An improved statistical model for taxonomic assignment of metagenomics

Report of the third conference on next-generation sequencing for adventitious virus detection in biologics for humans and animals

Next-generation sequencing (NGS) has been proven to address some of the limitations of the current testing methods for adventitious virus detection in biologics. The International Alliance for Biological Standardization (IABS), the U.S. Food and Drug Administration (FDA), and the European Directorate for the Quality of Medicines and Healthcare (EDQM) co-organized the "3rd Conference on Next-generation Sequencing for Adventitious Virus Detection in Biologics for Humans and Animals", which was held on September 27-28, 2022, in Rockville, Maryland, U.S.A. The meeting gathered international representatives from regulatory and public health authorities and other government agencies, industry, contract research organizations, and academia to present the current status of NGS applications and the progress on NGS standardization and validation for detection of viral adventitious agents in biologics, including human and animal vaccines, gene therapies, and biotherapeutics. Current regulatory expectations were discussed for developing a scientific consensus regarding using NGS for detection of adventitious viruses. Although there are ongoing improvements in the NGS workflow, the development of reference materials for facilitating method qualification and validation support the current use of NGS for adventitious virus detection.

Analysis and Allocation of Cancer-Related Genes Using Vague DNA Sequence Data

To test the equality of several independent multinomial distributions, the chi-square test for count data is applied. The existing test can be applied when complete information about the data is available. The complex process, such as DNA count, the existing test under classical statistics may mislead. To overcome the issue, the modification of the chi-square test for multinomial distribution under neutrosophic statistics is presented in this paper. The modified form of the chi-square test statistic under indeterminacy/uncertainty is presented and applied using the DNA count data. From the DNA count data analysis, simulation, and comparative studies, the proposed test is found to be informative, springy, and good as compared with the existing tests.