0
selected
-
1.
Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians.
Allaband, C, McDonald, D, Vázquez-Baeza, Y, Minich, JJ, Tripathi, A, Brenner, DA, Loomba, R, Smarr, L, Sandborn, WJ, Schnabl, B, et al
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2019;(2):218-230
-
-
Free full text
-
Abstract
Advances in technical capabilities for reading complex human microbiomes are leading to an explosion of microbiome research, leading in turn to intense interest among clinicians in applying these techniques to their patients. In this review, we discuss the content of the human microbiome, including intersubject and intrasubject variability, considerations of study design including important confounding factors, and different methods in the laboratory and on the computer to read the microbiome and its resulting gene products and metabolites. We highlight several common pitfalls for clinicians, including the expectation that an individual's microbiome will be stable, that diet can induce rapid changes that are large compared with the differences among subjects, that everyone has essentially the same core stool microbiome, and that different laboratory and computational methods will yield essentially the same results. We also highlight the current limitations and future promise of these techniques, with the expectation that an understanding of these considerations will help accelerate the path toward routine clinical application of these techniques developed in research settings.
-
2.
The entropic and symbolic components of information.
Dos Santos, WD
Bio Systems. 2019;:17-20
Abstract
At the turn of the 19th and 20th centuries, Boltzmann and Plank described entropy S as a logarithm function of the probability distribution of microstates w of a system (S = k ln w), where k is the Boltzmann constant equalling the gas constant per Avogadro's number (R NA-1). A few decades later, Shannon established that information, I, could be measured as the log of the number of stable microstates n of a system. Considering a system formed by binary information units, bit, I = log2bit From this, Brillouin deduced that information is inversely proportional to the number of microstates of a system, and equivalent to entropy taken with a negative signal -S or 'negentropy' (I = k ln (1/w) = -S). In contrast with these quantitative treatments, more recently, Barbieri approached the 'nominal' feature of information. In computing, semantics or molecular biology, information is transported in specific sequences (of bits, letters or monomers). As these sequences are not determined by the intrinsic properties of the components, they cannot be described by a physical law: information derives necessarily from a copying/coding process. Therefore, a piece of information, although an objective physical entity, is irreducible and immeasurable: it can only be described by naming their components in the exact order. Here, I review the mathematical rationale of Brillouin's identitification between information and negentropy to demonstrate that although a gain in information implies a necessary gain in negentropy, a gain in negentropy does not necessarily imply a gain in information.
-
3.
Application of Computational Biology to Decode Brain Transcriptomes.
Li, J, Wang, GZ
Genomics, proteomics & bioinformatics. 2019;(4):367-380
Abstract
The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these high-dimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.
-
4.
A guide to the application of Hill numbers to DNA-based diversity analyses.
Alberdi, A, Gilbert, MTP
Molecular ecology resources. 2019;(4):804-817
Abstract
With the advent of DNA sequencing-based techniques, the way we detect and measure biodiversity is undergoing a radical shift. There is also an increasing awareness of the need to employ intuitively meaningful diversity measures based on unified statistical frameworks, so that different results can be easily interpreted and compared. This article aimed to serve as a guide to implementing biodiversity assessment using the general statistical framework developed around Hill numbers into the analysis of systems characterized using DNA sequencing-based techniques (e.g., diet, microbiomes and ecosystem biodiversity). Specifically, we discuss (a) the DNA-based approaches for defining the types upon which diversity is measured, (b) how to weight the importance of each type, (c) the differences between abundance-based versus incidence-based approaches, (d) the implementation of phylogenetic information into diversity measurement, (e) hierarchical diversity partitioning, (f) dissimilarity and overlap measurement and (g) how to deal with zero-inflated, insufficient and biased data. All steps are reproduced with real data to also provide step-by-step bash and R scripts to enable straightforward implementation of the explained procedures.
-
5.
Structural insights into alcohol dehydrogenases catalyzing asymmetric reductions.
An, J, Nie, Y, Xu, Y
Critical reviews in biotechnology. 2019;(3):366-379
Abstract
Alcohol dehydrogenases are a group of oxidoreductases that specifically use NAD(P)+ or NAD(P)H as cofactors for electron acceptance or donation and catalyze interconversion between alcohols and corresponding carbonyl compounds. In addition to their physiological roles in metabolizing alcohols and aldehydes or ketones, alcohol dehydrogenases have received considerable attention with respect to their symmetry-breaking traits in catalyzing asymmetric reactions and have Accordingly, they have become widely applied in fine chemical synthesis, particularly in the production of chiral alcohols and hydroxyl compounds that are key elements in the synthesis of active pharmaceutical ingredients (API) employed in the pharmaceutical industry. The application of structural bioinformatics to the study of functional enzymes and recent scientific breakthroughs in modern molecular biotechnology provide us with an effective alternative to gain an understanding of the molecular mechanisms involved in asymmetric bioreactions and in overcoming the limitations of enzyme availability. In this review, we discuss molecular mechanisms underlying alcohol dehydrogenase-mediated asymmetric reactions, based on protein structure-function relationships from domain structure to functional active sites. The molecular principles of the catalytic machinery involving stereochemical recognition and molecular interaction are also addressed. In addition, the diversity of enzymatic functions and properties, for example, enantioselectivity, substrate specificity, cofactor dependence, metal requirement, and stability in terms of organic solvent tolerance and thermostability, are also discussed and based on a comparative analysis of high-resolution 3 D structures of representative alcohol dehydrogenases.
-
6.
Computational approaches for circular RNA analysis.
Jakobi, T, Dieterich, C
Wiley interdisciplinary reviews. RNA. 2019;(3):e1528
Abstract
Circular RNAs (circRNAs) are a recent addition to the expanding universe of RNA species and originate through back-splicing events from linear primary transcripts. CircRNAs show specific expression profiles with regards to cell type and developmental stage. Importantly, only few circRNAs have been functionally characterized to date. The detection of circRNAs from RNA sequencing data is a complex computational workflow that, depending on tissue and condition typically yields candidate sets of hundreds or thousands of circRNA candidates. Here, we provide an overview on different computational analysis tools and pipelines that became available throughout the last years. We outline technical and experimental requirements that are common to all approaches and point out potential pitfalls during the computational analysis. Although computational prediction of circRNAs has become quite mature in recent years, we provide a set of valuable validation strategies, in silico as well as in vitro-based approaches. In addition to circRNA detection via back-splicing junction, we present available analysis pipelines for delineating the primary sequence and for predicting possible functions of circRNAs. Finally, we outline the most important web resources for circRNA research. This article is categorized under: RNA Methods > RNA Analyses in vitro and In Silico RNA Evolution and Genomics > Computational Analyses of RNA.
-
7.
Copper trafficking in eukaryotic systems: current knowledge from experimental and computational efforts.
Magistrato, A, Pavlin, M, Qasem, Z, Ruthstein, S
Current opinion in structural biology. 2019;:26-33
Abstract
Copper plays a vital role in fundamental cellular functions, and its concentration in the cell must be tightly regulated, as dysfunction of copper homeostasis is linked to severe neurological diseases and cancer. This review provides a compendium of current knowledge regarding the mechanism of copper transfer from the blood system to the Golgi apparatus; this mechanism involves the copper transporter hCtr1, the metallochaperone Atox1, and the ATPases ATP7A/B. We discuss key insights regarding the structural and functional properties of the hCtr1-Atox1-ATP7B cycle, obtained from diverse studies relying on distinct yet complementary biophysical, biochemical, and computational methods. We further address the mechanistic aspects of the cycle that continue to remain elusive. These knowledge gaps must be filled in order to be able to harness our understanding of copper transfer to develop therapeutic approaches with the capacity to modulate copper metabolism.
-
8.
A Brief History of Protein Sorting Prediction.
Nielsen, H, Tsirigos, KD, Brunak, S, von Heijne, G
The protein journal. 2019;(3):200-216
-
-
Free full text
-
Abstract
Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.
-
9.
Recent Advances in Computational Methods for Identifying Anticancer Peptides.
Feng, P, Wang, Z
Current drug targets. 2019;(5):481-487
Abstract
Anticancer peptide (ACP) is a kind of small peptides that can kill cancer cells without damaging normal cells. In recent years, ACP has been pre-clinically used for cancer treatment. Therefore, accurate identification of ACPs will promote their clinical applications. In contrast to labor-intensive experimental techniques, a series of computational methods have been proposed for identifying ACPs. In this review, we briefly summarized the current progress in computational identification of ACPs. The challenges and future perspectives in developing reliable methods for identification of ACPs were also discussed. We anticipate that this review could provide novel insights into future researches on anticancer peptides.
-
10.
Methodological considerations for the identification of choline and carnitine-degrading bacteria in the gut.
Jameson, E, Quareshy, M, Chen, Y
Methods (San Diego, Calif.). 2018;:42-48
Abstract
The bacterial formation of trimethylamine (TMA) has been linked to cardiovascular disease. This review focuses on the methods employed to investigate the identity of the bacteria responsible for the formation of TMA from dietary choline and carnitine in the human gut. Recent studies have revealed the metabolic pathways responsible for bacterial TMA production, primarily the anaerobic glycyl radical-containing, choline-TMA lyase, CutC and the aerobic carnitine monooxygenase, CntA. Identification of these enzymes has enabled bioinformatics approaches to screen both human-associated bacterial isolate genomes and whole gut metagenomes to determine which bacteria are responsible for TMA formation in the human gut. We centre on several key methodological aspects for identifying the TMA-producing bacteria and report how these pathways can be identified in human gut microbiota through bioinformatics analysis of available bacterial genomes and gut metagenomes.