RNA splicing analysis using heterogeneous and large RNA-seq datasets.

Jorge Vaquero-Garcia, Joseph K Aicher, San Jewell, Matthew R Gazzara, Caleb M Radens, Anupama Jha, Scott S Norton, Nicholas F Lahens, Gregory R Grant, Yoseph Barash

March 2023 Nat Commun

Synopsis of Social media discussions

The discussions extensively reference the publication, frequently reiterating its title and the use of RNA-seq in splicing analysis. Phrases like 'everything is awesome when you're part of a team' and mentions of collaborative work indicate excitement and positivity around the findings. The tone across the posts is largely enthusiastic, showcasing the research's relevance within the fields of bioinformatics and genomics.

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The majority of discussions reflect positive sentiments towards the publication, indicating a favorable reception.

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While discussions reference the publication, they primarily echo its content rather than providing in-depth analysis.

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Impact
 High level of impact

Many participants assert that the research will significantly influence future studies in RNA splicing analysis.

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76 Posts

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257

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Effective RNA Splicing Analysis with MAJIQ v2 Algorithms

Effective RNA Splicing Analysis with MAJIQ v2 Algorithms

RNA-seq data is widely used to analyze RNA splicing variations. The MAJIQ v2 package offers new algorithms to detect and visualize splicing variations in complex datasets. This video discusses findings from analyzing 2,335 samples from 13 brain subregions, providing insights into splicing regulation.

STEM RTCL TV

June 17, 2023

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    They say "everything is awesome when you're part of a team," and it was awesome to work on "RNA splicing analysis using heterogeneous and large RNA-seq datasets" - check out our preprint! https://t.co/L4iObsei1Z @peregrinosun @jaicher @MR_Gazzara @AnupamaJha48 @YosephBarash
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Abstract Synopsis

  • RNAseq data is widely used to analyze RNA splicing variations, but existing methods struggle with large and diverse datasets that have high variability and many unexplained splice variants.
  • The MAJIQ v2 package offers new algorithms and tools to effectively detect, quantify, and visualize splicing variations in complex datasets.
  • In a study involving 2,335 samples from 13 brain subregions, MAJIQ v2 demonstrated its effectiveness in analyzing differential splicing, providing valuable insights into splicing regulation specific to different brain regions.