Computational methods and translational applications for targeted next-generation sequencing platforms

A unified DNA- and RNA-based NGS strategy for the analysis of multiple types of variants at the dual nucleic acid level in solid tumors

BACKGROUND

Targeted next-generation sequencing (NGS) is a powerful and suitable approach to comprehensively identify multiple types of variants in tumors. RNA-based NGS is increasingly playing an important role in precision oncology. Both parallel and sequential DNA- and RNA-based approaches are expensive, burdensome, and have long turnaround times, which can be impractical in clinical practice. A streamlined, unified DNA- and RNA-based NGS approach is urgently needed in clinical practice.

METHODS

A DNA/RNA co-hybrid capture sequencing (DRCC-Seq) approach was designed to capture pre-capture DNA and RNA libraries in a single tube and convert them into one NGS library. The performance of the DRCC-Seq approach was evaluated by a panel of reference standards and clinical samples.

RESULTS

The average depth, DNA data ratio, capture ratio, and target coverage 250 (×) of the DNA panel data had a negative correlation with an increase in the proportion of RNA probes. The SNVs, indels, fusions, and MSI status were not affected by the proportion of RNA probes, but the copy numbers of the target genes were higher than expected in the standard materials, and many unexpected gene amplifications were found using D:R (1:2) and D:R (1:4) probe panels. The optimal ratio of DNA and RNA probes in the combined probe panel was 1:1 using the DRCC-Seq approach. The DRCC-Seq approach was feasible and reliable for detecting multiple types of variants in reference standards and real-world clinical samples.

CONCLUSIONS

The DRCC-Seq approach is more cost-effective, with a shorter turnaround time and lower labor requirements than either parallel or sequential targeted DNA NGS and RNA NGS. It is feasible to identify multiple genetic variations at the DNA and RNA levels simultaneously in clinical practice.

Bioinformatic Tools for NGS-Based Metagenomics to Improve the Clinical Diagnosis of Emerging, Re-Emerging and New Viruses

Epidemics and pandemics have occurred since the beginning of time, resulting in millions of deaths. Many such disease outbreaks are caused by viruses. Some viruses, particularly RNA viruses, are characterized by their high genetic variability, and this can affect certain phenotypic features: tropism, antigenicity, and susceptibility to antiviral drugs, vaccines, and the host immune response. The best strategy to face the emergence of new infectious genomes is prompt identification. However, currently available diagnostic tests are often limited for detecting new agents. High-throughput next-generation sequencing technologies based on metagenomics may be the solution to detect new infectious genomes and properly diagnose certain diseases. Metagenomic techniques enable the identification and characterization of disease-causing agents, but they require a large amount of genetic material and involve complex bioinformatic analyses. A wide variety of analytical tools can be used in the quality control and pre-processing of metagenomic data, filtering of untargeted sequences, assembly and quality control of reads, and taxonomic profiling of sequences to identify new viruses and ones that have been sequenced and uploaded to dedicated databases. Although there have been huge advances in the field of metagenomics, there is still a lack of consensus about which of the various approaches should be used for specific data analysis tasks. In this review, we provide some background on the study of viral infections, describe the contribution of metagenomics to this field, and place special emphasis on the bioinformatic tools (with their capabilities and limitations) available for use in metagenomic analyses of viral pathogens.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.