Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

Liver cirrhosis is one of the most common cause of death in the world. The progress of liver cirrhosis involves health, liver cirrhosis and liver cancer, leading to great challenges in the diagnosis of the disease. Drug targets, which could be obtained conveniently, can help clinicians improve prognosis and treatment. Liver cirrhosis is associated with serum calcium levels. And studies reported Tanshinone IIA plays a therapeutic role in liver injury through activating calcium-dependent apoptosis. In this study, we explored the diagnostic key targets of Tanshinone IIA in liver cirrhosis through exploration of comprehensive dataset including health, liver cirrhosis and liver cancer patients. The unsupervised consensus clustering algorithm identified 3 novel subtypes in which differentially expressed genes (DEGs) between both subtypes were found by pairwise comparison. Then, 4 key drug targets of Tanshinone IIA were determined through the intersection of these DEGs. The diagnostic performance of target genes was assessed and further verified in the external dataset. We found that the 4 key drug targets could be used as effective diagnostic biomarkers. Then the immune scores in the high and low expression groups of target genes were estimated to identify significantly expressed immune cells. In addition, the immune infiltration of high and low target gene expression groups in several immune cells were significantly different. The findings suggest that 4 key drug targets may be a simple and useful diagnostic tool for predicting patients with cirrhosis. We further studied the carcinogenesis role of AKR1C3 and TPX2 in vitro. Both mRNA and protein expression in hepatoma carcinoma cells was detected using qRT-PCR and Western blot. And the knockdown of AKR1C3 and TPX2 significantly suppressed cell proliferation, migration and invasion.