functional genomics research

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Functional genomics articles from across Nature Portfolio

Functional genomics uses genomic data to study gene and protein expression and function on a global scale (genome-wide or system-wide), focusing on gene transcription, translation and protein-protein interactions, and often involving high-throughput methods.

Co-evolved genes improve the biosynthesis of secondary metabolites

A new engineering strategy for improving the biosynthesis of secondary metabolites in Streptomyces has been developed through the analysis of genes co-evolved with biosynthetic gene clusters. This strategy has been verified in 11 Streptomyces strains to enhance production of 16,385 metabolites, showing potential applications in drug discovery and industrial production.

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A CRISPRi/a screening platform to study cellular nutrient transport in diverse microenvironments

Chidley et al. report a CRISPR interference/activation screening platform to systematically interrogate the contribution of nutrient transporters to support cancer cell proliferation in environments of variable composition.

• Christopher Chidley
• Alicia M. Darnell
• Peter K. Sorger

A pan-genome of 69 Arabidopsis thaliana accessions reveals a conserved genome structure throughout the global species range

A pan-genome of Arabidopsis thaliana constructed using chromosome-level genome assemblies of 69 diverse accessions reveals a conserved genome structure throughout the global species range.

• Qichao Lian
• Bruno Huettel
• Raphael Mercier

Further evidence supporting the role of GTDC1 in glycine metabolism and neurodevelopmental disorders

• Edoardo Errichiello
• Mauro Lecca
• Maria Clara Bonaglia

Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers

In an observational study evaluating functional precision medicine in children and adolescents with relapsed or refractory solid and hematologic malignancies, it was feasible to provide personalized treatment recommendations to treating physicians on the basis of genomic profiling and ex vivo drug sensitivity testing within 4 weeks.

• Arlet M. Acanda De La Rocha
• Noah E. Berlow
• Diana J. Azzam

Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles

SCENT is a nonparametric method that models association between chromatin accessibility and gene expression in single-cell multimodal datasets, enabling construction of cell-type-specific enhancer–gene maps to aid mapping of candidate causal variants and genes for common diseases.

• Saori Sakaue
• Kathryn Weinand
• Soumya Raychaudhuri

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma

• Jianlin Wang

News and Comment

How transposable elements are spliced out.

• Chiara Anania

Transcription factor binding site affinity and the link to phenotype

• Michael Fletcher

MYC activates transcriptional enhancers to drive cancer progression

We show that in addition to promoter activation, MYC drives cancer progression by activating transcriptional enhancers via a distinct mechanism. MYC cooperates with several other proteins at these cis -regulatory regions to change the epigenome and promote recruitment of RNA polymerase II and enhancer transcription.

Unveiling the expanding protein universe of life

In this Journal Club, Hajk-Georg Drost highlights a recent study by Pavlopoulos et al. that organizes proteins at tree-of-life scale using massively parallel graph-based clustering.

• Hajk-Georg Drost

Unravelling the molecular mechanisms of skin color diversity in Africans

Skin color is highly variable in Africans, but the underlying molecular mechanisms remain poorly understood. Using population genetics and functional genomics, we identified key genetic variants, regulatory elements and genes that affect skin pigmentation, an adaptive trait, which provides valuable insights into the mechanisms underlying human skin color diversity and evolution.

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Functional Genomics Research

Cells with genes highlighted by florescent dyes grow in multi-well plates.

Functional genomics research examines the role of the genome in cancer. By testing hypotheses derived from structural genomics research , or by generating new ideas from experiments in cancer cells, functional genomics research reveals patterns in cancer biology that can sometimes be directly translated to precision cancer care. Studies like those from The Cancer Target Discovery and Development  (CTD 2 ) Network, have already discovered genomic vulnerabilities in cancer that can be exploited through targeted treatments. Many of CCG’s functional genomics experiments further investigate insights from structural genomics studies, which are carried out by CCG’s Genome Characterization Pipeline .

Key Questions

• How do altered  genes  in cancer work together within pathways to promote abnormal proliferation and survival?
• Can molecular pathways affected by genetic abnormalities in cancer genes be targeted with available drugs or new compounds?
• Can tumor models generated from patient biopsies be used to understand mechanisms of therapeutic efficacy or resistance?

Tools and Methods

CCG’s functional genomics studies use models of cancer for high-throughput drug screens, gene perturbation experiments using RNA interference and CRISPR-Cas9 technology, and many other genome-wide techniques. Currently, CCG researchers use cancer cell lines , tumor organoid cultures that grow in petri dishes, or mice bearing grafts from human tumors to determine the effects of particular genetic alterations. Recognizing the power of new methods for generating cancer models, CCG is supporting the development of cutting-edge organoid and conditionally reprogrammed cell models to promote the safe and effective translation of functional cancer genomics findings to clinical care.

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Cancer target discovery and development (ctd 2 ) network .

The CTD 2 Network bridges a major gap between cancer genomics and precision oncology by mining large-scale genomic datasets for alterations important in cancer development and translating those discoveries into treatment. The CTD² Network emphasizes collaborations between the funded Centers , which have expertise in various computational and functional genomic approaches. In addition to publishing novel results, the CTD 2  Network produces publicly available data , analytical tools , and  reagents .

All data generated are open-access and can be obtained from the websites listed below:

• Raw and analyzed primary data are available through the CTD 2 Data Portal
• Network-generated observations and results, with associated supporting evidence, are compiled in a web interface known as the  CTD 2 Dashboard

Human Cancer Models Initiative (HCMI)

HCMI is an international collaboration between the NCI, Cancer Research UK , the Wellcome Trust Sanger Institute , and the foundation Hubrecht Organoid Technology with the goal of generating a publicly available bank of 1,000 next-generation cancer models annotated with genomic and clinical data.

HCMI uses new, innovative technologies, including organoid and conditionally reprogrammed cell (CRC) culturing techniques, to create cancer models that more accurately represent the architecture and complexity of real tumors. The models also have associated genomic data and clinical data made available to the research community.

NCI is contributing to this international consortium by providing funding and support to two Cancer Model Development Centers (CMDCs), which will develop a subset of the HCMI's next-generation cancer models from patient tissues.

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Next-Generation Technologies supports the development of technology tools to facilitate and accelerate research using next-generation cancer models, such as organoids and conditionally reprogrammed cells. The tools will focus on utilizing models developed by NCI's  Human Cancer Models Initiative . Protocols, materials, and knowledge developed by the program will be shared broadly and expeditiously with the research community.

Plant genome information facilitates plant functional genomics

• Open access
• Published: 09 April 2024
• Volume 259 , article number  117 , ( 2024 )

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• Judith Jazmin Bernal-Gallardo 1 &
• Stefan de Folter   ORCID: orcid.org/0000-0003-4363-7274 1  

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Main conclusion

In this review, we give an overview of plant sequencing efforts and how this impacts plant functional genomics research.

Plant genome sequence information greatly facilitates the studies of plant biology, functional genomics, evolution of genomes and genes, domestication processes, phylogenetic relationships, among many others. More than two decades of sequencing efforts have boosted the number of available sequenced plant genomes. The first plant genome, of Arabidopsis, was published in the year 2000 and currently, 4604 plant genomes from 1482 plant species have been published. Various large sequence initiatives are running, which are planning to produce tens of thousands of sequenced plant genomes in the near future. In this review, we give an overview on the status of sequenced plant genomes and on the use of genome information in different research areas.

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Introduction

The blueprint of living organisms sits in its DNA. It contains the instructions for an organism to grow and develop. In the last two decades, genome sequencing has greatly advanced. Currently, the NCBI database ( https://www.ncbi.nlm.nih.gov/ ) holds information on 30,530 eukaryotic genomes (representing 12,205 species), of which 5119 are complete or at chromosome level (accessed on 5 March 2024; Fig.  1 ). From these sequencing efforts, it became clear that the complexity of an organism is not necessary in the number of its genes. For instance, the number of genes of human (International Human Genome Sequencing Consortium 2001 ; Venter et al. 2001 ) or a roundworm (C. elegans Sequencing Consortium 1998 ) are not that far apart. A big part of the complexity is in how gene expression is regulated, and finally in how many proteins this can result. Genome information drives the discovery of biological insights on how organisms are functioning and their evolutionary history, and as well for biotechnological innovations. In the field of agriculture, genome information helps modern breeding, facilitates climate adaptation and food security, among others. Though it does not stop here, genome sequence efforts continue around the world. To highlight one large effort, the Earth BioGenome Project, which aims to sequence every living eukaryotic organism with a name on our planet, which is around 2 million species (Lewin et al. 2018 ; Ebenezer et al. 2022 ). A genomic tree of life is intended to aid in our understanding of how species change, adapt, and rely on one another across an ecosystem. Through these discoveries, long-standing problems in phylogenetics, evolution, ecology, conservation, agriculture, the bioindustry, and medicine will be resolved (Blaxter et al. 2022 ).

Sequenced genomes of plant species. a The plant kingdom stands as the third-most sequenced domain of life, as evidenced by the cumulative number of sequenced species. b Boxplot of sequenced species across the main clades of the Plant Kingdom. c Graphical representation of the progression in plant genome sequencing since 2000. The bars illustrate the distribution of plant genomes at both chromosomal and non-chromosomal levels. The green line tracks the annual sequencing rate of species, while the salmon shadowed area represents the cumulative count of sequences through March 2024. For the latter two, use values on the right y-axis. d Chronology of sequenced key plants of agriculturally and scientifically important plant species. In a , the data for animals, fungi, protists, and other domains of life were acquired from the NCBI database ( https://www.ncbi.nlm.nih.gov/ ). Sequenced plant species counts were obtained from https://www.plabipd.de/ , with the information updated on 19 February 2024. In b , species count data and genome sequencing details at both chromosomal and non-chromosomal levels were obtained from the NCBI database. Species counts were verified and updated using information from https://www.plabipd.de/ . In ( c ), the chronology was constructed using data obtained from the NCBI database and some of the images were generated using BioRender.com

In this review, we give an overview of the status of (nuclear) plant genome sequencing efforts and how this has helped for studies on plant functional genomics.

The status of sequenced plant genomes

Information on plant genome sequences enormously facilitates studies on plant biology, genetics, development, evolution, molecular biology, among many others. The first sequenced plant genome, Arabidopsis thaliana , was published in the year 2000 (Arabidopsis Genome Initiative 2000 ). This model plant is widely used worldwide and with the genome sequence, it opened the plant field into the genomics era. For a historical overview of Arabidopsis, we refer to other reviews (Meyerowitz 2001 ; Provart et al. 2016 , 2021 ; Somssich 2019 ). Arabidopsis has a genome size of around 135 Mb, and based on the latest Araport11 re-annotation, has 27,655 protein-coding loci with 48,359 transcripts (Cheng et al. 2017 ). Various dedicated websites house data for the community such as The Arabidopsis Information Resource (TAIR; Rhee et al. 2003 ), Araport (Cheng et al. 2017 ; Pasha et al. 2020 ), ThaleMine (Krishnakumar et al. 2017 ; Pasha et al. 2020 ), and Bio-Analytic Resource (BAR; Toufighi et al. 2005 ).

Nowadays, plant genome sequencing is a very active field (Michael and Jackson 2013 ; Chen et al. 2018 ; Kersey 2019 ; Marks et al. 2021 ; Kress et al. 2022 ; Sun et al. 2022 ). Since the publication of the Arabidopsis genome in December 2000 (Arabidopsis Genome Initiative 2000 ) 4604 nuclear plant genomes have been sequenced, corresponding to 1482 plant species, most of them being from angiosperms (90%) (Figs. 1 and 2 ). This genome data are based on information from the NCBI database (accessed on 5 March 2024; https://www.ncbi.nlm.nih.gov/genome/browse#!/overview/plants ), and from the website Published Plant Genomes that visualizes sequenced plant genomes over time ( https://www.plabipd.de/ ; R. Schwacke, personal communication, 19 February 2024). The second plant species to have a genome sequenced was rice, with two subspecies of rice ( Oryza Sativa subsp. japonica and subsp. indica ; Goff et al. 2002 ; Yu et al. 2002 ); in 2006 the first genome of a tree, from poplar ( Populus trichocarpa ; Tuskan et al. 2006 ); and in 2007 the genome of grape, the first genome of a fruit producing species ( Vitis vinifera ; Velasco et al. 2007 ). In the second decade of sequencing, the number of genome reports per year went up exponentially (Fig.  1 ).

Genome size and species count across plant clades. a Range of genome size within each clade of plant classification, with data points denoting the minimum and maximum genome sizes for each clade. b Bars illustrating the distribution of the number of species within each clade of plant classification. The plant classification used is based on the taxonomy provided by https://www.plabipd.de/ , which was updated on 19 February 2024

Just in the last five year, numbers of sequenced nuclear plant genomes increased impressively from around 576 (reflecting 383 species) (Kersey 2019 ), 798 (reflecting 798 species) (Marks et al. 2021 ), 1031 (reflecting 788 species) (Sun et al. 2022 ), 1139 (reflecting 812 species) (Kress et al. 2022 ), to 4604 genome sequences (reflecting 1482 species) that have been reported (5 March 2024; Table S1 ). This has to do with improvements of sequence technologies and lower costs (Shendure et al. 2017 ; Michael and VanBuren 2020 ; Henry 2022 ). One of the descriptions of the quality of genome assemblies is the value of the Contig N50, which indicates the length of the shortest contig in the set of contigs containing at least 50% of the assembly length. This value greatly improved over the years (Fig.  3 a), which is low (< 1 kb or < 10 kb) when a short-read sequencing approach was used (e.g., Illumina), and nowadays, with the use of long-read sequencing approaches such as from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), the Contig N50 is hundreds of kb to several Mb, resulting in much higher quality genome assemblies (Michael and Jackson 2013 ; Belser et al. 2018 ; Kersey 2019 ; Michael and VanBuren 2020 ; Marks et al. 2021 ; Sharma et al 2021 ; Sun et al. 2022 ).

Comparative analysis of genome size and protein-coding genes in annotated plant genomes, and assembly statistics of contig N50 over time for sequenced plant species. a Distribution of assembly statistics: Contig N50 over time for the 1482 sequenced plant species; data obtained from the NCBI Database ( https://www.ncbi.nlm.nih.gov/ ). The green points represent assemblies based on long-read sequencing methods, while the purple points represent assemblies based on short-read sequencing methods. b The graph illustrates the distribution of the genome size and the number of protein-coding genes (the pink dashed line indicates the mean number of genes per genome: 34,071) in the 685 available annotated plant genomes, utilizing taxonomic classifications from the NCBI database ( https://www.ncbi.nlm.nih.gov/ ). Points are colored by assembly level, and the figure represents a clade of the Plant Kingdom

The estimated number of extant green plant species is around 450,000–500,000 (Corlett 2016 ; Lughadha et al. 2016 ). The number of green plant species with sequenced genomes (1482) represents around 0.26–0.29% of plant species, so only a fraction of them has been sequenced so far. Despite an uneven distribution, the reported genomes span around 500 million years of evolution and comprise the major clades of green plants (Viridiplantae) (Fig.  2 ). Nuclear plant genome size varies greatly among the sequenced species, from 9 Mb to 31 Gb (Fig.  2 ). In contrast to more than 3000-fold difference in genome size, the number of protein-coding genes per genome varies much less, only in the range of a few-fold difference (Fig.  3 b). Based on the 685 available annotated plant genomes depicted in Fig.  3 b, the mean number of protein-coding genes is 34,071 (Table S1 ). Large genome sizes are attributed in part to polyploidy events common in plants, but mainly to the activity of transposable elements (Michael and Jackson 2013 ; Michael 2014 ; Kersey 2019 ; Kress et al. 2022 ; Marks et al. 2021 ).

Furthermore, we can see that the model species and many agriculturally and economically important plant species have been sequenced (Figs. 1 and 2 ). Without doubt, the number of sequenced genomes and phylogenetic distributions of them will soon increase and expand, because of many current genome initiatives. Projects affiliated to the Earth BioGenome Project (Lewin et al. 2018 , 2022 ), is the Darwin Tree of Life Project that aims to sequence all 70,000 species in Britain and Ireland (Darwin Tree of Life Project Consortium 2022 ). Another example is the 10KP (10,000 Plants) Initiative, which aims to sequence genomes of 10,000 species representing every major clade of embryophytes (land plants), green algae (chlorophytes and streptophytes), and protists (photosynthetic and heterotrophic) (Cheng et al. 2018 ). Other initiatives are the African BioGenome Project (AfricaBP) aiming to sequence genomes of 105,000 endemic species, including plants (Ebenezer et al. 2022 ), the African Orphan Crops Consortium (AOCC) aiming to sequence 101 African orphan crops/trees (Hendre et al. 2019 ), and the Genomics for Australian Plants (GAP) consortium aiming to sequence representative Australian plant genomes across the plant tree of life (Genomics for Australian Plants Initiative 2018 ; McLay et al. 2022 ).

Mostly, when sequencing a genome, the genome of one individual species is sequenced, which will be used as the reference genome. However, this is unlikely to be the complete picture. Genetic differences among individual species may exist. To overcome this, the term pan-genome was coined. The first report was based on the sequencing of eight bacterial strains and the observation that not every gene was present in each strain (Tettelin et al. 2005 ). It refers to the ´whole´ genome within a species (Golicz et al. 2020 ; Bayer et al. 2020 ). A pan-genome can be made by sequencing different individuals, accessions, cultivars, or populations, and then by ´joining´ the information, the whole genetic diversity will be captured, in principle (Lei et al. 2021 ; Li et al. 2022 ). In plants, the first pan-genome was made for wild soybean ( Glycine soja ), by sequencing and de novo assembly of seven phylogenetically and geographically representative accessions (Li et al. 2014 ). To date, around 30 plant pan-genomes, mostly of crops, have been published (Li et al. 2022 ). To create pan-genomes, long read sequencing is used. Normally, for re-sequencing efforts, short read sequencing is used, which allows the detection of single nucleotide polymorphisms (SNPs), but structural variants (SVs) are more difficult to identify (Golicz et al. 2020 ).

For comparative plant genomics, we refer readers to the useful website Phytozome (Goodstein et al. 2012 ).

How plant genomes facilitate plant functional genomics

Gene function discovery using mutant collections.

With the availability of genome sequences, the identification of gene functions via mutant screens became much easier. To go from a phenotype to the probable casual mutation induced by ethyl methanesulfonate (EMS) mutagenesis using classical forward genetic screens involved long and laborious mapping strategies. Nowadays, mapping can be performed by sequencing the genomes of a population of backcrossed homozygous plants with the phenotype of interest, which allows the rapid identification of the casual mutation (Hartwig et al. 2012 ; Garcia et al. 2016 ).

In reverse genetic screens, starting with a gene of interest and determining the phenotype/function (Alonso and Ecker 2006 ), for 20 years the Arabidopsis community has used insertional T-DNA mutant collections where sequence information is available for most of the random T-DNA insertions in the genome, arguably, the most widely used is the SALK T-DNA collection (Alonso et al. 2003 ). Various other valuable sequenced collections of T-DNA, transposon insertion, or variations, are available for Arabidopsis (Samson et al. 2002 ; Sessions et al. 2002 ; Rosso et al. 2003 ; Woody et al. 2007 ), and for other model species such as rice (Wang et al. 2013 ; Wei et al. 2013 ), maize (Lu et al. 2018 ), and petunia (Vandenbussche et al. 2008 , 2016 ).

There are various other techniques available for gene function discovery where genome information is very useful. An example of a reverse genetics approach to find mutations is TILLING (Targeting Induced Local Lesions IN Genomes), which is a chemical random mutagenesis approach, followed by high-throughput screening of point mutations in targeted genomic regions. The screening part can be combined with high-throughput sequencing (Mccallum et al. 2000 ; Henikoff et al. 2004 ; Tadele 2016 ). Another frequently used approach is activation tagging to identify gain-of-function mutants. For this, a mutant population is made by random genome insertions of T-DNAs or transposons carrying an activation sequence, leading to the activation of nearby genes. Recovering the flanking sequence followed by the identification of the genome region leads to the discovery of the gene in question (Weigel et al. 2000 ; Marsch-Martinez et al. 2002 ; Tani et al. 2004 ).

Other reverse genetics approaches for gene function discovery, involve making dedicated constructs targeting genes of interests, which can be used to target one or more genes. RNA interference (RNAi) (Saurabh et al. 2014 ; Muhammad et al. 2019 ) or the fusion of a transcriptional repression domain (EAR domain) (Hiratsu et al. 2003 ; Mitsuda et al. 2011 ) can be used to obtain loss-of-function mutants. Another approach is the use of artificial miRNAs (amiRNAs) to silence genes. An amiRNA can be designed to silence one gene or a family of redundant genes (Schwab et al. 2006 ; Ossowski et al. 2008 ). A last example of an approach, still relatively new but already very actively used, is using a CRISPR-Cas system (Wada et al. 2020 ; Zhu et al. 2020 ; Gaillochet et al. 2021 ). The used guide RNAs (gRNAs) are typically directed towards coding regions, but can also be directed towards promoters or non-coding regions. Furthermore, multiple gRNAs can be cloned in the same vector to target different genes (Najera et al. 2019 ) or promoters (Rodríguez-Leal et al. 2017 ). Having the genome information, genome-wide screens can be made using pooled CRISPR libraries (Huang et al. 2022 ; Liu et al. 2023 ; Pan et al. 2023 ), and various reports have already been published such as in rice (Lu et al. 2017 ; Meng et al. 2017 ), tomato (Jacobs et al. 2017 ), soybean (Bai et al. 2020 ), maize (Liu et al. 2020 ), and canola (He et al. 2023 ).

The use of CRISPR systems, for ´traditional´ genome editing or for gene activation/repression, may fill the gap of functional genomics in plant species, beyond the model species currently used (Huang et al. 2022 ; Liu et al. 2023 ; Pan et al. 2023 ). With the use of pooled CRISPR libraries, massive plant transformation could be applied in different species. Sharing of whole genome gRNA library data, pooled libraries, and even complete transformed CRISPR mutant populations in the form of seeds could make a usage boost to functional studies. As mentioned above, 4,604 nuclear plant genomes have been sequenced, corresponding to 1482 plant species (Fig.  1 ), most functional genomics research is performed in a rough estimate of only 1–2% of plant species with genome information so far. The future holds interesting opportunities for the use of genome information.

OMICS technologies

In addition to genomics, there are now many other omics technologies available. All these technologies benefit greatly from genome information. Many efforts exist generating plant transcriptomes from model species but also non-model species, even from species with no genome information yet. For the latter, mapping of the sequence reads is done against the genome of the evolutionary closest species or reads can be mapped (and gene expression quantified) against a de novo assembled transcriptome from the target organism. In general, transcriptome information also helps to improve genome annotations. Many databases exist to explore transcriptome data such as BAR (Winter et al. 2007 ), Genevestigator (Zimmermann et al. 2004 ), and Plant Public RNA-seq Database (Yu et al. 2022 ). Other databases contain data from large initiatives like the 1KP (1000 Plants), where transcriptomes of 1124 species were sequenced to infer the phylogenomic relationships (Matasci et al. 2014 ; Leebens-Mack et al. 2019 ). Another initiative is the JGI Plant Gene Atlas, which contains almost 2100 RNA-Seq data sets collected from 18 plant species, with the aim to improve functional gene descriptions across the plant kingdom (Sreedasyam et al. 2023 ). Recently, a great number of specialized single cell and single nuclei transcriptome data sets are emerging (reviewed in: Seyfferth et al. 2021 ; Cervantes-Perez et al. 2022 ; Denyer and Timmermans 2022 ; Nolan and Shahan 2023 ; Zheng et al. 2023 ) and databases holding single cell transcriptome data (e.g., Ma et al. 2020 ; Wendrich et al. 2020 ; Chen et al. 2021a ; He et al. 2023 ).

Plant proteomics is also a large field and benefits from genome information, including transcriptome information, first to be able to predict all proteins and isoforms (Chen et al. 2021a , b ; Mergner and Kuster 2022 ). Many proteomic studies, from small studies to very large studies, and even pan-plant proteomes have been reported in the literature (e.g., McWhite et al. 2020 ; Mergner et al. 2020 ; van Wijk et al. 2021 , 2024 ).

An omics area that has a growing significance that can improve draft plant genomes, correct gene annotation, discover new translation initial sites, ORFs, and alternative splicing, and verify novel genes of the peptide/protein level is called proteogenomics (Nesvizhskii 2014 ; Song et al. 2023 ). The usefulness of proteogenomics has been illustrated for instance for the model organism Arabidopsis (e.g., Castellana et al. 2008 ; Zhu et al. 2017 ; Willems et al. 2017 , 2022 ). Recent examples of proteogenomics in other species are for sweet cherry and pear (Xanthopoulou et al. 2021; Wang et al. 2023 ).

Another big omics technology is metabolomics. Metabolomics is a good tool for functional genomics (Schauer and Fernie 2006 ). It is a powerful technique to analyze the metabolite content in plants and is less restricted to genome information or model species. Though limitations for metabolomics in some (non-model) plants are the lack of high-quality metabolite databases, such that some molecules cannot easily be unambiguously identified. On the other hand, combining different types of omics data can lead to the discovery of gene functions and help in future plant improvements (Kumar et al. 2017 ; Patel et al. 2021 ; Shen et al. 2023 ).

Evolution and domestication

Genome information facilitates the study of phylogenetic relationships among species. Furthermore, the importance of genes or gene families in the evolution of land plants can be studied (Yu et al. 2018 ; Leebens-Mack et al. 2019 ; Soltis and Soltis 2021 ; Guo et al. 2023 ). Another example facilitated by genome information is the study of domestication. Hundreds of plant species have been domesticated by humans by selecting for beneficial traits (Gepts 2004 ; Meyer and Purugganan 2013 ). Through candidate gene studies, quantitative trait locus (QTL) mapping and cloning, genome-wide association studies (GWASs), and whole-genome resequencing studies, a significant number of domestication or domestication-related genes have been discovered and isolated (Meyer and Purugganan 2013 ; Kantar et al. 2017 ). More recently, reports on pan-genomes also facilitate the study of evolution and domestication, and the identification of key genes associated with important agronomic traits (Li et al. 2022 ).

Interestingly, de novo domestication by genome editing has been used (Bartlett et al. 2023 ). For instance, using CRISPR-Cas9, this has been done in the wild tomato species (Li et al. 2018 ; Zsögön et al. 2018 ), in the Solanaceae species ´groundcherry´ (Lemmon et al. 2018 ), and in wild rice (Yu et al. 2021 ). Knowledge on domesticated genes was used to edit several of these genes at once, resulting directly in a ´crop´ with desirable agricultural traits.

Conclusion and perspective

In recent years, the number of sequenced plant genomes has increased at an incredible speed. It is clear that this will only continue, and in the near future we will have tens of thousands of sequenced plant genomes. This wealth of information will accelerate studies on plant biology, functional genomics, evolution of genomes and genes, domestication processes, phylogenetic relationships, among many others. In parallel, new and improved bioinformatics analysis methods will have to be developed.

The field of single cell genomics will also expand and will also come with technical challenges such as capturing more cells, capturing low-abundance cells, cell-type annotation, new sequencing and analysis methods (Efroni and Birnbaum 2016 ; Conde and Kirst 2022 ; Cuperus 2022 ). Moreover, this will not only apply to transcriptomics, but in all omics fields we are going to see a rapid expansion, from single cell omics , single cell multi- omics , spatial genomics and other omics , new omics analysis methods, and to inference of gene regulatory networks using single cell omics data, among others (Thibivilliers and Libault 2021 ; Clark et al. 2022 ; Yu et al. 2023 ; Baysoy et al. 2023 ).

The genome evolution and phylogenomic research field will have an ever-growing amount of data available for analyses. Furthermore, there is a great potential for the use of functional genomics data for genome-editing of crops and for the de novo domestication for future crops using this same technology (Fernie and Yan 2019 ; Zhou et al. 2020 ; Zaidi et al. 2020 ; Gao 2021 ; Kumar et al. 2022 ; Yu and Li 2022 ; Bartlett et al. 2023 ). Importantly, when it comes to crop yield, knowledge is required how to properly evaluate this (Khaipho-Burch et al. 2023 ).

Lastly, Artificial Intelligence (AI) is certainly going to play a role in the plant science fields discussed here. Predictive models or analysis methods are developed based on machine learning (ML) and deep learning (DL) (Wang et al. 2020 ; van Dijk et al. 2021 ; Xu et al. 2021 ; Holzinger et al. 2023 ). Besides ChatGPT as a tool to ask or write texts, among other tasks (OpenAI; https://chat.openai.com/chat ), probably one of the best-known tools now in life sciences, is AlphaFold and its successor Alphafold2, a model that can predict almost all protein tertiary structures (Senior et al. 2020 ; Jumper et al. 2021 ). Other examples are the use of AI in image analysis and image-based phenotyping, having autonomous robots and/or drones for plant phenotyping, pest management, fertilizer management, or harvesting (Harfouche et al. 2023 ; Holzinger et al. 2023 ; Murphy et al. 2024 ). Furthermore, AI can be applied in bioinformatic analysis, to improve genome annotations, predict with high accuracy specific motifs in regulatory regions, gene function prediction, or predict the import nucleotide region or gene(s) in EMS screens or QTL analysis, etc. These are just a few examples of the many possibilities of the use of AI now and in the near future.

In conclusion, plant genomics will undoubtedly remain a cornerstone, actively contributing to the ongoing advancement of plant science and its practical applications.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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We thank the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) from Mexico for a PhD fellowship to JJBG. The work in the SDF laboratory was financed by the CONAHCYT grants CB-2017–2018-A1-S-10126 and CF-2019–6360.

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Unidad de Genómica Avanzada (UGA-Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico

Judith Jazmin Bernal-Gallardo & Stefan de Folter

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SDF conceived the study. JJBG analysed genome sequence information and made the figures. SDF wrote the manuscript. Both read and approved the final version.

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Correspondence to Stefan de Folter .

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Bernal-Gallardo, J.J., de Folter, S. Plant genome information facilitates plant functional genomics. Planta 259 , 117 (2024). https://doi.org/10.1007/s00425-024-04397-z

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An integrated toolkit for human microglia functional genomics

Affiliations.

• 1 Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA.
• 2 Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA.
• 3 Department of Neurology, Columbia University Medical Center, New York, NY, USA.
• 4 Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY, USA.
• 5 Neuroimmunology Core, Center for Translational & Computational Neuroimmunology, Division of Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
• 6 Proteomics Core, Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.
• 7 Department of Medicine, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
• 8 Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
• 9 Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA. [email protected].
• 10 Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA. [email protected].
• 11 Department of Neurology, Columbia University Medical Center, New York, NY, USA. [email protected].
• PMID: 38600587
• PMCID: PMC11005142
• DOI: 10.1186/s13287-024-03700-9

Background: Microglia, the brain's resident immune cells, play vital roles in brain development, and disorders like Alzheimer's disease (AD). Human iPSC-derived microglia (iMG) provide a promising model to study these processes. However, existing iMG generation protocols face challenges, such as prolonged differentiation time, lack of detailed characterization, and limited gene function investigation via CRISPR-Cas9.

Methods: Our integrated toolkit for in-vitro microglia functional genomics optimizes iPSC differentiation into iMG through a streamlined two-step, 20-day process, producing iMG with a normal karyotype. We confirmed the iMG's authenticity and quality through single-cell RNA sequencing, chromatin accessibility profiles (ATAC-Seq), proteomics and functional tests. The toolkit also incorporates a drug-dependent CRISPR-ON/OFF system for temporally controlled gene expression. Further, we facilitate the use of multi-omic data by providing online searchable platform that compares new iMG profiles to human primary microglia: https://sherlab.shinyapps.io/IPSC-derived-Microglia/ .

Results: Our method generates iMG that closely align with human primary microglia in terms of transcriptomic, proteomic, and chromatin accessibility profiles. Functionally, these iMG exhibit Ca2 + transients, cytokine driven migration, immune responses to inflammatory signals, and active phagocytosis of CNS related substrates including synaptosomes, amyloid beta and myelin. Significantly, the toolkit facilitates repeated iMG harvesting, essential for large-scale experiments like CRISPR-Cas9 screens. The standalone ATAC-Seq profiles of our iMG closely resemble primary microglia, positioning them as ideal tools to study AD-associated single nucleotide variants (SNV) especially in the genome regulatory regions.

Conclusions: Our advanced two-step protocol rapidly and efficiently produces authentic iMG. With features like the CRISPR-ON/OFF system and a comprehensive multi-omic data platform, our toolkit equips researchers for robust microglial functional genomic studies. By facilitating detailed SNV investigation and offering a sustainable cell harvest mechanism, the toolkit heralds significant progress in neurodegenerative disease drug research and therapeutic advancement.

Keywords: CRISPR; Chromatin accessibility (ATAC-Seq); Functional genomics; Microglia; Neurodegenerative diseases; Proteomics; iPSC-derived microglia (iMG).

© 2024. The Author(s).

• Alzheimer Disease* / genetics
• Amyloid beta-Peptides
• Chromatin / genetics
• Chromatin / metabolism
• Microglia / metabolism
• Neurodegenerative Diseases*

Grants and funding

• R01AG070118/AG/NIA NIH HHS/United States

Funding opportunity: Human Functional Genomics Initiative clusters

Last updated: 31 August 2023 - see all updates

Apply for funding to lead a cluster as part of a coordinated Human Functional Genomics Initiative.

• be based at an organisation eligible for MRC funding
• meet individual eligibility requirements

Clusters could include:

• platforms using existing technologies at scale to interrogate functional readout of genetic variation
• development of novel tools, technologies and biological models for functional genomics research
• research focused on the interplay of genetic variance and physiological pathways, organs and systems in both healthy and disease states

The total fund is up to £16 million. MRC will fund up to 80% of the full economic cost (FEC) and funding will last for four years.

Please note the expression of interest stage (stage one) has closed with decisions communicated no later than 25 May 2023.

Full applications (stage two) are only permitted from invited applicants. The deadline for full applications is 27 July 2023.

Who can apply

Before applying to MRC for funding, please check the following:

• the  MRC eligibility guidance for applicants
• the  eligibility of your organisation
• your eligibility as an individual
• the  MRC guidance for applicants , which sets out the MRC funding rules

Eligibility

To be eligible to apply for MRC funding you must:

• be a researcher or technologist employed by an  eligible research organisation
• have at least a postgraduate degree, although we expect most applicants to have a PhD or medical degree and meet  individual eligibility requirements
• show that you will direct the project and be actively engaged in the work
• have the relevant expertise and experience to lead an MRC project

Applications can be from a single eligible organisation or a partnership of organisations.

When there are two or more eligible organisations involved, for administrative purposes it is necessary to identify a single principal investigator who must be affiliated with the lead research organisation. However, the balance of activity and leadership across the partner organisations can be equally shared if desirable. What is critical is for the approach to leadership and decision making across multiple organisations to be clearly specified.

Where appropriate, we encourage you to include one or more project partners in your application, from industry, charities or the wider third sector.

While international organisations cannot lead an application, it is possible for an international researcher to apply, as a co-investigator. We expect international co-investigators to offer expertise or facilities not available in the UK and to provide clear indicators of commitment to the project.

Equality, diversity and inclusion (EDI)

MRC is committed to achieving equality of opportunity for all funding applicants. We encourage applications from a diverse range of researchers and technologists.

We support people to work in a way that suits their personal circumstances. This includes:

• career breaks
• support for people with caring responsibilities
• flexible working
• alternative working patterns

Read MRC’s  guidance on flexible working and career breaks . You can also find out more about MRC’s current EDI initiatives  and  EDI at UK Research and Innovation .

Diversity is one of the core MRC values and we are committed to creating inclusive environments that encourage excellence in research through good equalities practice. We strongly encourage applications from currently underrepresented groups including female and ethnic minority researchers, and researchers with disabilities or long-term conditions. MRC expect funded projects and their leadership to be diverse. We encourage the leadership model to be inclusive, diverse, and creative, with rotation or succession of positions as appropriate.

Organisations that are not eligible to apply

These organisations cannot apply to host an award, but can participate as  project partners  on an application led by an eligible UK organisation:

• international research organisations

What we're looking for

The Human Functional Genomics Initiative will advance the UKRI ‘securing better health, ageing and wellbeing’ strategic priority, an initiative driven forward by all UKRI research councils.

The Human Functional Genomics Initiative aims to advance our understanding of the complexity of human physiology and how it changes over time and in disease, by exploiting recent advances in our ability to explore how genetic variation impacts complex phenotypes. This will enable the discovery and development of the next generation of genomically informed biomarkers, diagnostics, therapeutics, and preventative medicine strategies.

This is a timely opportunity to support functional genomics research as the convergence of recent advances in gene-editing technology, multimodal functional assessment at scale, artificial intelligence, and effective cell-based model systems allows us to answer questions that have previously been impossible.

In this initiative, we are seeking to fund a coordinated network of interdisciplinary clusters to drive a greater understanding of physiological pathways, organs and systems in both healthy and disease states through functional genomics research. The funded clusters are expected to complement each other, pro-actively coordinate across the initiative, work collaboratively, and provide access to the models, tools and data developed through the initiative on an open as possible, closed as necessary basis.

Read UK Research and Innovation’s (UKRI) guidance on making your research data open .

In addition to the clusters funded through this opportunity we will provide substantial ring-fenced funding to facilitate effective coordination across the initiative and with external partners, and support for platforms to curate, integrate and make available data generated via this initiative and related efforts.

Through this funding, we aim to:

• boost functional genomic capability
• develop causal models of biological processes in health and disease for functional assessment and validation
• develop new high throughput perturbation and readout technologies and tools for data analysis

This will be underpinned by support for data integration and findable, accessible, interoperable, and reusable (FAIR) data standards for data discoverability and accessibility.

Where relevant, each cluster proposal should articulate:

• the key biological and methodological challenges in functional genomics to be addressed
• health equity through population diversity of samples
• benefits of using diverse data in revealing new functional insights
• the limitations on the conclusions drawn from your own research and applicability of the research to UK and global populations
• how you will draw on or develop translatable models of human physiology across the life course for health and disease with a pathway, system, or organ focus. For example, this could include the use of longitudinal cohort or patient samples and data or development and validation of new technological approaches

You should also explain the intended outputs and impacts of the cluster proposal:

• how your work will drive deeper mechanistic understanding of the interplay of genetic variance in physiological function across the life course for healthy and disease states, and the potential impact. For example, new biomarkers, clinical positioning hypothesis or novel targets
• how the cluster will address a capability gap in the UK functional genomics research landscape
• why you are best placed to deliver it
• how what you are doing will connect with other relevant initiatives
• knowledge exchange
• sharing of tools, models and data

To be within scope, you must articulate an ambitious programme of work focused on human physiology in health and disease with clear potential to catalyse a step change in functional genomics research in a relevant biological context. The proposal could include, but is not limited to, the following:

• genome perturbation
• multiscale and multimodal functional analysis
• functional and regulatory screening
• development of novel and emerging tools and technologies to enable functional genomics research, for example, perturbation technologies, analytical methods or data methods
• bioinformatics
• computational biology and modelling
• artificial intelligence tools
• physiological and developmental states
• stage of the life course
• dynamic and spatial context
• the development of relevant translatable biological models that seek to delineate causal pathways. For example, models could include primary or induced pluripotent stem cells from healthy and patient donors

Where this is applicable to the research, we encourage the use of human biosamples and data to enhance the physiological and disease relevance and enable more rapid translation of the research.

You should also consider how the cluster will connect with and complement, rather than duplicate, existing infrastructure, initiatives and resources, including but not limited to:

• Joint AstraZeneca-Cancer Research Horizons Functional Genomics Centre
• Human Induced Pluripotent Stem Cells Initiative
• National Institute for Health and Care Research (NIHR) Bioresource
• NIHR-MRC Rare Disease Network
• Genomics England 100k Genomes Project
• UKRI artificial intelligence innovation to accelerate health research

We will not support:

• functional genomics approaches in oncology, as there is existing UK investment and expertise in this area. The greatest identified need is for investment in non-oncology approaches. The use of cancer cells lines in non-oncology projects is permitted, where appropriate
• proposals solely focused on high throughput arrayed or pooled clustered regularly interspaced short palindromic repeats (CRISPR) screening. The overall initiative will include a recent MRC investment in an industry-partnered functional genomics facility (details will be announced in due course). This will provide the UK research community with high throughput arrayed CRISPR screening capacity, expertise and technical support
• applications should primarily focus on human biological models and cells. Animal research that significantly enhances or complements activity may be considered, and, where appropriate, applicants are encouraged to engage with existing investments, for example, National Mouse Genetics Network

We also support functional genomics research focused on specific pathways, systems, or organs through MRC research boards, you should consider whether your proposed research is better aligned to these schemes:

• molecular and cellular medicine
• population and systems medicine
• infections and immunity
• neurosciences and mental health

We encourage you to contact us first at [email protected] to discuss your application.

Collaboration and principals of participation

Clusters should be organised around a defined research or methodological challenge and can include one or more eligible research organisations, as necessary. We strongly encourage applications from new groupings of researchers from multiple disciplines and research interests.

We strongly encourage collaboration with both small and large companies, including appropriate cash or in-kind contributions to the cluster, but it is not mandatory. Partnerships should be mutually beneficial and in line with MRC’s Industry Collaboration Framework .

We also encourage collaboration between academic and clinical researchers, where this is relevant to the cluster challenge.

We expect funded clusters to adopt an open and transparent approach to data sharing to enable access across the clusters, wider initiative and research community, as necessary. Read UKRI’s guidance on making your research data open .

Funded clusters are expected to champion a collaborative and open ethos to accelerate the collective impact and reach of the initiative and sustainability of its outputs.

Coordination and leadership

We will support an openly collaborative culture to enable the clusters to be outwardly facing, engaged with each other and relevant UK capabilities to allow the initiative to remain agile and responsive to emerging opportunities.

We will appoint a director of the initiative, and more information on the appointment process will be made available in due course. The role of the director will be to:

• oversee and champion the coordination of the clusters, both within the initiative and with the wider academic, clinical and industrial research communities
• advocate the principals of participation
• respond to emerging opportunities
• lead cluster engagement with an oversight board that reports to the funders

Additional funds will be available to the director to support these responsibilities.

The supporting data platform to curate, integrate and make available data generated via this initiative and related efforts will be commissioned separately by the funders

Funding and duration

• the total fund is up to £16 million and we anticipate funding four to five clusters
• we will fund 80% FEC
• projects should last four years
• projects should start by 1 April 2024

What costs we will fund

You can request funding for costs such as:

• a contribution to the salary of the principal investigator and co-investigators
• support for other posts such as research and technical
• research consumables
• travel costs
• data preservation, data sharing and dissemination costs
• estates and indirect costs

What costs we will not fund

• research involving randomised trials of clinical treatments
• costs for PhD studentships
• publication costs

Project partners

Where appropriate, we encourage the inclusion of project partners that will support the cluster through appropriate cash or in-kind contributions, such as:

• access to equipment
• sites or facilities
• the provision of data
• software or materials

We especially encourage collaboration between academic, clinical and industry researchers

At the full application stage, each project partner must provide a letter of support. If your application involves industry partners , they must provide a company letter of support if the project partner falls within the industry collaboration framework .

Find out more about subcontractors and dual roles .

Who cannot be included as a project partner

The individual named as the contact for the project partner organisation cannot also be named as staff.

How to apply

The Functional Genomics Initiative Clusters funding opportunity has two stages.

The expression of interest stage is mandatory. You must submit an expression of interest to MRC using the Joint Electronic Submission (Je-S) system and case for support template.

To manage demand, individuals may only submit one expression of interest as a principal investigator.

The expression for interest stage is open and will close on 4 May 2023 at 4:00pm UK time.

Following completion of your expression of interest, you will be notified and invited to apply to the full application stage (stage two). We will communicate expression of interest decisions no later than 25 May 2023. You can find advice on completing your Je-S expression of interest under the ‘How to apply stage one: expression of interest’ heading.

The full application stage will open on 4 April 2023, closing on 27 July 2023 at 4:00pm UK time.

How to apply stage one: expression of interest

We recommend you submit your expression of interest as soon as possible.

Your host organisation will also be able to provide advice and guidance.

Submitting your application

Before starting an application, you will need to log in or create an account in Je-S.

When applying:

• Select ‘documents’, then ‘new document’.
• Select ‘call search’.
• To find the opportunity, search for: Expression of Interest Functional genomics 2023.

This will populate:

• council: MRC
• document type: outline proposal
• scheme: standard outline
• call/type/mode: Expression of Interest Functional Genomics 2023

Once you have completed your application, make sure you ‘submit document’ to your research office for checking and approval.

You can save completed details in Je-S at any time and return to continue your application later.

MRC must receive your expression of interest application by 4 May 2023 at 4:00pm UK time.

You will not be able to apply after this time. Please leave enough time for your proposal to pass through your organisation’s Je-S submission route before this date.

You should ensure you are aware of and follow any internal institutional deadlines that may be in place.

What to include within your expression of interest application

You must follow the below guidance before accessing Je-S.

Je-S application

Project details section.

Search the database to add the lead research organisation and department, where the funded award will be held.

Project title

The title should be as informative as possible, capturing the essence of the research challenge. It should not exceed 150 characters. Avoid using specialist characters and symbols.

Start date and duration

The start date should be no later than 1 April 2024

The project duration will be 48 months.

Investigators

All applicants eligible to be included as part of the proposed leadership team, should be added to the Je-S application as an investigator.

If the application is to be delivered in partnership by two or more eligible organisations, then the principal investigator must be from the organisation which will lead the partnership (as detailed within project details section). All host organisations must be represented by an eligible co-investigator.

Each member of the leadership team will need an active Je-S account at the required level (proposal Je-S account type), to allow their inclusion as an investigator. The individual who will act as the grant holder with responsibilities to MRC at the start of the MRC award should be included within the application as principal investigator, this is for administrative purposes. Other leadership team members should be the application co-investigators (including international researchers if applicable).

The leadership team members’ application roles should not imply relative status or influence the leadership model which is for the applicants to propose.

These roles and people added do not limit who might be recruited to team at the full application.

If you are a researcher based in the UK, an overseas MRC unit or an international researcher who has not created a Je-S account, navigate to the  Je-S home page  and select  ‘create an account’ .

Principal investigator

Search the Je-S database to add the principal investigator and select ‘save’.

Co-investigator

Search the Je-S database to add all other members of the leadership team as co-investigator and select ‘save’. Repeat process to add all new co-investigators (including any international researchers assisting with the leadership of the project).

Researcher co-investigator

Search the Je-S database to add all other members of the leadership team as researcher co-investigator and select ‘save’. Repeat process to add all researcher co-investigators (including any international researcher co-investigators assisting with the leadership of the project).

Researcher co-investigator status is aimed at researchers who are currently not eligible to be a principal investigator or a co-investigator on a grant but who provide significant intellectual input to grant writing and design.

To be considered suitable for the status of researcher co-investigator I, applicants are expected to not be eligible as a principal investigator or co-investigator of a research grant in their own right (for example, because they do not have a contract of employment with any of the participating research organisations for the duration of the grant prior to application). This could include, but is not limited to:

• postdoctoral researchers
• technology specialists
• clinical fellows

For further information related to researcher co-investigator eligibility.

If project partners will be involved in the project, please note, they are not required to be detailed at the expression of interest stage.

If your application involves one or more industrial partners, you should review the information published within the  MRC Industry Collaboration Framework (ICF)  to decide if you should submit your full application under the ICF.

Select the option to indicate research grant and save.

Case for support attachment

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Clinical Functional Genomics

Simple summary.

Functional genomics refers to the activity of the genome, that is, how the information contained in DNA (the book) is read and ‘acted upon’ in a biological context. Genes are turned ‘on’ (resulting in the synthesis of RNA that is translated into proteins) or ‘off’ during development and in response to environmental stimuli. Mis-regulation of these process can manifest as disease. Functional genomics are currently being developed clinically to improve patient care, with some clear potential future goals within the field. This commentary discusses rapidly evolving clinical functional genomic pathways and the underpinning technologies that have allowed for recent research and scientific advancements, and addresses challenges faced in the field.

Functional genomics is the study of how the genome and its products, including RNA and proteins, function and interact to affect different biological processes. The field of functional genomics includes transcriptomics, proteomics, metabolomics and epigenomics, as these all relate to controlling the genome leading to expression of particular phenotypes. By studying whole genomes—clinical genomics, transcriptomes and epigenomes—functional genomics allows the exploration of the diverse relationship between genotype and phenotype, not only for humans as a species but also in individuals, allowing an understanding and evaluation of how the functional genome ‘contributes’ to different diseases. Functional variation in disease can help us better understand that disease, although it is currently limited in terms of ethnic diversity, and will ultimately give way to more personalized treatment plans.

1. Introduction

The human genome is arguably the most useful information we currently possess to help improve patient health and is a key to medical advancement, as whole genome sequencing (WGS) of an individual maps out their entire unique genome, making it possible to pinpoint abnormalities or monitor patient improvement as a treatment is given.

The functional genome, namely the transcriptome, proteome, metabolome and epigenome, together contribute to specifying the phenotype of an organism. Information in genes as DNA is transcribed into RNA then translated into proteins; this, plus epigenetic ‘chemical tags’ induced by environmental factors, write the biological code that causes a specific phenotype. Clinical functional genomics [ 1 ] explores how these processes influence disease development and is starting to play in increasingly important role in both diagnostic and prognostic procedures. The advent of more sophisticated technologies to identify the expression profile of RNA molecules within individual cells, and spatially resolved in diseased tissues, offers a new paradigm in both hematology and pathology. This, coupled with the advent of gene editing technologies, is leading to a new era of understanding diseases, and from which new diagnostics and therapeutics will emerge.

2.1. Clinical Functional Genomic Pathway

Functional genomic analysis can be performed on a patient sample using high-throughput technologies like bulk RNA sequencing or newer methods, including spatial transcriptomics, to provide insight into the cellular transcriptome [ 2 ]. These are compatible with next generation sequencing (NGS) which is a massively parallel, high-throughput technology that rapidly determines the order of nucleotides in entire genomes or targeted regions [ 3 ]. It is also possible to use microarrays to profile multi-gene expression, however, while this method is more cost-effective, it does not give the same complete picture as RNA sequencing or spatial transcriptomics. If only a small number of genes need to be tested, real-time PCR provides a highly sensitive and cost-efficient method of choice. Downstream bioinformatic analysis of the patient’s unique functional genome can identify diseases or disorders, allowing for a tailored treatment plan specific to the patient. This more accurate diagnosis and treatment results in better prognosis for patients and is the fundamental basis of precision medicine.

2.1.1. Current Clinical Research

The field of functional genomics is of great relevance clinically to cancer patients, where most research and development is currently focused. Changes in the genome or epigenome can cause cancer by promoting uncontrolled cell growth or causing the immune system to fail to destroy tumors. Using a clinical functional genomics approach can allow for earlier and more accurate cancer diagnosis, leading to more accurate treatment options and better prognosis for patients. The UK government is hopeful about staying at the forefront of genomic research and new discoveries using the initiative ‘National NHS Genomic Medicine Service’. In the UK, WGS has already begun to enter clinical practice and it is the government’s aim to increase this and to offer elements of the functional genomic pathway for diagnosis of cancer. Using functional genomic methods such as RNA sequencing has been shown to successfully detect relapsing cancer up to 200 days before the relapse appears on a CT scan. This success has led to RNA sequencing being introduced more routinely in cancer diagnostics [ 4 ]. Functional genomic techniques open the door to personalized medicine, especially for cancer patients, and allow doctors to form personalized treatment plans. WGS and epigenomics for an individual patient with cancer will identify genetic mutations and epigenetic alterations as circulating biomarkers; in cancers, these are circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) which can originate from the primary tumor, providing early detection. Biomarkers from metastasizing tumors are also extremely difficult to detect as they are present in such low abundance and can only be detected using highly sensitive technologies.

The Functional Genomics Centre at AstraZeneca is currently working with Cancer Research UK using clustered regularly interspaced short palindromic repeats (CRISPR) technology to study cancer biology and create better, more appropriate biological models. Alongside this, they are developing informatic processes to analyze ever-increasing dataset numbers [ 5 ]. The clinical implications are the possibility of developing more effective cancer treatments. Similarly, the company GSK joined forces with the University of California to form the Laboratory for Genomic Research (LGR). The main focus of the LGR is to use functional genomics to improve drug discovery methods using CRISPR to knock out or alter multiple genes in one experiment at scale. This allows scientists to explore the function of genetic variants quickly and simply, with the ultimate goal of new and improved drug production, leading to more novel therapies since these methods increase clinical success [ 6 ].

2.1.2. Current Clinical Applications

The rapid development of functional genomics in the past two decades has allowed for breakthrough discoveries in certain cancer research, such as in acute myeloid leukemia (AML) and breast cancer studies. Novel molecular biology technologies, such as short hairpin RNA (shRNA) and CRISPR-Cas9, and advances in bioinformatics have allowed for wider research into the physiopathology of AML, resulting in a cure being within reach [ 7 ]. Furthermore, functional genomics have been used effectively to guide drug development, including one of the earliest examples; the discovery that the gene HER2 is overexpressed in certain types of breast cancers, which led to development of a drug, Herceptin [ 8 , 9 ]. Identifying drug targets within diseases allows for more rapid drug development or the repurposing of existing drugs. This is majorly beneficial to the pharmaceutical industry as it lowers costs and speeds up development times. If similar studies and clinical trials were to be conducted on other diseases, it could rapidly lead to deeper understanding of the diseases and improve drug discovery.

2.1.3. Benefits of This Field to Science and Public Health

Variant interpretation—deciding if a genetic change is pathogenic or benign—is a common issue caused by lack of understanding of biological functions within the genome [ 1 ]. Functional genomic studies investigate biological processes and how genes, RNA and proteins interact to form phenotypes, rather than just exploring the genome per se. These processes and pathways are complicated due to the ever-changing nature of the transcriptome, epigenome, proteome and metabolome. A clinical functional genomic approach can improve this issue by linking WGS datasets with functional omics datasets. Interpretation can be improved by widening datasets, such as increasing ethnical diversity within studies. This could lead to significant clinical results for patients by improving understanding of biological processes to potentially reveal insights that were not possible to achieve with the genome alone.

2.1.4. Challenges Currently Being Faced within the Field

High costs are currently one of the major factors limiting the clinical expansion of the field. Lowering the cost of sequencing whole genomes would make it more accessible and easier to integrate into healthcare systems around the world. According to pricing from the US National Human Genome Research Institute, the cost per megabase of sequence data has plateaued since 2016 and remains fairly high, at around USD 1000 per genome for sequencing alone. The cost of genome sequencing per cancer case is roughly GPB 6850 in the UK [ 10 ], therefore despite the service being made available to the NHS, certain health trusts/boards may not offer it routinely due to high costs. However, these hospitals should be encouraged to perform genome sequencing as an investment in healthcare since treatment cost are significantly lower if the cancer is diagnosed early [ 11 ]; the UK governments’ ‘Life Science Vision’ is focused on such early diagnosis and prevention [ 12 ].

Functional genomic studies should lead to equitable personalized medicine. However, a challenge faced within the research is that there is not enough population and demographic diversity. Sirugo et al. determined that most genome-wide association studies (GWAS) are performed in high income countries and the percentage of individuals in GWAS based on ethnicity were 78% European, 10% Asian, 2% African, and all other ethnicities represented 1% or less of GWAS [ 13 ]. This is not equitable representation, and this information disparity can cause clinical genome interpretation to be less reliable for the underrepresented minorities. As use of WGS and functional genomics begins to increase clinically, we must ensure that reference genomes are diverse enough to be used for all populations and ethnicities. If ethnical diversity in genome studies is not increased, this will block large groups of people from accessing this form of healthcare.

Since functional genomics looks at more than just the genome, it is currently uncertain whether some functional aspects will be tissue- or cell-dependent and therefore it is unclear how well functional data obtained from blood samples can compare directly to difficult-to-collect tissue samples, e.g., from the brain. Comprehensive functionally annotated genomes are beginning to be assembled, enabling scientists to compare vast amounts of genomic data to assess this issue in depth [ 1 ].

Most multi-omics studies have been performed on animal models rather than human cells or tissues, and this creates the issue of then translating this functional genomic data into data that are useful or applicable in human disease. This issue can be resolved in the future by performing clinical trials on humans or by using the engineered humanized physiologically relevant animal models in research studies.

Ethical restrictions impact most areas of scientific research, with this field being no exception. The rapid growth of technologies and knowledge in this field has allowed for many discoveries, however the time taken to assess risks and benefits of clinical genomic testing has and will continue to slow down research, although thoroughness is necessary to ensure the safety of patients. The discussion around the ethics of human gene editing has led to many prominent scientists insisting on banning human germline editing while research is conducted to prove it can be done safely and effectively [ 14 ]. While this does slow down progress, there is a broad societal consensus that this is an important step since the technology is not yet properly understood.

2.2. Functional Genomics Technology Platforms

The rapid development of new technologies over recent years has increased and continues to increase our ability to analyze the genome, transcriptome, and quantify the proteome and metabolome of single cells. Functional genomics uses single cell analysis technologies, like single cell sequencing, alongside omics datasets and high-throughput technologies, which enable simultaneous analysis of thousands of single cells, to study and understand how genetic variants can affect disease pathogenesis [ 12 ]. Other techniques such as mass spectrometry have recently reemerged as key analytical tools for proteomic and metabolomic analysis of single cells.

2.2.1. Single Cell Analysis

Single cell analysis ( Figure 1 ) was developed only a decade ago in 2011 and has become majorly beneficial to the field of functional genomics. It has enabled a view of cell-specific and cell-cell interactions at a single cell level. This in-depth view of cells allows molecular profiling that previously could not be revealed and, moving forward, will be particularly beneficial in hematological cancer diagnosis and monitoring, as well as in monitoring and understanding immune responses to, for example, SARS-CoV-2 in COVID-19. A single-cell multi-omics approach is perhaps the best way forward in the characterization of cancer functional genomes as it is the only technique that can achieve full resolution of tumor heterogeneity [ 14 ].

Single cell transcriptomics is a technology used to resolve RNA-seq data at a single cell level, and thereby all mRNAs (and miRNA and lncRNA where the technology permits). Cells may be derived from homogenized tissues, but also from the immune cell component (Buffy coat) from whole blood samples. This technique allows for gene activity measurements from all cells present in a sample, assuming highly efficient cell labeling. Cell samples are isolated, and each cell is individually labeled using sequencing bar code technologies, e.g., microfluidics. Labeled cells can then be re-pooled and sequenced. Finally, data are analyzed, and gene clusters are associated into cell types or tissue domains. Adapted from Single Cell Sequencing, by BioRender.com (2021). Retrieved from https://app.biorender.com/biorender-templates .

2.2.2. NanoString

NanoString is a type of DNA microarray originally developed for use in cancer diagnostics but now has many clinical applications alongside oncology, such as immunology research. The amplification-free NanoString nCounter single cell assay system is highly sensitive and measures nucleic acid content by counting molecules and directly profiling them in a highly multiplexed single reaction to profile gene expression. This eliminates amplification bias, meaning this technology presents a potentially more reliable way to analyze DNA or RNA [ 15 ]. NanoString should currently be viewed as complementary to NGS and not a full replacement in all settings [ 16 ].

2.2.3. Spatial Transcriptomics

Spatial transcriptomics ( Figure 2 ) is a molecular profiling method that allows gene activity to be mapped across a tissue sample. The main method used to perform this is by positioning the sample on an array of spatially barcoded reverse-transcription primers that attach to mRNA with oligo(dT) tails. The library product is compatible with NGS technologies, which allows for massive transcriptional profiling. This method is incredibly useful as it gives scientists insight into an individual’s entire tissue sample which could help diagnose a disease, for example, through determining cellular heterogeneity, and even towards cancer stem cell identification [ 17 ].

Spatial transcriptomics is a technology used to spatially resolve RNA-seq data, and thereby all mRNAs, in individual tissue sections. This technique allows for gene activity measurements and mapping in tissue samples. Tissue samples are prepared on glass slides and, by means of tissue-domain oligo primers, genes are encoded. Finally, data are analyzed, and gene clusters are associated with tissue domains. Reprinted from Spatial Transcriptomics, by BioRender.com (2021). Retrieved from https://app.biorender.com/biorender-templates , accessed on 3 September 2021.

2.2.4. The Use of CRISPR-Cas9 within Functional Genomic Studies

CRISPR-Cas9 ( Figure 3 ) is a revolutionary discovery that allows for the editing of genes that have been found to be, for example, mutated in diseases, though studying such mutations in model systems using a functional genomics approach will allow the consequences of genetic mutations to be determined. From this, approaches that will change the way we treat certain diseases and identify drug targets will be discovered.

CRISPR/Cas9 is a powerful tool for genome engineering. The Cas9 complex with a sgRNA recognizes a specific sequence, the protospacer. This is only possible if this sequence is followed by a Protospacer Adjacent Motif (PAM). When Cas9 binds, a dsDNA break is generated. Then, non-homologous end joining or homology-directed repair can occur, leading to mutations or gene changes, respectively. Reprinted from CRISPR/Cas9 Gene Editing, by BioRender.com (2021). Retrieved from https://app.biorender.com/biorender-templates , accessed on 3 September 2021.

CRISPR edits genes by releasing guide RNA (gRNA) that targets and attaches to a specific section of DNA within the genome. This allows the enzyme Cas9 nuclease to cut the DNA at that section which activates the cell’s own DNA repair process. Sequences within the cut gene can be edited or deleted and replaced with a new DNA sequence. This process is quick and easy, making it very cost-efficient. The ability to edit genes quickly and inexpensively allows for experiments to determine causes of diseases that were previously unknown [ 5 ]. CRISPR-Cas9 could become a key part of the genetic screening industry as it can be used to engineer embryos, although there are many ethical debates surrounding this topic currently. All programmable nucleases, including CRISPR nucleases, are in the process of being clinically investigated to gather enough evidence to suggest whether they are safe and effective. If they are proven to be effective and safe in clinical trials, they could be used to treat patients with a wide variety of diseases, from hereditary blindness to cancers.

Despite the potential benefits, there are issues with the programmable nucleases that have come to light during clinical trials. They can cause unwanted and dangerous mutations; this is a big issue with regards to using the technology with humans, as mutations may contribute to oncogenesis. The enzyme Cas9 can be immunogenic, therefore initial clinical trials have only been conducted in an immunologically privileged organ, the eye. Certain editing approaches have started to be developed that appear to overcome some of the challenges of working with nucleases to edit genomes. These editors use a Cas9 nickase, which unlike a wild-type nuclease produces DNA single-strand breaks, not double-strand breaks. This means the nickase editors are unlikely to cause large deletions or chromosomal rearrangements during the DNA repair process. Ultimately, this engineered gene editing nuclease appears to work more reliably and efficiently at correcting genes. Due to the reduced risk of unwanted mutations, these base and prime editors would be ideal for germline or in utero editing in the future if these processes become legal. These processes could eventually be used to correct pathogenic mutations in human embryos, since gene editing in newborns is generally inefficient. This ethical discussion may come around sooner than expected as society is always looking for ways to improve public health, both after and potentially even before birth. However, it is important not to rush this process as, despite the promising results of current base and prime editors, they can still be improved [ 14 ].

When CRISPR-Cas9 are developed enough to resolve all issues and the process is safe for use in humans, this could become a pivotal part of the clinical functional genomic pathway and be especially useful to the genetic screening industry because in the future in utero editing may be favored over preimplantation genetic diagnosis since it will not require destruction of human embryos, although this will remain a highly controversial approach.

2.3. The Functional Genomic Market

The success and importance of genome sequencing during the COVID-19 pandemic drew government attention to the already rapidly expanding field. This, along with increasing cancer cases, resulted in more government interest and funding, such as in the UK with the launch of the GBP 200 m life science investment program in summer 2021. This investment is expected to generate around GBP 600 m long-term capital for the industry in the UK [ 12 ].

North America accounted for the largest share of the genomics market in 2020, with established companies such as Illumina, Inc. and Thermo Fisher Scientific dominating the market [ 18 ]. The main driving force of this market is the sequencing technology: This segment accounted for the largest technology share of the genomics market in 2019 due to its rapid advancements and usefulness to a wide variety of sectors. The drug discovery and development sector, which uses many of these technologies, accounted for the largest share by application of the genomics market in 2019 [ 19 ].

The value of the global genomics market is expected to more than double by 2025 [ 19 ] and could create around 133,000 jobs by 2030 [ 20 ]. This may cause issues within the industry, as there is already a shortage of trained professionals [ 19 ], unless companies start investing now in training more scientists in this area to fulfill the demand.

2.4. Future Goals

Drug production and diagnostics are currently still developed in ‘traditional’, ways with a relatively small number of businesses such as GSK, Novartis and AstraZeneca beginning to experiment with and integrate functional genomic methods into their workflows. However, functional genomics is likely to become an essential part of all drug discovery and development pathways in the near future. The main hinderance during drug production for pharmaceutical companies is that over 50% of drugs fail at phase III clinical trials due to lack of efficacy [ 21 ]. Failures are costly and time consuming for the industry. Functional genomic techniques appear to improve success rates during drug discovery trails by mapping out potential targets within a disease, and targeting these generally results in more efficient drugs, therefore higher clinical trial success rates ultimately will lower the time taken to discover and produce vital drugs.

3. Conclusions

To date, functional genomics is not being used to its full potential for personalized medicine. More clinical investigations should be performed using functional genomic pathways to increase our understanding of genes and diseases and allow this knowledge to become integrated into mainstream medicine, as it is very much needed. Personalized medicine using functional genomics and genomics to determine the most effective medication at the right time based on an individual’s unique profile will transform medicine since molecular changes precede clinical manifestations; therefore, we can treat patients earlier, resulting in better and more accurate prognosis than ever before.

Clinical trials should be performed using functional genomics to track patient progress when different drugs and treatments are used. This will deepen the understanding of diseases and how treatments affect them by measuring molecular changes compared to clinical phenotypes.

Author Contributions

Conceptualization, R.S.C.; writing—review and editing, R.S.C., S.C. Both authors have read and agreed to the published version of the manuscript.

This research was funded by the Swansea University Employability Academy Internship Programme 2021 (S.C.).

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Open access
• Published: 05 April 2024

Yak genome database: a multi-omics analysis platform

• Hui Jiang 1 , 2   na1 ,
• Zhi-Xin Chai 3   na1 ,
• Xiao-Ying Chen 1 , 2   na1 ,
• Cheng-Fu Zhang 1 , 2 ,
• Yong Zhu 1 , 2 ,
• Qiu-Mei Ji 1 , 2 &
• Jin-Wei Xin 1 , 2  

BMC Genomics volume  25 , Article number:  346 ( 2024 ) Cite this article

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Metrics details

The yak ( Bos grunniens ) is a large ruminant species that lives in high-altitude regions and exhibits excellent adaptation to the plateau environments. To further understand the genetic characteristics and adaptive mechanisms of yak, we have developed a multi-omics database of yak including genome, transcriptome, proteome, and DNA methylation data.

Description

The Yak Genome Database ( http://yakgenomics.com/ ) integrates the research results of genome, transcriptome, proteome, and DNA methylation, and provides an integrated platform for researchers to share and exchange omics data. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The genome module provides access to yak genome sequences, gene annotations and variant information. The transcriptome module offers transcriptome data from various tissues of yak and cattle strains at different developmental stages. The proteome module presents protein profiles from diverse yak organs. Additionally, the DNA methylation module shows the DNA methylation information at each base of the whole genome. Functions of data downloading and browsing, functional gene exploration, and experimental practice were available for the database.

This comprehensive database provides a valuable resource for further investigations on development, molecular mechanisms underlying high-altitude adaptation, and molecular breeding of yak.

Peer Review reports

Although single omics study provides information and insights into specific biological or molecular processes, it is hard to confirm the real molecular mechanisms underlying the functionality of an organism and the relationships between biological processes and environmental factors. Integrating and analyzing multiple omics data provide an effective and systematic approach to life science researchers. In general, genomics provides DNA sequence information, transcriptomics examines gene transcription patterns under specific conditions, proteomics explores the composition and expression levels of proteins in cells, and DNA methylation involves chemical modifications on DNA molecules [ 1 ]. Multi-omics analysis combines data at different levels to comprehensively explore biological processes. Multi-omics analysis reveals connections between genomics, transcriptomics, proteomics, and DNA methylation data, facilitating to understand how genomic variations impact gene transcription and protein expression, as well as the associations between DNA methylation and gene activities [ 2 ]. These pieces of information contribute novel information to the gene regulatory networks, which are important to molecular mechanisms underlying biological functions, development, metabolism, etiopathology, and environmental adaptation.

The yak ( Bos grunniens ) is a unique species in the Qinghai-Tibet Plateau, and widely distributes in high-altitude areas of Western China and neighboring regions. As a large mammal at the highest-altitude area, yak has survived and adapted to the harsh and cold environment after thousands of years of evolution [ 3 ]. Their unique biological features make them an ideal model for studying adaptive evolution and high-altitude ecosystems. Yak also plays important roles in agriculture and economic development. As a significant livestock species, yak provides meat, fur, and other economic resources. Their dung is also an important source of agricultural fertilizer and energy production. Moreover, yak positively impacts the ecological balance and vegetation restoration in the plateau grasslands through their grazing behaviors [ 4 ]. In recent years, we have analyzed yak using different omics approaches. These data preliminarily explored the yak genetic characteristics, gene transcription, protein expression, and DNA methylation patterns, as well as molecular regulatory mechanisms in response to different conditions [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ], providing novel insights into the mechanisms underlying evolution, and high-altitude adaptation in yak.

Currently, the data resources of yak omics researches are generally stored in public databases in their raw data format, such as NCBI. These databases primarily provide storage and retrieval functions, but lack an integrated platform for data integration and in-depth analysis. Hu et al. [ 12 ] developed a yak genome database ( http://me.lzu.edu.cn/yak ), which incorporated genome sequences, predicted genes and associated annotations, non-coding RNA sequences, transposable elements, and single nucleotide variants of yak, as well as three-way whole-genome alignments between human, cattle and yak. However, this database did not include other omics datasets, such as transcriptome, proteome, and DNA methylation. Given the vast and diverse nature of omics data, the traditional database retrieval methods could not fully explore the relationship between different types of datasets [ 13 ]. Thus, an integrated platform of different omics data is crucial to facilitate data integration, interaction, and analysis. An integrated platform can also offer advanced data mining and machine learning algorithms to help researchers discover the complex relationships among yak genomics, transcriptomics, proteomics, and other omics levels, further deepening our understanding of biological processes and diseases in yak.

In this study, the Yak Genome Database ( http://yakgenomics.com/ ) was constructed, which successfully assembled a comprehensive yak fine-scale genome map at the chromosome level, using PacBio sequencing, Illumina sequencing, Bionano assembly, and Hi-C three-dimensional genome scaffolding. Moreover, this platform also integrated transcriptome, proteome, and DNA methylation data of yak, which were not available in Yak Genome Database developed by Hu et al. [ 12 ]. This database provides basic information for yak researches in future, such as molecular breeding, molecular evolution, disease prevention and control.

Construction and content

The Yak Genome Database was deployed in the Ubuntu 20.04 operation system using the AKKA 2.13 (web server), MySQL 8.0.30 (database server), Scala 2.13.2, and SBT 1.3.9. All data were managed and stored using the MySQL Database Management System. The query function was enforced based on Slick 3.3.2 middleware tier. The Jbrowse 1.16.11 was used to visualize the genome. The website interfaces were designed and implemented using the Bootstrap 4.6.0 and the Play Framework 2.8.7. The software versions and statistical tools used for data analyses and plot preparation have been presented in Xin et al. [ 6 , 7 , 8 , 9 , 10 , 11 ]. The boxplots, and heatmaps were prepared using R 4.2.1. The website has been tested in several popular web browsers, including Firefox, Google Chrome, and Internet Explorer.

Utility and discussion

The yak genome database content.

The multi-omics data in the Yak Genome Database are categorized into two central functional domains: data resources and navigation (Fig.  1 ). The data resources contain four main modules, including genome, transcriptome, proteome, and methylation information. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The navigation page consists of Browser, Jbrowse, Search and Blast functions. Currently, the database supports individual download of images and gene data. In the future, we will add functions such as one-click download of whole genome information.

The homepage of yak genome database

Genome module

The Genome module incorporates the complete genomic DNA sequence of yaks obtained by the third-generation high-throughput sequencing platform (PacBio RSII) [ 14 ]. The yak genome was sequenced at a coverage of 70X, with the second-generation sequencing data used to correct errors. The Bionano assisted assembly technology was used for high-quality assembly, and analysis. Next, a refined physical map of the yak chromosome was generated, providing a more readable and complete genome database than the fragmented information in another Yak Genome Database (BosGru_v2.0) [ 15 ], and contributing a novel genome tool to yak researchers.

When accessing the ‘Genome’ section on the homepage, a new page will display information of genes at all locations, such as Gene ID, Chromosome, Start Position, End Position, Strand, GO (Gene Ontology) terms, Interpro, KEGG (Kyoto Encyclopedia of genes and Genomes), Swissprot, and Trembl in a user-friendly table format (Fig.  2 A). When clicking each gene, users can access detailed information of this gene, including annotations, transcriptional levels, proteome data, Jbrowse page, and nucleotide sequences associated with the gene (Fig.  2 B- 2 D). The ‘Annotation’ tav provides comprehensive gene annotation information, including GO terms, KEGG pathways, and Interpro annotations, which can be further explored by clicking them. The ‘Expression’ tab displays gene expression levels across different cattle breeds and tissues, and users can download the images in various formats by selecting the menu in the upper right corner of the image. ‘Jbrowse’ is used to display integrated information from annotated genomic datasets, while ‘Seqs’ provides the coding sequence (CDS) and protein sequence on the selected gene.

Features of the genome module. ( A ) Genome browse. ( B ) Basic information and annotation of a gene. ( C ) Gene expression. ( D ) Gene Jbrowse and sequences

Transcriptome module

Previously, comparative transcriptome sequencing was performed on lung, gluteal muscle, and mammary gland tissues of low-altitude cattle (Sanjiang and Holstein cattle), Tibetan cattle (living at a moderate altitude), and yaks (living at a high altitude). In addition, these tissues of yaks at different ages (6, 30, 60, and 90 months) were also subjected to transcriptome sequencing. These analyses identified the functional genes involved in the major biochemical, metabolic, and signal transduction pathways involved in yak development and high-altitude adaptation [ 10 , 11 ]. These data are included in the transcriptome module on the website, providing a valuable transcriptome database for specific tissue biomarkers, molecular research, and breeding of yaks. After clicking the “Transcriptome” button, users can select the strain in the ‘Sample’ dialog box, enter the gene ID in the ‘Gene ID’ dialog box, and then click ‘Search’ (Fig.  3 A), and then the website will return the transcriptional levels of the selected genes in selected samples in the forms of data table, Boxplot, Lineplot, and Heatmap (Fig.  3 B and D).

Features of the transcriptome module. ( A ) Transcriptome browse. ( B ) Box plot, ( C ) Line plot and ( D ) Heatmap of gene expression

Proteome module

Using the liquid chromatography-mass spectrometry (LC-MS) method, proteomic analyses were conducted for four specific tissues from four different species (yak, Tibetan cattle, Sanjiang cattle, and Holstein cattle) [ 7 , 8 , 9 ]. All the animals were female and 60 months of age. The proteome module provides two input dialog boxes. Users can select two samples and then click the “search” button. Next, the website will return the comparison results of the expression levels of all genes in the two selected samples, including log2(fold change) and statistical parameters (Fig.  4 ).

Browse of the proteome module

Methylation module

DNA methylation is a critical epigenetic modification that occurs in both animals and plants, playing pivotal roles in chromosome structure, gene expression and regulation [ 16 ]. The establishment of a comprehensive DNA methylation database for yak can significantly advance the comprehension of cellular gene expression and regulation, and provide deeper insights into the spatiotemporal specificity of DNA methylation across various developmental stages and organs [ 17 ]. The DNA methylation database of yak presents single-base methylation maps and tissue-specific methylation maps. The single-base methylation maps include: 1) DNA methylation levels at the single-base resolution, 2) DNA methylation levels specific to different base types, 3) DNA methylation levels specific to different gene structures, 4) DNA methylation levels in repetitive sequences, and 5) DNA methylation levels in non-coding sequences and regulatory regions. The tissue-specific methylation maps involve three tissues: mammary gland, lung, and muscle [ 6 ]. On the website, users can select ‘Sample’ and ‘Chromosome’ in the Methylation module, set the ‘Start Position’ and ‘End Position,’ and finally click ‘Search’ to obtain the corresponding DNA methylation results on the selected sequences (Fig.  5 ).

Features of the methylation module. ( B ) Box plot, ( C ) Line plot and ( D ) Heatmap of methylated gene expression

‘Browse’ allows users to read the yak genome directly. ‘JBrowse’ is a next-generation genome browser built with JavaScript and HTML5. The Jbrowse of Yak Genome Database includes tracks describing gene, gene sequence, mRNAs, structure, and other gene-related features, and provides a graphical display of annotations on the yak genome (Fig.  6 ). Users can browse gene models on chromosomes and unanchored contigs. For example, if user set the genomic region from 4,454,001 bp to 5,878,000 bp on Chr1 for browsing, all genes in this region will appear in order (Fig.  6 A). When clicking on ‘BmuPB021145’, an extra layer will appear with the detailed information, such as mRNAs, CDS and other features (Fig.  6 B). For more operational details, users can click the ‘Help’ button, which provides comprehensive instructions and guidance.

Regional view of the genome using Jbrowse. ( A ) A graphic view of the region 4,454,001 bp to 5,878,000 bp on Chr1. ( B ) The interface after clicking on ‘BmuPB021145’.

The ‘Search’ tab supplies users with two methods (search by gene ID or range) for genome searching. When users click on ‘Blast’, three options ‘Blastn Gene’, ‘Blastn Genome’ and ‘Blastp’ will display. Users can select the Blast type and enter a DNA or protein sequence, and set the parameters of ‘Evalue’, ‘Word size’ and ‘Max target seqs’. After clicking the ‘Search’ button, the nucleotide or protein sequence complying the search conditions will display and could be downloaded by the users.

Additional tools

The Yak Genome Database also provides users with several convenient online tools, including Primer designer, GO and KEGG enrichment. The ‘Primer designer’ tool offers primer design function to amplify a selected sequence. The ‘GO enrichment’ and ‘KEGG enrichment’ tools facilitate the users to obtain the GO and KEGG enrichment results of a set of genes.

Maintenance of the yak genome database in future

To ensure continuous operation of the Yak Genome Database, we would assign an administrator to manage the website regularly. We would keep omics studies on yak in future, and all the omics data we obtained would be uploaded to this database. In addition, we would keep cooperations with other investigators and find more cooperators who work on yak. Next, all the progresses on yak omics would also be encouraged to supplement in this database.

Conclusions

The Yak Genome Database is a comprehensive platform of genomic physical map, which integrates genome, transcriptome, proteome, and DNA methylation data. Information in the database can be downloaded, and shared through the Internet. Users who want to upload their own data can contact the administrator of the website. By providing timely updates on yak research progress, the Yak Genome Database enables efficient and interactive sharing of existing scientific data among researchers worldwide who are interested in yak, cattle, livestock, ruminant animals, and even medical research. Comparative analysis of multidimensional data from key yak tissues aims to uncover the mechanisms underlying high-altitude adaptation, disease resistance, cold tolerance, and starvation resistance of large animals in the plateau. These findings contribute to molecular breeding of livestock animals and the understanding of human responses to harsh environments.

Data availability

The datasets generated and analyzed in the current study are freely available on the Download page of Yak database with the web link: http://yakgenomics.com/ .

Abbreviations

Coding Sequence

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

National Center for Biotechnology Information

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This work was supported by the Program of Provincial Department of Finance of the Tibet Autonomous Region, the Major Special Projects of Tibet Autonomous Region (XZ202101ZD0002N-01), the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0501), and the program National Beef Cattle and Yak Industrial Technology System (CARS-37).

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Hui Jiang, Zhi-Xin Chai and Xiao-Ying Chen contributed equally to this work.

Authors and Affiliations

State Key Laboratory of Hulless Barley and Yak Germplasm Resources and Genetic Improvement, 850000, Lhasa, Tibet, China

Hui Jiang, Xiao-Ying Chen, Cheng-Fu Zhang, Yong Zhu, Qiu-Mei Ji & Jin-Wei Xin

Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences, 850000, Lhasa, Tibet, China

Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, 610041, Chengdu, Sichuan, China

Zhi-Xin Chai

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HJ and ZXC performed the analysis. XYC conducted the database. CFZ and YZ wrote the paper. QMJ supervised the database and JWX revised the manuscript. All authors have approved the final article.

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Correspondence to Qiu-Mei Ji or Jin-Wei Xin .

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All procedures and experiments involving animals followed the guidelines for the Care and Use of Laboratory Animals. The Ethics Committee at Institute of Animal Science and Veterinary, Tibet Academy of Agricultural and Animal Husbandry Sciences (Permit Number: 2015 − 216) approved this study.

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Jiang, H., Chai, ZX., Chen, XY. et al. Yak genome database: a multi-omics analysis platform. BMC Genomics 25 , 346 (2024). https://doi.org/10.1186/s12864-024-10274-6

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Received : 30 October 2023

Accepted : 31 March 2024

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DOI : https://doi.org/10.1186/s12864-024-10274-6

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