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PubMed2026

Spatial multiomics in biomedical research: advances beyond transcriptomics.

Coordinated changes in gene expression, epigenetic regulation, protein and metabolic activities together drive disease progression and determine clinical outcomes. While spatially resolved transcriptomics has been widely adopted across biomedical fields, it offers an incomplete picture limited to transcriptomic levels. Here, we survey the latest developments in spatial multiomics technologies, with particular emphasis on platforms that extend beyond conventional transcriptomics and profile genomics, epigenomics, proteomics, or metabolomics within intact tissues. These approaches are rapidly becoming commercialized, and here we highlight major technical breakthroughs, enhanced sample compatibility, emerging applications, and computational tools for data analysis. This Review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of spatial multiomics methods in biomedical research.

Humans↗

PubMed2002

Plant functional genomics.

Functional genome analysis of plants has entered the high-throughput stage. The complete genome information from key species such as Arabidopsis thaliana and rice is now available and will further boost the application of a range of new technologies to functional plant gene analysis. To broadly assign functions to unknown genes, different fast and multiparallel approaches are currently used and developed. These new technologies are based on known methods but are adapted and improved to accommodate for comprehensive, large-scale gene analysis, i.e. such techniques are novel in the sense that their design allows researchers to analyse many genes at the same time and at an unprecedented pace. Such methods allow analysis of the different constituents of the cell that help to deduce gene function, namely the transcripts, proteins and metabolites. Similarly the phenotypic variations of entire mutant collections can now be analysed in a much faster and more efficient way than before. The different methodologies have developed to form their own fields within the functional genomics technological platform and are termed transcriptomics, proteomics, metabolomics and phenomics. Gene function, however, cannot solely be inferred by using only one such approach. Rather, it is only by bringing together all the information collected by different functional genomic tools that one will be able to unequivocally assign functions to unknown plant genes. This review focuses on current technical developments and their impact on the field of plant functional genomics. The lower plant Physcomitrella is introduced as a new model system for gene function analysis, owing to its high rate of homologous recombination.

Databases, Factual↗

PubMed2026

Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits.

Gene regulatory networks encode the fundamental logic of cellular functions, but systematic network mapping remains challenging, especially in cell states relevant to human biology and disease. Here, we perturbed all expressed genes across 22 million primary human CD4+ T cells from four donors and developed a probe-based perturb-seq platform to measure the transcriptome effects in cells at rest and after stimulation. These data allowed us to map genes regulating immune pathways, including previously uncharacterized regulators of cytokine production. Importantly, active regulators and the gene programs they control changed dramatically across stimulation conditions. Perturbation signatures enabled us to model T cell states observed in population-scale transcriptomic atlases, nominating regulators of T cell polarization and of age-related phenotypes. Finally, we leveraged perturb-seq to implicate context-specific gene regulatory pathways in autoimmune disease risk. Our study provides a foundational resource and new approaches to decode T cell function and human immune traits.

CD4(+) T cell polarization↗

PubMed2026

Hyperprogression Upon Cemiplimab Alone or With Short Course Chemotherapy in PD-L1 ≥ 50% Non-small Cell Lung Cancer: A Biomarker Guided Multicenter International Phase 2 Trial-HYPERBOLIC Study.

BACKGROUND: Immune checkpoint inhibitor (ICI) monotherapy is the standard first-line treatment for advanced non-small cell lung cancer (NSCLC) with PD-L1 ≥ 50%; however, up to 30% of patients experience early progression or death, including cases of hyperprogressive disease (HPD). High baseline levels (≥ 30.5%) of circulating CD10- low-density neutrophils (LDNs) have been associated with increased HPD occurrence. Emerging evidence suggests that combining ICI with platinum-based chemotherapy (PCT) may mitigate the risk of HPD. Currently, no prospective studies have addressed HPD prevention in this context. PATIENTS AND METHODS: HYPERBOLIC (NCT07274384) is a phase 2, randomized, open-label, multicenter, international trial evaluating whether adding 3 cycles of PCT to first-line cemiplimab reduces HPD rate in stage IV NSCLC with PD-L1 ≥ 50% and CD10- LDNs (identified by flow cytometry as CD15⁺CD11b⁺ within the PBMC fraction, with immature cells defined by loss of CD10) ≥ 30.5%. Seventy-four patients will be randomized (1:1 ratio) to receive cemiplimab alone or cemiplimab plus 3 PCT cycles, followed by cemiplimab maintenance. Randomization will be stratified by Lung Immune Prognostic Index. The first computed tomography scan at week 7 after treatment start will assess HPD occurrence, defined as RECIST v 1.1. disease progression with a delta tumor growth rate (ΔTGR) ≥ 50% and/or TGR ratio ≥ 2. The primary endpoint will be the combined rate of HPD and early death (death within 12 weeks with no radiological evaluation). Secondary endpoints will be HPD rate according to alternative definitions, overall survival, progression free survival, objective response rate, and safety. An extensive translational research platform will include spatial transcriptomics of tumor tissue, single-cell RNA sequencing of PBMCs, circulating-free DNA and plasma factors profiling, and saliva/stool microbiome genomics and metabolomics, to longitudinally explore tumor-host dynamic interactions during treatment. CONCLUSION: to our knowledge, HYPERBOLIC is the first prospective, biomarker-driven trial investigating early treatment escalation based on HPD risk in PD-L1-high NSCLC.

CD10↗

PubMed2026

PRDX1 as a novel urinary biomarker for bladder cancer: Development of an integrated fiber optic sensing platform.

In this study, integrated proteomic and transcriptomic analyses identified peroxiredoxin 1 (PRDX1) as a novel urinary biomarker for bladder cancer (BC). PRDX1 was significantly upregulated in BC tissues and was associated with poorer overall survival. In vitro experiments further demonstrated that PRDX1 promotes malignant phenotypes of BC cells, including proliferation, migration, and invasion. Silencing PRDX1 in BC cells significantly reduced the invasiveness and proliferation ability.To address the clinical need for rapid and non-invasive detection, we developed an innovative optical fiber biosensor based on surface plasmon resonance (SPR) technology for the quantitative detection of urinary PRDX1. The biosensor exhibited excellent analytical performance, including high sensitivity (limit of detection: 0.06 ng/mL), a wide linear range (0-25 ng/mL), rapid response (∼14 s), as well as good stability and selectivity. In clinical validation involving 97 BC patients and 30 healthy controls, the biosensor demonstrated outstanding diagnostic performance, with an area under the receiver operating characteristic curve (AUC) of 0.91 and an overall diagnostic accuracy of 86.6%, outperforming conventional enzyme-linked immunosorbent assay (ELISA). Collectively, this study not only identifies PRDX1 as a promising biomarker for non-invasive diagnosis and prognostic evaluation of BC, but also establishes an efficient SPR-based optical fiber sensing platform, providing new insights into both clinical detection and the functional role of PRDX1 in BC progression.

Humans↗

PubMed2026

High-throughput single-cell proteomics and transcriptomics from same cells with a nanoliter-scale, spin-transfer approach.

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

Single-Cell Analysis↗

PubMed2025

Analysis of Leishbuviridae from Trypanosomatids.

Over the last decade, considerable progress has been made in unraveling RNA virus diversity. This has contributed to our understanding of the evolution of these viruses, which include emerging zoonotic human pathogens. Current success has been greatly facilitated by the development of next-generation sequencing platforms instrumental for meta-transcriptomic studies. However, due to the rapid evolution of RNA viruses, there are numerous "blind spots" waiting to be explored; one of those is the RNA virome of unicellular eukaryotes. Here, we present the pipeline, which has been successfully used to characterize various types of RNA viruses, including Leishbuviridae (Bunyaviricetes, Hareavirales) in the parasitic flagellates of the family Trypanosomatidae. The pipeline relies on axenic in vitro cell culture and double-stranded RNA enrichment, followed by direct RNA-sequencing. A detailed procedure description starting from the initial total RNA preparation to the final assembly of the viral segments is provided.

High-Throughput Nucleotide Sequencing↗

PubMed2004

RAD and the RAD Study-Annotator: an approach to collection, organization and exchange of all relevant information for high-throughput gene expression studies.

MOTIVATION: Gene expression array technology has become increasingly widespread among researchers who recognize its numerous promises. At the same time, bench biologists and bioinformaticians have come to appreciate increasingly the importance of establishing a collaborative dialog from the onset of a study and of collecting and exchanging detailed information on the many experimental and computational procedures using a structured mechanism. This is crucial for adequate analyses of this kind of data. RESULTS: The RNA Abundance Database (RAD; http://www.cbil.upenn.edu/RAD) provides a comprehensive MIAME-supportive infrastructure for gene expression data management and makes extensive use of ontologies. Specific details on protocols, biomaterials, study designs, etc. are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format (e.g. ArrayExpress). RAD is part of a more general Genomics Unified Schema (http://www.gusdb.org), which includes a richly annotated gene index (http://www.allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. This infrastructure enables a large variety of queries that incorporate visualization and analysis tools and have been tailored to serve the specific needs of projects focusing on particular organisms or biological systems.

Abstracting and Indexing↗

PubMed2026

Microplastics and nanoplastics-related genes signature predicts prognosis in pancreatic ductal adenocarcinoma and functional validation of interleukin 1 alpha.

BACKGROUND: Microplastics and nanoplastics (MNPs), as emerging environmental pollutants, have garnered significant attention from the global scientific community due to their potential threats to human health, particularly their association with the occurrence and development of cancer. The goal of our study is to create a predictive marker for pancreatic ductal adenocarcinoma (PAAD) based on MNPs-related genes, with the purposes of predicting survival outcomes and assessing the tumor immune microenvironment. METHODS: Using multi-cohort data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC), we assessed the association between MNPs and PAAD prognosis through the Xiantao Academic (https://www.xiantao.love/). The development of a prognostic signature was followed by an assessment of its significance through the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and decision curve analysis (DCA). The validity of the risk model was confirmed through the ICGC and GSE71729 cohorts. The model was then assessed for levels of tumor immune infiltration. To explore MNPs-related genes expression characteristics within immune cells in PAAD, we performed single-cell RNA sequencing and spatial transcriptomics analysis through the Sparkle Platform (https://grswsci.top/). Finally, in vitro experiments were conducted to investigate the biological function of interleukin 1 alpha (IL1A). RESULTS: A four-gene signature comprising XDH, IL1A, KIF20A, and ASPM, based on MNPs, was developed to stratify PAAD patients into two distinct risk groups. The high-risk group showed a significantly poorer prognosis. A similar trend was verified in the external cohorts ICGC and GSE71729. The signature risk score affected immune cell infiltration in the PAAD microenvironment. The infiltration of B cells, CD8+ T cells, cytotoxic cells, immature dendritic cells (iDCs), mast cells, plasmacytoid dendritic cell (pDC), T cells, Tem cells, T follicular helper (TFH) cells, and T helper 17 (Th17) cells had a positive correlation with the low-risk group. In contrast, high-risk patients tended to have increased number of T helper (Th2) cells and higher expression of SIGLEC15, CD274, IGSF8. Knockdown of IL1A in PAAD cells inhibited their tumor proliferation ability in vitro. CONCLUSIONS: Using MNPs-related genes, we built a prognostic model for PAAD, revealing that patients with high-risk scores are likely to have a worse prognosis. This model is designed to develop personalized treatment strategies tailored to the specific needs of each patient, thereby improving clinical outcomes for PAAD patients. Furthermore, IL1A could be a promising therapeutic candidate for PAAD.

Microplastics↗

PubMed2005

Benchmarking the CATMA microarray. A novel tool for Arabidopsis transcriptome analysis.

Transcript profiling is crucial to study biological systems, and various platforms have been implemented to survey mRNAs at the genome scale. We have assessed the performance of the CATMA microarray designed for Arabidopsis (Arabidopsis thaliana) transcriptome analysis and compared it with the Agilent and Affymetrix commercial platforms. The CATMA array consists of gene-specific sequence tags of 150 to 500 bp, the Agilent (Arabidopsis 2) array of 60mer oligonucleotides, and the Affymetrix gene chip (ATH1) of 25mer oligonucleotide sets. We have matched each probe repertoire with the Arabidopsis genome annotation (The Institute for Genomic Research release 5.0) and determined the correspondence between them. Array performance was analyzed by hybridization with labeled targets derived from eight RNA samples made of shoot total RNA spiked with a calibrated series of 14 control transcripts. CATMA arrays showed the largest dynamic range extending over three to four logs. Agilent and Affymetrix arrays displayed a narrower range, presumably because signal saturation occurred for transcripts at concentrations beyond 1,000 copies per cell. Sensitivity was comparable for all three platforms. For Affymetrix GeneChip data, the RMA software package outperformed Microarray Suite 5.0 for all investigated criteria, confirming that the information provided by the mismatch oligonucleotides has no added value. In addition, taking advantage of replicates in our dataset, we conducted a robust statistical analysis of the platform propensity to yield false positive and false negative differentially expressed genes, and all gave satisfactory results. The results establish the CATMA array as a mature alternative to the Affymetrix and Agilent platforms.

Arabidopsis↗

PubMed2025

RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study.

BACKGROUND: Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. RESULTS: Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings. CONCLUSION: We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

Humans↗

PubMed2004

Transcriptional signature of an adult brain tumor in Drosophila.

BACKGROUND: Mutations and gene expression alterations in brain tumors have been extensively investigated, however the causes of brain tumorigenesis are largely unknown. Animal models are necessary to correlate altered transcriptional activity and tumor phenotype and to better understand how these alterations cause malignant growth. In order to gain insights into the in vivo transcriptional activity associated with a brain tumor, we carried out genome-wide microarray expression analyses of an adult brain tumor in Drosophila caused by homozygous mutation in the tumor suppressor gene brain tumor (brat). RESULTS: Two independent genome-wide gene expression studies using two different oligonucleotide microarray platforms were used to compare the transcriptome of adult wildtype flies with mutants displaying the adult bratk06028 mutant brain tumor. Cross-validation and stringent statistical criteria identified a core transcriptional signature of brat(k06028) neoplastic tissue. We find significant expression level changes for 321 annotated genes associated with the adult neoplastic brat(k06028) tissue indicating elevated and aberrant metabolic and cell cycle activity, upregulation of the basal transcriptional machinery, as well as elevated and aberrant activity of ribosome synthesis and translation control. One fifth of these genes show homology to known mammalian genes involved in cancer formation. CONCLUSION: Our results identify for the first time the genome-wide transcriptional alterations associated with an adult brain tumor in Drosophila and reveal insights into the possible mechanisms of tumor formation caused by homozygous mutation of the translational repressor brat.

Animals↗

PubMed2006

Transcriptome analysis of a cnidarian-dinoflagellate mutualism reveals complex modulation of host gene expression.

BACKGROUND: Cnidarian-dinoflagellate intracellular symbioses are one of the most important mutualisms in the marine environment. They form the trophic and structural foundation of coral reef ecosystems, and have played a key role in the evolutionary radiation and biodiversity of cnidarian species. Despite the prevalence of these symbioses, we still know very little about the molecular modulators that initiate, regulate, and maintain the interaction between these two different biological entities. In this study, we conducted a comparative host anemone transcriptome analysis using a cDNA microarray platform to identify genes involved in cnidarian-algal symbiosis. RESULTS: We detected statistically significant differences in host gene expression profiles between sea anemones (Anthopleura elegantissima) in a symbiotic and non-symbiotic state. The group of genes, whose expression is altered, is diverse, suggesting that the molecular regulation of the symbiosis is governed by changes in multiple cellular processes. In the context of cnidarian-dinoflagellate symbioses, we discuss pivotal host gene expression changes involved in lipid metabolism, cell adhesion, cell proliferation, apoptosis, and oxidative stress. CONCLUSION: Our data do not support the existence of symbiosis-specific genes involved in controlling and regulating the symbiosis. Instead, it appears that the symbiosis is maintained by altering expression of existing genes involved in vital cellular processes. Specifically, the finding of key genes involved in cell cycle progression and apoptosis have led us to hypothesize that a suppression of apoptosis, together with a deregulation of the host cell cycle, create a platform that might be necessary for symbiont and/or symbiont-containing host cell survival. This first comprehensive molecular examination of the cnidarian-dinoflagellate associations provides critical insights into the maintenance and regulation of the symbiosis.

Animals↗

PubMed2006

Microarray RNA transcriptional profiling: part I. Platforms, experimental design and standardization.

This review summarizes, in a balanced and comprehensive manner, the various components of microarrays and their types, substrate architecture, platforms for microarray probe implementation, standardizations and confounders. The review is intended to familiarize the beginner with the principles of experimental design and the selection of an appropriate microarray platform. This parallel technology has revolutionized transcriptomic approaches to data profiling and has a major role in the identification of expressed genes, classification and diagnosis studies. The technology is still evolving and guidelines for standardization and reporting have been developed and are being improved.

Equipment Design↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2004

Integrated transcriptome and proteome data: the challenges ahead.

The recent availability of platform technologies for high throughput proteome analysis has led to the emergence of integrated messenger RNA and protein expression data. The Pearson correlation coefficients for these data range from 0.46 to 0.76. In these integrated studies, serial analyses of gene expression and DNA microarrays have been used to quantify the transcriptome, while proteome analysis has been based on two-dimensional gel electrophoresis, isotope coded affinity tags and multidimensional protein identification technology. This paper provides a comprehensive review of the analytical techniques used in these studies and explores the extent to which the choice of experimental methodology can bias the correlation or the ability to detect proteins.

Animals↗

PubMed2006

Application of transcriptional and biological network analyses in mouse germ-cell transcriptomes.

Serial analysis of gene expression (SAGE) provides a global analysis platform for profiling mRNA populations present in cells of interest without the constraint of gene selection and the ambiguous nature of data obtained. However, most of the reports on SAGE and germ cell development are limited to descriptive analyses. Here, we report a series of bioinformatic analyses using recently published SAGE data on the transcriptome of mouse type A spermatogonia (Spga), pachytene spermatocytes (Spcy), and round spermatids (Sptd). Tags with a total count of > or =20 in three SAGE libraries were examined. Our aim was to identify and discover potential transcriptional regulators and pathways involved at different stages of spermatogenesis. Unsupervised hierarchical clustering based on tag expression and Gene Ontology analysis were applied to identify genes and biological processes overrepresented at a particular stage of development. The 5' cis-regulatory elements were examined for common regulators in different functional clusters. Potential biological networks were also constructed to reveal the link between the gene candidates. Biological pathways related to the three germ cell stages were constructed. A number of known transcription regulators in spermatogenesis, including NF-kappaB, SP1, AP-1, and EGR, were identified. Novel promoter elements such as the E box in Spga-specific genes, GATA in Spcy-specific genes, and GKLF in Sptd-specific genes were also observed. Taken together, our approach is reliable and provides a foundation for the generation of novel biological hypotheses for studying spermatogenesis.

Animals↗

PubMed2004

Dynamic transcriptome of mice.

Life science in the 21st century is developing rapidly through the structural analysis of biomolecules, the completion of the human genome sequence and the analysis of transcriptomes. The mouse transcriptome has been comprehensively analyzed using a gene discovery approach to collect full-length cDNA (FL-cDNA) clones. The framework of the transcriptome was then mapped out by an international Functional ANnoTation Of Mouse cDNA (FANTOM) effort, and a significant new population of noncoding transcripts was discovered. The geographical analogy of a second "RNA continent," separate from the "continent" of expressed proteins, aids the visualization of this concept. An unexpected number of variations was discovered in the mouse transcriptome. The animal transcriptome has evolved to produce several transcripts and proteins from a single "transcriptional unit". Transcriptome analysis has given rise to the FL-cDNA database and to the 60 770 FANTOM FL-cDNA clone set, and the DNABook was developed as an easier way to distribute these clones. In conjunction with genome sequence databases, transcriptome databases and clone banks will be platforms for developing advanced databases of gene function (e.g. the Genome Function Database). This will enable life science to make rapid progress towards understanding life as a system of molecules.

Animals↗