Open Access Highly Accessed Research article

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver

Jonathan Schug1, Lindsay B McKenna1, Gabriel Walton1, Nicholas Hand2, Sarmistha Mukherjee3, Kow Essuman3, Zhongjie Shi3, Yan Gao4, Karen Markley3, Momo Nakagawa3, Vasumathi Kameswaran1, Anastassios Vourekas5, Joshua R Friedman2, Klaus H Kaestner1 and Linda E Greenbaum1*

Author Affiliations

1 Department of Genetics and Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

2 Department of Pediatrics, The Children’s Hospital, University of Pennsylvania, Philadelphia, Pennsylvania, USA

3 Departments of Cancer Biology and Medicine, Thomas Jefferson University, 519 BLSB, 233 S. 10th Street, Philadelphia, PA, 19107, USA

4 Department of Cancer Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA

5 Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

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BMC Genomics 2013, 14:264  doi:10.1186/1471-2164-14-264

Published: 18 April 2013

Additional files

Additional file 1:

Schug Figure S1.

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Additional file 2: Table S1:

This contains the maximum (Max), minimum (Min), and max/min fold change (FC) across the quiescent liver and post-partial hepatectomy time course, as well as the average and individual values for each time point. All data are in reads per million.

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Additional file 3:

Schug Figure S2.

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Additional file 4: Table S2:

This file contains the average total regulatory load (TRL) for RefSeq sequences. We show the average values at each time point as well as the minimum (Min), maximum (Max), and max/min fold change (FC). The values are calculated by adding the total RPM values for all Ago footprints on each RefSeq, then dividing by the RNA-Seq expression values for RefSeqs in reads per million per kilobase (RPKM).

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Additional file 5: Table S3:

This file contains the significantly enriched Ingenuity functions in the K-means clustering of the total regulatory load (TRL) profiles. The functions are sorted by increasing p-value and by function. A function is first listed with its best Benjamini-Hochberg corrected p-value (FDR), and subsequent rows list any other clusters where the function is enriched. The genes listed are those in the cluster that are associated with the function. The p-value is corrected for multiple testing. The Cluster number corresponds to those in Figure 2A.

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Additional file 6: Table S4:

This file contains the significantly enriched Ingenuity canonical pathways in the K-means clustering of the total regulatory load (TRL) profiles. The pathways are sorted by decreasing statistical significance and by name. A function is first listed with its best significance, and subsequent rows list any other clusters where the pathway is enriched. The genes listed are those in the cluster that are associated with the pathway. The FDR is the Benjamini-Hochberg corrected p-value calculated by Ingenuity. The Cluster number corresponds to those in Figure 2B.

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Additional file 7: Table S5:

Positions and strengths of mRNA footprints and targeting miRNAs.

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Additional file 8:

Supplemental methods.

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Additional file 9:

Schug Figure S3.

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