Department of Microbiology Immunology and Tropical Medicine, George Washington University Medical Center, Washington DC 20037, USA.
Department of Biological Sciences, George Washington University, Science and Engineering Hall, Suite 6000, 800 22nd Street NW, Washington DC 20052, USA.
Int J Parasitol. 2018 Jul;48(8):585-590. doi: 10.1016/j.ijpara.2018.02.001. Epub 2018 Mar 9.
Interest has recently grown in developing the entomopathogenic nematode Heterorhabditis bacteriophora as a model to genetically dissect the process of parasitic infection. Despite the availability of a full genome assembly, there is substantial variation in gene model accuracy. Here, a methodology is presented for leveraging RNA-seq evidence to generate improved annotations using ab initio gene prediction software. After alignment of reads and subsequent generation of a RNA-seq supported annotation, the new gene prediction models were verified on a selection of genes by comparison with sequenced 5' and 3' rapid amplification of cDNA ends products. By utilising a whole transcriptome for genome annotation, the current reference annotation was enriched, demonstrating the importance of coupling transcriptional data with genome assemblies.
最近人们对利用昆虫病原线虫 Heterorhabditis bacteriophora 作为模型来从遗传学上剖析寄生感染过程产生了浓厚的兴趣。尽管已经获得了完整的基因组组装,但基因模型的准确性仍存在很大差异。在这里,提出了一种利用 RNA-seq 证据的方法,通过从头预测软件生成改进的注释。在对reads 进行比对并生成 RNA-seq 支持的注释后,通过与测序的 5' 和 3' cDNA 末端产物的比较,对新的基因预测模型在选择的基因上进行了验证。通过利用整个转录组进行基因组注释,当前的参考注释得到了丰富,这表明将转录组数据与基因组组装相结合的重要性。