In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR
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.
Key wordsRNA modification RNA posttranscriptional modification RNA covalent modification Small RNA Small RNA sequencing Messenger RNA RNA sequencing Machine learning Classification
High-throughput annotation of modified ribonucleotides
messenger RNA sequencing
Small RNA sequencing
single nucleotide polymorphism
This work is supported by the National Institute of General Medical Sciences [R01-GM099962 to P.P.K, Y.Y.L, B.D.G., and L.S.W], National Institute on Aging [U24-AG041689 to L.S.W.], National Science Foundation [CAREER Award MCB-1053846, MCB-1243947, and IOS-1444490 to B.D.G.]. We thank Alexandre Amlie-Wolf and other members of the Wang and Gregory labs for their comments and help with this work.
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