Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-ofthe-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.
Relative terms often appear together in the literature. Methods have been presented for weighting relativity of pairwise terms by their co-occurring literature and inferring new relationship. Terms in the literature are also in the directed acyclic graph of ontologies, such as Gene Ontology and Disease Ontology. Therefore, semantic association between terms may help for establishing relativities between terms in literature. However, current methods do not use these associations. In this paper, an adjusted R-scaled score (ARSS) based on information content (ARSSIC) method is introduced to infer new relationship between terms. First, set inclusion relationship between terms of ontology was exploited to extend relationships between these terms and literature. Next, the ARSS method was presented to measure relativity between terms across ontologies according to these extensional relationships. Then, the ARSSIC method using ratios of information shared of term's ancestors was designed to infer new relationship between terms across ontologies. The result of the experiment shows that ARSS identified more pairs of statistically significant terms based on corresponding gene sets than other methods. And the high average area under the receiver operating characteristic curve (0.9293) shows that ARSSIC achieved a high true positive rate and a low false positive rate. Data is available at http://mlg.hit.edu.cn/ARSSIC/.
Advances in high-throughput sequencing technologies have brought us into the individual genome era. Projects such as the 1000 Genomes Project have led the individual genome sequencing to become more and more popular. How to visualize, analyse and annotate individual genomes with knowledge bases to support genome studies and personalized healthcare is still a big challenge. The Personal Genome Browser (PGB) is developed to provide comprehensive functional annotation and visualization for individual genomes based on the genetic–molecular–phenotypic model. Investigators can easily view individual genetic variants, such as single nucleotide variants (SNVs), INDELs and structural variations (SVs), as well as genomic features and phenotypes associated to the individual genetic variants. The PGB especially highlights potential functional variants using the PGB built-in method or SIFT/PolyPhen2 scores. Moreover, the functional risks of genes could be evaluated by scanning individual genetic variants on the whole genome, a chromosome, or a cytoband based on functional implications of the variants. Investigators can then navigate to high risk genes on the scanned individual genome. The PGB accepts Variant Call Format (VCF) and Genetic Variation Format (GVF) files as the input. The functional annotation of input individual genome variants can be visualized in real time by well-defined symbols and shapes. The PGB is available at http://www.pgbrowser.org/.
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer, and it usually occurs following chronic liver disease. Although some progress has been made in the treatment of HCC, the prognosis of patients with advanced HCC is not optimistic, mainly because of the inevitable development of drug resistance. Therefore, multi-target kinase inhibitors for the treatment of HCC, such as sorafenib, lenvatinib, cabozantinib, and regorafenib, produce small clinical benefits for patients with HCC. It is necessary to study the mechanism of kinase inhibitor resistance and explore possible solutions to overcome this resistance to improve clinical benefits. In this study, we reviewed the mechanisms of resistance to multi-target kinase inhibitors in HCC and discussed strategies that can be used to improve treatment outcomes.
There is still a lack of fast and accurate classification tools to identify the taxonomies of noisy long reads, which is a bottleneck to the use of the promising long-read metagenomic sequencing technologies. Herein, we propose de Bruijn graph-based Sparse Approximate Match Block Analyzer (deSAMBA), a tailored long-read classification approach that uses a novel pseudo alignment algorithm based on sparse approximate match block (SAMB). Benchmarks on real sequencing datasets demonstrate that deSAMBA enables to achieve high yields and fast speed simultaneously, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.
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