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This article is part of a special issue on epigenomics.

Highly Accessed Opinion

The birth of the Epitranscriptome: deciphering the function of RNA modifications

Yogesh Saletore123, Kate Meyer4, Jonas Korlach5, Igor D Vilfan5, Samie Jaffrey4 and Christopher E Mason12*

Author Affiliations

1 Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA

2 The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10065, USA

3 Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY10065, USA

4 Department of Pharmacology, Weill Cornell Medical College, New York, NY 10065, USA

5 Pacific Biosciences, 1380 Willow Rd, Menlo Park, CA 94025, USA

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Genome Biology 2012, 13:175  doi:10.1186/gb-2012-13-10-175

Published: 31 October 2012

Additional files

Additional file 1:

Alignment of reads to various gene categories. The percentage of bases mapping to each category was plotted for three m6A samples and three matching control samples from the MeRIP-seq study's human HEK293T dataset. The controls are samples sequenced prior to the IP and the high number of 3' UTR reads represents control regions that do not contain m6A peaks, as well as the fact that most peaks fall right between the last CDS and the beginning of the 3' UTR.

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Additional file 2:

Effect of aligner on peak detection. Realigning the MeRIP-seq HEK293T data using three different aligners, with MeRIPPeR as the peak caller, shows that the peak distribution is dependent on the aligner chosen. Using TopHat 2 increases the number of 5' UTR peaks detected.

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Additional file 3:

Peak and transcript detection as a function of read depth. (a) Increasing the number of aligned reads sampled from three different aligners (BWA, GSNAP and TopHat 2) shows an increased peak detection (linear regression R2 = 0.83). Reads were sampled from aligned reads of the MeRIP-seq HEK293T sample 2 dataset. (b) The number of transcripts that contain peaks for each of the subsampled levels. While increasing the read depth results in a dramatic increase in the number of peaks, the number of transcripts shows a much slower increase, indicating that increasing the read depth likely finds peaks with lower m6A stoichiometry.

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Additional file 4:

Antibody peak distribution. Peak distribution shown across gene bodies for each individual antibody, with the SySy antibody shown as a solid line and the NEB antibody shown as a dashed line. The distributions suggest that the two antibodies produce similar binding profiles. The NEB antibody does show a slightly higher peak in the 5' UTR for the HEK293T sample 3, but the sample is a separate biological replicate and there is no SySy run for comparison, so the distribution could also be attributed to some change in the biological sample itself.

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Additional file 5:

The increasing complexity of the central dogma of molecular biology. Top: the originally proposed central dogma, with unidirectional information flow. Bottom: the current view of the central dogma, wherein information content can flow backwards or sideways with RT, RNA-binding proteins (RbPs) and RNA editing. In addition, information can be copied within each of the three realms: genetic (blue) copying such as with transposable elements; transcriptional (red) copying with ribozymes and rich levels of RNA regulation using small RNAs (micro, piwi, si, viRNAs); and proteomic (green) copying using prions. Information can also move between generations or be transmitted between species, such as with epigenetic marks, viRNAs and prions (gray area below).

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