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Background: Transposable elements (TEs) are powerful creators of genotypic and phenotypic diversity due to their inherent mutagenic capabilities and in this way they serve as a deep reservoir of sequences for genomic variation. As agents of genetic disruption, a TE's potential to impact phenotype is partially a factor of its location in the genome. Previous research has shown TEs' ability to impact the expression of neighboring genes, however our understanding of this trend is hampered by the exceptional amount of diversity in the TE world, and a lack of publicly available computational methods that quantify the presence of TEs relative to genes. Results: Here, we have developed a tool to more easily quantify TE presence relative to genes through the use of only a gene and TE annotation, yielding a new metric we call TE Density. Briefly defined as the proportion of TE-occupied base-pairs relative to a window-size of the genome. This new pipeline reports TE density for each gene in the genome, for each type descriptor of TE (order and superfamily), and for multiple positions and distances relative to the gene (upstream, intragenic, and downstream) over sliding, user-defined windows. In this way, we overcome previous limitations to the study of TE-gene relationships by focusing on all TE types present in the genome, utilizing flexible genomic distances for measurement, and reporting a TE presence metric for every gene in the genome. Conclusions: Together, this new tool opens up new avenues for studying TE-gene relationships, genome architecture, comparative genomics, and the tremendous diversity present of the TE world. TE Density is open-source and freely available at: https://github.com/sjteresi/TE_Density. [ABSTRACT FROM AUTHOR]

Systems biology is an academic field that attempts to integrate different levels of information to understand how biological systems function. It is the study of the composition of all components of a biological system and their interactions under specific conditions. The core of systems biology is holistic and systematic research, which is different from the manner of thinking and research of all other branches of biology to date. Chinese herbal formulae (CHF) are the main form of Chinese medicine and are composed of single Chinese herbal medicines (CHMs) with pharmacological and pharmacodynamic compatibility. When single CHMs are combined into CHF, the result is different from the original effect of a single drug and can be better adapted to more diseases with complex symptoms. CHF represent a complex system with multiple components, targets and effects. Therefore, the use of systems biology is conducive to revealing the complex characteristics of CHF. With the rapid development of omics technologies, systems biology has been widely and increasingly applied to the study of the basis of the pharmacological substances, action targets and mechanisms of CHF. To meet the challenges of multiomics synthesis-intensive studies and system dynamics research in CHF, this paper reviews the common techniques of genomics, transcriptomics, proteomics, metabolomics, and metagenomics and their applications in research on CHF. [ABSTRACT FROM AUTHOR]

Selected papers from the 13th International Conference on Bioinformatics (InCoB2014), July 31-2 August, 2014 in Sydney, Australia have been compiled in this supplement. These range from network analysis and gene regulatory networks to systems level biological analysis, providing the 2014 update to InCoB's computational systems biology research. [ABSTRACT FROM AUTHOR]

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was initially established in 2012. Due to the coronavirus (COVID-19) pandemic, the ICIBM 2020 was held for the first time as a virtual online conference on August 9 to 10. The virtual conference had ~ 300 registered participants and featured 41 online real-time presentations. ICIBM 2020 received a total of 75 manuscript submissions, and 12 were selected to be published in this special issue of BMC Bioinformatics. These 12 manuscripts cover a wide range of bioinformatics topics including network analysis, imaging analysis, machine learning, gene expression analysis, and sequence analysis. [ABSTRACT FROM AUTHOR]