Current Genomics - Volume 20, Issue 1, 2019

Background: In bioinformatics, estimation of k-mer abundance histograms or just enumerating the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequencing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estimation in sequencing data have been developed in recent years. Objective: In this article, we present a comparative assessment of the different k-mer frequency estimation programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits. Methods: Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods. Results: The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie. Conclusion: The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appropriate method. Such results analysis also help researchers to discover remaining open research questions, effective combinations of existing techniques and possible avenues for future research.

Introduction: The most basic aspect of modern engineering is the design of operators to act on physical systems in an optimal manner relative to a desired objective – for instance, designing a control policy to autonomously direct a system or designing a classifier to make decisions regarding the system. These kinds of problems appear in biomedical science, where physical models are created with the intention of using them to design tools for diagnosis, prognosis, and therapy. Methods: In the classical paradigm, our knowledge regarding the model is certain; however, in practice, especially with complex systems, our knowledge is uncertain and operators must be designed while taking this uncertainty into account. The related concepts of intrinsically Bayesian robust operators and optimal Bayesian operators treat operator design under uncertainty. An objective-based experimental design procedure is naturally related to operator design: We would like to perform an experiment that maximally reduces our uncertainty as it pertains to our objective. Results & Discussion: This paper provides a nonmathematical review of optimal Bayesian operators directed at biomedical scientists. It considers two applications important to genomics, structural intervention in gene regulatory networks and classification. Conclusion: The salient point regarding intrinsically Bayesian operators is that uncertainty is quantified relative to the scientific model, and the prior distribution is on the parameters of this model. Optimization has direct physical (biological) meaning. This is opposed to the common method of placing prior distributions on the parameters of the operator, in which case there is a scientific gap between operator design and the phenomena.

The peculiar ability of skeletal muscle tissue to operate adaptive changes during post-natal development and adulthood has been associated with the existence of adult somatic stem cells. Satellite cells, occupying an exclusive niche within the adult muscle tissue, are considered bona fide stem cells with both stem-like properties and myogenic activities. Indeed, satellite cells retain the capability to both maintain the quiescence in uninjured muscles and to be promptly activated in response to growth or regenerative signals, re-engaging the cell cycle. Activated cells can undergo myogenic differentiation or self-renewal moving back to the quiescent state. Satellite cells behavior and their fate decision are finely controlled by mechanisms involving both cell-autonomous and external stimuli. Alterations in these regulatory networks profoundly affect muscle homeostasis and the dynamic response to tissue damage, contributing to the decline of skeletal muscle that occurs under physio-pathologic conditions. Although the clear myogenic activity of satellite cells has been described and their pivotal role in muscle growth and regeneration has been reported, a comprehensive picture of inter-related mechanisms guiding muscle stem cell activity has still to be defined. Here, we reviewed the main regulatory networks determining satellite cell behavior. In particular, we focused on genetic and epigenetic mechanisms underlining satellite cell maintenance and commitment. Besides intrinsic regulations, we reported current evidences about the influence of environmental stimuli, derived from other cell populations within muscle tissue, on satellite cell biology.

Background: Cancer is a complex disease with a lucid etiology and in understanding the causation, we need to appreciate this complexity. Objective: Here we are aiming to gain insights into the genetic associations of prostate cancer through a network-based systems approach using the BC3Net algorithm. Methods: Specifically, we infer a prostate cancer Gene Regulatory Network (GRN) from a large-scale gene expression data set of 333 patient RNA-seq profiles obtained from The Cancer Genome Atlas (TCGA) database. Results: We analyze the functional components of the inferred network by extracting subnetworks based on biological process information and interpret the role of known cancer genes within each process. Furthermore, we investigate the local landscape of prostate cancer genes and discuss pathological associations that may be relevant in the development of new targeted cancer therapies. Conclusion: Our network-based analysis provides a practical systems biology approach to reveal the collective gene-interactions of prostate cancer. This allows a close interpretation of biological activity in terms of the hallmarks of cancer.

Background: Skin-derived precursors (SKPs) display the characteristics of self-renewal and multilineage differentiation. Objective: The study aimed to explore the molecular mechanisms of mouse SKPs differentiation into SKP-derived fibroblasts (SFBs). Methods: We compared the microRNA (miRNA) profile in mouse SKPs and SFBs by RNA sequencing. Real-time quantitative reverse transcription PCR (qRT-PCR) was performed to validate the miRNA expression. The integrated analysis of miRNA and mRNA expression data was performed to explore the potential crosstalk of miRNA-mRNA in SKP differentiation. Results: 207 differentially expressed miRNAs and 835 miRNA target genes in the gene list of integrated mRNA expression profiling were identified. Gene Ontology (GO) enrichment analysis revealed that cell differentiation and cell proliferation process were significantly enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed the target genes were significantly most enriched in the cytokine-cytokine receptor interaction, cancer pathways and axon guidance signaling pathway. The most upregulated and downregulated target genes were involved in the Wnt, Notch, cytokine- cytokine receptor interaction, TGF-β, p53 and apoptotic signaling pathway. The miRNAmRNA regulatory networks and 507 miRNA-mRNA pairs were constructed. Seven miRNAs (miR- 486-3p, miR-504-5p, miR-149-3p, miR-31-5p, miR-484, miR-328-5p and miR-22-5p) and their target genes Wnt4, Dlx2, Sema4f, Kit, Kitl, Inpp5d, Igfbp3, Prdm16, Sfn, Irf6 and Clu were identified as miRNA-mRNA crosstalk pairs. Conclusion: These genes and signaling pathways might control SKPs proliferation and SKPs differentiation into SFBs during the process of SKPs differentiation, which might promote the application of SKPs in the clinical treatment of skin-related diseases by regulating SKPs proliferation and SKPs differentiation.

Background: Hepatitis B Viral (HBV) infection is one of the major causes of Hepatocellular Carcinoma (HCC). Mounting evidence had provided that the HBV integration might be a critical contributor of HCC carcinogenesis. Objective and Methods: To explore the profile of HBV integration in the plasma DNA, the method of next-generation sequencing, HBV capture and bioinformatics had been employed to screen for HBV integration sites in the plasma samples. Results: In the initial experiment, a total of 87 breakpoints were detected in the 20 plasma samples. The distribution of breakpoints showed that there was significant enrichment of breakpoints in the region of intron. Furthermore, the HBV breakpoints were prone to occur in the region of X protein (1,700-2,000bp) in the plasma samples. The pathway analysis had revealed that the HBV integrations sites were specifically enriched in the cancer pathway. Conclusion: Altogether, our results had provided direct evidence for the HBV integration in plasma DNA, and they might be potentially useful for future HCC prognosis and diagnosis.

Background: Complexity and dynamicity of biological events is a reason to use comprehensive and holistic approaches to deal with their difficulty. Currently with advances in omics data generation, network-based approaches are used frequently in different areas of computational biology and bioinformatics to solve problems in a systematic way. Also, there are many applications and tools for network data analysis and manipulation which their goal is to facilitate the way of improving our understandings of inter/intra cellular interactions. Methods: In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion: CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.