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https://doi.org/10.1109/itng.2...
Article . 2013 . Peer-reviewed
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Parser Combinators: A Practical Application for Generating Parsers for NMR Data

Authors: Heidi J.C. Ellis; Michael R. Gryk; Matthew Fenwick; Gerard Weatherby;

Parser Combinators: A Practical Application for Generating Parsers for NMR Data

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is a technique for acquiring protein data at atomic resolution and determining the three-dimensional structure of large protein molecules. A typical structure determination process results in the deposition of a large data sets to the BMRB (Bio-Magnetic Resonance Data Bank). This data is stored and shared in a file format called NMR-Star. This format is syntactically and semantically complex making it challenging to parse. Nevertheless, parsing these files is crucial to applying the vast amounts of biological information stored in NMR-Star files, allowing researchers to harness the results of previous studies to direct and validate future work. One powerful approach for parsing files is to apply a Backus-Naur Form (BNF) grammar, which is a high-level model of a file format. Translation of the grammatical model to an executable parser may be automatically accomplished. This paper will show how we applied a model BNF grammar of the NMR-Star format to create a free, open-source parser, using a method that originated in the functional programming world known as "parser combinators". This paper demonstrates the effectiveness of a principled approach to file specification and parsing. This paper also builds upon our previous work [1], in that 1) it applies concepts from Functional Programming (which is relevant even though the implementation language, Java, is more mainstream than Functional Programming), and 2) all work and accomplishments from this project will be made available under standard open source licenses to provide the community with the opportunity to learn from our techniques and methods.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average