BIG to CNCB:An Exploratory Journey from Genomics to Bioinformation

The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human Genome Project(HGP)in completing the“1%task”to independently accomplishing the super-hybrid rice genome and other several national and international genome projects,BIG has made tremendous contributions in genomics research and development in China.In 2024,bearing great ambition and responsibility,BIG is transformed to the China National Center for Bioinformation(CNCB),aiming to become a global hub in bioinformatics big data services,innovation,and entrepreneurship.With the completion of its new infrastructure in 2027,CNCB is looking into a brighter future.

Chromosome-level genome and population genomics of the intermediate horseshoe bat(Rhinolophus affinis)reveal the molecular basis of virus tolerance in Rhinolophus and echolocation call frequency variation

Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.

Rice-wheat comparative genomics:Gains and gaps

Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits.Several wheat genes and gene families have been characterized based on their rice orthologs.Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops.Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping,deduce their putative functions and biochemical pathways,and develop molecular markers for marker-assisted breeding.A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering,gene editing,or wide crossing.

Conservation genomics provides insights into genetic resilience and adaptation of the endangered Chinese hazelnut, Corylus chinensis

Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.

Metabologenomics and network pharmacology to understand the molecular mechanism of cancer research

In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cases 2023 November 6;11(31):7593-7609.Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats.Numerous methods for chemical warfare,communication,nutrition acquisition,and stress prevention are made possible by these specialized metabolites.Metabolomics is a popular technique for collecting direct mea-surements of metabolic activity from many biological systems.However,con-fusing metabolite identification is a typical issue,and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity.Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologe-nomics.This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes.Combining metabolomics and genomics data is beneficial.Furthermore,computer networking proposes that using meta-bolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.CONCLUSION The genome and metabolites of biological organisms are not fully characterized with current technologies.However,increasing high-throughput metabolomics and genomics data provide promising generation of paired data sets to understand the molecular mechanism of biochemical processes as well as determining targets for pharmaceutical drug design.Contemporary network infrastructures to integrate omics analysis can provide molecular mechanism of biochemical pathways.Furthermore,clinical data may be integrated to gene expression–metabolite expression by system genetics approach.Calculating pair-wise correlations and weighted correlation network analysis provide the basis of this integration[11-13].The occurrence of strong correlations between classified metabolites and co-expression transcripts implies either various roles of metabolites or linkages between metabolic pathways and the immune system.

Exploring the Unknown: The Application and Prospects of Artificial Intelligence in Genomics and Bioinformatics

This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.

从Genomics,Proteomics到Cytomics,还是从Cytometry到Cytomics

Cytomics是一个特殊名词,她将cyto-与-omics连在一起,代表着一个崭新的领域。Cyto-主要来自分析细胞学(analyticalcytology或cytometry),而-omics主要来自蛋白质组学(proteomics)及其决定者基因组学(genomics)。经过三十余年的发展,分析细胞学可以通过分子探针将异质性的或混杂的细胞群体分为不同的细胞组份(cytomes),而基因组学和蛋白质组学的技术已能鉴定出某一物种的整套基因组和蛋白质组。因此,我们认为,细胞组学的任务是分离细胞组,并在基因组和蛋白质组层面确定其赖以存在和转变的分子基础。细胞组学的实施需要两大系列技术,一是细胞组分离技术,二是分离后再分析技术。这些技术的发展将使我们最终理解基因组并利用之,也必将拓展出更新的领域。

Genomics-based plant germplasm research(GPGR)

Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abundant resource. Genomics-based plant germplasm research （GPGR） or ＂Cenoplasmics＂ is a novel cross-disciplinary research field that seeks to apply the principles and techniques of genomics to germplasm research. We describe in this paper the concept, strategy, and approach behind GPGR, and summarize current progress in the areas of the definition and construction of core collections, enhancement of germplasm with core collections, and gene discovery from core collections. GPGR is opening a new era in germplasm research. The contribution, progress and achievements of GPGR in the future are predicted.

Approaches to functional genomics in filamentous fungi

The study of gene function in filamentous fungi is a field of research that has made great advances in very recent years. A number of transformation and gene manipulation strategies have been developed and applied to a diverse and rapidly expanding list of economically important filamentous fungi and oomycetes. With the significant number of fungal genomes now sequenced or being sequenced, functional genomics promises to uncover a great deal of new information in coming years. This review discusses recent advances that have been made in examining gene function in filamentous fungi and describes the advantages and limitations of the different approaches.

Recent progress in alfalfa(Medicago sativa L.)genomics and genomic selection

Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced agronomic traits is challenging and progress has historically not been rapid. Methods that make use of genotypic information and statistical models to generate a genomic estimated breeding value(GEBV) for each plant at a young age hold a great deal of promise to accelerate breeding gains. An emerging genomic breeding pipeline employs SNP chips or genotyping-by-sequencing(GBS) to identify SNP markers in a training population, followed by the use of a statistical model to find associations between the discovered SNPs and traits of interest, followed by genomic selection(GS), a breeding program utilizing the trained model to predict breeding values and making selections based on the estimated breeding value(EBV). Much work has been done in recent years in all of these areas, to generate marker sets and discover SNPs associated with desirable traits, and the application of these technologies in alfalfa breeding programs is under way. However, GBS/GWAS/GS is still a new breeding paradigm,and work is ongoing to evaluate different models, software, and methods for use in such programs. In this review, we look at the progress of alfalfa genomics over the past halfdecade, and review work comparing models and methods relevant to this new type of breeding strategy.

Development and Characterization of SSR Markers in Proso Millet Based on Switchgrass Genomics

Proso millet (Panicummiliaceum) has highwater use efficiency (WUE), a short growing-season, and is highly adapted to a semi-arid climate. Genomic resources for proso millet are very limited. Large numbers of DNA markers and other genomic tools in proso millet can readily be developed by using genomic resources in related grasses. The objectives of the present report were to 1) test and characterize switchgrass SSR markers for use in proso millet, and 2) elucidate repeat-motifs in proso millet based on new SSR marker analysis. A total of 548 SSR markers were tested on 8 proso millet genotypes. Out of these, 339 amplified SSR markers in proso millet. This showed that 62% of the switchgrass SSR markers were transferable to proso millet. Of these 339 markers, 254 were highly polymorphic among the 8 proso genotypes. The resolving power of these 254 polymorphic SSR markers ranged from 0.25-14.75 with an average of 2.71. The 254 polymorphic SSR markers amplified 984 alleles in the ranges of 50 bp to 1300 bp. The majority of the SSR markers (221 of 254) amplified dinucleotide repeats. Based on SSR marker analysis, AG/GA was the most abundant repeat-motifs in proso millet. Switchgrass genomic information seems to be the most useful for developing DNA markers in proso millet. Markers developed in this study will be helpful for linkage map construction, mapping agronomic traits and future molecular breeding efforts in proso millet.

Integrating pathomics with radiomics and genomics for cancer prognosis:A brief review

In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"prognostic image cues from the histopathological image.While we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay.In this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis.It includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and radiomics.We also present challenges,potential opportunities,and avenues for future work.

Plant phylogenomics based on genome-partitioning strategies:Progress and prospects

The rapid expansion of next-generation sequencing （NGS） has generated a powerful array of approaches to address fundamental questions in biology. Several genome-partitioning strategies to sequence selected subsets of the genome have emerged in the fields of phylogenomics and evolutionary genomics. In this review, we summarize the applications, advantages and limitations of four NGS-based genome- partitioning approaches in plant phylogenomics： genome skimming, transcriptome sequencing （RNA- seq）, restriction site associated DNA sequencing （RAD-Seq）, and targeted capture （Hyb-seq）. Of these four genome-partitioning approaches, targeted capture （especially Hyb-seq） shows the greatest promise for plant phy^ogenetics over the next fex~ years. This reviex~ wi~ aid ~esea^chers in their selection of appropriate genome-partitioning approaches to address questions of evolutionary scale, where we anticipate continued development and expansion ofwhole-genome sequencing strategies in the fields of plant phylogenomics and evolutionary biology research.

Experimental genomics:The application of DNA microarrays in cellular and molecular biology studies

The genome sequence information in combination with DNA microarrays promises to revolutionize the way of cellu-lar and molecular biological research by allowing complex mixtures of RNA and DNA to interrogated in a parallel and quantita-tive fashion. DNA microarrays can be used to measure levels of gene expression for tens of thousands of gene simultane-ously and take advantage of all available sequence information for experimental design and data interpretation in pursuit of biological understanding. Recent progress in experimental genomics allows DNA microarrays not simply to provide a cata-logue of all the genes and information about their function, but to understand how the components work together to comprise functioning cells and organisms. This brief review gives a survey of DNA microarrays technology and its applications in ge-nome and gene function analysis, gene expression studies, biological signal and defense system, cell cycle regulation, mechanism of transcriptional regulation, proteomics, and the functionality of food component.

Population genomics reveals that natural variation in PRDM16 contributes to cold tolerance in domestic cattle

Environmental temperature serves as a major driver of adaptive changes in wild organisms.To discover the mechanisms underpinning cold tolerance in domestic animals,we sequenced the genomes of 28 cattle from warm and cold areas across China.By characterizing the population structure and demographic history,we identified two genetic clusters,i.e.,northern and southern groups,as well as a common historic population peak at 30 kilo years ago.Genomic scan of cold-tolerant breeds determined potential candidate genes in the thermogenesis-related pathways that were under selection.Specifically,functional analysis identified a substitution of PRDM16(p.P779 L)in northern cattle,which maintains brown adipocyte formation by boosting thermogenesis-related gene expression,indicating a vital role of this gene in cold tolerance.These findings provide a basis for genetic variation in domestic cattle shaped by environmental temperature and highlight the role of reverse mutation in livestock species.

Functional genomics in the rice blast fungus to unravel the fungal pathogenicity

A rapidly growing number of successful genome sequencing projects in plant pathogenic fungi greatly increase the demands for tools and methodologies to study fungal pathogenicity at genomic scale. Magnaporthe oryzae is an economically important plant pathogenic fungus whose genome is fully sequenced. Recently we have reported the development and application of functional genomics platform technologies in M. oryzae. This model approach would have many practical ramifications in design and implementation of upcoming functional genomics studies of filamentous fungi aimed at understanding fungal pathogenicity.

Functional genomics: Gene identification via T-DNA mediated gene trap tagging in plants

The fully sequenced genomes of Arabidopsis, rice, tomato, potato, ma ize, wheat, and soybean offer large amounts of information about cellular and de velopmental biology. It is a central challenge of genomics to use this informati on in discovering the function of proteins and identifying developmentally impor tant genes. Although classical genetic approaches to gene identification which r ely on disruption of a gene leading to a recognizable phenotype continues to be an extremely successful one, T-DNA mediated gene trap tagging which has been dev eloped that utilize random integration of reporter gene constructs has also prov en to be an extremely powerful tool in plant cellular developmental biology. In this review, how gene trap tagging, promoter trap tagging, and enhancer trap tag ging detection systems have been applied to plant biology is described and these gene identification techniques could be useful to the plant molecular biology a nd plant biotechnology community.

Comparative genomics of Helicobacter pylori

Genomic sequences have been determined for a number of strains of Helicobacter pylori （H pylori） and related bacteria. With the development of microarray analysis and the wide use of subtractive hybridization techniques, comparative studies have been carried out with respect to the interstrain differences between H pylori and inter-species differences in the genome of related bacteria. It was found that the core genome of H pylori constitutes 1111 genes that are determinants of the species properties. A great pool of auxiliary genes are mainly from the categories of cag pathogenicity islands, outer membrane proteins, restriction-modification system and hypothetical proteins of unknown function. Persistence of H pylori in the human stomach leads to the diversification of the genome. Comparative genomics suggest that a host jump has occurs from humans to felines. Candidate genes specific for the development of the gastric diseases were identified. With the aid of proteomics, population genetics and other molecular methods, future comparative genomic studies would dramatically promote our understanding of the evolution, pathogenesis and microbiology of Hpylori.

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallogra-phy based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of in- silico methods for sequence-based prediction/ assessment of the protein crystallization pro-pensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discov-ered to correlate with the ability to form crystals. We also contrast and empirically compare mo- dern sequence-based predictors of crystalliza-tion propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and compli-mentary predictions. Although their average ac- curacy is similar at around 70%, we show that application of a simple majority-vote based en-semble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystalli-zation propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

WheatGene: A genomics database for common wheat and its related species

Common wheat(Triticum aestivum) is a hexaploid plant(AABBDD) derived from genetically related tetraploid wheat T. turgidum(AABB) and a diploid goatgrass Aegilops tauschii(DD). Recent advances in sequencing technology and genome assembly strategies allow the acquisition of multiple wheat genomes, calling for a centralized database to store, manage and query the genomics information in a manner to reflect their evolutionary relationship and to perform effective comparative genome analysis. Here,we built WheatGene, a database that contains five wheat genomes of 318,102 genes and 945,900 transcripts and their expression information in 998 RNA-seq samples that can be searched and compared in an interactive manner. WheatGene was developed with Drupal, a popular content management system and the toolkit Tripal managed the biological information. The database was accessible through a web browser with species, search, gene expression, tools, and literature entries. Tools available were BLAST,synteny viewer, map viewer, JBrowse, data downloads, gene expression heatmap and bar chart, and homologs viewer. Moreover, the map viewer connected genomics data with genetic maps and QTL that can be searched for markers for molecular breeding. WheatGene was developed with open-source modules and libraries. WheatGene is available at http://wheatgene.agrinome.org.