During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the “microbiome data space”.Type 1 diabetes (T1D) can cause brain region-specific metabolic disorders, but whether gender influences T1D-related brain metabolic changes is rarely reported. Therefore, here we examined metabolic changes in six different brain regions of male and female mice under normal and T1D conditions using an integrated method of NMR-based metabolomics and linear mixed-model, and aimed to explore sex-specific metabolic changes from normal to T1D. The results demonstrate that metabolic differences occurred in all brain regions between two genders, while the hippocampal metabolism is more likely to be affected by T1D. At the 4th week after streptozotocin treatment, brain metabolic disorders mainly occurred in the cortex and hippocampus in female T1D mice, but the striatum and hippocampus in male T1D mice. In addition, anaerobic glycolysis was significantly altered in male mice, mainly in the striatum, midbrain, hypothalamus and hippocampus, but not in female mice. We also found that female mice exhibited a hypometabolism status relative to male mice from normal to T1D. Collectively, this study suggests that T1D affected brain region-specific metabolic alterations in a sex-specific manner, and may provide a metabolic view on diabetic brain diseases between genders.Recent advances in optical mapping have allowed the construction of improved genome assemblies with greater contiguity. Optical mapping also enables genome comparison and identification of large-scale structural variations. Association of these large-scale genomic features with biological functions is an important goal in plant and animal breeding and in medical research. Optical mapping has also been used in microbiology and still plays an important role in strain typing and epidemiological studies. Here, we review the development of optical mapping in recent decades to illustrate its importance in genomic research. We detail its applications and algorithms to show its specific advantages. Finally, we discuss the challenges required to facilitate the optimization of optical mapping and improve its future development and application.Protein tertiary structure is important information in various areas of biological research, however, the experimental cost associated with structure determination is high, and computational prediction methods have been developed to facilitate a more economical approach. Currently, template-based modeling methods are considered to be the most practical because the resulting predicted structures are often accurate, provided an appropriate template protein is available. During the first stage of template-based modeling, sensitive homology detection is essential for accurate structure prediction. However, sufficient structural models cannot always be obtained due to a lack of quality in the sequence alignment generated by a homology detection program. Therefore, an automated method that detects remote homologs accurately and generates appropriate alignments for accurate structure prediction is needed. In this paper, we propose an algorithm for suitable alignment generation using an intermediate sequence search for use with template-based modeling. We used intermediate sequence search for remote homology detection and intermediate sequences for alignment generation of remote homologs. ON-01910 cell line We then evaluated the proposed method by comparing the sensitivity and selectivity of homology detection. Furthermore, based on the accuracy of the predicted structure model, we verify the accuracy of the alignments generated by our method. We demonstrate that our method generates more appropriate alignments for template-based modeling, especially for remote homologs. All source codes are available at https//github.com/shuichiro-makigaki/agora.Mutations in genes encoding for histone methylation proteins are associated with several developmental disorders. Among them, KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease. While nonsense mutations and short insertions/deletions are known to trigger pathogenic mechanisms, the functional effects of missense mutations are still uncharacterized. In this study, we demonstrate that a selected set of missense mutations significantly hamper the interaction between KDM6A and the histone H3, by modifying the dynamics of the linker domain, and then causing a loss of function effect.Collaboration of transcription factors (TFs) and their recognition motifs in DNA is the result of coevolution and forms the basis of gene regulation. However, the way how these short genomic sequences contribute to setting the level of gene products is not understood in sufficient detail. The biological problem to be solved by the cell is complex, because each gene requires a unique regulatory network in each cellular condition using the same genome. Thus far, only some components of these networks have been uncovered. In this review, we compiled the features and principles of the motif grammar, which dictates the characteristics and thus the likelihood of the interactions of the binding TFs and their coregulators. We present how sequence features provide specificity using, as examples, two major TF superfamilies, the bZIP proteins and nuclear receptors. We also discuss the phenomenon of “weak” (low affinity) binding sites, which appear to be components of several important genomic regulatory regions, but paradoxically are barely detectable by the currently used approaches.