微生物组数据分析方法与应用

高通量测序技术的发展衍生出一系列微生物组(microbiome)研究技术,如扩增子、宏基因组、宏转录组等,快速推动了微生物组领域的发展。微生物组数据分析涉及的基础知识、软件和数据库较多,对于同领域研究者开展学习和选择合适的分析方法具有一定困难。本文系统概述了微生物组数据分析的基本思想和基础知识,详细总结比较了扩增子和宏基因组分析中的常用软件和数据库,并对高通量数据下游分析中常用的几种方法,包括统计和可视化、网络分析、进化分析、机器学习和关联分析等,从可用性、软件选择以及应用等几个方面进行了概述。本文拟通过对当前微生物组主流分析方法的整理和总结,为同领域研究者更方便、灵活的开展数据分析,快速选择研究分析工具,高效挖掘数据背后的生物学意义提供参考,进一步推动微生物组研究在生物学领域的发展。

Journal of Genetics and Genomics in 2023:progresses and beyond

Journal of Genetics and Genomics(JGG),launched in 1974,celebrates its 50th birthday in 2023.With continuous support from our authors,reviewers,readers,and the editorial board,JGG has made remarkable progress in the past year by publishing 122 papers covering major disciplines in life sciences and medical genetics with exciting discoveries.It is worthwhile to note that we have recruited 66 young talents to join JGG as junior editors,resulting in significant improvement in manuscript evaluation and journal promotion.Here,we summarize major progresses reported in JGG in 2023 fromaneditorial view.

JGG in 2022:challenges,opportunities,and prospects

During the past 2022,the Journal of Genetics and Genomics(JGG)has seen a variety of challenges as well as opportunities.At the difficult time of the COVID-19 pandemic,we have gone through unforeseen difficulties,including the cancellation of academic conferences,drop in submissions,work from home,and infections.Despite all these challenges,however,our authors,readers,reviewers,and editorial members have all supported and witnessed the progress of JGG.In 2022,JGG has published a record number of 136 papers reporting cutting-edge studies covering a range of fields in life sciences.Here,we briefly highlight important progresses in 2022 in an editorial view.

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.

Root microbiota shift in rice correlates with resident time in the field and developmental stage

Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.

Host-Associated Quantitative Abundance Profiling Reveals the Microbial Load Variation of Root Microbiome

Plant-associated microbes are critical for plant growth and survival under natural environmental conditions.To date,most plant microbiome studies involving high-throughput amplicon sequencing have focused on the relative abundance of microbial taxa.However,this technique does not assess the total microbial load and the abundance of individual microbes relative to the amount of host plant tissues.Here,we report the development of a host-associated quantitative abundance profiling(HA-QAP)method that can accurately examine total microbial load and colonization of individual root microbiome members relative to host plants by the copy-number ratio of microbial marker gene to plant genome.We validate the HAQAP method using mock experiments,perturbation experiments,and metagenomic sequencing.The HA-QAP method eliminates the generation of spurious outputs in the classical method based on microbial relative abundance,and reveals the load of root microbiome to host plants.Using the HA-QAP method,we found that the copy-number ratios of microbial marker genes to plant genome range from 1.07 to 6.61 for bacterial 16S rRNA genes and from 0.40 to 2.26 for fungal internal transcribed spacers in the root microbiome samples from healthy rice and wheat.Furthermore,using HA-QAP we found that an increase in total microbial load represents a key feature of changes in root microbiome of rice plants exposed to drought stress and of wheat plants with root rot disease,which significantly influences patterns of differential taxa and species interaction networks.Given its accuracy and technical feasibility,HA-QAP would facilitate our understanding of genuine interactions between root microbiome and plants.

Novel compound heterozygous cadherin 3 mutations in hypotrichosis and juvenile macular dystrophy

To the Editor:Hypotrichosis with juvenile macular dystrophy(HJMD,OMIM:601553)is a rare autosomal recessive disorder characterized by short and sparse hair,progressive macular degeneration,decreased visual acuity,andevenblindness in early life.