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Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
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http://dx.doi.org/10.1016/j.molp.2020.01.008 | DOI Listing |
Theor Appl Genet
September 2025
Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Sanitz, 18190, Germany.
Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations.
View Article and Find Full Text PDFNat Biotechnol
September 2025
Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.
Targeted protein degraders hold potential as therapeutic agents to target conventionally 'undruggable' proteins. Here, we develop a high-throughput screen, DEath FUSion Escaper (DEFUSE), to identify small-molecule protein degraders. By conjugating the protein of interest to a fast-acting triggerable death protein, this approach translates target protein degradation into a cell survival phenotype to illustrate the presence of degraders.
View Article and Find Full Text PDFSci Justice
September 2025
School of Life Sciences, University of KwaZulu-Natal, Private Bag X54001, Westville, Durban 4000, South Africa. Electronic address:
A compound marker integrates two or more genetic markers into a single assay. The application of compound markers enhances the predictive accuracy of genetic testing by leveraging the strengths of different genetic variations while mitigating the limitations of individual markers. Compound markers include SNP-SNPs, SNP-STRs, DIP-SNPs, DIP-STRs, Multi-In/Dels, CpG-SNPs, CpG-STRs/CpG-In/Del, and Methylation-Microhaplotypes.
View Article and Find Full Text PDFCell Rep Methods
September 2025
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland. Electronic address:
In cancer research, multiplexed imaging allows detailed characterization of the tumor microenvironment (TME) and its link to patient prognosis. The integrated immunoprofiling of large adaptive cancer patient cohorts (IMMUcan) consortium collects multi-modal imaging data from thousands of patients with cancer to perform broad molecular and cellular spatial profiling. Here, we describe and compare two workflows for multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) developed within IMMUcan to enable the generation of standardized data for cancer tissue analysis.
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