Whole Genome Assembly of Human Papillomavirus by Nanopore Long-Read Sequencing.

Human papillomavirus (HPV) is a causal agent for most cervical cancers. The physical status of the HPV genome in these cancers could be episomal, integrated, or both. HPV integration could serve as a biomarker for clinical diagnosis, treatment, and prognosis. Although whole-genome sequencing by next-generation sequencing (NGS) technologies, such as the Illumina sequencing platform, have been used for detecting integrated HPV genome in cervical cancer, it faces challenges of analyzing long repeats and translocated sequences. In contrast, Oxford nanopore sequencing technology can generate ultra-long reads, which could be a very useful tool for determining HPV genome sequence and its physical status in cervical cancer. As a proof of concept, in this study, we completed whole genome sequencing from a cervical cancer tissue and a CaSki cell line with Oxford Nanopore Technologies. From the cervical cancer tissue, a 7,894 bp-long HPV35 genomic sequence was assembled from 678 reads at 97-fold coverage of HPV genome, sharing 99.96% identity with the HPV sequence obtained by Sanger sequencing. A 7904 bp-long HPV16 genomic sequence was assembled from data generated from the CaSki cell line at 3857-fold coverage, sharing 99.99% identity with the reference genome (NCBI: U89348). Intriguingly, long reads generated by nanopore sequencing directly revealed chimeric cellular-viral sequences and concatemeric genomic sequences, leading to the discovery of 448 unique integration breakpoints in the CaSki cell line and 60 breakpoints in the cervical cancer sample. Taken together, nanopore sequencing is a unique tool to identify HPV sequences and would shed light on the physical status of HPV genome in its associated cancers.

Background correction in near-infrared spectra of plant extracts by orthogonal signal correction.

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.