Expert Rev Mol Diagn

Mass spectrometry (MS) has found numerous applications in life sciences. It has high accuracy, sensitivity and wide dynamic range in addition to medium- to high-throughput capabilities. These features make MS a superior platform for analysis of various biomolecules including proteins, lipids, nucleic acids and carbohydrates. Until recently, MS was applied for protein detection and characterization. During the last decade, however, MS has successfully been used for molecular diagnostics of microbial and viral infections with the most notable applications being identification of pathogens, genomic sequencing, mutation detection, DNA methylation analysis, tracking of transmissions, and characterization of genetic heterogeneity. These new developments vastly expand the MS application from experimental research to public health and clinical fields. Matching of molecular techniques with specific requirements of the major MS platforms has produced powerful technologies for molecular diagnostics, which will further benefit from coupling with computational tools for extracting clinical information from MS-derived data.CC999999/Intramural CDC HHS/United States2018-03-01T00:00:00Z23638820PMC58310797469vault:2743

Bioinformatics Methods For Studying Intra-Host and Inter-Host Evolution Of Highly Mutable Viruses

Reproducibility and robustness of genomic tools are two important factors to assess the reliability of bioinformatics analysis. Such assessment based on these criteria requires repetition of experiments across lab facilities which is usually costly and time consuming. In this study we propose methods that are able to generate computational replicates, allowing the assessment of the reproducibility of genomic tools. We analyzed three different groups of genomic tools: DNA-seq read alignment tools, structural variant (SV) detection tools and RNA-seq gene expression quantification tools. We tested these tools with different technical replicate data. We observed that while some tools were impacted by the technical replicate data some remained robust. We observed the importance of the choice of read alignment tools for SV detection as well. On the other hand, we found out that the RNA-seq quantification tools (Kallisto and Salmon) that we chose were not affected by the shuffled data but were affected by reverse complement data. Using these findings, our proposed method here may help biomedical communities to advice on the robustness and reproducibility factors of genomic tools and help them to choose the most appropriate tools in terms of their needs. Furthermore, this study will give an insight to genomic tool developers about the importance of a good balance between technical improvements and reliable results

Applications of next-generation sequencing technologies and computational tools in molecular evolution and aquatic animals conservation studies : a short review

Aquatic ecosystems that form major biodiversity hotspots are critically threatened due to environmental and anthropogenic stressors. We believe that, in this genomic era, computational methods can be applied to promote aquatic biodiversity conservation by addressing questions related to the evolutionary history of aquatic organisms at the molecular level. However, huge amounts of genomics data generated can only be discerned through the use of bioinformatics. Here, we examine the applications of next-generation sequencing technologies and bioinformatics tools to study the molecular evolution of aquatic animals and discuss the current challenges and future perspectives of using bioinformatics toward aquatic animal conservation efforts

Algorithms for Analysis of Heterogeneous Cancer and Viral Populations Using High-Throughput Sequencing Data

Next-generation sequencing (NGS) technologies experienced giant leaps in recent years. Short read samples reach millions of reads, and the number of samples has been growing enormously in the wake of the COVID-19 pandemic. This data can expose essential aspects of disease transmission and development and reveal the key to its treatment. At the same time, single-cell sequencing saw the progress of getting from dozens to tens of thousands of cells per sample. These technological advances bring new challenges for computational biology and require the development of scalable, robust methods to deal with a wide range of problems varying from epidemiology to cancer studies. The first part of this work is focused on processing virus NGS data. It proposes algorithms that can facilitate the initial data analysis steps by filtering genetically related sequencing and the tool investigating intra-host virus diversity vital for biomedical research and epidemiology. The second part addresses single-cell data in cancer studies. It develops evolutionary cancer models involving new quantitative parameters of cancer subclones to understand the underlying processes of cancer development better

Computational pan-genomics: status, promises and challenges

International audienceMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains

Open questions in the social lives of viruses

Social interactions among viruses occur whenever multiple viral genomes infect the same cells, hosts, or populations of hosts. Viral social interactions range from cooperation to conflict, occur throughout the viral world, and affect every stage of the viral lifecycle. The ubiquity of these social interactions means that they can determine the population dynamics, evolutionary trajectory, and clinical progression of viral infections. At the same time, social interactions in viruses raise new questions for evolutionary theory, providing opportunities to test and extend existing frameworks within social evolution. Many opportunities exist at this interface: Insights into the evolution of viral social interactions have immediate implications for our understanding of the fundamental biology and clinical manifestation of viral diseases. However, these opportunities are currently limited because evolutionary biologists only rarely study social evolution in viruses. Here, we bridge this gap by (1) summarizing the ways in which viruses can interact socially, including consequences for social evolution and evolvability; (2) outlining some open questions raised by viruses that could challenge concepts within social evolution theory; and (3) providing some illustrative examples, data sources, and conceptual questions, for studying the natural history of social viruses