Evidence map›Paper›PMID 40987990›Full record

ReviewFunctional & integrative genomics2025

Next-generation translational genomics for developing future crops.

Udita Basu, Swarup K Parida

Abstract readReview
PubMed Publisher
In one paragraph

Review in Functional & integrative genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Udita BasuBiological Sciences and Technology Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, Assam, India. uditabasu@neist.res.in.ORCID http://orcid.org/0000-0002-8231-5238
Swarup K ParidaBRIC-National Institute of Plant Genome Research, New Delhi, 110067, India. swarup@nipgr.ac.in.ORCID http://orcid.org/0000-0001-7843-3031

Funding

CSIR-North East Institute of Science and Technology OLP2503A
6 · The paper itself

Abstract

Advancements in translational genomics have revolutionized crop breeding, driving us from traditional breeding methods towards next-generation strategies that integrate genomic, transcriptomic, and phenotypic data to expedite crop improvement. There has been a shift from single genomes to pan-genomes, which better capture intraspecific diversity, and from bulk transcriptome analyses to single-cell transcriptomics, enabling cell-specific insights into gene regulation and functional genomics. Both high throughput genopyting and phenotyping approaches are now possible due to rapid technological advancement in the field of translational genomics. Large-scale phenotyping data from multi-environment field trials is now possible due to AI-enabled digital and drone-based scanning. In the era of artificial intelligence and machine learning we have developed flexible models to handle complex genetic architecture of trait regulation using various tools and approaches. These genetic and genomic resources are the foundation for generating novel, adaptable, and high-yielding varieties, accelerating trait discovery and mapping. This review explores the comprehensive landscape of modern translational genomics, highlighting key shifts and innovations that enhance our capacity to address agricultural challenges. Integrative pipelines that unify these next-generation approaches could facilitate faster, more precise, and sustainable crop improvement, ultimately meeting the growing demands for future-ready crops.

Indexed as

Crops, AgriculturalGenomicsPlant BreedingArtificial IntelligenceChromosome MappingGene Expression ProfilingGenome, PlantPhenotypeQuantitative Trait LociArtificial intelligenceGenome editingMapping populationPangenomeQTL/eQTLSpeed breeding

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.