Dataset Information

Goslin: A Grammar of Succinct Lipid Nomenclature.

ABSTRACT: We introduce Goslin, a polyglot grammar for common lipid shorthand nomenclatures based on the LIPID MAPS nomenclature and the shorthand nomenclature established by Liebisch and coauthors and used by LipidHome and SwissLipids. Goslin was designed to address the following pressing issues in the lipidomics field: (1) to simplify the implementation of lipid name handling for developers of mass spectrometry-based lipidomics tools, (2) to offer a tool that unifies and normalizes the main existing lipid name dialects enabling a lipidomics analysis in a high-throughput fashion, and (3) to provide a consistent mapping from lipid shorthand names to lipid building blocks and structural properties. We provide implementations of Goslin in four major programming languages, namely, C++, Java, Python 3, and R to kick-start adoption and integration. Further, we set up a web service for users to work with Goslin directly. All implementations are available free of charge under a permissive open source license.

SUBMITTER: Kopczynski D

PROVIDER: S-EPMC7467413 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Goslin: A Grammar of Succinct Lipid Nomenclature.

Kopczynski Dominik D Hoffmann Nils N Peng Bing B Ahrends Robert R

Analytical chemistry 20200730 16

We introduce Goslin, a polyglot grammar for common lipid shorthand nomenclatures based on the LIPID MAPS nomenclature and the shorthand nomenclature established by Liebisch and coauthors and used by LipidHome and SwissLipids. Goslin was designed to address the following pressing issues in the lipidomics field: (1) to simplify the implementation of lipid name handling for developers of mass spectrometry-based lipidomics tools, (2) to offer a tool that unifies and normalizes the main existing lipi ...[more]

PMID: 32589019

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Project description:BACKGROUND: The high throughput of modern NGS sequencers coupled with the huge sizes of genomes currently analysed, poses always higher algorithmic challenges to align short reads quickly and accurately against a reference sequence. A crucial, additional, requirement is that the data structures used should be light. The available modern solutions usually are a compromise between the mentioned constraints: in particular, indexes based on the Burrows-Wheeler transform offer reduced memory requirements at the price of lower sensitivity, while hash-based text indexes guarantee high sensitivity at the price of significant memory consumption. METHODS: In this work we describe a technique that permits to attain the advantages granted by both classes of indexes. This is achieved using Hamming-aware hash functions--hash functions designed to search the entire Hamming sphere in reduced time--which are also homomorphisms on de Bruijn graphs. We show that, using this particular class of hash functions, the corresponding hash index can be represented in linear space introducing only a logarithmic slowdown (in the query length) for the lookup operation. We point out that our data structure reaches its goals without compressing its input: another positive feature, as in biological applications data is often very close to be un-compressible. RESULTS: The new data structure introduced in this work is called dB-hash and we show how its implementation--BW-ERNE--maintains the high sensitivity and speed of its (hash-based) predecessor ERNE, while drastically reducing space consumption. Extensive comparison experiments conducted with several popular alignment tools on both simulated and real NGS data, show, finally, that BW-ERNE is able to attain both the positive features of succinct data structures (that is, small space) and hash indexes (that is, sensitivity). CONCLUSIONS: In applications where space and speed are both a concern, standard methods often sacrifice accuracy to obtain competitive throughputs and memory footprints. In this work we show that, combining hashing and succinct indexing techniques, we can attain good performances and accuracy with a memory footprint comparable to that of the most popular compressed indexes.

Dataset Information

Goslin: A Grammar of Succinct Lipid Nomenclature.

Publications

Goslin: A Grammar of Succinct Lipid Nomenclature.

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