scholarly journals From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 308 ◽  
Author(s):  
Julijana Ivanisevic ◽  
Elizabeth J. Want
Keyword(s):  
Statistical Power ◽  
Biomarker Discovery ◽  
Disease Onset ◽  
Metabolite Identification ◽  
Holistic Approach ◽  
Data Interpretation ◽  
Relevant Information ◽  
Metabolomics Data ◽  
Biologically Relevant ◽  
Key Steps

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

scholarly journals Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

2021 ◽  
Author(s):  
Florence Anne Castelli ◽  
Giulio Rosati ◽  
Christian Moguet ◽  
Celia Fuentes ◽  
Jose Marrugo-Ramírez ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Personalized Medicine ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Point Of Care ◽  
Metabolite Identification ◽  
Individual Characteristics ◽  
Molecular Signatures ◽  
Laboratory Equipment ◽  
Metabolomics Data

AbstractMetabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine. Graphical abstract

scholarly journals MetPC: Metabolite Pipeline Consisting of Metabolite Identification and Biomarker Discovery Under the Control of Two-Dimensional FDR

Metabolites ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 103
Author(s):  
Jaehwi Kim ◽  
Jaesik Jeong
Keyword(s):  
Expectation Maximization ◽  
Latent Variables ◽  
Biomarker Discovery ◽  
Metabolite Identification ◽  
Two Dimensional ◽  
Bioinformatics Tool ◽  
Metabolomics Data ◽  
False Discovery ◽  
Hierarchical Statistical Model ◽  
Complex Features

Due to the complex features of metabolomics data, the development of a unified platform, which covers preprocessing steps to data analysis, has been in high demand over the last few decades. Thus, we developed a new bioinformatics tool that includes a few of preprocessing steps and biomarker discovery procedure. For metabolite identification, we considered a hierarchical statistical model coupled with an Expectation–Maximization (EM) algorithm to take care of latent variables. For biomarker metabolite discovery, our procedure controls two-dimensional false discovery rate (fdr2d) when testing for multiple hypotheses simultaneously.

scholarly journals The Process Chain for Peptidomic Biomarker Discovery

2006 ◽  
Vol 22 (1-2) ◽  
pp. 27-37 ◽  
Author(s):  
Michael Schrader ◽  
Hartmut Selle
Keyword(s):  
Clinical Studies ◽  
Biomarker Discovery ◽  
Data Interpretation ◽  
Underlying Disease ◽  
Biologically Relevant ◽  
Physiological Processes ◽  
Small Proteins ◽  
Drug Substances ◽  
Therapeutic Measures ◽  
New Biomarkers

Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics®methods) was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide biomarkes has potential for improving clinical studies.

Deep learning meets metabolomics: a methodological perspective

2020 ◽  
Author(s):  
Partho Sen ◽  
Santosh Lamichhane ◽  
Vivek B Mathema ◽  
Aidan McGlinchey ◽  
Alex M Dickens ◽  
...  
Keyword(s):  
Deep Learning ◽  
Biomarker Discovery ◽  
Metabolite Identification ◽  
Metabolomics Data ◽  
Metabolic Phenotyping ◽  
Metabolic Modelling ◽  
Actionable Knowledge ◽  
The Past ◽  
Genome Scale ◽  
Biological Outcomes

Abstract Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of ‘big data’, including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

Power-Enhanced Funnel Plots for Meta-Analysis

2020 ◽  
Vol 228 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Michael Kossmeier ◽  
Ulrich S. Tran ◽  
Martin Voracek
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Relevant Information ◽  
Selective Reporting ◽  
Graphical Display ◽  
Funnel Plot ◽  
Evidential Value ◽  
Graphical Displays ◽  
Funnel Plots ◽  
Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

scholarly journals NoRCE: non-coding RNA sets cis enrichment tool

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gulden Olgun ◽  
Afshan Nabi ◽  
Oznur Tastan
Keyword(s):  
Expression Patterns ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Fruit Fly ◽  
Relevant Information ◽  
R Package ◽  
Data Repository ◽  
Biologically Relevant ◽  
Gene Sets ◽  
Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

scholarly journals Noise matters: elephants show risk-avoidance behaviour in response to human-generated seismic cues

2021 ◽  
Vol 288 (1953) ◽  
pp. 20210774
Author(s):  
Beth Mortimer ◽  
James A. Walker ◽  
David S. Lolchuragi ◽  
Michael Reinwald ◽  
David Daballen
Keyword(s):  
Seismic Noise ◽  
Information Transfer ◽  
Conservation Management ◽  
Sensory Ecology ◽  
Loxodonta Africana ◽  
Relevant Information ◽  
African Elephants ◽  
Risk Avoidance ◽  
Biologically Relevant ◽  
Seismic Vibrations

African elephants ( Loxodonta africana ) use many sensory modes to gather information about their environment, including the detection of seismic, or ground-based, vibrations. Seismic information is known to include elephant-generated signals, but also potentially encompasses biotic cues that are commonly referred to as ‘noise’. To investigate seismic information transfer in elephants beyond communication, here we tested the hypothesis that wild elephants detect and discriminate between seismic vibrations that differ in their noise types, whether elephant- or human-generated. We played three types of seismic vibrations to elephants: seismic recordings of elephants (elephant-generated), white noise (human-generated) and a combined track (elephant- and human-generated). We found evidence of both detection of seismic noise and discrimination between the two treatments containing human-generated noise. In particular, we found evidence of retreat behaviour, where seismic tracks with human-generated noise caused elephants to move further away from the trial location. We conclude that seismic noise are cues that contain biologically relevant information for elephants that they can associate with risk. This expands our understanding of how elephants use seismic information, with implications for elephant sensory ecology and conservation management.

Exploring Dimensions of the Media Dream

2016 ◽  
pp. 1-39 ◽  
Author(s):  
Rollin McCraty ◽  
Stephen Brock Schafer
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Electromagnetic Coupling ◽  
Relevant Information ◽  
Living Systems ◽  
Biologically Relevant ◽  
Dream Analysis ◽  
Psychological Analysis ◽  
Dramatic Structure ◽  
The Media

The earth's magnetic fields are carriers of biologically relevant information that connects all living systems. The electromagnetic coupling of the human brain, cardiovascular and nervous systems, and geomagnetic frequencies supports the hypothesis that the mediated reality of electromagnetic bandwidths can be correlated with bio-energetic and geomagnetic frequencies. Understood as bio-energetic functions (Thinking, Feeling, Sensing, & Intuiting), the media-sphere becomes measurable according to principles of coherency (measured as heart-rate variability, HRV) and principles of Jungian dream analysis (compensation and dramatic structure). It has been demonstrated that the rhythmic patterns in beat-to-beat heart rate variability reflect emotional functions, permeate every bodily cell, and play a central role in the generation and transmission of system-wide information via the electromagnetic field. So, the “media dream” becomes susceptible to psychological analysis leading to a better understanding of unconscious cognitive archetypal patterns of contextual collectives.

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