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Antibiotic resistance is a worldwide problem that imposes a devastating effect on developing countries and requires immediate interventions. Initially, most of the antibiotic drugs were identified by culturing soil microbes. However, this method is prone to discovering the same antibiotics repeatedly. The present study employed a shotgun metagenomics approach to investigate the taxonomic diversity, functional potential, and biosynthetic capacity of microbiomes from two natural agricultural farmlands located in Bekeka and Welmera Choke Kebelle in Ethiopia for the first time. Analysis of the small subunit rRNA revealed bacterial domain accounting for 83.33% and 87.24% in the two selected natural farmlands. Additionally, the analysis showed the dominance of Proteobacteria representing 27.27% and 28.79% followed by Actinobacteria making up 12.73% and 13.64% of the phyla composition. Furthermore, the analysis revealed the presence of unassigned bacteria in the studied samples. The metagenome functional analysis showed 176,961 and 104, 636 number of protein-coding sequences (pCDS) from the two samples found a match with 172,655 and 102, 275 numbers of InterPro entries, respectively. The Genome ontology annotation suggests the presence of 5517 and 3293 pCDS assigned to the "biosynthesis process". Numerous Kyoto Encyclopedia of Genes and Genomes modules (KEGG modules) involved in the biosynthesis of terpenoids and polyketides were identified. Furthermore, both known and novel Biosynthetic gene clusters, responsible for the production of secondary metabolites, such as polyketide synthases, non-ribosomal peptide synthetase, ribosomally synthesized and post-translationally modified peptides (Ripp), and Terpene, were discovered. Generally, from the results it can be concluded that the microbiomes in the selected sampling sites have a hidden functional potential for the biosynthesis of secondary metabolites. Overall, this study can serve as a strong preliminary step in the long journey of bringing new antibiotics to the market.
Pubmed ID: 38956049
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Web application to search nucleotide databases using a nucleotide query. Algorithms: blastn, megablast, discontiguous megablast.
View all literature mentionsA database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).
View all literature mentionsSoftware package for functional analysis of sequences by classifying them into families and predicting presence of domains and sites. Scans sequences against InterPro's signatures. Characterizes nucleotide or protein function by matching it with models from several different databases. Used in large scale analysis of whole proteomes, genomes and metagenomes. Available as Web based version and standalone Perl version and SOAP Web Service.
View all literature mentionsOpen, web-based platform providing bioinformatics tools and services for data intensive genomic research. Platform may be used as a service or installed locally to perform, reproduce, and share complete analyses. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Community has created Galaxy instances in many different forms and for many different applications including Galaxy servers, cloud services that support Galaxy instances, and virtual machines and containers that can be easily deployed for your own server.The Galaxy team is a part of BX at Penn State, and the Biology and Mathematics and Computer Science departments at Emory University.Training Infrastructure as a Service (TIaaS) is a service offered by some UseGalaxy servers to specifically support training use cases.
View all literature mentionsService providing functional analysis of proteins by classifying them into families and predicting domains and important sites. They combine protein signatures from a number of member databases into a single searchable resource, capitalizing on their individual strengths to produce a powerful integrated database and diagnostic tool. This integrated database of predictive protein signatures is used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures. You can access the data programmatically, via Web Services. The member databases use a number of approaches: # ProDom: provider of sequence-clusters built from UniProtKB using PSI-BLAST. # PROSITE patterns: provider of simple regular expressions. # PROSITE and HAMAP profiles: provide sequence matrices. # PRINTS provider of fingerprints, which are groups of aligned, un-weighted Position Specific Sequence Matrices (PSSMs). # PANTHER, PIRSF, Pfam, SMART, TIGRFAMs, Gene3D and SUPERFAMILY: are providers of hidden Markov models (HMMs). Your contributions are welcome. You are encouraged to use the ''''Add your annotation'''' button on InterPro entry pages to suggest updated or improved annotation for individual InterPro entries.
View all literature mentionsThe Rfam database is a collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models (CMs). The families in Rfam break down into three broad functional classes: Non-coding RNA genes, structured cis-regulatory elements and self-splicing RNAs. Typically these functional RNAs often have a conserved secondary structure which may be better preserved than the RNA sequence. The CMs used to describe each family are a slightly more complicated relative of the profile hidden Markov models (HMMs) used by Pfam. CMs can simultaneously model RNA sequence and the structure in an elegant and accurate fashion. Rfam is also available via FTP. You can find data in Rfam in various ways... * Analyze your RNA sequence for Rfam matches * View Rfam family annotation and alignments * View Rfam clan details * Query Rfam by keywords * Fetch families or sequences by NCBI taxonomy * Enter any type of accession or ID to jump to the page for a Rfam family, sequence or genome
View all literature mentionsA software application for finding fragmented genes in short reads and may be applied to predict prokaryotic genes in incomplete assemblies or complete genomes.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware tool for sequence mapping.The next version of BWA-MEM. Used for aligning sequencing reads against large reference genome.
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