RESUMO
Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.
RESUMO
Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.
RESUMO
Vulvar squamous cell carcinoma (SCC) consists of two different etiologic categories: human papilloma virus (HPV)-associated (HPV (+)) and HPV-non-associated (HPV (−)). There have been no genome-wide studies on the genetic alterations of vulvar SCCs or on the differences between HPV (+) and HPV (−) vulvar SCCs. In this study, we performed whole-exome sequencing and copy number profiling of 6 HPV (+) and 9 HPV (−) vulvar SCCs and found known mutations (TP53, CDKN2A and HRAS) and copy number alterations (CNAs) (7p and 8q gains and 2q loss) in HPV (−) SCCs. In HPV (+), we found novel mutations in PIK3CA, BRCA2 and FBXW7 that had not been reported in vulvar SCCs. HPV (−) SCCs exhibited more mutational loads (numbers of nonsilent mutations and driver mutations) than HPV (+) SCCs, but the CNA loads and mutation signatures between HPV (+) and HPV (−) SCCs did not differ. Of note, 40% and 40% of the 15 vulvar SCCs harbored PIK3CA and FAT1 alterations, respectively. In addition, we found that the SCCs harbored kataegis (a localized hypermutation) in 2 HPV (+) SCCs and copy-neutral losses of heterozygosity in 4 (one HPV (+) and 3 HPV (−)) SCCs. Our data indicate that HPV (+) and HPV (−) vulvar SCCs may have different mutation and CNA profiles but that there are genomic features common to SCCs. Our data provide useful information for both HPV (+) and HPV (−) vulvar SCCs and may aid in the development of clinical treatment strategies.