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Methods Mol Biol. 2017;1562:211-229. doi: 10.1007/978-1-4939-6807-7_14.

In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR.

Author information

1
Department of Pathology and Laboratory Medicine, University of Pennsylvania, D102 Richards Medical Research Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
2
Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
3
Cell and Molecular Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA.
4
Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
5
Department of Pathology and Laboratory Medicine, University of Pennsylvania, D102 Richards Medical Research Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA. lswang@upenn.edu.
6
Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA. lswang@upenn.edu.
7
Institute on Aging, University of Pennsylvania, Philadelphia, PA, 19104, USA. lswang@upenn.edu.

Abstract

RNA molecules are often altered post-transcriptionally by the covalent modification of their nucleotides. These modifications are known to modulate the structure, function, and activity of RNAs. When reverse transcribed into cDNA during RNA sequencing library preparation, atypical (modified) ribonucleotides that affect Watson-Crick base pairing will interfere with reverse transcriptase (RT), resulting in cDNA products with mis-incorporated bases or prematurely terminated RNA products. These interactions with RT can therefore be inferred from mismatch patterns in the sequencing reads, and are distinguishable from simple base-calling errors, single-nucleotide polymorphisms (SNPs), or RNA editing sites. Here, we describe a computational protocol for the in silico identification of modified ribonucleotides from RT-based RNA-seq read-out using the High-throughput Analysis of Modified Ribonucleotides (HAMR) software. HAMR can identify these modifications transcriptome-wide with single nucleotide resolution, and also differentiate between different types of modifications to predict modification identity. Researchers can use HAMR to identify and characterize RNA modifications using RNA-seq data from a variety of common RT-based sequencing protocols such as Poly(A), total RNA-seq, and small RNA-seq.

KEYWORDS:

Classification; Machine learning; Messenger RNA; RNA covalent modification; RNA modification; RNA posttranscriptional modification; RNA sequencing; Small RNA; Small RNA sequencing

PMID:
28349463
DOI:
10.1007/978-1-4939-6807-7_14
[Indexed for MEDLINE]

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