A practical guide to amplicon and metagenomic analysis of microbiome data

Advances in high-throughput sequencing(HTS)have fostered rapid developments in the field of microbiome research,and massive microbiome datasets are now being generated.However,the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field.Here,we systematically summarize the advantages and limitations of microbiome methods.Then,we recommend specific pipelines for amplicon and metagenomic analyses,and describe commonly-used software and databases,to help researchers select the appropriate tools.Furthermore,we introduce statistical and visualization methods suitable for microbiome analysis,including alpha-and betadiversity,taxonomic composition,difference comparisons,correlation,networks,machine learning,evolution,source tracing,and common visualization styles to help researchers make informed choices.Finally,a stepby-step reproducible analysis guide is introduced.We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

Toward Training and Assessing Reproducible Data Analysis in Data Science Education

Reproducibility is a cornerstone of scientific research.Data science is not an exception.In recent years scientists were concerned about a large number of irreproducible studies.Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy.Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training.In this study,we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis.The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports,only one-third of reports provided all necessary information for replication.The actual replication results also include conflicting claims;some lacked comparisons of original and replication results,indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results.The findings suggest that more training is needed to help data science students communicating reproducible data analysis.

Determination of symptoms associated with hiesho among young females using hie rating surveys

Objective： Hie （cold sensation） is one of the most well-known health complaints in Japan and elsewhere in East Asia. Those who suffer from severe hie are considered to have hiesho （cold disorder）. This study was conducted to determine symptoms associated with hie in young females using a survey consisting of the hie scale and hie diary. Methods： Two hundred and seventy-one participants were included for the analysis. Survey forms were distributed to the participants. Diagnosis of hiesho was determined by using the hie scale. A discriminant score of over -0.38 was considered hiesho. The Short Form-8 Health Survey Standard Version （SF-8） was used to measure health-related quality of life （QOL）. The participants were also asked to respond to the questionnaire evaluating 14 physical and emotional symptoms, utilizing a six-level Likert scale item. Results： The 1st factor （hie factor） was correlated with hie （r= 0.546）, dry mouth （r= 0.332）, lower- extremity edema （r = 0.450）, headrushes （r=0.470）, shoulder stiffness （r = 0.311 ）, headrushes with chills （r = 0.726）, and fatigue （r= 0.359）. Cronbach＇s α of the 1st factor was 0.748, which indicated reliability between the items. When hie factor was the dependent variable, standardized partial regression coefficient was β=-0.387 for physical component score （P 〈 0.001） and β=-0.243 for mental component score （P 〈 0.001 ）. Conclusion： This study indicated that hiesho symptoms among young female adults were associated with bodily pain and general health perceptions of the SF-8 QOL survey.