KEGG_Extractor: An Effective Extraction Tool for KEGG Orthologs

HMMER-Extractor: an auxiliary toolkit for identifying genomic macromolecular metabolites based on Hidden Markov Models

Human microbiome contains various microbial macromolecules with important biological functions. The Hidden Markov Models (HMMs) can overcome the problem of low similarity sequences with distant relationships and are widely implemented within various sequence alignment softwares. However, the HMM-based sequence alignments can generate a large number of results, how to quickly screen and batch extract target homologs from microbiomes is the major sticking points. It is necessary to develop an integrated gene filter and extraction pipeline to quickly and accurately screen homologs. Here, we introduced the HMMER-Extractor for amino acids or nucleotide sequences extraction, which was a supporting toolkit through provided filtering scores and an iterative keyword matching (IKM) logic. To make it more user-friendly and accessible, we further presented a visualized web server platform. An interactive HTML output provided a user-friendly way to browse homologous annotations and sequence extraction. The web server provided the community with a streamlined and user-friendly interface to analyze microbiomes. Through the HMMER-Extractor, we constructed a cardiovascular disease related gene dataset of the macromolecular metabolite trimethylamine (TMA) and lipopolysaccharide (LPS) based on 46,699 bacterial genomes from human gut. Approximately 21,014 and 1961 bacterial strains were identified to contain the cnt or cut operon of TMA, and the waa gene cluster of LPS, respectively. The Escherichia coli occupied the largest proportion among all the bacterial species, which belonged to the phyla Firmicutes. The HMMER-Extractor toolkit is an integrated pipeline and has been proven to be accurate and fast in extracting target macromolecular encoding genes from microbial genomes.

Characterization the microbial diversity and metabolites of four varieties of Dry-Cured ham in western Yunnan of China

In this study, high-throughput sequencing and metabolomics analysis were conducted to analyze the microbial and metabolites of dry-cured Sanchuan ham, Laowo ham, Nuodeng ham, and Heqing ham that have fermented for two years produced from western Yunnan China. Results showed that at the genus level, the dominant bacteria in the four types of ham were Halomonas and Staphylococcus, while the dominant fungi were Aspergillus and Yamadazyma. A total 422 different metabolites were identified in four types of ham, mainly amino acids, peptides, fatty acids, and their structural analogs, which were involved in pantothenate and coenzyme A biosynthesis, caffeine, and tyrosine metabolism. The dominant microorganisms of the four types of ham were mainly related to the metabolism of fatty acids and amino acids. This research enhances the identification degree of these four types of dry-cured ham and provides a theoretical basis for developing innovative and distinctive ham products.

Small RNA-Seq to Unveil the miRNA Expression Patterns and Identify the Target Genes in Panax ginseng

Panax ginseng, renowned for its medicinal properties, relies on adventitious roots and hairy roots as crucial sources for the production of ginsenosides. Despite the widespread utilization of ginseng, investigations into its miRNAs have remained scarce. To address this gap, two samples of ginseng adventitious roots and ginseng hairy roots were collected, and subsequent construction and sequencing of small RNA libraries of ginseng adventitious roots and hairy roots were performed using the Illumina HiSeq X Ten platform. The analysis of the sequencing data unveiled total miRNAs 2432. The miR166 and miR396 were the most highly expressed miRNA families in ginseng. The miRNA expression analysis results were used to validate the qRT-PCR. Target genes of miRNA were predicted and GO function annotation and KEGG pathway analysis were performed on target genes. It was found that miRNAs are mainly involved in synthetic pathways and biological processes in plants, which include metabolic and bioregulatory processes. The plant miRNAs enriched KEGG pathways are associated with some metabolism, especially amino acid metabolism and carbohydrate metabolism. These results provide valuable insights miRNAs and their roles in metabolic processes in ginseng.