SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:  evbc@unj-jena.de

Summary The development of de novo DNA synthesis, polymerase chain reaction (PCR), DNA sequencing and molecular cloning gave researchers unprecedented control over DNA and DNA-mediated processes. To reduce the error probabilities of these techniques, DNA composition has to adhere to method-dependent restrictions. To comply with such restrictions, a synthetic DNA fragment is often adjusted manually or by using custom-made scripts. In this article, we present MESA (Mosla Error Simulator), a web application for the assessment of DNA fragments based on limitations of DNA synthesis, amplification, cloning, sequencing methods and biological restrictions of host organisms. Furthermore, MESA can be used to simulate errors during synthesis, PCR, storage and sequencing processes. Availability and implementation MESA is available at mesa.mosla.de, with the source code available at github.com/umr-ds/mesa_dna_sim. Contact dominik.heider@uni-marburg.de Supplementary information Supplementary data are available at Bioinformatics online.

The extensive information capacity of DNA, coupled with decreasing costs for DNA synthesis and sequencing, makes DNA an attractive alternative to traditional data storage. The processes of writing, storing, and reading DNA exhibit specific error profiles and constraints DNA sequences have to adhere to. We present DNA-Aeon, a concatenated coding scheme for DNA data storage. It supports the generation of variable-sized encoded sequences with a user-defined Guanine-Cytosine (GC) content, homopolymer length limitation, and the avoidance of undesired motifs. It further enables users to provide custom codebooks adhering to further constraints. DNA-Aeon can correct substitution errors, insertions, deletions, and the loss of whole DNA strands. Comparisons with other codes show better error-correction capabilities of DNA-Aeon at similar redundancy levels with decreased DNA synthesis costs. In-vitro tests indicate high reliability of DNA-Aeon even in the case of skewed sequencing read distributions and high read-dropout.

Since the outbreak in 2019, researchers are trying to find effective drugs against the SARS-CoV-2 virus based on de novo drug design and drug repurposing. The former approach is very time consuming and needs extensive testing in humans, whereas drug repurposing is more promising, as the drugs have already been tested for side effects, etc. At present, there is no treatment for COVID-19 that is clinically effective, but there is a huge amount of data from studies that analyze potential drugs. We developed CORDITE to efficiently combine state-of-the-art knowledge on potential drugs and make it accessible to scientists and clinicians. The web interface also provides access to an easy-to-use API that allows a wide use for other software and applications, e.g., for meta-analysis, design of new clinical studies, or simple literature search. CORDITE is currently empowering many scientists across all continents and accelerates research in the knowledge domains of virology and drug design.

The use of complex biological molecules to solve computational problems is an emerging field at the interface between biology and computer science. There are two main categories in which biological molecules, especially DNA, are investigated as alternatives to silicon-based computer technologies. One is to use DNA as a storage medium, and the other is to use DNA for computing. Both strategies come with certain constraints. In the current study, we present a novel approach derived from chaos game representation for DNA to generate DNA code words that fulfill user-defined constraints, namely GC content, homopolymers, and undesired motifs, and thus, can be used to build codes for reliable DNA storage systems.

Due to the rapid cost decline of synthesizing and sequencing deoxyribonucleic acid (DNA), high information density, and its durability of up to centuries, utilizing DNA as an information storage medium has received the attention of many scientists. State-of-the-art DNA storage systems exploit the high capacity of DNA and enable random access (predominantly random reads) by primers, which serve as unique identifiers for directly accessing data. However, primers come with a significant limitation regarding the maximum available number per DNA library. The number of different primers within a library is typically very small (e.g. ≈10). We propose a method to overcome this deficiency and present a general-purpose technique for addressing and directly accessing thousands to potentially millions of different data objects within the same DNA pool. Our approach utilizes a fountain code, sophisticated probe design, and microarray technologies. A key component is locality-sensitive hashing, making checks for dissimilarity among such a large number of probes and data objects feasible.

Background Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires efficient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an efficient workflow management system. Results We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix) or as a Docker container on DockerHub (https://hub.docker.com/r/mw55/natrix). Conclusion Natrix is a user-friendly and highly extensible workflow for processing Illumina amplicon data.

Patients with end-stage renal disease (ESRD) suffer from a progressively increasing low-grade systemic inflammation, which is associated with higher morbidity and mortality. Regulatory T cells (Tregs) play an important role in regulation of the inflammatory process. Previously, it has been demonstrated that short-chain fatty acids reduce inflammation in the central nervous system in a murine model of multiple sclerosis through an increase in tissue infiltrating Tregs. Here, we evaluated the effect of the short-chain fatty acid propionate on the chronic inflammatory state and T-cell composition in ESRD patients. Analyzing ESRD patients and healthy blood donors before, during, and 60 days after the propionate supplementation by multiparametric flow cytometry we observed a gradual and significant expansion in the frequencies of CD25 high CD127 − Tregs in both groups. Phenotypic characterization suggests that polarization of naïve T cells towards Tregs is responsible for the observed expansion. In line with this, we observed a significant reduction of inflammatory marker CRP under propionate supplementation. Of interest, the observed anti-inflammatory surroundings did not affect the protective pathogen-specific immunity as demonstrated by the stable frequencies of effector/memory T cells specific for tetanus/diphtheria recall antigens. Collectively, our data suggest that dietary supplements with propionate have a beneficial effect on the elevated systemic inflammation of ESRD patients. The effect can be achieved through an expansion of circulating Tregs without affecting the protective pathogen-reactive immunity.