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 ◽  
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 ◽  
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 Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i516-i524
Author(s):  
Midori Iida ◽  
Michio Iwata ◽  
Yoshihiro Yamanishi
Keyword(s):  
Large Scale ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Molecular Networks ◽  
Supplementary Information ◽  
New Associations ◽  
Disease States ◽  
Molecular Features ◽  
Novel Approach

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification and Characterization of miRNAs in Self-Rooted and Grafted Malus Reveals Critical Networks Associated with Flowering

2018 ◽  
Vol 19 (8) ◽  
pp. 2384 ◽  
Author(s):  
Na An ◽  
Sheng Fan ◽  
Yang Yang ◽  
Xilong Chen ◽  
Feng Dong ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Expression Patterns ◽  
Fruit Trees ◽  
Differentially Expressed ◽  
Flowering Genes ◽  
Differentially Expressed Mirnas ◽  
Flowering Rate ◽  
Carbohydrate Contents

Grafting can improve the agricultural traits of crop plants, especially fruit trees. However, the regulatory networks and differentially expressed microRNAs (miRNAs) related to grafting in apple remain unclear. Herein, we conducted high-throughput sequencing and identified differentially expressed miRNAs among self-rooted Fuji, self-rooted M9, and grafted Fuji/M9. We analyzed the flowering rate, leaf morphology, and nutrient and carbohydrate contents in the three materials. The flowering rate, element and carbohydrate contents, and expression levels of flowering genes were higher in Fuji/M9 than in Fuji. We detected 206 known miRNAs and 976 novel miRNAs in the three materials, and identified those that were up- or downregulated in response to grafting. miR156 was most abundant in Fuji, followed by Fuji/M9, and then self-rooted M9, while miR172 was most abundant in M9, followed by Fuji/M9, and then Fuji. These expression patterns suggest that that these miRNAs were related to grafting. A Gene Ontology (GO) analysis showed that the differentially expressed miRNAs controlled genes involved in various biological processes, including cellular biosynthesis and metabolism. The expression of differentially expressed miRNAs and flowering-related genes was verified by qRT-PCR. Altogether, this comprehensive analysis of miRNAs related to grafting provides valuable information for breeding and grafting of apple and other fruit trees.

scholarly journals Suppressive Subtractive Hybridization of and Differences in Gene Expression Content of Calcifying and Noncalcifying Cultures of Emiliania huxleyi Strain 1516

2005 ◽  
Vol 71 (5) ◽  
pp. 2564-2575 ◽  
Author(s):  
Binh Nguyen ◽  
Robert M. Bowers ◽  
Thomas M. Wahlund ◽  
Betsy A. Read
Keyword(s):  
Time Course ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Subtractive Hybridization ◽  
Emiliania Huxleyi ◽  
Marine Ecology ◽  
Differentially Expressed ◽  
Suppressive Subtractive Hybridization ◽  
Pcr Analysis

ABSTRACT The marine coccolithophorid Emiliania huxleyi is a cosmopolitan alga intensely studied in relation to global carbon cycling, biogeochemistry, marine ecology, and biomineralization processes. The biomineralization capabilities of coccolithophorids have attracted the attention of scientists interested in exploiting this ability for the development of materials science and biomedical and biotechnological applications. Although it has been well documented that biomineralization in E. huxleyi is promoted by growth under phosphate-limited conditions, the genes and proteins that govern the processes of calcification and coccolithogenesis remain unknown. Suppressive subtractive hybridization (SSH) libraries were constructed from cultures grown in phosphate-limited and phosphate-replete media as tester and driver populations for reciprocal SSH procedures. Positive clones from each of the two libraries were randomly selected, and dot blotting was performed for the analysis of expression patterns. A total of 513 clones from the phosphate-replete library and 423 clones from the phosphate-limited library were sequenced, assembled, and compared to sequences in GenBank using BLASTX. Of the 103 differentially expressed gene fragments from the phosphate-replete library, 34% showed significant homology to other known proteins, while only 23% of the 65 differentially expressed gene fragments from the phosphate-limited library showed homology to other proteins. To further assess mRNA expression, real-time RT-PCR analysis was employed and expression profiles were generated over a 14-day time course for three clones from the phosphate-replete library and five clones from the phosphate-limited library. The fragments isolated provide the basis for future cloning of full-length genes and functional analysis.

scholarly journals A Novel Approach to the Determination and Characterization of HIV Dementia

1996 ◽  
Vol 23 (2) ◽  
pp. 104-109 ◽  
Author(s):  
P. Brouwers ◽  
E. Mohr ◽  
K. Hildebrand ◽  
M. Hendricks ◽  
J.J. Claus ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Subcortical Dementia ◽  
Neuropsychological Measures ◽  
Linear Discriminant ◽  
Psychiatric Status ◽  
Novel Approach ◽  
Objective Classification ◽  
Neurodegenerative Dementias

ABSTRACT:Background: Neuropsychological studies of the pattern and extent of cognitive impairment in HIV-infected patients have mostly used deviations from control values and/or cut-off scores as criteria for classification of dementia. There is, however, no agreement as to how to define impairment, and classification is imprecise. Method: The current study used a dementia classification matrix, developed with a step-wise linear discriminant analysis of neuropsychological data from patients with primary neurodegenerative dementias, to classify symptomatic HIV patients as demented or non-demented, and further to differentiate cortical and subcortical dementia patterns. Thirty-two male and 2 female patients (mean age 39 ± 2) with symptomatic HIV disease (mean absolute CD4 count 195 ±41) participated in the study. Results: Thirty-five per cent of patients were classified as demented. Of these, 83% showed a subcortical pattern and 17% a cortical profile of deficits. Significant differences between patients classified as subcortically demented and those categorized as normal on neuropsychological measures associated with subcortical integrity further validated the classification. Measures of psychiatric status between subgroups were similar. Conclusion: Since certain treatments may delay or reverse cognitive deficits, the use of an objective classification method based on discriminant analysis may help to identify patients who may benefit from therapy.

scholarly journals Differential Gene Expression Responding to Low Phosphate Stress in Leaves and Roots of Maize by cDNA-SRAP

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Lei Yan ◽  
Liang Su ◽  
Rui Li ◽  
Hao Li ◽  
Jianrong Bai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Complex Adaptive ◽  
Differential Gene ◽  
Low Pi Stress ◽  
Leaves And Roots ◽  
Diverse Groups ◽  
Pi Stress

Phosphate (Pi) deficiency in soil can have severe impacts on the growth, development, and production of maize worldwide. In this study, a cDNA-sequence-related amplified polymorphism (cDNA-SRAP) transcript profiling technique was used to evaluate the gene expression in leaves and roots of maize under Pi stress for seven days. A total of 2494 differentially expressed fragments (DEFs) were identified in response to Pi starvation with 1202 and 1292 DEFs in leaves and roots, respectively, using a total of 60 primer pairs in the cDNA-SRAP analysis. These DEFs were categorized into 13 differential gene expression patterns. Results of sequencing and functional analysis showed that 63 DEFs (33 in leaves and 30 in roots) were annotated to a total of 54 genes involved in diverse groups of biological pathways, including metabolism, photosynthesis, signal transduction, transcription, transport, cellular processes, genetic information, and organismal system. This study demonstrated that (1) the cDNA-SRAP transcriptomic profiling technique is a powerful method to analyze differential gene expression in maize showing advantageous features among several transcriptomic methods; (2) maize undergoes a complex adaptive process in response to low Pi stress; and (3) a total of seven differentially expressed genes were identified in response to low Pi stress in leaves or roots of maize and could be used in the genetic modification of maize.

scholarly journals Transcriptomic Time Series Analysis in Wound Healing: Challenges and Perspectives on Data Interpretation

2021 ◽  
Author(s):  
Ksenia Zlobina ◽  
Eric Malekos ◽  
Han Chen ◽  
Marcella Gomez
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Wound Healing ◽  
Expression Patterns ◽  
Data Interpretation ◽  
Differentially Expressed ◽  
Expression Data ◽  
Sample Collection ◽  
Individual Gene ◽  
Experimental Approaches

Abstract Background: Wound transcriptomic analysis can be used to quantify wound healing stages and identify leverage points for wound healing intervention. However, individual gene signatures corresponding to wound healing stages vary from one experiment to another and are highly dependent on both experimental setup and bioinformatics methods. Methods: We develop a systematic approach to informatively compare time series from publicly available wound transcriptomic datasets, including mouse and human wounds, and identify consistent gene expression patterns. Results: We reveal the limitations of gene expression data collection, interpretation, and comparison. For example, the sample rate of wound transcriptomic sample collection must be higher than the rate of changes in the wound healing processes, otherwise, important changes in gene expression may be missed. This may lead to mis finding the most significant genes, as peaks of expression for highly differentially expressed genes are lost. Nevertheless, we derived a short list of genes highly differentially expressed in all datasets under consideration. After clustering and normalization, these genes clearly demonstrate similarly changing dynamics of expression between the wounds and may be used for wound healing stage detection.Conclusions: A list of genes that may be used for transcriptomics-based wound healing stage detection is provided. In addition, we suggest experimental approaches that could help researchers to extract more meaningful results.

scholarly journals Comparison of characteristics of long noncoding RNA in Hanwoo according to sex

2020 ◽  
Vol 33 (5) ◽  
pp. 696-703
Author(s):  
Jae-Young Choi ◽  
KyeongHye Won ◽  
Seungwoo Son ◽  
Donghyun Shin ◽  
Jae-Don Oh
Keyword(s):  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Skeletal Muscles ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Physiological Mechanisms ◽  
Protein Coding ◽  
Future Studies ◽  
Hanwoo Cattle

Objective: Cattle were some of the first animals domesticated by humans for the production of milk, meat, etc. Long noncoding RNA (lncRNA) is defined as longer than 200 bp in non-protein coding transcripts. lncRNA is known to function in regulating gene expression and is currently being studied in a variety of livestock including cattle. The purpose of this study is to analyze the characteristics of lncRNA according to sex in Hanwoo cattle.Methods: This study was conducted using the skeletal muscles of 9 Hanwoo cattle include bulls, steers and cows. RNA was extracted from skeletal muscle of Hanwoo. Sequencing was conducted using Illumina HiSeq2000 and mapped to the Bovine Taurus genome. The expression levels of lncRNAs were measured by DEGseq and quantitative trait loci (QTL) data base was used to identify QTLs associated with lncRNA. The python script was used to match the nearby genesResults: In this study, the expression patterns of transcripts of bulls, steers and cows were identified. And we identified significantly differentially expressed lncRNAs in bulls, steers and cows. In addition, characteristics of lncRNA which express differentially in muscles according to the sex of Hanwoo were identified. As a result, we found differentially expressed lncRNAs according to sex were related to shear force and body weight.Conclusion: This study was classified and characterized lncRNA which differentially expressed by sex in Hanwoo cattle. We believe that the characterization of lncRNA by sex of Hanwoo will be helpful for future studies of the physiological mechanisms of Hanwoo cattle.

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