Hi everybody,
Join us for our monthly webinar next Wednesday, Sept. 4th, at 12 PM CDT.
Dr. David Emms from InstaDeep will discuss AgroNT, a foundational large
language model for plant genomics.
I have included more details about the webinar and the Zoom link to attend
the webinar below.
I hope you will join us.
Best,
Annarita
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*Abstracts:*
*AgroNT: A Foundational Large Language Model for Plant Genomics*
Foundational large language models can be pre-trained on large unlabelled
datasets and subsequently fine-tuned to a wide range of specific tasks.
We’ll present AgroNT (Agro Nucleotide Transformer), a foundational DNA
large language model pre-trained on reference genomes from 48 plant species
with a predominant focus on crops. We have shown that AgroNT can be
fine-tuned to obtain state-of-the-art predictions of many genomic elements,
including polyadenylation sites, splice sites, open chromatin and enhancer
regions. Furthermore, AgroNT can be fine-tuned to e.g. predict
tissue-specific gene expression levels or to prioritize functional variants.
Building on our Nucleotide Transformer, the novel SegmentNT model is able
to make nucleotide resolution predictions, well suited to tasks such as de
novo genome annotation of previously unseen species. Both our AgroNT and
SegmentNT models are open-sourced for academic research and non-commercial
uses on our GitHub repository
https://github.com/instadeepai/nucleotide-transformer and HuggingFace space
https://huggingface.co/InstaDeepAI.
*| 1P ET | 12P CT | 11A MT | 10A PT |*
Find your local time here
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*Join Zoom
Meetinghttps://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09
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Meeting ID: 820 3835 6125
Passcode: 160683
Hi everybody,
This is a friendly reminder of tomorrow's webinar at 12 PM CDT. *Seth
Murray <https://soilcrop.tamu.edu/people/murray-seth-c/> *(Texas A&M
University, TAMU) will present on temporal field phenomics.
I have included below more details about the webinar and the Zoom link to
attend the webinar.
I hope you will join us.
Best,
Annarita
:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:
*Abstracts:*
*Capturing Nature AND Nurture with Temporal Field Phenomics to Breed Better
Crops*
An organism’s phenome results from genotype (nature), environment and
management effects (nurture) and their interactions, as well as measurement
error. For over 30 years, DNA sequencing and genomics tools have advanced
genotyping to where genomes can now be routinely saturated with
measurements. In contrast, most focus in high throughput phenotyping and
phenomics to date has been on automating previously known “traits” as
measurable and interpretable phenotypes; akin to focusing on measuring a
single DNA marker rather than measuring a saturated genome. Tools such as
unoccupied aerial systems (UAS, aka UAVs, drones) collecting temporal
phenomic measurements in the field now allow novel methods in plant
breeding and new insights into plant biology. Viewing phenomics as a
platform for discovery, similar to genomics, opens new methods for
capturing phenomena in nature and nurture. To date, our experience with
phenomic prediction from UAS in maize breeding for cumulative, complex
phenotypes such as grain yield suggests it’s possible to predict organismal
performance in untested environments; in fact possibly better than
gold-standard genomic methods. Surprising insights into biology have also
been made in through these activities predicting plant disease and
resistance, evaluating genotypic resilience to stress, and identifying
early season growth periods for crop improvement that have not been able to
be selected. Method development and data analytics in phenomics are large
investments, but worth making. Successfully measuring the phenome will
impact every aspect of science and society, in biological disciplines from
germplasm curators, physiologists to breeders, to education, the courtroom
and policy.
*| 1P ET | 12P CT | 11A MT | 10A PT |*
Find your local time here
<https://www.timeanddate.com/worldclock/fixedtime.html?msg=AgBioData+Monthly…>
*Join Zoom
Meetinghttps://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09
<https://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09>*
Meeting ID: 820 3835 6125
Passcode: 160683