WhatsHap: Software to take Genome Research and Clinical Diagnostics to the Haplotype Level

WhatsHap:将基因组研究和临床诊断提升到单倍型水平的软件

基本信息

项目摘要

The rapid advances in genome sequencing technology are currently transforming biomedical research and health care. The genomes of any two patients are distinct, leading to differences in clinically relevant traits such as susceptibility to disease or intolerance to specific drugs. Personalized medicine aims to exploit such genomic information for improving diagnosis and therapy allocation. That is, an individual's genomic makeup is taken into account for clinical decision making. Genome sequencing machines produce big data, which are analyzed by complex data processing pipelines. Obviously, software solutions used for clinical purposes need to meet highest quality standards. This requirement, however, is in stark contrast to many current bioinformatics tools that remain in a research prototype stadium, often due to a lack of dedicated software engineers.The emergence of second-generation sequencing technologies from 2006 onwards has enabled genome sequencing at manageable costs, with prizes for sequencing a single human genome dropping from hundreds of millions of dollars in 2001 to less than one thousand dollars today. This massive decrease in prices has enabled an equally massive increase in data volume which has, by far, outpaced Moore's law.Today, we are witnessing the emergence of third-generation sequencing technologies. These technologies enable a paradigm shift in the field of genomics: while many studies have focussed on genotype data in the past, we are now in a position to reconstruct human haplotypes. The availability of haplotype information is critically important for a large number of analyses, in particular for studies of human ancestry, migration and admixture, but also for understanding and diagnosing haplotype-specific clinical conditions. Therefore, third-generation sequencing technologies are first impacting genomic and clinical research and will subsequently become part of routine clinical diagnostics and care. In order to turn these opportunities into reality, sustainably maintained production-quality software to process these data is a prerequisite.Our WhatsHap software provides an excellent starting point for this. It is already in use in multiple large-scale projects, is rapidly gaining attention of life sciences researchers as well as of vendors of sequencing machines, and is provided as high-quality open source software. It has hence already attained the status of a demonstrator. The swiftly expanding sector of genome sequencing and precision medicine provides an outstanding environment to raise funds for a non-profit foundation to sustainably maintain WhatsHap after the funding period. Although a long history of foundations that support open source projects exists, this model is -- in our view -- under-explored for academic software. With this project, we aim to increase awareness for this approach and will document our experiences in a public report.
基因组测序技术的快速发展正在改变生物医学研究和卫生保健。任何两名患者的基因组都是不同的,导致临床相关特征的差异,如对疾病的易感性或对特定药物的不耐受。个性化医疗旨在利用这些基因组信息来改进诊断和治疗分配。也就是说,临床决策要考虑到个体的基因组组成。基因组测序机产生大数据,这些数据通过复杂的数据处理管道进行分析。显然,用于临床目的的软件解决方案需要满足最高的质量标准。然而,这一要求与目前许多生物信息学工具形成鲜明对比,这些工具通常是由于缺乏专门的软件工程师而停留在研究原型体育场中。自2006年以来,第二代测序技术的出现使得基因组测序的成本可控,单个人类基因组测序的奖金从2001年的数亿美元下降到今天的不到1000美元。价格的大幅下降使得数据量同样大幅增长,这远远超过了摩尔定律。今天,我们正在见证第三代测序技术的出现。这些技术使基因组学领域的范式发生了转变:虽然过去许多研究都集中在基因型数据上,但我们现在能够重建人类的单倍型。单倍型信息的可用性对大量分析至关重要,特别是对人类祖先,迁移和混合的研究,以及对单倍型特异性临床条件的理解和诊断。因此,第三代测序技术首先影响基因组和临床研究,随后将成为常规临床诊断和护理的一部分。为了将这些机会变为现实,持续维护生产质量的软件来处理这些数据是先决条件。我们的WhatsHap软件提供了一个很好的起点。它已经在多个大型项目中使用,正在迅速引起生命科学研究人员和测序设备供应商的注意,并作为高质量的开源软件提供。因此,它已经达到了一个示范的地位。快速发展的基因组测序和精准医疗领域为非营利基金会筹集资金提供了良好的环境,以便在资助期结束后持续维护WhatsHap。尽管支持开源项目的基金会有着悠久的历史,但在我们看来,这种模式在学术软件方面还没有得到充分的探索。通过这个项目,我们的目标是提高人们对这种方法的认识,并将我们的经验记录在一份公开报告中。

项目成果

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Professor Dr. Tobias Marschall其他文献

Professor Dr. Tobias Marschall的其他文献

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{{ truncateString('Professor Dr. Tobias Marschall', 18)}}的其他基金

Multi-omics approach including structural variations in the genome of medulloblastoma to detect novel targets
多组学方法,包括髓母细胞瘤基因组的结构变异,以检测新靶点
  • 批准号:
    497790467
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

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