Profª Drª Maria Lúcia Carneiro Vieira, deste Depto. de Genética, eleita Membro Titular na área de Ciências Agrárias da Academia de Ciências do Estado de São Paulo (ACIESP - @aciespsp).
#ESALQGenetics
https://t.co/4PhTAHetPA
O Dr. Fernando Henrique Correr ( @Fernando_Correr ), orientado do Prof. Dr. Gabriel Rodrigues Alves Margarido ( @gramarga ), do Departamento de Genética, ESALQ/USP, premiado no PRÊMIO CAPES DE TESE - EDIÇÃO 2022.
https://t.co/Tv1J4G6Ovn
Our new book chapter, for any one interested in applying transcriptomics in plants: https://t.co/GXbyXLdE5y with @gramarga, Hector Fabio Espitia-Navarro and John Jaime Riascos
Today we publish a paper in @ScienceMagazine that expands nanopore readings to the proteome:
a nanopore-based scanner to read off PROTEINS at the single-molecule level! 🤩
Awesome experiments by postdoc Henry Brinkerhoff of our #CDlab, with MD simulations of @aksimentievLab
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This is a great overview of machine learning written for a biological audience. It covers not only different algorithms but also data leakage, evaluating articles that use machine learning, etc.
https://t.co/n3kjeFRnVT
@nilshomer I work with plant genomics and have often used Picard. Imposter syndrome can definitely hit us all, but you're one (of many) that I look up to. Thanks for your contributions!
Is finally out in the world! New preprint: Genomic prediction with allele dosage information in highly polyploid species. My PhD paper together with @gramarga and @anetepsouza
https://t.co/Sb03s2qHwA
Happy to share our new publication in BMC Genomics (@BMC_series) on differential expression in leaves of Saccharum genotypes contrasting in biomass production. Thanks to @gramarga, @GuilhermeHosaka and all the other authors for their great contributions!
https://t.co/SDCgxWczLq
A new (3rd) release of the "Population and Quantitative Genetics" book. As usual, the book is completely open access as are all of the underlying source code, figures, illustrations.
https://t.co/cVwohT4rMj
The more papers I read for a review article I'm writing about ML pitfalls in genomics, the more my faith is shaken in the results from papers that apply machine learning to methylation arrays. A salty thread. 1/
SRA plans to drop base quality. This would make resequencing data largely useless for var calling. There are better ways to save space: quality binning and CRAM. If you use reseq data from SRA, please voice yourself in this RFI (deadline in 2 weeks): https://t.co/Ii4d4wyH0s