scholarly journals MSF: Modulated Sub-graph Finder

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1346
Author(s):  
Mariam R. Farman ◽  
Ivo L. Hofacker ◽  
Fabian Amman
Keyword(s):  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Data Interpretation ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Novel Approach ◽  
Differential Gene ◽  
External Stimuli ◽  
Flow Of Information

High throughput techniques such as RNA-seq or microarray analysis have proven to be invaluable for the characterizing of global transcriptional gene activity changes due to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and most of the methods rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. To this end, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information and identify the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https://github.com/Modulated-Subgraph-Finder/MSF.

scholarly journals MSF: Modulated Sub-graph Finder

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1346
Author(s):  
Mariam R. Farman ◽  
Ivo L. Hofacker ◽  
Fabian Amman
Keyword(s):  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Data Interpretation ◽  
Differentially Expressed ◽  
Novel Approach ◽  
Differential Gene ◽  
External Stimuli ◽  
Flow Of Information

High throughput techniques such as RNA-seq or microarray analysis have proven tobe invaluable for the characterization of global transcriptional gene activity changesdue to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. To this end, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information and identify the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https://github.com/Modulated-Subgraph-Finder/MSF.

scholarly journals MSF: Modulated Sub-graph Finder

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1346 ◽  
Author(s):  
Mariam R. Farman ◽  
Ivo L. Hofacker ◽  
Fabian Amman
Keyword(s):  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Data Interpretation ◽  
Differentially Expressed ◽  
P Value ◽  
Novel Approach ◽  
Perturbation Source ◽  
Differential Gene ◽  
External Stimuli ◽  
Flow Of Information

High throughput techniques such as RNA-seq or microarray analysis have proven to be invaluable for the characterization of global transcriptional gene activity changes due to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. Thereby, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information from the perturbation source to the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https: //github.com/Modulated-Subgraph-Finder/MSF.

scholarly journals Transcriptomic Analysis for the Identification of Metabolic Pathway Genes Related to Toluene Response in Ardisia pusilla

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1011
Author(s):  
Junping Xu ◽  
Chang Ho Ahn ◽  
Ju Young Shin ◽  
Pil Man Park ◽  
Hye Ryun An ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Metabolic Pathways ◽  
Differentially Expressed Gene ◽  
Carotenoid Biosynthesis ◽  
Raw Material ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Quantitative Real Time Pcr ◽  
Phenylpropanoid Biosynthesis ◽  
Differential Gene

Toluene is an industrial raw material and solvent that can be found abundantly in our daily life products. The amount of toluene vapor is one of the most important measurements for evaluating air quality. The evaluation of toluene scavenging ability of different plants has been reported, but the mechanism of plant response to toluene is only partially understood. In this study, we performed RNA sequencing (RNA-seq) analysis to detect differential gene expression in toluene-treated and untreated leaves of Ardisiapusilla. A total of 88,444 unigenes were identified by RNA-seq analysis, of which 49,623 were successfully annotated and 4101 were differentially expressed. Gene ontology analysis revealed several subcategories of genes related to toluene response, including cell part, cellular process, organelle, and metabolic processes. We mapped the main metabolic pathways of genes related to toluene response and found that the differentially expressed genes were mainly involved in glycolysis/gluconeogenesis, starch and sucrose metabolism, glycerophospholipid metabolism, carotenoid biosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis. In addition, 53 transcription factors belonging to 13 transcription factor families were identified. We verified 10 differentially expressed genes related to metabolic pathways using quantitative real-time PCR and found that the results of RNA-seq were positively correlated with them, indicating that the transcriptome data were reliable. This study provides insights into the metabolic pathways involved in toluene response in plants.

scholarly journals RNA-seq analysis of workers' brain reveals that queen and brood affect bumble bee worker reproduction via similar genetic pathways

2021 ◽  
Author(s):  
Priscila Santos ◽  
David Galbraith ◽  
Jesse Starkey ◽  
Etya Amsalem
Keyword(s):  
Gene Expression ◽  
Fat Body ◽  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Worker Reproduction ◽  
Differentially Expressed ◽  
Bumble Bee ◽  
Rna Seq ◽  
Evolution Of Sociality ◽  
Worker Sterility

Worker reproduction in social insects is often regulated by the queen’s presence but can be regulated by other colony members, such as the brood and nestmates. Adults and brood may induce the same outcomes in subordinates but may use different mechanisms. Here, we compared gene expression patterns in bumble bee workers (Bombus impatiens) in response to the queen, the brood, both or none. RNA‐seq analysis of workers’ brain identified 27 differentially expressed genes regulated by the queen and the brood. Expression levels of 8 candidate genes were re-tested using qRT-PCR in worker brain and fat body. Our results show that the brood’s effect on gene expression is substantially weaker than the queen, and a greater impact on gene expression was caused by the combined presence of the queen and the brood. All the genes that were explained by the brood presence were also regulated by the queen presence. A significant amount of the variation in gene expression was explained by the queen, that regulated the expression of key regulators of reproduction and brood care across insects, such as neuroparsin and vitellogenin. A comparison of the data with similar datasets in the honeybee and the raider ant revealed that neuroparsin is the only differentially expressed gene shared by all species. These data highlight the need to consider components other than the queen when examining mechanisms regulating worker sterility and provide information on key genes regulating reproduction that are likely to play an important role in the evolution of sociality.

scholarly journals Different Disease Endotypes in Phenotypically Similar Vasculitides Affecting Small-to-Medium Sized Blood Vessels

2021 ◽  
Vol 12 ◽  
Author(s):  
Erin E. Gill ◽  
Maren L. Smith ◽  
Kristen M. Gibson ◽  
Kimberly A. Morishita ◽  
Amy H. Y. Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Cell Receptor ◽  
Rna Seq ◽  
Adaptive Immune ◽  
T Cell Receptor Signaling ◽  
Neutrophil Degranulation ◽  
Differential Gene ◽  
Disease Subtypes

Objectives: Chronic primary vasculitis describes a group of complex and rare diseases that are characterized by blood vessel inflammation. Classification of vasculitis subtypes is based predominantly on the size of the involved vessels and clinical phenotype. There is a recognized need to improve classification, especially for small-to-medium sized vessel vasculitides, that, ideally, is based on the underlying biology with a view to informing treatment.Methods: We performed RNA-Seq on blood samples from children (n = 41) and from adults (n = 11) with small-to-medium sized vessel vasculitis, and used unsupervised hierarchical clustering of gene expression patterns in combination with clinical metadata to define disease subtypes.Results: Differential gene expression at the time of diagnosis separated patients into two primary endotypes that differed in the expression of ~3,800 genes in children, and ~1,600 genes in adults. These endotypes were also present during disease flares, and both adult and pediatric endotypes could be discriminated based on the expression of just 20 differentially expressed genes. Endotypes were associated with distinct biological processes, namely neutrophil degranulation and T cell receptor signaling.Conclusions: Phenotypically similar subsets of small-to-medium sized vessel vasculitis may have different mechanistic drivers involving innate vs. adaptive immune processes. Discovery of these differentiating immune features provides a mechanistic-based alternative for subclassification of vasculitis.

scholarly journals De novo gonad transcriptome analysis of the common littoral shrimp Palaemon serratus: novel insights into sex-related genes

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Inés González-Castellano ◽  
Chiara Manfrin ◽  
Alberto Pallavicini ◽  
Andrés Martínez-Lage
Keyword(s):  
Transcriptome Analysis ◽  
De Novo ◽  
Expression Patterns ◽  
Gonadal Development ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Specific Expression ◽  
Development Processes ◽  
The Common

Abstract Background The common littoral shrimp Palaemon serratus is an economically important decapod resource in some European communities. Aquaculture practices prevent the genetic deterioration of wild stocks caused by overfishing and at the same time enhance the production. The biotechnological manipulation of sex-related genes has the proved potential to improve the aquaculture production but the scarcity of genomic data about P. serratus hinders these applications. RNA-Seq analysis has been performed on ovary and testis samples to generate a reference gonadal transcriptome. Differential expression analyses were conducted between three ovary and three testis samples sequenced by Illumina HiSeq 4000 PE100 to reveal sex-related genes with sex-biased or sex-specific expression patterns. Results A total of 224.5 and 281.1 million paired-end reads were produced from ovary and testis samples, respectively. De novo assembly of ovary and testis trimmed reads yielded a transcriptome with 39,186 transcripts. The 29.57% of the transcriptome retrieved at least one annotation and 11,087 differentially expressed genes (DEGs) were detected between ovary and testis replicates. Six thousand two hundred seven genes were up-regulated in ovaries meanwhile 4880 genes were up-regulated in testes. Candidate genes to be involved in sexual development and gonadal development processes were retrieved from the transcriptome. These sex-related genes were discussed taking into account whether they were up-regulated in ovary, up-regulated in testis or not differentially expressed between gonads and in the framework of previous findings in other crustacean species. Conclusions This is the first transcriptome analysis of P. serratus gonads using RNA-Seq technology. Interesting findings about sex-related genes from an evolutionary perspective (such as Dmrt1) and for putative future aquaculture applications (Iag or vitellogenesis genes) are reported here. We provide a valuable dataset that will facilitate further research into the reproductive biology of this shrimp.

scholarly journals Identification of Novel lncRNAs Differentially Expressed in Placentas of Chinese Ningqiang Pony and Yili Horse Breeds

Animals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 119
Author(s):  
Yabin Pu ◽  
Yanli Zhang ◽  
Tian Zhang ◽  
Jianlin Han ◽  
Yuehui Ma ◽  
...  
Keyword(s):  
Body Size ◽  
Limb Development ◽  
Small Body ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Myoblast Differentiation ◽  
Rna Seq ◽  
Skeletal Myoblast ◽  
Control Procedures ◽  
Non Coding Rnas

As a nutrient sensor, the placenta plays a key role in regulating fetus growth and development. Long non-coding RNAs (lncRNAs) have been shown to regulate growth-related traits. However, the biological function of lncRNAs in horse placentas remains unclear. To compare the expression patterns of lncRNAs in the placentas of the Chinese Ningqiang (NQ) and Yili (YL) breeds, we performed a transcriptome analysis using RNA sequencing (RNA-seq) technology. NQ is a pony breed with an average adult height at the withers of less than 106 cm, whereas that of YL is around 148 cm. Based on 813 million high-quality reads and stringent quality control procedures, 3011 transcripts coding for 1464 placental lncRNAs were identified and mapped to the horse reference genome. We found 107 differentially expressed lncRNAs (DELs) between NQ and YL, including 68 up-regulated and 39 down-regulated DELs in YL. Six (TBX3, CACNA1F, EDN3, KAT5, ZNF281, TMED2, and TGFB1) out of the 233 genes targeted by DELs were identified as being involved in limb development, skeletal myoblast differentiation, and embryo development. Two DELs were predicted to target the TBX3 gene, which was found to be under strong selection and associated with small body size in the Chinese Debao pony breed. This finding suggests the potential functional significance of placental lncRNAs in regulating horse body size.

scholarly journals Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data

2019 ◽  
Vol 13 ◽  
pp. 117793221986081 ◽  
Author(s):  
Takayuki Osabe ◽  
Kentaro Shimizu ◽  
Koji Kadota
Keyword(s):  
Differential Expression ◽  
Bayesian Methods ◽  
Count Data ◽  
Empirical Bayes ◽  
Expression Patterns ◽  
Rna Seq ◽  
Model Framework ◽  
Gene Ranking ◽  
Normalization Methods

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.

scholarly journals Integration of mRNA and miRNA Analysis Reveals the Molecular Mechanism Underlying Salt and Alkali Stress Tolerance in Tobacco

2019 ◽  
Vol 20 (10) ◽  
pp. 2391 ◽  
Author(s):  
Jiayang Xu ◽  
Qiansi Chen ◽  
Pingping Liu ◽  
Wei Jia ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Salinity Stress ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Crop Yields ◽  
Antioxidant Activities ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Alkali Stress ◽  
Rna Seq ◽  
Alkali Tolerance

Salinity is one of the most severe forms of abiotic stress and affects crop yields worldwide. Plants respond to salinity stress via a sophisticated mechanism at the physiological, transcriptional and metabolic levels. However, the molecular regulatory networks involved in salt and alkali tolerance have not yet been elucidated. We developed an RNA-seq technique to perform mRNA and small RNA (sRNA) sequencing of plants under salt (NaCl) and alkali (NaHCO3) stress in tobacco. Overall, 8064 differentially expressed genes (DEGs) and 33 differentially expressed microRNAs (DE miRNAs) were identified in response to salt and alkali stress. A total of 1578 overlapping DEGs, which exhibit the same expression patterns and are involved in ion channel, aquaporin (AQP) and antioxidant activities, were identified. Furthermore, genes involved in several biological processes, such as “photosynthesis” and “starch and sucrose metabolism,” were specifically enriched under NaHCO3 treatment. We also identified 15 and 22 miRNAs that were differentially expressed in response to NaCl and NaHCO3, respectively. Analysis of inverse correlations between miRNAs and target mRNAs revealed 26 mRNA-miRNA interactions under NaCl treatment and 139 mRNA-miRNA interactions under NaHCO3 treatment. This study provides new insights into the molecular mechanisms underlying the response of tobacco to salinity stress.

scholarly journals Distinct gene signatures predict insulin resistance in young mice with high fat diet-induced obesity

2018 ◽  
Vol 50 (3) ◽  
pp. 144-157 ◽  
Author(s):  
Katherine Chen ◽  
Alice Jih ◽  
Olivia Osborn ◽  
Sarah T. Kavaler ◽  
Wenxian Fu ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
High Fat Diet ◽  
Expression Patterns ◽  
Gene Families ◽  
Differentially Expressed ◽  
Rna Seq ◽  
High Fat ◽  
Diet Induced Obesity ◽  
Before And After ◽  
Young Mice

Highly inbred C57BL/6 mice show wide variation in their degree of insulin resistance in response to diet-induced obesity even though they are almost genetically identical. Here we employed transcriptional profiling by RNA sequencing (RNA-Seq) of visceral adipose tissue (VAT) and liver in young mice to determine how gene expression patterns correlate with the later development of high-fat diet (HFD)-induced insulin resistance in adulthood. To accomplish this goal, we partially removed and banked tissues from pubertal mice. Mice subsequently received HFD followed by metabolic phenotyping to identify two well-defined groups of mice with either severe or mild insulin resistance. The remaining tissues were collected at study termination. We then applied RNA-Seq to generate transcriptome profiles associated with worsened insulin resistance before and after the initiation of HFD. We found 244 up- and 109 downregulated genes in VAT of the most insulin-resistant mice even before HFD exposure. Downregulated genes included serine protease inhibitor, major urinary protein, and complement genes; upregulated genes represented mostly muscle constituents. These gene families were also differentially expressed in VAT of mice with high or low insulin resistance after HFD. Inflammatory genes predicted insulin resistance in liver, but not in VAT. In contrast, when we compared VAT of all mice before and after HFD, differentially expressed genes were predominantly composed of immune response genes. These data show a distinct set of gene transcripts in young mice correlates with the severity of insulin resistance in adulthood, providing insight into the pathogenesis of insulin resistance in early life.

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