Special Issue "Application of Bioinformatics in Microbiome"
Deadline for manuscript submissions: 25 December 2023 | Viewed by 5233
The microorganisms found in an environment and their activities are collectively referred to as the microbiome. The advent of sequencing technologies has enabled new means of studying microbiomes; it is now possible to answer the questions of “who are there”, “What can they do” and “what are they doing” for a habitat by sequencing its marker genes, meta-genomes and meta-transcriptomes, respectively. With high-throughput next-generation sequencing (NGS), there has been an explosion of sequencing data, which require novel and efficient computing algorithms, software and pipelines to process, manage and interpret their embedded information. Bioinformatics is an interdisciplinary field of science that combines biology, computer science, informatics, mathematics and statistics to analyze and interpret biological and clinical data. Bioinformatics is not only crucial but essential to extracting meaningful information from cryptic NGS data.
This Special Issue aims to demonstrate the latest developments in bioinformatics in the NGS era that have helped advanced our understanding of the microbiome. The issue’s scope includes, but is not limited to, the following topics:
- New computing algorithms for analyzing NGS data;
- Data management platforms (online or standalone desktop databases) for NGS data;
- Pipeline development (a collection of analytic software streamlined from upstream to downstream applications);
- Bioinformatics application in the cloud platform;
- Artificial intelligence (AI) in microbiome research;
- New microbiome research discovery, both medical and non-medical, with a significant bioinformatics component;
- Reviews of the above applications.
Dr. Tsute Chen
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- next-generation sequencing
- artificial intelligence
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Abstract: DNA synthesis is widely used in synthetic biology to construct and assemble sequences ranging from short RBS to ultra-long synthetic genomes. Many sequence features, such as GC content and repeat sequences, are known to affect the synthesis difficulty and subsequently the synthesis cost. In addition, there are latent sequence features, especially local characteristics of the sequence, which might affect the DNA synthesis process as well. Reliable prediction of synthesis difficulty for a given sequence is important for reducing cost, but this remains a challenge. In this study, we propose a new Automated Machine Learning (AutoML) approach to predict DNA synthesis difficulty, which achieves an F1 score of 0.930 and outperforms the current state-of-the-art model. We found local sequence features that were neglected in previous methods, which might also affect the difficulty DNA synthesis. Moreover, experimental validation based on 10 genes of Escherichia coli MG1655 shows that our model can achieve 80% accuracy, which is also better than the state-of-the-art. The standalone version is provided at https://github.com/tibbdc/scp4ssd. Moreover, we developed the cloud platform SCP4SSD (https://scp4ssd.biodesign.ac.cn), using an entirely cloud-based serverless architecture.
2. Title: Pulmonary Bacteriobiota as a prognostic factor in critically ill patients
Abstract: This study aims to make use of this technology and identify the bacterial composition of bronchial secretion samples from mechanically ventilated patients and establish this as a prognostic factor for survival using high-throughput sequencing platforms Illumina's. An observational, longitudinal, prospective study of critical patients mechanically ventilated for non-respiratory indications, among other exclusion criteria, in a polyvalent intensive care unit, was carried out; the sample was extracted by endotracheal aspiration and subsequently characterized by sequencing the 16S ribosomal RNA gene. The predominant species were Proteobacteria, Firmicutes and Bacteroidata. In the group of surviving patients, they were Proteobacteria, Bacteroidata, and Firmicutes and in the group of deceased patients were Firmicutes, Proteobacteria, and Bacteroidata. The alpha diversity found no significant difference between both, as did the beta diversity. In this group of patients, the microbial composition could not be associated with disease severity.
3. Title: "Three Clusters of Caries Status Identified for Thai Mother-Child Dyads Based on the Oral Microbiome and Maternal Relatedness”.