Briefings in Functional Genomics最新文献

As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

Genetic variability is essential for the development of new crop varieties with economically beneficial traits. The traits can be inherited from wild relatives or induced through mutagenesis. Novel genetic elements can then be identified and new gene functions can be predicted. In this study, forward and reverse genetics approaches were described, in addition to their applications in modern crop improvement programs and functional genomics. By using heritable phenotypes and linked genetic markers, forward genetics searches for genes by using traditional genetic mapping and allele frequency estimation. Despite recent advances in sequencing technology, omics and computation, genetic redundancy remains a major challenge in forward genetics. By analyzing close-related genes, we will be able to dissect their functional redundancy and predict possible traits and gene activity patterns. In addition to these predictions, sophisticated reverse gene editing tools can be used to verify them, including TILLING, targeted insertional mutagenesis, gene silencing, gene targeting and genome editing. By using gene knock-down, knock-up and knock-out strategies, these tools are able to detect genetic changes in cells. In addition, epigenome analysis and editing enable the development of novel traits in existing crop cultivars without affecting their genetic makeup by increasing epiallelic variants. Our understanding of gene functions and molecular dynamics of various biological phenomena has been revised by all of these findings. The study also identifies novel genetic targets in crop species to improve yields and stress tolerances through conventional and non-conventional methods. In this article, genetic techniques and functional genomics are specifically discussed and assessed for their potential in crop improvement.

Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) have been around for more than 3 years now. However, due to constant viral evolution, novel variants are emerging, leaving old treatment protocols redundant. As treatment options dwindle, infection rates continue to rise and seasonal infection surges become progressively common across the world, rapid solutions are required. With genomic and proteomic methods generating enormous amounts of data to expand our understanding of SARS-CoV-2 biology, there is an urgent requirement for the development of novel therapeutic methods that can allow translational research to flourish. In this review, we highlight the current state of COVID-19 in the world and the effects of post-infection sequelae. We present the contribution of translational research in COVID-19, with various current and novel therapeutic approaches, including antivirals, monoclonal antibodies and vaccines, as well as alternate treatment methods such as immunomodulators, currently being studied and reiterate the importance of translational research in the development of various strategies to contain COVID-19.