Recent high-throughput multi-omics techniques such as (meta) genomics, transcriptomics, proteomics, culturomics and metabolomics have immensely contributed in in-depth high-resolution data generation and improved understanding of the diversity, structure and function of the microbial community in an environment. Such unprecedented data have enhanced the blueprint of the microbial world and subsequently helped in understanding the correlation between genotype to phenotype in diverse ecological settings. Thus, using these recent technologies, in the pursuit of characterizing novel microorganisms, genes, and metabolic compounds; microbiologists have been exploring numerous geographically and physiologically diverse ecosystems. Furthermore, this approach has taken a central stage and transforming the multi-omics data into better health outcomes and disease management across various fields of biology. Additionally, we have witnessed several discoveries such as identification of prognostic biomarkers, advancements in health prognosis, identification of novel drug targets as well as the event of repurposing of the drugs for the treatment of a variety of diseases. Therefore, the current research topic tends to bring the omics-data integration with the microbial diversity and linking their functional aspects to understand the specific role in their respective ecological niche.
Through this issue, the goal is to encourage the researchers to describe datasets using multi-omics profiling technologies supported by wet-lab experiments and to get a better understanding of the adaptation/interaction of the microbes/microbial community in their respective environmental settings. Furthermore, the identification of novel gene clusters and metabolites through genomic and metagenome data-mining is also the focus of this issue.
Based on the goals defined above, manuscripts that broadly fall into below mentioned topics are invited:
1. The data mining of multi-omics datasets to establish the relationship of microbes to their natural settings.
2. Studies on novel microbes with extensive comparison along with identification of novel pathways/metabolites.
3. Studies related to multi-omics research focusing on state-of-the-art computational or experimental methods for high-resolution microbial community profiling and novel pathways/metabolites.
4. Clear hypothesis-driven analysis using sequencing datasets complemented with the laboratory studies.
(Please consider the quality and content requirements for experimental studies as outlined below:
Systems Microbiology does not consider descriptive studies that are solely based on amplicon (eg. 16S rRNA) profiles, unless they are accompanied by a clear hypothesis and experimentation and provide insight into the microbiological system or process being studied. If your manuscript include amplicon profiles please make sure that your abstract include a clear statement on why we should consider your manuscript. Especially helpful would be to specify in your abstract the methods used to test the hypothesis and how the reported results support and validate this hypothesis.)
In this issue, we invite the manuscripts in the form of original research, review, mini-review, perspective, and concept-driven methodologies in this current issue. In particular, we tend to focus on the data-mining of multi-omics datasets and establishing the relationship of microbes to their natural settings. Studies on novel microbes with extensive comparison along with the identification of novel pathways/metabolites are also invited in this issue. We tend to encourage those researchers, who feel that their datasets are being complemented with the laboratory studies. Studies based on amplicon sequencing datasets should be driven by a clear hypothesis and supported by wet-lab experiments that provide an understanding of microbiological systems are encouraged.
Recent high-throughput multi-omics techniques such as (meta) genomics, transcriptomics, proteomics, culturomics and metabolomics have immensely contributed in in-depth high-resolution data generation and improved understanding of the diversity, structure and function of the microbial community in an environment. Such unprecedented data have enhanced the blueprint of the microbial world and subsequently helped in understanding the correlation between genotype to phenotype in diverse ecological settings. Thus, using these recent technologies, in the pursuit of characterizing novel microorganisms, genes, and metabolic compounds; microbiologists have been exploring numerous geographically and physiologically diverse ecosystems. Furthermore, this approach has taken a central stage and transforming the multi-omics data into better health outcomes and disease management across various fields of biology. Additionally, we have witnessed several discoveries such as identification of prognostic biomarkers, advancements in health prognosis, identification of novel drug targets as well as the event of repurposing of the drugs for the treatment of a variety of diseases. Therefore, the current research topic tends to bring the omics-data integration with the microbial diversity and linking their functional aspects to understand the specific role in their respective ecological niche.
Through this issue, the goal is to encourage the researchers to describe datasets using multi-omics profiling technologies supported by wet-lab experiments and to get a better understanding of the adaptation/interaction of the microbes/microbial community in their respective environmental settings. Furthermore, the identification of novel gene clusters and metabolites through genomic and metagenome data-mining is also the focus of this issue.
Based on the goals defined above, manuscripts that broadly fall into below mentioned topics are invited:
1. The data mining of multi-omics datasets to establish the relationship of microbes to their natural settings.
2. Studies on novel microbes with extensive comparison along with identification of novel pathways/metabolites.
3. Studies related to multi-omics research focusing on state-of-the-art computational or experimental methods for high-resolution microbial community profiling and novel pathways/metabolites.
4. Clear hypothesis-driven analysis using sequencing datasets complemented with the laboratory studies.
(Please consider the quality and content requirements for experimental studies as outlined below:
Systems Microbiology does not consider descriptive studies that are solely based on amplicon (eg. 16S rRNA) profiles, unless they are accompanied by a clear hypothesis and experimentation and provide insight into the microbiological system or process being studied. If your manuscript include amplicon profiles please make sure that your abstract include a clear statement on why we should consider your manuscript. Especially helpful would be to specify in your abstract the methods used to test the hypothesis and how the reported results support and validate this hypothesis.)
In this issue, we invite the manuscripts in the form of original research, review, mini-review, perspective, and concept-driven methodologies in this current issue. In particular, we tend to focus on the data-mining of multi-omics datasets and establishing the relationship of microbes to their natural settings. Studies on novel microbes with extensive comparison along with the identification of novel pathways/metabolites are also invited in this issue. We tend to encourage those researchers, who feel that their datasets are being complemented with the laboratory studies. Studies based on amplicon sequencing datasets should be driven by a clear hypothesis and supported by wet-lab experiments that provide an understanding of microbiological systems are encouraged.
