A Transcriptomic Pipeline Adapted for Genomic Sequence Discovery of Germline-Restricted Sequence in Zebra Finch, Taeniopygia guttata.
A Transcriptomic Pipeline Adapted for Genomic Sequence Discovery of Germline-Restricted Sequence in Zebra Finch, Taeniopygia guttata.
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DOI：
10.1093/gbe/evab088
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发表时间：
2021-06-08
影响因子：
3.3
通讯作者：
Bracht JR
中科院分区：
文献类型：
--
作者：
Asalone KC;Takkar AK;Saldanha CJ;Bracht JR
鸣禽有一个不寻常的基因组元素，只在它们的生殖系细胞中发现，被称为生殖系限制性染色体（GRC）。由于生殖细胞包含GRC和非GRC（或a染色体）序列，从基因组组装中确定GRC衍生元件被证明是困难的。在此，我们介绍了一种新的转录组学方法在GRC序列鉴定中的应用。通过将Stringtie/Ballgown流水线应用于体细胞和种系DNA读取，我们发现每千碱基每百万映射读取的片段比例可以用来确定GRC的组份。通过比较覆盖分析，我们成功地鉴定出733个高置信度的GRC序列（其中720个是本研究新鉴定的）和51个通过定量聚合酶链反应验证的序列。我们还发现了两个新的GRC基因，一个假设的蛋白质和一个编码RNase h -样结构域的基因，并将16个先前发现但未放置的基因放置到它们的宿主contigs上。由于目前的重点是对不同鸣禽的GRC进行测序，我们的工作增加了基因组工具包来识别GRC元素，我们提供了详细的协议和GitHub存储库https://github.com/brachtlab/Comparative_Coverage_Analysis（最后访问日期为2021年5月12日）。
Songbirds have an unusual genomic element which is only found in their germline cells, known as the germline-restricted chromosome (GRC). Because germ cells contain both GRC and non-GRC (or A-chromosome) sequences, confidently identifying the GRC-derived elements from genome assemblies has proven difficult. Here, we introduce a new application of a transcriptomic method for GRC sequence identification. By adapting the Stringtie/Ballgown pipeline to use somatic and germline DNA reads, we find that the ratio of fragments per kilobase per million mapped reads can be used to confidently assign contigs to the GRC. Using this comparative coverage analysis, we successfully identify 733 contigs as high confidence GRC sequences (720 newly identified in this study) and 51 contigs which were validated using quantitative polymerase chain reaction. We also identified two new GRC genes, one hypothetical protein and one gene encoding an RNase H-like domain, and placed 16 previously identified but unplaced genes onto their host contigs. With the current focus on sequencing GRCs from different songbirds, our work adds to the genomic toolkit to identify GRC elements, and we provide a detailed protocol and GitHub repository at https://github.com/brachtlab/Comparative_Coverage_Analysis (last accessed May 12, 2021).
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