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A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support.

ABSTRACT: This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.

SUBMITTER: Alm E

PROVIDER: S-EPMC5834917 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support.

Alm Erik E Torgrip Ralf J O RJ Aberg K Magnus KM Schuppe-Koistinen Ina I Lindberg Johan J

Analytical and bioanalytical chemistry 20090722 1

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological s ...[more]

PMID: 19629457

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Project description:BackgroundThe development of Next Generation Sequencing (NGS) has had a major impact on the study of genetic sequences. Among problems that researchers in the field have to face, one of the most challenging is the taxonomic classification of metagenomic reads, i.e., identifying the microorganisms that are present in a sample collected directly from the environment. The analysis of environmental samples (metagenomes) are particularly important to figure out the microbial composition of different ecosystems and it is used in a wide variety of fields: for instance, metagenomic studies in agriculture can help understanding the interactions between plants and microbes, or in ecology, they can provide valuable insights into the functions of environmental communities.ResultsIn this paper, we describe a new lightweight alignment-free and assembly-free framework for metagenomic classification that compares each unknown sequence in the sample to a collection of known genomes. We take advantage of the combinatorial properties of an extension of the Burrows-Wheeler transform, and we sequentially scan the required data structures, so that we can analyze unknown sequences of large collections using little internal memory. The tool LiME (Lightweight Metagenomics via eBWT) is available at https://github.com/veronicaguerrini/LiME .ConclusionsIn order to assess the reliability of our approach, we run several experiments on NGS data from two simulated metagenomes among those provided in benchmarking analysis and on a real metagenome from the Human Microbiome Project. The experiment results on the simulated data show that LiME is competitive with the widely used taxonomic classifiers. It achieves high levels of precision and specificity - e.g. 99.9% of the positive control reads are correctly assigned and the percentage of classified reads of the negative control is less than 0.01% - while keeping a high sensitivity. On the real metagenome, we show that LiME is able to deliver classification results comparable to that of MagicBlast. Overall, the experiments confirm the effectiveness of our method and its high accuracy even in negative control samples.

Dataset Information

A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support.

Publications

A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support.

Similar Datasets

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