5 datasets found
  1. Additional file 2 of Strategies for detecting and identifying biological...

    • springernature.figshare.com
    xlsx
    Updated Jun 5, 2023
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    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski (2023). Additional file 2 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data [Dataset]. http://doi.org/10.6084/m9.figshare.14532350.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 2. Summary of Statistical Measures used to Evaluate Gene Expression before and after Minimum Value Adjustment (MVA).

  2. Additional file 10 of Strategies for detecting and identifying biological...

    • springernature.figshare.com
    xlsx
    Updated Jun 10, 2023
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    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski (2023). Additional file 10 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data [Dataset]. http://doi.org/10.6084/m9.figshare.14532341.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 10. Reference Genes with Raw Counts Greater than 5 and R2 Values > 0.9 in three separate RNA sequencing Studies.

  3. Additional file 6 of Strategies for detecting and identifying biological...

    • springernature.figshare.com
    xlsx
    Updated Jun 10, 2023
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    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski (2023). Additional file 6 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data [Dataset]. http://doi.org/10.6084/m9.figshare.14532362.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 6. STRING db Analysis of Intra-individual Positional Gene Rankings in 35 Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.

  4. CreativeWork

    • pfocr.wikipathways.org
    Updated Nov 7, 2022
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    WikiPathways (2022). CreativeWork [Dataset]. https://pfocr.wikipathways.org/figures/PMC3046063_673fig7.html
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    Dataset updated
    Nov 7, 2022
    Dataset authored and provided by
    WikiPathwayshttp://wikipathways.org/
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Proposed working model and protein–protein interaction map. (A) Working model. In the absence of growth factors (top left), Gαi3 preferentially interacts with AGS3, its GDI, which maintains it inactive (Gαi3–GDP). The Gαi3–AGS3 complex localizes to LC3-positive structures via a direct interaction between AGS3 and LC3, thereby promoting autophagy (i.e., maturation of preautophagosomal structures into mature LC3-positive structures; green panel, bottom). In the presence of growth factors (top panel, right), Gαi3 is activated (Gαi3–GTP) by GIV, its GEF, which 1) enhances the anti-autophagic class I PI3K-Akt-mTOR signaling pathway at the PM and 2) releases active Gαi3 from Gαi3–AGS3–LC3 complexes assembled on membranes, thereby directly inhibiting autophagosome formation and maturation. It is possible that signaling programs modulated by GIV’s GEF function at the PM also influence the equilibrium between Gαi3–AGS3 and Gαi3–GIV as a regulatory feedback loop (interrupted arrow). Further studies will be required to pinpoint the specific LC3-positive compartment where Gαi3 localizes and the precise role of this G protein in regulating autophagosome maturation. (B) Proposed protein–protein interaction map for Gαi3, GIV, and AGS3 showing the shortest functional links that integrate pro- or anti-autophagic signaling by growth factors and G protein pathways in balancing the process (initiation and reversal) of autophagy. Functional interactions between Gαi3 and its modulators analyzed in this (GIV and AGS3) and other studies (RGS19/GAIP; Ogier-Denis et al., ) in the context of autophagy are shown. These are based on interactions listed in the I2D (Brown and Jurisica, ; Brown et al., ), STRING (http://string-db.org/), and MitoCheck (www.mitocheck.org) databases and validated in the literature. For simplicity, this interaction map includes only the experimentally validated functional interactions that are relevant to G protein and growth factor signaling during autophagy. The proteins of the autophagic pathway (green) are linked to Gαi3 by a direct interaction (identified in this work and highlighted with a bold red line) between the GDI AGS3 (GPSM1) and LC3 (ATG8). The proteins of the anti-autophagic growth factor/nutrients (growth factor receptors, G protein–coupled receptors) and mTOR signaling pathways (red) are linked to G protein signaling via GIV (Girdin), the GEF that directly interacts with and activates Gαi3 (resulting in Gαi3–GTP). The protein network also reveals that processes such as apoptosis (blue) and asymmetric cell division (yellow) are likely to be influenced by modulation of G protein activity by either this or other such pairs of GDI(s) and GEF(s).

  5. Additional file 11 of Strategies for detecting and identifying biological...

    • springernature.figshare.com
    xlsx
    Updated Jun 1, 2023
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    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski (2023). Additional file 11 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data [Dataset]. http://doi.org/10.6084/m9.figshare.14532344.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 11. Control Reference Data Files.

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William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski (2023). Additional file 2 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data [Dataset]. http://doi.org/10.6084/m9.figshare.14532350.v1
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Additional file 2 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

Related Article
Explore at:
xlsxAvailable download formats
Dataset updated
Jun 5, 2023
Dataset provided by
Figsharehttp://figshare.com/
Authors
William W. Wilfinger; Robert Miller; Hamid R. Eghbalnia; Karol Mackey; Piotr Chomczynski
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Additional file 2. Summary of Statistical Measures used to Evaluate Gene Expression before and after Minimum Value Adjustment (MVA).

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