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Expectation-Maximization Model for Substitution of Missing Values Characterizing Greenness of Organic Solvents.

ABSTRACT: Organic solvents are ubiquitous in chemical laboratories and the Green Chemistry trend forces their detailed assessments in terms of greenness. Unfortunately, some of them are not fully characterized, especially in terms of toxicological endpoints that are time consuming and expensive to be determined. Missing values in the datasets are serious obstacles, as they prevent the full greenness characterization of chemicals. A featured method to deal with this problem is the application of Expectation-Maximization algorithm. In this study, the dataset consists of 155 solvents that are characterized by 13 variables is treated with Expectation-Maximization algorithm to predict missing data for toxicological endpoints, bioavailability, and biodegradability data. The approach may be particularly useful for substitution of missing values of environmental, health, and safety parameters of new solvents. The presented approach has high potential to deal with missing values, while assessing environmental, health, and safety parameters of other chemicals.

SUBMITTER: Luczynska G

PROVIDER: S-EPMC6100055 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Expectation-Maximization Model for Substitution of Missing Values Characterizing Greenness of Organic Solvents.

Łuczyńska Gabriela G Pena-Pereira Francisco F Tobiszewski Marek M Namieśnik Jacek J

Molecules (Basel, Switzerland) 20180528 6

Organic solvents are ubiquitous in chemical laboratories and the Green Chemistry trend forces their detailed assessments in terms of greenness. Unfortunately, some of them are not fully characterized, especially in terms of toxicological endpoints that are time consuming and expensive to be determined. Missing values in the datasets are serious obstacles, as they prevent the full greenness characterization of chemicals. A featured method to deal with this problem is the application of Expectatio ...[more]

PMID: 29843437

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Project description:Genome-wide nucleosome profiles are predominantly characterized using MNase-seq, which involves extensive MNase digestion and size selection to enrich for mono-nucleosome-sized fragments. Most available MNase-seq analysis packages assume that nucleosomes uniformly protect 147bp DNA fragments. However, some nucleosomes with atypical histone or chemical compositions protect shorter lengths of DNA. The rigid assumptions imposed by current nucleosome analysis packages ignore variation in nucleosome lengths, potentially blinding investigators to regulatory roles played by atypical nucleosomes. To enable the characterization of different nucleosome types from MNase-seq data, we introduce the Size-based Expectation Maximization (SEM) nucleosome calling package. SEM employs a hierarchical Gaussian mixture model to estimate the positions and subtype identity of nucleosomes from MNase-seq fragments. Nucleosome subtypes are automatically identified based on the distribution of protected DNA fragment lengths at nucleosome positions. Benchmark analysis indicates that SEM is on par with existing packages in terms of standard nucleosome-calling accuracy metrics, while uniquely providing the ability to characterize nucleosome subtype identities. Using SEM on a low-dose MNase H2B MNase-ChIP-seq dataset from mouse embryonic stem cells, we identified three nucleosome types: short-fragment nucleosomes, canonical nucleosomes, and di-nucleosomes. The short-fragment nucleosomes can be divided further into two subtypes based on their chromatin accessibility. Interestingly, the subset of short-fragment nucleosomes in accessible regions exhibit high MNase sensitivity and display distribution patterns around transcription start sites (TSSs) and CTCF peaks, similar to the previously reported "fragile nucleosomes". These SEM-defined accessible short-fragment nucleosomes are found not just in promoters, but also in enhancers and other regulatory regions. Additional investigations reveal their co-localization with the chromatin remodelers Chd6, Chd8, and Ep400. In summary, SEM provides an effective platform for distinguishing various nucleosome subtypes, paving the way for future exploration of non-standard nucleosomes.

Dataset Information

Expectation-Maximization Model for Substitution of Missing Values Characterizing Greenness of Organic Solvents.

Publications

Expectation-Maximization Model for Substitution of Missing Values Characterizing Greenness of Organic Solvents.

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