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Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
13.2K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.1K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.1K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

10.5K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
10.5K
Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.5K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.5K
Sequences01:29

Sequences

504
Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Updated: May 5, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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Predecir el empalme desde la secuencia primaria con aprendizaje profundo

Kishore Jaganathan1, Sofia Kyriazopoulou Panagiotopoulou1, Jeremy F McRae1

  • 1Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.

Cell
|January 22, 2019
PubMed
Resumen
Este resumen es generado por máquina.

Una nueva red neuronal profunda predice con precisión las uniones de empalme, identificando el empalme críptico causado por variantes genéticas. Este hallazgo revela una causa previamente subestimada de trastornos genéticos raros y afecciones del desarrollo neurológico.

Palabras clave:
Inteligencia artificialaprendizaje profundogenéticael empalme

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Área de la Ciencia:

  • Biología molecular
  • La genética
  • La bioinformática

Sus antecedentes:

  • La precisión del empalme es crucial para la expresión génica, sin embargo, los mecanismos subyacentes siguen sin estar claros.
  • El empalme críptico, provocado por variantes genéticas, puede conducir a enfermedades pero es difícil de predecir.
  • Comprender la predicción de la unión de empalme es vital para diagnosticar trastornos genéticos.

Objetivo del estudio:

  • Desarrollar una red neuronal profunda para una predicción precisa de la unión de empalme.
  • Para identificar las variantes genéticas no codificantes que causan el empalme críptico.
  • Evaluar el papel de las variantes modificadoras del empalme en las enfermedades humanas.

Principales métodos:

  • Desarrolló un modelo de red neuronal profunda para la predicción de uniones de empalme.
  • Se analizaron las mutaciones sinónimas e intrónicas para las consecuencias que alteran el empalme.
  • Predicciones validadas utilizando datos de secuenciación de ARN.
  • Se examinaron mutaciones de novo en pacientes con autismo y discapacidad intelectual.

Principales resultados:

  • La red neuronal profunda predice con precisión las uniones de empalme de las secuencias de pre-ARNm.
  • Las mutaciones predictivas que alteran el empalme muestran altas tasas de validación con ARN-seq y son perjudiciales en la población humana.
  • Las mutaciones de novo con consecuencias de alteración del empalme se enriquecen en pacientes con autismo y discapacidad intelectual.
  • Las mutaciones predictivas de alteración del empalme validadas por secuencia de ARN en 21 de los 28 pacientes.

Conclusiones:

  • Las redes neuronales profundas pueden predecir con precisión las uniones de empalme e identificar el empalme críptico.
  • Las variantes genéticas que alteran el empalme representan una causa significativa y subestimada de trastornos genéticos raros y afecciones del desarrollo neurológico.
  • Este enfoque ayuda en el diagnóstico de enfermedades genéticas y la comprensión de los impactos de la mutación.