RoboOligo: software for mass spectrometry data to support manual and de novo sequencing of post-transcriptionally modified ribonucleic acids

Ribosomal ribonucleic acid (RNA), transfer RNA and other biological or synthetic RNA polymers can contain nucleotides that have been modified by the addition of chemical groups. Traditional Sanger sequencing methods cannot establish the chemical nature and sequence of these modified-nucleotide conta...

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Bibliographic Details
Published in:Nucleic acids research 2015-05, Vol.43 (10), p.e64-e64
Main Authors: Sample, Paul J, Gaston, Kirk W, Alfonzo, Juan D, Limbach, Patrick A
Format: Article
Language:English
Subjects:
Algorithms
High-Throughput Nucleotide Sequencing - methods
Methods Online
RNA Processing, Post-Transcriptional
RNA, Ribosomal - chemistry
RNA, Ribosomal - metabolism
RNA, Transfer - chemistry
RNA, Transfer - metabolism
Sequence Analysis, RNA - methods
Software
Tandem Mass Spectrometry
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Summary:Ribosomal ribonucleic acid (RNA), transfer RNA and other biological or synthetic RNA polymers can contain nucleotides that have been modified by the addition of chemical groups. Traditional Sanger sequencing methods cannot establish the chemical nature and sequence of these modified-nucleotide containing oligomers. Mass spectrometry (MS) has become the conventional approach for determining the nucleotide composition, modification status and sequence of modified RNAs. Modified RNAs are analyzed by MS using collision-induced dissociation tandem mass spectrometry (CID MS/MS), which produces a complex dataset of oligomeric fragments that must be interpreted to identify and place modified nucleosides within the RNA sequence. Here we report the development of RoboOligo, an interactive software program for the robust analysis of data generated by CID MS/MS of RNA oligomers. There are three main functions of RoboOligo: (i) automated de novo sequencing via the local search paradigm. (ii) Manual sequencing with real-time spectrum labeling and cumulative intensity scoring. (iii) A hybrid approach, coined 'variable sequencing', which combines the user intuition of manual sequencing with the high-throughput sampling of automated de novo sequencing.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkv145