Gigwa-MCP Connects to GIGWA Database for Genomic Data Analysis

Every plant is controlled by thousands of genetic switches. For most of scientific history, reading them accurately, and predicting what they do, required enormous time and resources. AI is changing that. Researchers have developed a model trained on Arabidopsis DNA-binding data that can accurately predict transcription factor binding and gene-control patterns and apply those insights to maize. Traits linked to flowering time, disease resistance, seedling growth, and heat stress regulation are now more accessible to breeders than ever before. Faster genetic insight means faster breeding cycles. Faster breeding cycles mean better varieties reaching Indian farmers sooner. Read more: https://lnkd.in/g8kV2tw7 Phys.org | Raghavan Sampathkumar | Surabhi Jajodia | Dr Urvashi Vashist | Kuldeep Tyagi #AIPlantScience #GenomicsAI #CropBreeding #Plant #Biotechnology #SeedInnovation #FSII #VoiceOfTheSeed #BeejKiBaat #TransformationTuesday 

📢 Recently Published in The Plant Phenome Journal 📢 ❓ Can phenomic prediction models explain why plants perform the way they do or only predict their performance? 💡 A new review explores the philosophy behind phenomic prediction, highlighting that while these models are powerful tools for predicting complex traits, they should not be interpreted as evidence of cause-and-effect relationships. Instead, the authors propose modeling approaches that better separate genetic and environmental influences for more reliable biological interpretation. Key highlights: 💥 Explains the fundamental differences between genomic prediction and phenomic prediction 💥 Discusses how confounding, collider, and selection bias can affect interpretation of phenomic models 💥 Recommends multivariate linear mixed models (MvLMMs) as a more robust framework for integrating phenomic data while improving biological interpretation.   This perspective provides valuable guidance for researchers using high-throughput phenotyping and phenomic prediction in plant breeding, emphasizing the importance of distinguishing prediction from causal interpretation. 📖 Read the article: https://lnkd.in/gpM4yJiH #ThePlantPhenome #TPPJ #Phenomics #PlantBreeding #HighThroughputPhenotyping #MachineLearning #QuantitativeGenetics #PrecisionAgriculture

I enjoyed reading this thought provoking review article from the minds of Mitchell Feldmann Fangyi Wang Daniel Runcie Please check out The Plant Phenome Journal for this and more articles reporting findings at the forefront of plant phenomics.

Hundreds of millions of people across Africa eat cassava every single day. It is a staple crop, a calorie lifeline, and yet almost none of its real genetic diversity is captured by the genome scientists actually use to study it. That reference genome comes from a single South American line. One plant, standing in for an entire species' worth of variation across a continent where it feeds the most people. I'm trying to breed better, more resilient cassava for African farmers using a genetic map that barely resembles what grows in their fields. That gap is where this prompted this project. I will be sharing the process of building a pangenome for cassava, a genome structure that holds many varieties at once instead of just one, and then testing whether it actually predicts plant performance better than the single reference does. Not just building it and calling it done, but proving whether it is worth building at all. If you work in genomics, bioinformatics, or plant breeding, or if you just care about food security in places that rarely make it into mainstream science coverage, I would love for you to follow along. #Cassava #Genomics #Pangenome #FoodSecurity #Bioinformatics

🧬 The Filter of Gold ( Finding What Matters ) 🧬 📌 After genotyping of the DNA samples & before filtering genetic data, we must look at Allele Frequency ( the percentage of a specific DNA letter in a herd) . 🔸 Think of it as counting colors of 100 bottle in a bowl: if you have 95 blue bottle and 5 red ones, the red frequency is 5%. 🔹 Ex:- In a herd of 100 cows 🐮 , if 99 carry the genetic letter A at a specific spot and only 1 has G, the frequency of G is 1% , so the minor allele frequency (MAF ) is 1 % . 🔹 That ultra-rare letter G is what we call the Minor Allele, and its low frequency makes it a statistical ghost. 💀 🔸 If you test parentage here, finding AA in a calf proves nothing because every candidate bull in the herd also has AA. 🔸 Since almost the entire population carries A, this specific genetic marker has zero power to distinguish the real father. ( A SNP of no value !! ) 💠 So tracking hundreds of these static, low-frequency letters across the genome is mathematically useless for breeding. 🔸 It wastes massive computer processing power and creates heavy background noise in our genomic data pipelines. 🔸 This is why we apply a strict threshold called Minor Allele Frequency (MAF) in tools like PLINK . 🔹 By instantly wiping out any SNP with a MAF below 5% ; 10 %, we focus only on the highly informative letters that drive profit. #Genomics #ParentageVerification #SmartFarming #Bioinformatics #DataScience #AnimalGenetics #AgTech #BigData

What if the same science that helps diagnose human diseases could also improve crops, protect animals, and build a healthier planet? 🌍🧬 In this exclusive conversation, discover how GeneInsight is using genomics and bioinformatics to create real-world impact across multiple industries—not just healthcare. ✨ From precision agriculture to animal genomics and environmental research, the possibilities are far greater than you think. 🎥 Watch till the end—you'll never look at genetics the same way again. 💬 Which application impressed you the most? Share your thoughts below! 📌 Follow GeneInsight Informatics Pvt. Ltd. for more expert insights on genomics, biotechnology, and the future of innovation #GeneInsight #GeneInsightInformatics #Genomics #Bioinformatics #Biotechnology #GeneticTesting #PrecisionMedicine #LifeSciences #HealthcareInnovation #MedicalResearch #Agritech #AnimalHealth #EnvironmentalScience #Innovation #FutureOfScience #Diagnostics #DNA

Henderson's Mixed Model Equations are far more than a computational tool—they are a philosophy of scientific reasoning. In multi-trait and multi-environment breeding, we are not simply searching for the "best estimate." We are reconstructing biological reality from a network of interconnected information. One trait informs another, one environment explains another, genomic relationships transfer information across individuals, and G×E reminds us that genetic superiority is never absolute—it is always contextual. Every block in Henderson's equations asks a different scientific question: What is fixed? What is genetic? What is environmental? What is interaction? What is noise? The final solution emerges only when all of these questions are solved simultaneously. This is why I believe the next frontier in quantitative genetics will not come from collecting more data alone, but from building better mathematical representations of biological relationships. The quality of our decisions ultimately depends on the quality of the structure we use to represent reality. Quantitative genetics is not merely about predicting breeding values. It is about discovering the hidden structure that generates phenotypic variation. Know What → Know Why → Know How → Do How → Know When. Better representations lead to better decisions. Better decisions lead to greater genetic gain. #dataanalysis #MixedModel #molecularbreeding #PlantBreeding #genomics #genomicselection

**AI-Driven Genetics: Transforming Angus Breeding** At Campus Consortium Foundation (CCF), we recognize the groundbreaking work being done in genetics, such as Cody Deluisio's innovative approach to Angus breeding. By leveraging AI models to inform mating decisions and optimize genetic outcomes, we witness a critical fusion of technology with biology that enhances efficiency and results. How can we further integrate AI in agriculture to maximize genetic potential? #investing #innovation #r&d #gentech #artificialintelligence #biology #genetics

Excited to share our recently published review paper, "Unlocking the genetic potential of Triticum urartu for wheat improvement." As the wild A-genome donor of modern wheat, Triticum urartu represents a remarkable source of genetic diversity that has the potential to strengthen wheat breeding for the future. Our review explores how this underutilized wild relative can contribute valuable traits for disease resistance, tolerance to heat and drought, improved grain quality, and overall crop resilience. We also discuss how advances in genomics, molecular markers, and doubled haploid technologies are making it increasingly feasible to identify, transfer, and utilize these beneficial alleles in breeding programs. A sincere thank you to the co-authors. #CIMMYT #UniversityofNottingham #JohnInnesCentre #LUANAR #RothamstedResearch

Edition 21(S) - 2021 - Article: Next generation breeding in pulses: Present status and future directions Abstract - Human population growth in combination with changing patterns of global food consumption under climate change is posing formidable challenge to attaining sustainable global food security. Besides being economically viable sources of plant based protein for human consumption, pulses are also beneficial for the environment owing to their inherent capacity of nitrogen fixation. Hence, further development of pulses has become imperative in the vigorously transitional global scenario where flourishing anthropogenic activities are triggering irreplaceable depletion of natural resources. During past years, considerable attention has been given on the use of next generation sequencing for enriching the genomic resources in pulse crops including high-throughput DNA markers, candidate gene(s) and QTLs for predicting plant phenotypes, and whole genome sequences. With refinements in DNA sequencing technologies and computational analytical tools, the rapidly grown numbers of sequenced pulse genomes offer novel insights on crop evolution and breeding history. Integration of new-generation genomic and phenomic tools with generation acceleration procedures like genomic selection and speed breeding could greatly accelerate progress in pulses genetic improvement. The present review discusses current status and future scope of using next-generation breeding approaches in pulses that will cause not only an increase in the rate of developing climate resilient superior cultivars but also help to reach to goal of global food security. Keywords: Pulses, DNA marker, Genome, Gene, QTL, Haplotype, Genetic gain 🔗 https://lnkd.in/dn_KYbZB #GlobalFoodSecurity #ClimateResilience #PulseGenomics #SustainableAgriculture #NextGenBreeding