scholarly journals Stratification of Gene Coexpression Patterns and GO Function Mining for a RNA-Seq Data Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Zhao ◽  
Fenglin Cao ◽  
Yonghui Gong ◽  
Huafeng Xu ◽  
Yiping Fei ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Physiological State ◽  
Biological Research ◽  
Data Series ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Functional Relationships ◽  
Differential Coexpression ◽  
Functional Mechanisms ◽  
Coexpression Pattern

RNA-Seq is emerging as an increasingly important tool in biological research, and it provides the most direct evidence of the relationship between the physiological state and molecular changes in cells. A large amount of RNA-Seq data across diverse experimental conditions have been generated and deposited in public databases. However, most developed approaches for coexpression analyses focus on the coexpression pattern mining of the transcriptome, thereby ignoring the magnitude of gene differences in one pattern. Furthermore, the functional relationships of genes in one pattern, and notably among patterns, were not always recognized. In this study, we developed an integrated strategy to identify differential coexpression patterns of genes and probed the functional mechanisms of the modules. Two real datasets were used to validate the method and allow comparisons with other methods. One of the datasets was selected to illustrate the flow of a typical analysis. In summary, we present an approach to robustly detect coexpression patterns in transcriptomes and to stratify patterns according to their relative differences. Furthermore, a global relationship between patterns and biological functions was constructed. In addition, a freely accessible web toolkit “coexpression pattern mining and GO functional analysis” (COGO) was developed.

scholarly journals Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 665
Author(s):  
Hui Yu ◽  
Yan Guo ◽  
Jingchun Chen ◽  
Xiangning Chen ◽  
Peilin Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Focal Adhesion ◽  
Biological Pathways ◽  
Postmortem Brain ◽  
Rna Seq ◽  
Hub Genes ◽  
Differential Coexpression ◽  
Pathway Crosstalk ◽  
Microarray Datasets ◽  
Transcriptomic Studies

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

Antimicrobial Effects of d-3-Phenyllactic Acid on Listeria monocytogenes in TSB-YE Medium, Milk, and Cheese

1998 ◽  
Vol 61 (10) ◽  
pp. 1281-1285 ◽  
Author(s):  
VIRGINIE DIEULEVEUX ◽  
MICHELINE GUÉGUEN
Keyword(s):  
Listeria Monocytogenes ◽  
Growth Phase ◽  
Physiological State ◽  
Geotrichum Candidum ◽  
Bactericidal Effect ◽  
Exponential Growth Phase ◽  
Bacteriostatic Effect ◽  
Experimental Conditions ◽  
Phenyllactic Acid ◽  
Whole Milk

d-3-Phenyllactic acid is a compound with anti-Listeria activity which is produced and secreted by the yeastlike fungus, Geotrichum candidum. This compound has a bactericidal effect independent of the physiological State of Listeria monocytogenes when added at a concentration of 7 mg/ml to tryptic soy broth supplemented with yeast extract (TSB-YE). An initial L. monocytogenes population of 105 CFU/ml was reduced 100-fold (2 log) after 4 days of culture at 25 °C in TSB-YE containing d-3-phenyllactic acid. The Listeria population was reduced 1,000-fold (3 log) when the compound was added during the exponential growth phase, and was reduced to less than 10 CFU/ml when it was added during the stationary phase. d-3-Phenyllactic acid had a bacteriostatic effect in UHT whole milk, reducing the population by 4.5 log, to give fewer cells than in the control after 5 days of culture. The results obtained with L. monocytogenes at concentrations of 105 and 103 CFU/ml in cheese curds were less conclusive. d-3-Phenyllactic acid was 10 times less active than nisin in our experimental conditions (TSB-YE at 25°C).

scholarly journals Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dimitra Sarantopoulou ◽  
Soon Yew Tang ◽  
Emanuela Ricciotti ◽  
Nicholas F. Lahens ◽  
Damien Lekkas ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Differential Expression Analysis ◽  
Functional Results ◽  
Starting Material ◽  
The Other ◽  
Library Preparation ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Preparation Methods ◽  
Low Input

Abstract Library preparation is a key step in sequencing. For RNA sequencing there are advantages to both strand specificity and working with minute starting material, yet until recently there was no kit available enabling both. The Illumina TruSeq stranded mRNA Sample Preparation kit (TruSeq) requires abundant starting material while the Takara Bio SMART-Seq v4 Ultra Low Input RNA kit (V4) sacrifices strand specificity. The SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Pico) by Takara Bio claims to overcome these limitations. Comparative evaluation of these kits is important for selecting the appropriate protocol. We compared the three kits in a realistic differential expression analysis. We prepared and sequenced samples from two experimental conditions of biological interest with each of the three kits. We report differences between the kits at the level of differential gene expression; for example, the Pico kit results in 55% fewer differentially expressed genes than TruSeq. Nevertheless, the agreement of the observed enriched pathways suggests that comparable functional results can be obtained. In summary we conclude that the Pico kit sufficiently reproduces the results of the other kits at the level of pathway analysis while providing a combination of options that is not available in the other kits.

Biological Model of Fisheries Production Based on Physiological and Ecological Scalings of Body Size

1989 ◽  
Vol 46 (4) ◽  
pp. 614-623 ◽  
Author(s):  
P. R. Boudreau ◽  
L. M. Dickie
Keyword(s):  
Body Size ◽  
Individual Species ◽  
Data Series ◽  
Biomass Density ◽  
Fish Stocks ◽  
Population Sampling ◽  
Functional Relationships ◽  
Relationship Of ◽  
The Relationship ◽  
Population Equilibrium

Earlier ecological studies showing regularity in the relationship of certain indices of production to body size are used to develop a predictive equation of fish production on a year to year basis, with biomass and body size as independent stock variables. The prediction system makes use of the observed regular adjustments of local biomass density with body size and the parallelism of the functional relationships of production and biomass with body size both between and within stock cohorts. The method obviates the need to invoke assumptions of population equilibrium. The model is applied to three data series for individual species exploited by commercial fisheries on the Scotian Shelf. The results suggest that despite the vagaries of population sampling, ecological information can provide practical estimates of the production potential of fish stocks.

scholarly journals Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

2020 ◽  
Author(s):  
Peng Yu ◽  
Jin Li ◽  
Su-Ping Deng ◽  
Feiran Zhang ◽  
Petar N. Grozdanov ◽  
...  
Keyword(s):  
Rett Syndrome ◽  
Neuronal Morphology ◽  
Biological Research ◽  
Integrated Analysis ◽  
Splicing Factors ◽  
Biological Processes ◽  
Rna Seq ◽  
Key Factors ◽  
Mouse Tissues ◽  
Public Data

AbstractA vast amount of public RNA-sequencing datasets have been generated and used widely to study transcriptome mechanisms. These data offer precious opportunity for advancing biological research in transcriptome studies such as alternative splicing. We report the first large-scale integrated analysis of RNA-Seq data of splicing factors for systematically identifying key factors in diseases and biological processes. We analyzed 1,321 RNA-Seq libraries of various mouse tissues and cell lines, comprising more than 6.6 TB sequences from 75 independent studies that experimentally manipulated 56 splicing factors. Using these data, RNA splicing signatures and gene expression signatures were computed, and signature comparison analysis identified a list of key splicing factors in Rett syndrome and cold-induced thermogenesis. We show that cold-induced RNA-binding proteins rescue the neurite outgrowth defects in Rett syndrome using neuronal morphology analysis, and we also reveal that SRSF1 and PTBP1 are required for energy expenditure in adipocytes using metabolic flux analysis. Our study provides an integrated analysis for identifying key factors in diseases and biological processes and highlights the importance of public data resources for identifying hypotheses for experimental testing.

scholarly journals β-aminobutyric acid (BABA): towards understanding its biosynthesis, localization and hormonal regulation in "Arabidopsis thaliana"

2020 ◽  
Author(s):  
◽  
Francesco Stefanelli
Keyword(s):  
Plant Hormones ◽  
Hormonal Regulation ◽  
Compensation Effect ◽  
Physiological State ◽  
Aminobutyric Acid ◽  
Rna Seq ◽  
Wild Type ◽  
Biotic And Abiotic Stresses ◽  
Organ Type ◽  
A Cell

Priming is defined as a physiological state induced by a priming stimulus that allows a plant to deploy a more rapid and more robust defense response to stresses compared with a non-primed plant. β-aminobutyric acid (BABA) has emerged as one of the best stimuli to study priming. Plants can synthesize BABA and accumulate it after being exposed to both biotic or abiotic stress. The plant immune system regulates BABA accumulation during pathogen infection. BABA concentrations vary depending on organ type and with the developmental stage. Flowers, senescent leaves and seeds are the sites of major accumulation. Interestingly, the early senescence and constitutive priming mutant cpr5-2 shows higher basal and induced BABA concentrations compared to its wild type. Besides cpr5-2, no other mutants related to BABA have been characterized up to now. Therefore, we performed RNA-seq analysis to identify common genes expressed during various BABA-inducing biotic and abiotic stresses in Arabidopsis Col-0 and cpr5-2. The analysis revealed ten genes up-regulated in common and one down-regulated gene. Nevertheless, T-DNA insertional lines of the up-regulated genes did not show a wild-type BABA concentration after salt stress application, keeping unsolved the search for genes involved in BABA metabolism in plants. Furthermore, we are looking at the relation between BABA and plant hormones. Exposition of Arabidopsis to different plant hormones revealed that only ABA led to an increase of the BABA concentration. The bioactive form of ABA, (+)-ABA, showed a more robust BABA induction phenotype. Mutants of the three SnRK2 genes, key regulators of ABA signaling, showed BABA induction after (+)-ABA application, suggesting a compensation effect. Finally, to get further information on BABA localization in plants, BABA has been modified chemically, adding an alkyne tag. Successively, a copper-catalyzed azide-alkyne cycloaddition (CuAAC) between tagged BABA and an azide Alexa fluor® was performed, generating fluorescence tagged BABA molecules. These molecules were then visualized on confocal microscopy, showing a cell wall localization of BABA in Arabidopsis roots and globular subcellular structures. Leaves showed no labeling. However, the results are too early to define precisely the exact localization of BABA in plants.

scholarly journals Point Process Temporal Structure Characterizes Electrodermal Activity

2020 ◽  
Author(s):  
Sandya Subramanian ◽  
Riccardo Barbieri ◽  
Emery N. Brown
Keyword(s):  
Point Process ◽  
Electrodermal Activity ◽  
Temporal Structure ◽  
Physiological State ◽  
Electrical Conductance ◽  
Pulse Interval ◽  
Data Series ◽  
Inverse Gaussian ◽  
Probability Models ◽  
Gaussian Models

AbstractElectrodermal activity (EDA) is a read-out of the body’s sympathetic nervous system measured as sweat-induced changes in the electrical conductance properties of the skin. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality and emotional states. Standardized EDA data analysis methods are readily available. However, none considers two established physiological features of EDA: 1) sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process; 2) inter-pulse interval times vary depending upon the local physiological state of the skin. Based on the anatomy and physiology that underlie feature 1, we postulate that inverse Gaussian probability models would accurately describe EDA inter-pulse intervals. Given feature 2, we postulate that under fluctuating local physiological states, the inter-pulse intervals would follow mixtures of inverse Gaussian models, that can be represented as lognormal models if the conditions favor longer intervals (heavy tails) or by gamma models if the conditions favor shorter intervals (light tails). To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 to 2 hours of quiet wakefulness. We assess the tail behavior of the probability models by computing their settling rates. All data series were accurately described by one or more of the models: two by inverse Gaussian models; five by lognormal models and three by gamma models. These probability models suggest a highly succinct point process framework for real-time tracking of sympathetically-mediated changes in physiological state.

scholarly journals Molecular Characterization of the Oncogene BTF3 and Its Targets in Colorectal Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Hantao Wang ◽  
Junjie Xing ◽  
Wei Wang ◽  
Guifen Lv ◽  
Haiyan He ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transcription Factor ◽  
Ubiquitin Ligase ◽  
E3 Ubiquitin Ligase ◽  
Rna Seq ◽  
A Subunit ◽  
Cancer Types ◽  
Functional Mechanisms ◽  
Transcriptional Target

Colorectal cancer (CRC) is one of the most commonly diagnosed and leading causes of cancer mortality worldwide, and the prognosis of patients with CRC remains unsatisfactory. Basic transcription factor 3 (BTF3) is an oncogene and hazardous prognosticator in CRC. Although two distinct functional mechanisms of BTF3 in different cancer types have been reported, its role in CRC is still unclear. In this study, we aimed to molecularly characterize the oncogene BTF3 and its targets in CRC. Here, we first identified the transcriptional targets of BTF3 by applying combined RNA-Seq and ChIP-Seq analysis, identifying CHD1L as a transcriptional target of BTF3. Thereafter, we conducted immunoprecipitation (IP)-MS and E3 ubiquitin ligase analysis to identify potential interacting targets of BTF3 as a subunit of the nascent-polypeptide-associated complex (NAC). The analysis revealed that BTF3 might also inhibit E3 ubiquitin ligase HERC2-mediated p53 degradation. Finally, miRNAs targeting BTF3 were predicted and validated. Decreased miR-497-5p expression is responsible for higher levels of BTF3 post-transcriptionally. Collectively, we concluded that BTF3 is an oncogene, and there may exist a transcription factor and NAC-related proteolysis mechanism in CRC. This study provides a comprehensive basis for understanding the oncogenic mechanisms of BTF3 in CRC.

scholarly journals Sashimi plots: Quantitative visualization of alternative isoform expression from RNA-seq data

2014 ◽  
Author(s):  
Yarden Katz ◽  
Eric T Wang ◽  
Jacob Stilterra ◽  
Schraga Schwartz ◽  
Bang Wong ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Splice Junction ◽  
Genomic Region ◽  
Rna Seq ◽  
Mrna Isoforms ◽  
Experimental Conditions ◽  
Human Genes ◽  
Quantitative Visualization ◽  
Isoform Expression ◽  
Multiple Samples

Analysis of RNA sequencing (RNA-Seq) data revealed that the vast majority of human genes express multiple mRNA isoforms, produced by alternative pre-mRNA splicing and other mechanisms, and that most alternative isoforms vary in expression between human tissues. As RNA-Seq datasets grow in size, it remains challenging to visualize isoform expression across multiple samples. We present Sashimi plots, a quantitative multi-sample visualization of RNA-Seq reads aligned to gene annotations, which enables quantitative comparison of isoform usage across samples or experimental conditions. Given an input annotation and spliced alignments of reads from a sample, a region of interest is visualized in a Sashimi plot as follows: (i) alignments in exons are represented as read densities (optionally normalized by length of genomic region and coverage), and (ii) splice junction reads are drawn as arcs connecting a pair of exons, where arc width is drawn proportional to the number of reads aligning to the junction.

scholarly journals Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Kayla A. Johnson ◽  
Arjun Krishnan
Keyword(s):  
Gene Expression ◽  
Expression Data ◽  
Rna Seq ◽  
Functional Relationships ◽  
Gene Coexpression ◽  
Transformation Methods ◽  
Network Transformation ◽  
Almost All ◽  
Coexpression Networks ◽  
The Impact

Abstract Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

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