BackgroundSince the first HIV-1 case in 1989, Hebei province has presented a clearly rising trend of HIV-1 prevalence, and HIV-1 genetic diversity has become the vital barrier to HIV prevention and control in this area. To obtain detailed information of HIV-1 spread in different populations and in different areas of Hebei, a cross-sectional HIV-1 molecular epidemiological investigation was performed across the province.MethodsBlood samples of 154 newly diagnosed HIV-1 individuals were collected from ten prefectures in Hebei using stratified sampling. Partial gag and env genes were amplified and sequenced. HIV-1 genotypes were identified by phylogenetic tree analyses.ResultsAmong the 139 subjects genotyped, six HIV-1 subtypes were identified successfully, including subtype B (41.0 %), CRF01_AE (40.3 %), CRF07_BC (11.5 %), CRF08_BC (4.3 %), unique recombinant forms (URFs) (1.4 %) and subtype C (1.4 %). Subtype B was identified as the most frequent subtype. Two URF recombination patterns were the same as CRF01_AE/B. HIV-1 genotype distribution showed a significant statistical difference in different demographic characteristics, such as source (P < 0.05), occupation (P < 0.05) and ethnicity (P < 0.05). The distributions of subtype B (P < 0.05), CRF01_AE (P < 0.05), CRF07_BC (P < 0.05) and subtype C (P < 0.05) showed significant differences in all ten prefectures, and the distributions of all six subtypes were significantly different in Shijiazhuang (P < 0.05) and Xingtai (P < 0.05), but not in other prefectures (P > 0.05). The differences in HIV-1 genotype distribution were closely associated with transmission routes. Particularly, all six subtype strains were found in heterosexuals, showing that HIV-1 has spread from the high-risk populations to the general populations in Hebei, China. In addition, CRF01_AE instead of subtype B has become the major strain of HIV-1 infection among homosexuals.ConclusionsOur study revealed HIV-1 evolution and genotype distribution by investigating newly diagnosed HIV-1 individuals in Hebei, China. This study provides important information to enhance the strategic plan for HIV prevention and control in China.

Circular RNAs (circRNAs) are a novel class of endogenous noncoding RNAs that have well-conserved sequences. Emerging evidence has shown that circRNAs can be novel biomarkers or therapeutic targets for many diseases and play an important role in the development of various pathological conditions. Therefore, identifying potential disease-related circRNAs is helpful in improving the efficiency of finding therapeutic targets for diseases. Here, we propose a computational model (PreCDA) to predict potential circRNA–disease associations. First, we calculated the circRNA expression similarity based on circRNA expression profiles. The circRNA functional similarity is calculated based on cosine similarity, and the disease similarity is used as the dimension of each circRNA vector. The associations between circRNAs and diseases are defined based on the circRNA functional similarity and expression similarity. We constructed a disease-related circRNA association network and used a graph-based recommendation algorithm (PersonalRank) to sort candidate disease-related circRNAs. As a result, PreCDA has an average area under the receiver operating characteristic curve value of 78.15% in predicting candidate disease-related circRNAs. In addition, we discuss the factors that affect the performance of this method and find some unknown circRNAs related to diseases, with several common diseases used as case studies. These results show that PreCDA has good performance in predicting potential circRNA–disease associations and is helpful for the diagnosis and treatment of human diseases.

No abstract

State-of-the-art next-generation sequencing (NGS)-based subclonal reconstruction methods perform poorly on somatic copy number alternations (SCNAs), due to not only it needs to simultaneously estimate the subclonal population frequency and the absolute copy number for each SCNA, but also there exist complex bias and noise in the tumor and its paired normal sequencing data. Both existing NGS-based SCNA detection methods and SCNA's subclonal population frequency inferring tools use the read count on radio (RCR) of tumor to its paired normal as the key feature of tumor sequencing data; however, the sequencing error and bias have great impact on RCR, which leads to a large number of redundant SCNA segments that make the subsequent process of SCNA's subclonal population frequency inferring and subclonal reconstruction time-consuming and inaccurate. We perform a mathematical analysis of the solution number of SCNA's subclonal frequency, and we propose a computational algorithm to reduce the impact of false breakpoints based on it. We construct a new probability model that incorporates the RCR bias correction algorithm, and by stringing it with the false breakpoint filtering algorithm, we construct a whole SCNA's subclonal population reconstruction pipeline. The experimental result shows that our pipeline outperforms the existing subclonal reconstruction programs both on simulated data and TCGA data.

No abstract