Publications by authors named "Gemma I Martinez-Redondo"

Recent advances in high-throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, generating a new genome is not an easy task due to the high cost of genome sequencing, the high complexity of assembly, and the lack of standardized protocols for genome annotation. The lack of consensus in the annotation and publication of genome files hinders research by making researchers lose time in reformatting the files for their purposes but can also reduce the quality of the genetic repertoire for an evolutionary study.

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Article Synopsis
  • Multiple sequence alignments and phylogenetic trees are essential tools in biology research, providing detailed biological insights.
  • PhyKIT is a software tool designed to streamline the analysis of these alignments and trees, offering various functionalities like constructing supermatrices and identifying errors in orthology inference.
  • The text outlines several protocols for using PhyKIT, covering installation, data processing, and features that aid in understanding gene functions and evolutionary relationships.
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  • A genomic database encompassing all eukaryotic species on Earth is crucial for scientific advancements, yet most species lack genomic data.
  • The Earth BioGenome Project (EBP) was initiated in 2018 by global scientists to compile high-quality reference genomes for approximately 1.5 million recognized eukaryotic species.
  • The European Reference Genome Atlas (ERGA) launched a Pilot Project to create a decentralized model for reference genome production by testing it on 98 species, providing valuable insights into scalability, equity, and inclusiveness for genomic projects.
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  • Protein language models have shown strong results on curated datasets, but their application to entire proteomes is still untested.
  • In our study, we compared two machine learning methods for decoding functional information in model organism proteomes and found that protein language models outperformed deep learning methods in precision and informativeness.
  • Our findings suggest that protein language models could be highly effective for large-scale protein annotation and further analytical tasks, leading us to recommend a guide for their implementation.
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One of the most important physiological challenges animals had to overcome during terrestrialization (i.e., the transition from sea to land) was water loss, which alters their osmotic and hydric homeostasis.

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