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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Predicción de variantes de enfermedades con modelos generativos profundos de datos evolutivos

Jonathan Frazer1, Pascal Notin2, Mafalda Dias1

  • 1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

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Un nuevo modelo computacional, EVE (modelo evolutivo del efecto variante), predice la patogenicidad de la variante proteica sin etiquetas de enfermedad. Este enfoque supera a los métodos y ayudas existentes para clasificar millones de variantes genéticas de importancia desconocida.

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Área de la Ciencia:

  • La genómica
  • Biología computacional
  • La genética humana

Sus antecedentes:

  • Más del 98% de las variantes de proteínas en los genes de enfermedades humanas tienen consecuencias clínicas desconocidas, lo que dificulta el diagnóstico y el tratamiento precisos.
  • Los métodos computacionales actuales para la interpretación de variantes se basan en etiquetas de enfermedades limitadas, sesgadas y de calidad variable, lo que lleva a predicciones no confiables.
  • Los métodos experimentales de alto rendimiento se utilizan cada vez más, pero consumen muchos recursos.

Objetivo del estudio:

  • Desarrollar un nuevo enfoque computacional para predecir la patogenicidad de la variante de proteínas que no requiera datos de enfermedades etiquetadas.
  • Aprovechar modelos generativos profundos y información evolutiva para evaluar con precisión los efectos de las variantes.
  • Proporcionar una herramienta escalable y confiable para clasificar millones de variantes genéticas de significado desconocido.

Principales métodos:

  • Desarrolló EVE (modelo evolutivo de efecto variante), un modelo generativo profundo entrenado en la variación de la secuencia evolutiva en los organismos.
  • Modelado la distribución de las variaciones de la secuencia para capturar implícitamente las restricciones de la secuencia de proteínas esenciales para la aptitud.
  • Se evaluó el rendimiento de EVE en comparación con los métodos computacionales existentes que dependen de la etiqueta y las predicciones experimentales de alto rendimiento.

Principales resultados:

  • EVE supera a los métodos computacionales de última generación que se basan en datos etiquetados.
  • El rendimiento predictivo de EVE es comparable o mejor que las predicciones experimentales de alto rendimiento.
  • Se predijo la patogenicidad de más de 36 millones de variantes en 3.219 genes de enfermedades, clasificando más de 256.000 variantes de importancia desconocida.

Conclusiones:

  • Los modelos generativos profundos que aprovechan la información evolutiva ofrecen un enfoque poderoso y libre de etiquetas para la predicción de la patogenicidad de las variantes.
  • EVE proporciona evidencia valiosa e independiente para la interpretación de variantes tanto en la investigación como en el entorno clínico.
  • Este método mejora significativamente la capacidad de interpretar las variantes genéticas y su impacto en la salud humana.