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: 10687972
• 负责人: David E. Hill
• 金额: $ 66.35万
• 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/10687972
• 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; Technology; Time; Tissues; Transcript; Translations; analysis pipeline; biotypes; cell type; data archive; experimental study; follow-up; genetic disorder diagnosis; genetic testing; genome-wide; improved; insight; molecular sequence database; response; transcriptome; transcriptome sequencing; 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.

摘要 阐明基因组的编码潜力得益于精确的基因组序列和广泛的 转录组测序允许蛋白质编码序列（CDS）或开放阅读框架的详细模型 （ORF）。虽然至少有一个可靠的全长转录模型已被分配给每一个蛋白质编码， 由于i）表达水平的巨大差异， 从共同基因表达的同种型之间的差异，以及ii）获得全长（FL）转录物的困难 序列的此外，2004年和2005年的记录誊本总数之间仍有很大差异， 注释数据库以及具有实验证据的注释FL转录本的数量。 编码转录物的谱包括具有多个维度的巨大但有限的“异构体空间”： 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。正如 表达水平在细胞和组织中不同，选择性剪接转录物的相对丰度也不同。 如果没有经验知识， 完整注释编码的功能蛋白质的整个互补序列。 历史上，基因注释主要由来自INSDC数据库的EST和mRNA支持， 自动化注释方法正被应用于全基因组和转录组。但目前的 自动注释不能提供与手动注释相同质量的数据。灵敏度和 特异性降低，捕获较少的功能注释，并且所有自动化方法都缺乏自动化的能力。 手动注释器，用于介绍其他正交数据类型和科学文献的解释，但 手动注释是高度劳动密集型的。GENCODE版本v36代表了近10种 100万个EST、cDNA和蛋白质同源性。考虑到预期的数据量， 产生的数据比整个INSDC目录还多，目前的手工注释方法没有按比例缩放。的 长转录组测序方法的出现提供了历史数据类型的替换， 基因和转录本注释的好处。然而，大量的数据量已经被 存放在公共数据档案超过人工管理能力，要求实施自动化 解决方案而不影响注释质量。此外，由于非靶向测序方法非常困难， 由于他们发现不太丰富的转录本的效率很低，因此产生的大多数序列数据给我们提供了非常 对可复制的转录本多样性了解甚少。为了克服这些挑战，我们两个小组分别 联合力量，以增加完全实验验证的全长人类蛋白质编码转录本的目录。 这项建议的重点是整合实验方法， 人类蛋白质编码转录本的计数，随着 一个自动化注释管道，允许将该资源整合到GENCODE基因注释中。