A phylogenetic tree can reflect the evolutionary relationships between species or gene families, and they play a critical role in modern biological research. In this review, we summarize common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). Here we discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees from molecular data using packages and algorithms in R. This review aims to provide comprehensive guidance and reference for researchers seeking to construct phylogenetic trees while also promoting further development and innovation in this field. By offering a clear and concise overview of the different methods available, we hope to enable researchers to select the most appropriate approach for their specific research questions and datasets.

Food-derived bioactive peptides (FBPs) have demonstrated their significance in pharmaceuticals, diets and nutraceuticals, benefiting public health and global ecology. While significant efforts have been made to discover FBPs and to elucidate the underlying bioactivity mechanisms, there is lack of a systemic study of sequence–structure–activity relationship of FBPs in a large dataset. Here, we construct a database of food-derived bioactive peptides (DFBP), containing a total of 6276 peptide entries in 31 types from different sources. Further, we develop a series of analysis tools for function discovery/repurposing, traceability, multifunctional bioactive exploration and physiochemical property assessment of peptides. Finally, we apply this database and data-mining techniques to discover new FBPs as potential drugs for cardiovascular diseases. The DFBP serves as a useful platform for not only the fundamental understanding of sequence–structure–activity of FBPs but also the design, discovery, and repurposing of peptide-based drugs, vaccines, materials and food ingredients. DFBP service can be accessed freely via http://www.cqudfbp.net/. All data are incorporated into the article and its online supplementary material. Supplementary data are available at Bioinformatics online.

Background: To explore the hub gene closely related to the progression of gastric cancer (GC), so as to provide a theoretical basis for revealing the therapeutic mechanism of GC.

Effective and safe implementation of precision oncology for breast cancer is a vital strategy to improve patient outcomes, which relies on the application of reliable biomarkers. As ‘liquid biopsy’ and novel resource for biomarkers, exosomes provide a promising avenue for the diagnosis and treatment of breast cancer. Although several exosome-related databases have been developed, there is still lacking of an integrated database for exosome-based biomarker discovery. To this end, a comprehensive database ExoBCD (https://exobcd.liumwei.org) was constructed with the combination of robust analysis of four high-throughput datasets, transcriptome validation of 1191 TCGA cases and manual mining of 950 studies. In ExoBCD, approximately 20 900 annotation entries were integrated from 25 external sources and 306 exosomal molecules (49 potential biomarkers and 257 biologically interesting molecules). The latter could be divided into 3 molecule types, including 121 mRNAs, 172 miRNAs and 13 lncRNAs. Thus, the well-linked information about molecular characters, experimental biology, gene expression patterns, overall survival, functional evidence, tumour stage and clinical use were fully integrated. As a data-driven and literature-based paradigm proposed of biomarker discovery, this study also demonstrated the corroborative analysis and identified 36 promising molecules, as well as the most promising prognostic biomarkers, IGF1R and FRS2. Taken together, ExoBCD is the first well-corroborated knowledge base for exosomal studies of breast cancer. It not only lays a foundation for subsequent studies but also strengthens the studies of probing molecular mechanisms, discovering biomarkers and developing meaningful clinical use.