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PubMed2025

An elegant co-transformation strategy for recalcitrant wheat using morphogenic regulators.

Common wheat (Triticum aestivum L.) is a vital global crop, but many elite cultivars remain recalcitrant to genetic transformation, hindering functional genomics and crop improvement. Here, we developed an efficient co-transformation strategy for recalcitrant wheat varieties (e.g., Aikang58 and Xinong979) using the morphogenic gene mTaGRF4-TaGIF1. This approach entails mixing Agrobacterium tumefaciens cultures carrying two separate vectors: a standard gene-of-interest (GOI) vector (containing a selectable marker) and a gene-of-co-transformation vector (GOC, expressing mTaGRF4-TaGIF1 without a selectable marker). Co-transformation enhanced regeneration efficiency to ~37.38% in AK58, a marked improvement over conventional methods, enabling consistent recovery of transgenic plants. Among regenerants, ~63.25% carried both GOI and GOC (GOI&GOC), while ~11.92% contained only the GOI. Only-GOI plants could also be obtained through progeny segregation from GOI&GOC lines. We successfully generated GUS- and RUBY-expressing transgenic lines, as well as CRISPR-Cas9-edited mutants targeting Q and Ph1 genes, confirming the method's efficacy for both gain-of-function and genome editing application. Furthermore, the strategy was successfully extended to another recalcitrant variety Xinong979, demonstrating its potential for broad applicability. Unlike existing methods dependent on complex excision systems or tissue-specific promoters, our co-transformation methodology significantly simplifies both vector design and procedural workflow while maintaining high efficiency. Collectively, these findings establish a technically advanced yet operationally simplified transformation platform that addresses the long-standing challenge of genetic transformation in recalcitrant wheat varieties, providing researchers with a powerful tool for functional genomics studies and accelerating precision breeding programs in elite wheat cultivars.

Triticum↗
PubMed2006

ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements.

Genomic sequence signals - such as base composition, presence of particular motifs, or evolutionary constraint - have been used effectively to identify functional elements. However, approaches based only on specific signals known to correlate with function can be quite limiting. When training data are available, application of computational learning algorithms to multispecies alignments has the potential to capture broader and more informative sequence and evolutionary patterns that better characterize a class of elements. However, effective exploitation of patterns in multispecies alignments is impeded by the vast number of possible alignment columns and by a limited understanding of which particular strings of columns may characterize a given class. We have developed a computational method, called ESPERR (evolutionary and sequence pattern extraction through reduced representations), which uses training examples to learn encodings of multispecies alignments into reduced forms tailored for the prediction of chosen classes of functional elements. ESPERR produces a greatly improved Regulatory Potential score, which can discriminate regulatory regions from neutral sites with excellent accuracy ( approximately 94%). This score captures strong signals (GC content and conservation), as well as subtler signals (with small contributions from many different alignment patterns) that characterize the regulatory elements in our training set. ESPERR is also effective for predicting other classes of functional elements, as we show for DNaseI hypersensitive sites and highly conserved regions with developmental enhancer activity. Our software, training data, and genome-wide predictions are available from our Web site (http://www.bx.psu.edu/projects/esperr).

Algorithms↗
PubMed2026

Unraveling the genomic blueprint of the Indian black soldier fly: From genome assembly to evolutionary insights.

The black soldier fly (BSF) (Hermetia illucens) has been renowned for its sustainable bioconversion capabilities, resulting in smart protein production with wide applications in animal feed, bioenergy, and biofertilizer. However, the genetic mechanisms underlying efficient bioconversion and productivity remain poorly understood. To advance strain-specific applications and strengthen genetic resource availability, we present the whole genome sequencing (WGS) data for an Indian isolate of black soldier fly. The assembled genome was 1.46 Gb with a scaffold N50 of 172.7 Mb, and a GC content of 42.6%. Furthermore, 64.17% of genomic sequences were masked as repeated, and 14,317 protein-coding sequences were identified. Variant analysis against the reference genome identified 34.44 million variants (∼33.25 million SNPs and ∼ 1.18 million INDELs), with the majority (99.3%) classified as MODIFIER, 0.54% as LOW impact, 0.14% as MODERATE, and only 0.003% as HIGH impact. Comparative genomic analysis with other related species revealed expansions of gene families in BSF associated with Immune effector (Antimicrobial peptides (AMPs), Lysozymes, and Peptidoglycan Recognition Protein (PGRP) and Detoxification (cytochrome P450 enzymes). Notably, AMPs in the Indian isolate showed enhanced copy number variation in defensin (27) and PGRP (40) compared to reference BSF, suggesting potential regional adaptations to pathogen exposure. Collectively, this genomic data provides an improved resource for evolutionary studies, functional genomics, and targeted genetic improvement of BSF for sustainable bioconversion applications.

Comparative genomics↗
PubMed2026

Integrating genomics, multi-omics, CRISPR and speed breeding for stress-resilient vegetable legume improvement.

Vegetable legumes are nutritionally and ecologically important crops. However, their genetic improvement has not kept pace with the increasing challenges posed by climate change due to the polygenic nature of stress tolerance, narrow genetic diversity, and the persistent gap between molecular discoveries and field-level cultivar development. Although recent reviews have examined individual genomic tools or specific stress responses, a comprehensive synthesis integrating genomics-assisted breeding, multi-omics technologies, genome editing, and speed breeding within a unified crop improvement framework has been lacking. This review addresses that gap by critically evaluating how these complementary approaches can accelerate the development of stress-resilient vegetable legumes, including pea, common bean, cowpea, faba bean, cluster bean, yard-long bean, and hyacinth bean. This review synthesizes advances in QTL mapping, genome-wide association studies, transcriptomics, metabolomics, and CRISPR-based functional genomics that have identified key regulators and pathways underlying resistance to major biotic and abiotic stresses. Rather than considering these technologies independently, the review emphasizes their convergence into a systems-level breeding framework integrating genomic discovery, functional validation, predictive breeding, and accelerated generation advancement to improve breeding efficiency. Speed breeding, enabling up to seven to eight generations annually under optimized controlled-environment experimental conditions in cowpea, is discussed as a complementary strategy with genomic selection and genome editing. The review further identifies major translational bottlenecks, including transformation recalcitrance, limited genomic resources for underutilized vegetable legumes, inadequate multi-environment validation, and fragmented omics integration, and presents an integrated systems-breeding framework to bridge the gap between gene discovery and cultivar development.

Fabaceae↗
PubMed1991

The nuclear matrix: a heuristic model for investigating genomic organization and function in the cell nucleus.

Despite significant advances in deciphering the molecular events underlying genomic function, our understanding of these integrated processes inside the functioning cell nucleus has, until recently, met with only very limited success. A major conundrum has been the "layers of complexity" characteristic of all cell structure and function. To understand how the cell nucleus functions, we must also understand how the cell nucleus is put together and functions as a whole. The value of this neo-holistic approach is demonstrated by the enormous progress made in recent years in identifying a wide variety of nuclear functions associated with the nuclear matrix. In this article we summarize basic properties of in situ nuclear structure, isolated nuclear matrix systems, nuclear matrix-associated functions, and DNA replication in particular. Emphasis is placed on identifying current problems and directions of research in this field and illustrating the intrinsic heuristic value of this global approach to genomic organization and function.

Amino Acid Sequence↗
PubMed2026

The Saccharomyces Genome Database-a history of ideas and accomplishments, 1994-2026.

The Saccharomyces Genome Database (SGD) is one of the longest-running and most consequential biological databases in the world. Founded in the early 1990s at Stanford University under the visionary leadership of David Botstein and developed under the long-term technical direction of J. Michael Cherry, SGD has served for more than three decades not only as the authoritative knowledge center for the budding yeast Saccharomyces cerevisiae, but also as the source for much of the fundamentals of eukaryotic biology. This history traces the arc of a remarkable intellectual and scientific project: beginning with the challenge of building the very first integrated eukaryotic genome database and evolving across 30 years into a global knowledge hub for genetics, functional genomics, and human disease research. The history is organized chronologically, with each section highlighting the central ideas, technical developments, and concrete accomplishments of that period.

Databases, Genetic↗
PubMed2004

Transposable elements for transgenesis and insertional mutagenesis in vertebrates: a contemporary review of experimental strategies.

Functional genomic analyses in vertebrate model systems, including fish, frogs, and mice, have greatly contributed to our understanding of embryonic development and human disease. However, new molecular tools and strategies are needed to meet the increasing demands of linking sequence information to gene function. Transposable elements (TEs) are very efficient at integrating into DNA, and are therefore useful vectors for transferring new genetic material into genomes. In particular, members of the Tc1/mariner superfamily of elements are able to transpose in species other than their hosts, and are therefore emerging tools for functional genomics in several organisms. This chapter describes strategies of using retrovirus vectors and DNA-based TEs for transgenesis and insertional mutagenesis in vertebrates, with special emphasis on the Sleeping Beauty (SB) element, a reconstructed Tc1/mariner-like transposon from fish. SB jumps efficiently in cells of diverse vertebrate species in culture, as well as in somatic and germline tissues of the mouse in vivo. Simple structure and easy laboratory handling of transposon vectors are coupled with efficient and stable transgene integration and persistent, long-term transgene expression by transposon-mediated gene transfer. These features all contribute to the usefulness of TEs as tools for vertebrate functional genomics, as well as for animal biotechnology and human gene therapy.

Animals↗
PubMed2006

Drosophila melanogaster as a model for studying protein-encoding genes that are resident in constitutive heterochromatin.

The organization of chromosomes into euchromatin and heterochromatin is one of the most enigmatic aspects of genome evolution. For a long time, heterochromatin was considered to be a genomic wasteland, incompatible with gene expression. However, recent studies--primarily conducted in Drosophila melanogaster--have shown that this peculiar genomic component performs important cellular functions and carries essential genes. New research on the molecular organization, function and evolution of heterochromatin has been facilitated by the sequencing and annotation of heterochromatic DNA. About 450 predicted genes have been identified in the heterochromatin of D. melanogaster, indicating that the number of active genes is higher than had been suggested by genetic analysis. Most of the essential genes are still unknown at the molecular level, and a detailed functional analysis of the predicted genes is difficult owing to the lack of mutant alleles. Far from being a peculiarity of Drosophila, heterochromatic genes have also been found in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Oryza sativa and Arabidopsis thaliana, as well as in humans. The presence of expressed genes in heterochromatin seems paradoxical because they appear to function in an environment that has been considered incompatible with gene expression. In the future, genetic, functional genomic and proteomic analyses will offer powerful approaches with which to explore the functions of heterochromatic genes and to elucidate the mechanisms driving their expression.

Animals↗
PubMed2001

Modeling genetic networks and their evolution: a complex dynamical systems perspective.

After finishing the sequence of the human genome, a functional understanding of genome dynamics is the next major step on the agenda of the biosciences. New approaches, such as microarray techniques, and new methods of bioinformatics provide powerful tools aiming in this direction. In the last few years, important parts of genome organization and dynamics in a number of model organisms have been determined. However, an integrated view of gene regulation on a genomic scale is still lacking. Here, genome function is discussed from a complex dynamical systems perspective: which dynamical properties can a large genomic system exhibit in principle, given the local mechanisms governing the small subsystems that we know today? Models of artificial genetic networks are used to explore dynamical principles and possible emergent dynamical phenomena in networks of genetic switches. One observes evolution of robustness and dynamical self-organization in large networks of artificial regulators that are based on the dynamic mechanism of transcriptional regulators as observed in biological gene regulation. Possible biological observables and ways of experimental testing of global phenomena in genome function and dynamics are discussed. Models of artificial genetic networks provide a tool to address questions in genome dynamics and their evolution and allow simulation studies in evolutionary genomics.

Biological Evolution↗
PubMed2002

Protein sequence analysis in silico: application of structure-based bioinformatics to genomic initiatives.

The current pace of high-throughput genome sequencing programs coupled with high-throughput functional genomic screens has provided researchers with a bewildering array of sequence and biological data to contend with. Identification of proteins of interest from a particular biological study requires the application of bioinformatic tools to process and prioritise the data. From a protein function standpoint, transfer of annotation from known proteins to a novel target is currently the only practical way to convert vast quantities of raw sequence data into meaningful information. New bioinformatics tools now provide more sophisticated methods to transfer functional annotation, integrating sequence, family profile and structural search methodology. The importance of these approaches to medical research is increasing as we move to annotate the proteome through functional and structural genomic efforts.

Animals↗
PubMed2003

Identification and characterization of multi-species conserved sequences.

Comparative sequence analysis has become an essential component of studies aiming to elucidate genome function. The increasing availability of genomic sequences from multiple vertebrates is creating the need for computational methods that can detect highly conserved regions in a robust fashion. Towards that end, we are developing approaches for identifying sequences that are conserved across multiple species; we call these "Multi-species Conserved Sequences" (or MCSs). Here we report two strategies for MCS identification, demonstrating their ability to detect virtually all known actively conserved sequences (specifically, coding sequences) but very little neutrally evolving sequence (specifically, ancestral repeats). Importantly, we find that a substantial fraction of the bases within MCSs (approximately 70%) resides within non-coding regions; thus, the majority of sequences conserved across multiple vertebrate species has no known function. Initial characterization of these MCSs has revealed sequences that correspond to clusters of transcription factor-binding sites, non-coding RNA transcripts, and other candidate functional elements. Finally, the ability to detect MCSs represents a valuable metric for assessing the relative contribution of a species' sequence to identifying genomic regions of interest, and our results indicate that the currently available genome sequences are insufficient for the comprehensive identification of MCSs in the human genome.

Animals↗
PubMed2005

What it will take to feed 5.0 billion rice consumers in 2030.

Major advances have occurred in rice production due to adoption of green revolution technology. Between 1966 and 2000, the population of densely populated low income countries grew by 90% but rice production increased by 130% from 257 million tons in 1966 to 600 million tons in 2000. However, the population of rice consuming countries continues to grow and it is estimated that we will have to produce 40 more rice in 2030. This increased demand will have to be met from less land, with less water, less labor and fewer chemicals. To meet the challenge of producing more rice from suitable lands we need rice varieties with higher yield potential and greater yield stability. Various strategies for increasing the rice yield potential being employed include: (1) conventional hybridization and selection procedures, (2) ideotype breeding, (3) hybrid breeding, (4) wide hybridization and (5) genetic engineering. Various conventional and biotechnology approach are being employed to develop durable resistance to diseases and insect and for tolerance to abiotic stresses. The availability of the rice genome sequence will now permit identification of the function of each of 60,000 rice genes through functional genomics. Once the function of a gene is identified, it will be possible to develop new rice varieties by introduction of the gene through traditional breeding in combination with marker aided selection or direct engineering of genes into rice varieties.

Agriculture↗
PubMed2006

Microbial biochemistry, physiology, and biotechnology of hyperthermophilic Thermotoga species.

High-throughput sequencing of microbial genomes has allowed the application of functional genomics methods to species lacking well-developed genetic systems. For the model hyperthermophile Thermotoga maritima, microarrays have been used in comparative genomic hybridization studies to investigate diversity among Thermotoga species. Transcriptional data have assisted in prediction of pathways for carbohydrate utilization, iron-sulfur cluster synthesis and repair, expolysaccharide formation, and quorum sensing. Structural genomics efforts aimed at the T. maritima proteome have yielded hundreds of high-resolution datasets and predicted functions for uncharacterized proteins. The information gained from genomics studies will be particularly useful for developing new biotechnology applications for T. maritima enzymes.

Bacterial Proteins↗
PubMed2006

Approaches to lipid metabolism gene identification and characterization in the postgenomic era.

The availability of genomic resources has already had a tremendous impact on biomedical research. In this review, we describe how whole genome sequence and high-throughput functional genomics projects have facilitated the identification and characterization of important genes in lipid metabolism and disease. We review key approaches and lipid genes identified in the first years of this century and discuss how genomic resources are likely to streamline gene identification and functional characterization in the future.

Animals↗
PubMed2003

Calcium/calmodulin-mediated signal network in plants.

Various extracellular stimuli elicit specific calcium signatures that can be recognized by different calcium sensors. Calmodulin, the predominant calcium receptor, is one of the best-characterized calcium sensors in eukaryotes. In recent years, completion of the Arabidopsis genome project and advances in functional genomics have helped to identify and characterize numerous calmodulin-binding proteins in plants. There are some similarities in Ca(2+)/calmodulin-mediated signaling in plants and animals. However, plants possess multiple calmodulin genes and many calmodulin target proteins, including unique protein kinases and transcription factors. Some of these proteins are likely to act as "hubs" during calcium signal transduction. Hence, a better understanding of the function of these calmodulin target proteins should help in deciphering the Ca(2+)/calmodulin-mediated signal network and its role in plant growth, development and response to environmental stimuli.

Arabidopsis↗
PubMed2002

The use and analysis of microarray data.

Functional genomics is the study of gene function through the parallel expression measurements of genomes, most commonly using the technologies of microarrays and serial analysis of gene expression. Microarray usage in drug discovery is expanding, and its applications include basic research and target discovery, biomarker determination, pharmacology, toxicogenomics, target selectivity, development of prognostic tests and disease-subclass determination. This article reviews the different ways to analyse large sets of microarray data, including the questions that can be asked and the challenges in interpreting the measurements.

Animals↗
PubMed2001

Recent advances in computational genomics.

In the post-genomic era, the new discipline of functional genomics is now facing the challenge of associating a function (as well as estimating its relevance to industrial applications) to about 100,000 microbial, plant or animal genes of known sequence but unknown function. Besides the design of databases, computational methods are increasingly becoming intimately linked with the various experimental approaches. Consequently, bioinformatics is rapidly evolving into independent fields addressing the specific problems of interpreting i) genomic sequences, ii) protein sequences and 3D-structures, as well as iii) transcriptome and macromolecular interaction data. It is thus increasingly difficult for the biologist to choose the computational approaches that perform best in these various areas. This paper attempts to review the most useful developments of the last 2 years.

Computational Biology↗
PubMed2004

Detection of functional modules from protein interaction networks.

Complex cellular processes are modular and are accomplished by the concerted action of functional modules (Ravasz et al., Science 2002;297:1551-1555; Hartwell et al., Nature 1999;402:C47-52). These modules encompass groups of genes or proteins involved in common elementary biological functions. One important and largely unsolved goal of functional genomics is the identification of functional modules from genomewide information, such as transcription profiles or protein interactions. To cope with the ever-increasing volume and complexity of protein interaction data (Bader et al., Nucleic Acids Res 2001;29:242-245; Xenarios et al., Nucleic Acids Res 2002;30:303-305), new automated approaches for pattern discovery in these densely connected interaction networks are required (Ravasz et al., Science 2002;297:1551-1555; Bader and Hogue, Nat Biotechnol 2002;20:991-997; Snel et al., Proc Natl Acad Sci USA 2002;99:5890-5895). In this study, we successfully isolate 1046 functional modules from the known protein interaction network of Saccharomyces cerevisiae involving 8046 individual pair-wise interactions by using an entirely automated and unsupervised graph clustering algorithm. This systems biology approach is able to detect many well-known protein complexes or biological processes, without reference to any additional information. We use an extensive statistical validation procedure to establish the biological significance of the detected modules and explore this complex, hierarchical network of modular interactions from which pathways can be inferred.

Algorithms↗