Urinary biomonitoring of fuel ether oxygenates using a needle trap device packed with a novel molecularly imprinted polymer surface modified Zeolite Y.

This study introduces an innovative needle trap device (NTD) featuring a molecularly imprinted polymer (MIP) surface-modified Zeolite Y. The developed NTD was integrated with gas chromatography-flame ionization detector (GC-FID) and employed for analysis of fuel ether oxygenates (methyl tert‑butyl ether, MTBE, ethyl tert‑butyl ether, ETBE, and tert‑butyl formate, TBF) in urine samples. To optimize the key experimental variables including extraction temperature, extraction time, salt concentration, and stirring speed, a central composite design-response surface methodology (CCD-RSM) was employed.

Comparative study of molecularly imprinted polymer surface modified magnetic silica aerogel, zeolite Y, and MIL-101(Cr) for dispersive solid phase extraction of fuel ether oxygenates in drinking water.

The study was performed in two phases. First, the polymerization was carried out upon three magnetized surfaces of silica aerogel, zeolite Y, and MIL-101(Cr). Then, optimal molecularly imprinted polymer and optimal extraction conditions were determined by the central composite design-response surface method.

Rescuing biologically relevant consensus regions across replicated samples.

Background: Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites.

Results: We rescue weak binding sites using MSPC, which efficiently exploits replicates to lower the threshold required to identify a site while keeping a low false-positive rate, and we compare it to IDR, a widely used post-processing method for identifying highly reproducible peaks across replicates.

Microextraction and Determination of Poly- and Perfluoroalkyl Substances, Challenges, and Future Trends.

Per- and polyfluoroalkyl substances (PFAS) are fluorocarbon compounds in which hydrogen atoms have been partly or entirely replaced by fluorine. They have a very wide range of applications, while they are persistent in the environment and exhibit bioaccumulative and toxic properties. Neither chemical nor biological mechanisms can decompose PFAS due to their strong C-F bonds.

Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.

Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update.

Galaxy (https://galaxyproject.org) is a web-based computational workbench used by tens of thousands of scientists across the world to analyze large biomedical datasets. Since 2005, the Galaxy project has fostered a global community focused on achieving accessible, reproducible, and collaborative research.

How Machine Learning Will Transform Biomedicine.

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning.

Cloud bursting galaxy: federated identity and access management.

Motivation: Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to high-speed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms.

Federated Galaxy: Biomedical Computing at the Frontier.

Biomedical data exploration requires integrative analyses of large datasets using a diverse ecosystem of tools. For more than a decade, the Galaxy project (https://galaxyproject.org) has provided researchers with a web-based, user-friendly, scalable data analysis framework complemented by a rich ecosystem of tools (https://usegalaxy.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging.

Explorative visual analytics on interval-based genomic data and their metadata.

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines.

Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE).

MuSERA: Multiple Sample Enriched Region Assessment.

Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support.

Using combined evidence from replicates to evaluate ChIP-seq peaks.

Motivation: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA-protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives; replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence.

GenoMetric Query Language: a novel approach to large-scale genomic data management.

Motivation: Improvement of sequencing technologies and data processing pipelines is rapidly providing sequencing data, with associated high-level features, of many individual genomes in multiple biological and clinical conditions. They allow for data-driven genomic, transcriptomic and epigenomic characterizations, but require state-of-the-art 'big data' computing strategies, with abstraction levels beyond available tool capabilities.

Results: We propose a high-level, declarative GenoMetric Query Language (GMQL) and a toolkit for its use.