledge and complete annotation of the entire complement of encoded functional proteins. Historically, gene annotation was supported predominantly by ESTs and mRNAs from INSDC databases while automated approaches to annotation are being applied to whole genomes and transcriptomes. However, current automated annotation does not provide the same quality data as does manual annotation. Sensitivity and specificity are reduced, less functional annotation is captured, and all automated methods lack the capacity of a manual annotator to introduce additional orthogonal data types and interpretation of the scientific literature, but manual annotation is highly labor-intensive. GENCODE release v36 represents the interpretation of nearly 10 million EST, cDNA and protein homologies. Given the anticipated volumes of data, with single experiments producing more data than the entire INSDC catalogue, current methods of manual annotation do not scale. The emergence of long transcriptomic sequencing methods provides for the replacement of historical data types to the benefit of gene and transcript annotation. However, the massively greater data volumes already being deposited in public data archives exceed manual curation capability, demanding implementation of automated solutions without compromising annotation quality. Furthermore, as untargeted sequencing approaches are very inefficient in their discovery of less abundant transcripts, the majority of sequence data generated gives us very little insight into discoverable transcript diversity. To overcome these challenges, our two respective groups have joined forces to increase the catalog of fully experimentally verified full length human protein-coding transcripts. This proposal focuses on the integration of experimental approaches that will provide a comprehensive enumeration of human protein-coding transcripts, a “Reference Human Transcriptome” with the development of an automated annotation pipeline to allow the integration of this resource into GENCODE gene annotation.

抽象的 阐明基因组的编码潜力得益于准确的基因组序列和广泛的研究 转录组测序可提供蛋白质编码序列 (CDS) 或开放阅读框的详细模型 （ORF）虽然已经为每个蛋白质编码分配了至少一个可靠的全长转录模型。 基因，由于 i) 表达水平的巨大差异，大多数替代同工型仍未表征 共同基因表达的亚型之间的差异，以及 ii) 获得全长 (FL) 转录本的难度 此外，转录本总数之间仍然存在很大差异。 注释数据库以及带有实验证据的注释 FL 转录本的数量。 编码转录本的范围包含一个巨大但有限的多维“异构体空间”：i) 基因，ii) 组织和细胞类型，iii) 发育和时间 iv) 疾病，以及 v) 对刺激的反应。 不同细胞和组织的表达水平不同，选择性剪接转录本的相对丰度也不同。 如果没有经验知识和经验，就不可能对人类基因组进行全面、功能性的理解。 编码功能蛋白的完整注释。 从历史上看，基因注释主要由 INSDC 数据库中的 EST 和 mRNA 支持，而 然而，自动化注释方法正在应用于整个基因组和转录组。 自动注释不能提供与手动注释相同的质量数据。 特异性降低，捕获的功能注释较少，并且所有自动化方法都缺乏分析能力 手动注释器引入额外的正交数据类型和科学文献的解释，但是 手动注释是高度劳动密集型的，GENCODE 版本 v36 代表了近 10 个解释。 考虑到预期的数据量，通过单个实验获得数百万个 EST、cDNA 和蛋白质同源物。 产生的数据比整个 INSDC 目录还要多，当前的手动注释方法无法扩展。 长转录组测序方法的出现提供了历史数据类型的替代 然而，基因和转录本注释的好处已经大大增加。 存放在公共数据档案中的数据超出了人工管理能力，需要实施自动化 此外，由于非靶向测序方法非常困难。 他们发现不太丰富的转录本效率低下，生成的大多数序列数据给了我们非常多的信息 为了克服这些挑战，我们两个小组对可发现的转录本多样性知之甚少。 共同努力增加经过充分实验验证的全长人类蛋白质编码转录本的目录。 该提案侧重于实验方法的整合，这将提供全面的 人类蛋白质编码转录本的计数，“参考人类转录组”的发展 一个自动化注释管道，允许将此资源集成到 GENCODE 基因注释中。