The advent of next-generation sequencing technologies is accompanied with the development of many whole-genome sequence assembly methods and software, especially for de novo fragment assembly. Due to the poor knowledge about the applicability and performance of these software tools, choosing a befitting assembler becomes a tough task. Here, we provide the information of adaptivity for each program, then above all, compare the performance of eight distinct tools against eight groups of simulated datasets from Solexa sequencing platform. Considering the computational time, maximum random access memory (RAM) occupancy, assembly accuracy and integrity, our study indicate that string-based assemblers, overlap-layout-consensus (OLC) assemblers are well-suited for very short reads and longer reads of small genomes respectively. For large datasets of more than hundred millions of short reads, De Bruijn graph-based assemblers would be more appropriate. In terms of software implementation, string-based assemblers are superior to graph-based ones, of which SOAPdenovo is complex for the creation of configuration file. Our comparison study will assist researchers in selecting a well-suited assembler and offer essential information for the improvement of existing assemblers or the developing of novel assemblers.

BackgroundClear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis.MethodsWe collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis.ResultsUsing this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC.ConclusionsThe integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.

Next-generation sequencing (NGS) technology has rapidly advanced and generated the massive data volumes. To align and map the NGS data, biologists often randomly select a number of aligners without concerning their suitable feature, high performance, and high accuracy as well as sequence variations and polymorphisms existing on reference genome. This study aims to systematically evaluate and compare the capability of multiple aligners for NGS data analysis. To explore this capability, we firstly performed alignment algorithms comparison and classification. We further used long-read and short-read datasets from both real-life and in silico NGS data for comparative analysis and evaluation of these aligners focusing on three criteria, namely, application-specific alignment feature, computational performance, and alignment accuracy. Our study demonstrated the overall evaluation and comparison of multiple aligners for NGS data analysis. This serves as an important guiding resource for biologists to gain further insight into suitable selection of aligners for specific and broad applications.

Kawasaki disease (KD) is a complex disease, leading to the damage of multisystems. The pathogen that triggers this sophisticated disease is still unknown since it was first reported in 1967. To increase our knowledge on the effects of genes in KD, we extracted statistically significant genes so far associated with this mysterious illness from candidate gene studies and genome-wide association studies. These genes contributed to susceptibility to KD, coronary artery lesions, resistance to initial IVIG treatment, incomplete KD, and so on. Gene ontology category and pathways were analyzed for relationships among these statistically significant genes. These genes were represented in a variety of functional categories, including immune response, inflammatory response, and cellular calcium ion homeostasis. They were mainly enriched in the pathway of immune response. We further highlighted the compelling immune pathway of NF-AT signal and leukocyte interactions combined with another transcription factor NF-κB in the pathogenesis of KD. STRING analysis, a network analysis focusing on protein interactions, validated close contact between these genes and implied the importance of this pathway. This data will contribute to understanding pathogenesis of KD.

BackgroundImmunotherapy for hepatocellular carcinoma (HCC) exhibits limited clinical efficacy due to immunosuppressive tumor microenvironment (TME). Tumor-infiltrating macrophages (TIMs) account for the major component in the TME, and the dominance of M2 phenotype over M1 phenotype in the TIMs plays the pivotal role in sustaining the immunosuppressive character. We thus investigate the effect of bufalin on promoting TIMs polarization toward M1 phenotype to improve HCC immunotherapy.MethodsThe impact of bufalin on evoking antitumor immune response was evaluated in the immunocompetent mouse HCC model. The expression profiling of macrophage-associated genes, surface markers and cytokines on bufalin treatment in vitro and in vivo were detected using flow cytometry, immunofluorescence, western blot analysis, ELISA and RT-qPCR. Cell signaling involved in M1 macrophage polarization was identified via the analysis of gene sequencing, and bufalin-governed target was explored by immunoprecipitation, western blot analysis and gain-and-loss of antitumor immune response. The combination of bufalin and antiprogrammed cell death protein 1 (anti-PD-1) antibody was also assessed in orthotopic HCC mouse model.ResultsIn this study, we showed that bufalin can function as an antitumor immune modulator that governs the polarization of TIMs from tumor-promoting M2 toward tumor-inhibitory M1, which induces HCC suppression through the activation of effector T cell immune response. Mechanistically, bufalin inhibits overexpression of p50 nuclear factor kappa B (NF-κB) factor, leading to the predominance of p65-p50 heterodimers over p50 homodimers in the nuclei. The accumulation of p65-p50 heterodimers activates NF-κB signaling, which is responsible for the production of immunostimulatory cytokines, thus resulting in the activation of antitumor T cell immune response. Moreover, bufalin enhances the antitumor activity of anti-PD-1 antibody, and the combination exerts synergistic effect on HCC suppression.ConclusionsThese data expound a novel antitumor mechanism of bufalin, and facilitate exploitation of a new potential macrophage-based HCC immunotherapeutic modality.