Genomics, proteomics & bioinformatics最新文献

Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous research focused on mono-functional peptides, but a growing number of multi-functional peptides have been discovered. Although there have been enormous experimental efforts to assay multi-functional peptides, only a small fraction of millions of known peptides have been explored. Effective and precise techniques for identifying multi-functional peptides can facilitate their discovery and mechanistic understanding. In this article, we presented a method iMFP-LG for identifying multi-functional peptides based on protein language models (pLMs) and graph attention networks (GATs). Comparison results showed that iMFP-LG outperforms state-of-the-art methods on both multi-functional bioactive peptides and multi-functional therapeutic peptides datasets. The interpretability of iMFP-LG was also illustrated by visualizing attention patterns in pLMs and GATs. Regarding the outstanding performance of iMFP-LG on the identification of multi-functional peptides, we employed iMFP-LG to screen novel candidate peptides with both ACP and AMP functions from millions of known peptides in the UniRef90. As a result, 8 candidate peptides were identified, and 1 candidate that exhibits both antibacterial and anticancer effects was confirmed through molecular structure alignment and biological experiments. We anticipate that iMFP-LG can assist in the discovery of multi-functional peptides and contribute to the advancement of peptide drug design.

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and Major histocompatibility complex (MHC) -peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical postprocessing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available online at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

The rapid expansion of the number of viral genome sequences in public databases necessitates a scalable, universal, and automated preliminary taxonomic framework for comprehensive virus studies. Here, we introduce VISTA (Virus Sequence-based Taxonomy Assignment), a computational tool that employs a novel pairwise sequence comparison system and an automatic demarcation threshold identification framework for virus taxonomy. Leveraging physio-chemical property sequences, k-mer profiles, and machine learning techniques, VISTA constructs a robust distance-based framework for taxonomic assignment. Functionally similar to PASC (Pairwise Sequence Comparison), a widely used virus assignment tool based on pairwise sequence comparison, VISTA demonstrates superior performance by providing significantly improved separation for taxonomic groups, more objective taxonomic demarcation thresholds, greatly enhanced speed, and a wider application scope. We successfully applied VISTA to 38 virus families, as well as to the class Caudoviricetes. This demonstrates VISTA's scalability, robustness, and ability to automatically and accurately assign taxonomy to both prokaryotic and eukaryotic viruses. Furthermore, the application of VISTA to 679 unclassified prokaryotic virus genomes recovered from metagenomic data identified 46 novel virus families. VISTA is available as both a command line tool and a user-friendly web portal at https://ngdc.cncb.ac.cn/vista.

Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics studies has been carried out, generating datasets ranging from genotype to phenotype. In order to efficiently utilize these data for basic and applied research, a soybean multi-omics database with extensive data coverage and comprehensive data analysis tools was established. The Soybean Omics Database (SoyOD) integrates important new datasets with existing public datasets to form the most comprehensive collection of soybean multi-omics information. Compared to existing soybean databases, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptome datasets from seeds at different developmental stages, 53 phenotypic datasets, and more than 2500 phenotypic images. In addition, SoyOD integrates existing data resources, including 59 assembled genomes, genetic variation data from 3904 soybean accessions, 225 sets of phenotypic data, and 1097 transcriptomic sequences covering 507 different tissues and treatment conditions. Moreover, SoyOD can be used to mine candidate genes for important agronomic traits, as shown in a case study on plant height. Additionally, powerful analytical and easy-to-use toolkits enable users to easily access the available multi-omics datasets, and to rapidly search genotypic and phenotypic data in a particular germplasm. The novelty, comprehensiveness, and user-friendly features of SoyOD make it a valuable resource for soybean molecular breeding and biological research. SoyOD is publicly accessible at https://bis.zju.edu.cn/soyod.

Pigs are among the most essential sources of high-quality protein in human diets. Structural variants (SVs) are a major source of genetic variants associated with diverse traits and evolutionary events. However, the current linear reference genome of pigs limits the presentation of position information for SVs. In this study, we generated a pangenome of pigs and a genome variation map of 599 deep-sequenced genomes across Eurasia. Moreover, a section-wide gene repertoire was constructed, which indicated that core genes were more evolutionarily conserved than variable genes. Subsequently, we identified 546,137 SVs, their enrichment regions, and relationships with genomic features and found significant divergence across Eurasian pigs. More importantly, the pangenome-detected SVs could complement heritability estimates and genome-wide association studies based only on single nucleotide polymorphisms. Among the SVs shaped by selection, we identified an insertion in the promoter region of the TBX19 gene, which may be related to the development, growth, and timidity traits of Asian pigs and may affect the gene expression. Our constructed pig pangenome and the identified SVs provide rich resources for future functional genomic research on pigs.

The rapid development of sequencing technology has led to an explosion of plant genome data, opening up more opportunities for research in the field of comparative evolutionary analysis of plant genomes. In this review, we take changes in plant genome size and composition as a starting point and describe the effects of polyploidy, whole genome duplication and transposable elements changes on plant genome architecture and evolution, respectively. In addition, to address the lack of relevant information in some areas, we also collected and analyzed 234 representative plant genome data as a supplement. We aim to provide a global, up-to-date summary of information on plant genome architecture and evolution in this review.

Lycium barbarum, a member of the Solanaceae family, represents an important eudicot lineage with homology of food and medicine. Lycium barbarum pectin polysaccharides (LBPPs) are key bioactive ingredients of Lycium barbarum, and are among the few polysaccharides with both biocompatibility and biomedical activity. While previous studies have primarily focused on the functional properties of LBPPs, the mechanisms of biosynthesis and transport by key enzymes remain poorly understood. Here, we reported the completion of a 2.18-gigabase reference genome of Lycium barbarum, reconstructed the first entire biosynthesis of pectin polysaccharides and sugar transport, and characterized the important genes responsible for backbone extending, sidechain synthesis, and modification of pectin polysaccharides. Additionally, we characterized long non-coding RNAs (lncRNAs) associated with polysaccharide metabolism and identified a specific rhamnogalacturonan I (RG-I) rhamnosyltransferase, RRT3020, which enhances RG-I biosynthesis in LBPPs. These newly identified enzymes and pivotal genes endow L. barbarum with specific pectin biosynthesis capabilities, distinguishing it from other Solanaceae species. Our findings provide a foundation for evolutionary studies and molecular breeding to enhance the diverse applications of L. barbarum.

The rapid development of multi-omics (transcriptome, proteome, cistrome, imaging, and regulome) mediated wide association studies methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multi-omics mediated wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association studies (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multi-omics mediated wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

High-fidelity (HiFi) sequencing has facilitated the assembly and analysis of the most repetitive region of the genome, the centromere. Nevertheless, our current understanding of human centromeres is based on a relatively small number of telomere-to-telomere assemblies, which has not yet captured its full diversity. In this study, we investigated the genomic diversity of human centromere higher order repeats (HORs) via both HiFi reads and haplotype-resolved assemblies from hundreds of samples drawn from ongoing pangenome-sequencing projects and reprocessed them via a novel HOR annotation pipeline, HiCAT-human. We used this wealth of data to provide a global survey of the centromeric HOR landscape; in particular, we found that 23 HORs presented significant copy number variability between populations. We detected three centromere genotypes with unbalanced population frequencies on chromosomes 5, 8, and 17. An inter-assembly comparison of HOR loci further revealed that while HOR array structures are diverse, they nevertheless tend to form a number of specific landscapes, each exhibiting different levels of HOR subunit expansion and possibly reflecting a cyclical evolutionary transition from homogeneous to nested structures and back.

Historically, there have been many outbreaks of viral diseases that have continued to claim millions of lives. Research on human-virus protein-protein interactions (PPIs) is vital to understanding the principles of human-virus relationships, providing an essential foundation for developing virus control strategies to combat diseases. The rapidly accumulating data on human-virus PPIs offer unprecedented opportunities for bioinformatics research around human-virus PPIs. However, available detailed analyses and summaries to help use these resources systematically and efficiently are lacking. Here, we comprehensively review the bioinformatic tools used in human-virus PPIs research, discuss and compare the function, performance, and limitations of these web resources. This study aims to provide researchers with a bioinformatic toolbox that will hopefully better facilitate the exploration of human-virus PPIs based on binding modes.