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Related Concept Videos

MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...

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Updated: May 14, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice
07:07

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

Poisson factor models with applications to non-normalized microRNA profiling.

Seonjoo Lee1, Pauline E Chugh, Haipeng Shen

  • 1Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA.

Bioinformatics (Oxford, England)
|February 23, 2013
PubMed
Summary
This summary is machine-generated.

We developed a new Poisson factor model, Poisson Singular Value Decomposition with Offset (PSVDOS), for analyzing next-generation sequencing count data. This method improves microRNA (miRNA) profiling and classification for identifying viral infections.

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs

Published on: August 3, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) is a powerful tool for transcriptional profiling, including microRNA (miRNA) analysis.
  • miRNAs play crucial roles in cellular processes and are implicated in diseases like cancer and infections.
  • Analyzing NGS count data presents statistical challenges due to skewness, non-negativity, and variable total read counts across samples.

Purpose of the Study:

  • To develop a statistical method for analyzing NGS count data, specifically for miRNA profiling.
  • To address the limitations of existing methods, such as those used for microarray data, in analyzing NGS data.
  • To identify viral infections by detecting changes in host miRNA profiles.

Main Methods:

  • Proposed a family of Poisson factor models tailored for count data.
  • Developed an efficient algorithm, Poisson Singular Value Decomposition with Offset (PSVDOS), for model estimation.
  • Incorporated automatic sample normalization using offsets within the model.

Main Results:

  • PSVDOS demonstrated superior performance compared to other normalization and dimension reduction methods in simulations.
  • The method achieved insightful dimension reduction of miRNA profiles from 18 samples.
  • Extracted factors from PSVDOS led to more accurate and meaningful clustering of cell lines.

Conclusions:

  • Poisson factor models, particularly PSVDOS, are effective for analyzing NGS count data.
  • PSVDOS offers improved normalization and dimension reduction for miRNA profiling.
  • The method facilitates more accurate classification and clustering of biological samples based on miRNA expression.