BMC Genomics March 2019 PMID 30866806

Linear models enable powerful differential activity analysis in massively parallel reporter assays.

Leslie Myint, Dimitrios G Avramopoulos, Loyal A Goff, Kasper D Hansen

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Mentions

Peak: 7 in Mar 2019

across 2 sources since Mar 2019

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56,612

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Mar 2019 – Sep 2023

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The analysis captures a collectively positive and engaged attitude toward the publication, exemplified by expressions like 'out today!' and anticipation for speaking events, reflecting strong interest and recognition of its practical application. The tone and words emphasize support and excitement rather than skepticism or critique.

AgreementModerate agreement

The discussion reflects strong support or positive acknowledgment of the publication, especially highlighting the significance of the new methods and software, as seen in comments like 'our methods paper is out today' and mentions of the associated packag

InterestHigh interest

Participants demonstrate high interest, with mentions of future talks and the practical aspects of the study, suggesting engagement with the research's implications.

EngagementHigh engagement

Posts show active involvement, including sharing the publication, discussing its methods, and expressing anticipation for upcoming presentations, indicating a deep level of engagement.

ImpactModerate impact

The discussion hints at potential influence, with mentions of tool availability on Bioconductor and enthusiastic remarks about the method’s usefulness, though some posts are more informational than explicitly evaluative.

Abstract in brief

This study introduces the mpralm method, a linear modeling approach for analyzing data from Massively Parallel Reporter Assays (MPRAs), and demonstrates its superior performance and calibration compared to existing methods across multiple datasets.
The research examines the impact of different barcode summarization techniques, highlighting how they can significantly affect analysis results, and provides practical recommendations for improving experimental design, such as using at least 4 replicates per condition for better power.
An R package implementing the mpralm method is available through Bioconductor, offering tools for more accurate differential activity analysis, group comparisons, and power calculations to enhance MPRA experiment design and interpretation.]

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All mentions

Linear Models for Enhanced Differential Activity Analysis in MPRAs

This study introduces the mpralm linear modeling method for analyzing Massively Parallel Reporter Assays, demonstrating its superior performance, calibration, and practical recommendations for experimental design to…

STEM RTCL TV · Apr 7, 2023 62 views

Enhanced Differential Activity Analysis in MPRAs Using Linear Models

This study introduces the mpralm linear modeling approach for analyzing data from Massively Parallel Reporter Assays, demonstrating improved performance and calibration over existing methods. Practical recommendations…

STEM RTCL TV · Aug 7, 2023 8 views

Advanced Linear Models for Differential Activity Analysis in MPRAs

This study introduces the mpralm method, a linear modeling approach for analyzing data from Massively Parallel Reporter Assays, demonstrating its superior performance and calibration across datasets. It offers practical…

STEM RTCL TV · Sep 2, 2023 6 views

Advanced Linear Modeling for Differential Activity Analysis in MPRAs

This study introduces the mpralm linear modeling approach for analyzing Massively Parallel Reporter Assays, demonstrating its superior performance and calibration across datasets. It offers practical recommendations for…

STEM RTCL TV · Jul 3, 2023 5 views

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@razoralign · X

mpralm: Linear models enable powerful differential activity analysis in massively parallel reporter assays https://t.co/TmpAw2iEHs

Apr 17, 2019 · 16,212 followers View post →

antisense.

@razoralign · X