scholarly journals The Drosophila Gene Expression Tool (DGET) for expression analyses

2016 ◽  
Author(s):  
Yanhui Hu ◽  
Aram Comjean ◽  
Norbert Perrimon ◽  
Stephanie Mohr
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Data Retrieval ◽  
Design Stage ◽  
Expression Data ◽  
Similar Expression ◽  
Public Data

AbstractBackgroundNext-generation sequencing technologies have greatly increased our ability to identify gene expression levels, including at specific developmental stages and in specific tissues. Gene expression data can help researchers understand the diverse functions of genes and gene networks, as well as help in the design of specific and efficient functional studies, such as by helping researchers choose the most appropriate tissue for a study of a group of genes, or conversely, by limiting a long list of gene candidates to the subset that are normally expressed at a given stage or in a given tissue.ResultsWe report a Drosophila Gene Expression Tool (DGET, www.flyrnai.org/tools/dget/web/), which stores and facilitates search of RNA-Seq based expression profiles available from the modENCODE consortium and other public data sets. Using DGET, researchers are able to look up gene expression profiles, filter results based on threshold expression values, and compare expression data across different developmental stages, tissues and treatments. In addition, at DGET a researcher can analyze tissue or stage-specific enrichment for an inputted list of genes (e.g. ‘hits’ from a screen) and search for additional genes with similar expression patterns. We performed a number of analyses to demonstrate the quality and robustness of the resource. In particular, we show that evolutionary conserved genes expressed at high or moderate levels in both fly and human tend to be expressed in similar tissues. Using DGET, we compared whole tissue profile and sub-region/cell-type specific datasets and estimated the potential cause of false positives in one dataset. We also demonstrated the usefulness of DGET for synexpression studies by querying genes with similar expression profile to the mesodermal master regulator Twist.ConclusionAltogether, DGET provides a flexible tool for expression data retrieval and analysis with short or long lists of Drosophila genes, which can help scientists to design stage- or tissue-specific in vivo studies and do other subsequent analyses.

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

Building Gene Networks by Analyzing Gene Expression Profiles

2018 ◽  
pp. 440-454
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter we examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

scholarly journals Visual Comparison of Multiple Gene Expression Datasets in a Genomic Context

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Krzysztof Borowski ◽  
Jung Soh ◽  
Christoph W. Sensen
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Gene Function ◽  
Graphical Representation ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Expression Data ◽  
Genomic Context ◽  
Multiple Gene ◽  
Microarray Datasets

SummaryThe need for novel methods of visualizing microarray data is growing. New perspectives are beneficial to finding patterns in expression data. The Bluejay genome browser provides an integrative way of visualizing gene expression datasets in a genomic context. We have now developed the functionality to display multiple microarray datasets simultaneously in Bluejay, in order to provide researchers with a comprehensive view of their datasets linked to a graphical representation of gene function. This will enable biologists to obtain valuable insights on expression patterns, by allowing them to analyze the expression values in relation to the gene locations as well as to compare expression profiles of related genomes or of di erent experiments for the same genome.

scholarly journals ExAtlas: An interactive online tool for meta-analysis of gene expression data

2015 ◽  
Vol 13 (06) ◽  
pp. 1550019 ◽  
Author(s):  
Alexei A. Sharov ◽  
David Schlessinger ◽  
Minoru S. H. Ko
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Fixed Effects ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Gene Set ◽  
Public Data

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users’ own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher’s methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein–protein interaction) are pre-loaded and can be used for functional annotations.

scholarly journals Systems Biology Analysis of the Antagonizing Effects of HIV-1 Tat Expression in the Brain over Transcriptional Changes Caused by Methamphetamine Sensitization

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 426 ◽  
Author(s):  
Liana V. Basova ◽  
James P. Kesby ◽  
Marcus Kaul ◽  
Svetlana Semenova ◽  
Maria Cecilia Garibaldi Marcondes
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Gene Transcription ◽  
Gene Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Sirtuin 1 ◽  
The Brain ◽  
Hiv 1

Methamphetamine (Meth) abuse is common among humans with immunodeficiency virus (HIV). The HIV-1 regulatory protein, trans-activator of transcription (Tat), has been described to induce changes in brain gene transcription that can result in impaired reward circuitry, as well as in inflammatory processes. In transgenic mice with doxycycline-induced Tat protein expression in the brain, i.e., a mouse model of neuroHIV, we tested global gene expression patterns induced by Meth sensitization. Meth-induced locomotor sensitization included repeated daily Meth or saline injections for seven days and Meth challenge after a seven-day abstinence period. Brain samples were collected 30 min after the Meth challenge. We investigated global gene expression changes in the caudate putamen, an area with relevance in behavior and HIV pathogenesis, and performed pathway and transcriptional factor usage predictions using systems biology strategies. We found that Tat expression alone had a very limited impact in gene transcription after the Meth challenge. In contrast, Meth-induced sensitization in the absence of Tat induced a global suppression of gene transcription. Interestingly, the interaction between Tat and Meth broadly prevented the Meth-induced global transcriptional suppression, by maintaining regulation pathways, and resulting in gene expression profiles that were more similar to the controls. Pathways associated with mitochondrial health, initiation of transcription and translation, as well as with epigenetic control, were heavily affected by Meth, and by its interaction with Tat in anti-directional ways. A series of systems strategies have predicted several components impacted by these interactions, including mitochondrial pathways, mTOR/RICTOR, AP-1 transcription factor, and eukaryotic initiation factors involved in transcription and translation. In spite of the antagonizing effects of Tat, a few genes identified in relevant gene networks remained downregulated, such as sirtuin 1, and the amyloid precursor protein (APP). In conclusion, Tat expression in the brain had a low acute transcriptional impact but strongly interacted with Meth sensitization, to modify effects in the global transcriptome.

AN IMPROVED BICLUSTERING METHOD FOR ANALYZING GENE EXPRESSION PROFILES

2005 ◽  
Vol 14 (05) ◽  
pp. 771-789 ◽  
Author(s):  
JIONG YANG ◽  
HAIXUN WANG ◽  
WEI WANG ◽  
PHILIP S. YU
Keyword(s):  
Gene Expression ◽  
Heuristic Algorithms ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Yeast Gene ◽  
Huge Amount ◽  
Probabilistic Algorithm ◽  
Null Values

Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church1 to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data2 shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.

scholarly journals De novo characterization of the Lycium ruthenicum transcriptome and analysis of its digital gene expression profiles during fruit development and ripening

2017 ◽  
Vol 69 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Yong Peng ◽  
Huiqin Ma ◽  
Shangwu Chen
Keyword(s):  
Gene Expression ◽  
Developmental Stages ◽  
De Novo ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Digital Gene Expression ◽  
Orthologous Group ◽  
Functional Studies ◽  
Lycium Ruthenicum

Lycium ruthenicum Murr., which belongs to the family Solanaceae, is a resource plant for Chinese traditional medicine and nutraceutical foods. In this study, RNA sequencing was applied to obtain raw reads of L. ruthenicum fruit at different stages of ripening, and a de novo assembly of its sequence was performed. Approximately 52.45 million 100-bp paired-end raw reads were generated from the samples by deep RNA-seq analysis. These short reads were assembled to obtain 164814 contigs, and the contigs were assembled into 84968 non-redundant unigenes using the Trinity method. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group and KEGG (Kyoto Encyclopedia of Genes and Genomes)pathway terms. Digital gene expression analysis was applied to compare gene-expression patterns at different fruit developmental stages. These results contribute to existing sequence resources for Lycium spp. during the fruit-ripening stages, which is valuable for further functional studies of genes involved in L. ruthenicum fruit nutraceutical quality.

scholarly journals BrassicaEDB: A Gene Expression Database for Brassica Crops

2020 ◽  
Vol 21 (16) ◽  
pp. 5831 ◽  
Author(s):  
Haoyu Chao ◽  
Tian Li ◽  
Chaoyu Luo ◽  
Hualei Huang ◽  
Yingfei Ruan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Level ◽  
Level Data ◽  
Rapeseed Cultivar ◽  
Transcriptome Level

The genus Brassica contains several economically important crops, including rapeseed (Brassica napus, 2n = 38, AACC), the second largest source of seed oil and protein meal worldwide. However, research in rapeseed is hampered because it is complicated and time-consuming for researchers to access different types of expression data. We therefore developed the Brassica Expression Database (BrassicaEDB) for the research community. In the current BrassicaEDB, we only focused on the transcriptome level in rapeseed. We conducted RNA sequencing (RNA-Seq) of 103 tissues from rapeseed cultivar ZhongShuang11 (ZS11) at seven developmental stages (seed germination, seedling, bolting, initial flowering, full-bloom, podding, and maturation). We determined the expression patterns of 101,040 genes via FPKM analysis and displayed the results using the eFP browser. We also analyzed transcriptome data for rapeseed from 70 BioProjects in the SRA database and obtained three types of expression level data (FPKM, TPM, and read counts). We used this information to develop the BrassicaEDB, including “eFP”, “Treatment”, “Coexpression”, and “SRA Project” modules based on gene expression profiles and “Gene Feature”, “qPCR Primer”, and “BLAST” modules based on gene sequences. The BrassicaEDB provides comprehensive gene expression profile information and a user-friendly visualization interface for rapeseed researchers. Using this database, researchers can quickly retrieve the expression level data for target genes in different tissues and in response to different treatments to elucidate gene functions and explore the biology of rapeseed at the transcriptome level.

Human cord blood CD34+Pax-5+ B-cell progenitors: single-cell analyses of their gene expression profiles

Blood ◽  
2003 ◽  
Vol 101 (9) ◽  
pp. 3424-3430 ◽  
Author(s):  
Eva Sanz ◽  
Melchor Alvarez-Mon ◽  
Carlos Martı́nez-A ◽  
Antonio de la Hera
Keyword(s):  
Gene Expression ◽  
Cord Blood ◽  
B Cell ◽  
Single Cell ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Terminal Deoxynucleotidyl Transferase ◽  
Human Cord Blood

Circulating CD34+ cells are used in reparative medicine as a stem cell source, but they contain cells already committed to different lineages. Many think that B-cell progenitors (BCPs) are confined to bone marrow (BM) niches until they differentiate into B cells and that they do not circulate in blood. The prevailing convention is that BCP transit a CD34+CD19−10+early-B→CD34+CD19+CD10+B-cell progenitor (pro-B)→CD34−CD19+CD10+ B-cell precursor (pre-B) differentiation pathway within BM. However, populations of CD34+CD10+ and CD34+CD19+ cells circulate in adult peripheral blood and neonatal umbilical cord blood (CB) that are operationally taken as BCPs on the basis of their phenotypes, although they have not been submitted to a systematic characterization of their gene expression profiles. Here, conventional CD34+CD19+CD10+ and novel CD34+CD19+CD10− BCP populations are characterized in CB by single-cell sorting and multiplex analyses of gene expression patterns. Circulating BCP are Pax-5+cells that span the early-B, pro-B, and pre-B developmental stages, defined by the profiles of rearranged V-D-JH, CD79, VpreB, recombination activating gene (RAG), and terminal deoxynucleotidyl transferase (TdT) expression. Contrary to the expectation, circulating CD34+CD19−CD10+ cells are essentially devoid of Pax-5+ BCP. Interestingly, the novel CD34+CD19+CD10− BCP appears to be the normal counterpart of circulating preleukemic BCPs that undergo chromosomal translocations in utero months or years before their promotion into infant acute lymphoblastic B-cell leukemia after secondary postnatal mutations. The results underscore the power of single-cell analyses to characterize the gene expression profiles in a minor population of rare cells, which has broad implications in biomedicine.

scholarly journals Dynamic gene expression profiles during postnatal development of porcine subcutaneous adipose

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1768 ◽  
Author(s):  
Jie Zhang ◽  
Jideng Ma ◽  
Keren Long ◽  
Long Jin ◽  
Yihui Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Adipose Tissue ◽  
Subcutaneous Adipose Tissue ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Model Organism ◽  
Digital Gene Expression ◽  
Subcutaneous Adipose

A better understanding of the control of lipogenesis is of critical importance for both human and animal physiology. This requires a better knowledge of the changes of gene expression during the process of adipose tissue development. Thus, the objective of the current study was to determine the effects of development on subcutaneous adipose tissue gene expression in growing and adult pigs. Here, we present a comprehensive investigation of mRNA transcriptomes in porcine subcutaneous adipose tissue across four developmental stages using digital gene expression profiling. We identified 3,274 differential expressed genes associated with oxidative stress, immune processes, apoptosis, energy metabolism, insulin stimulus, cell cycle, angiogenesis and translation. A set of universally abundant genes (ATP8,COX2,COX3,ND1, ND2,SCDandTUBA1B) was found across all four developmental stages. This set of genes may play important roles in lipogenesis and development. We also identified development-related gene expression patterns that are linked to the different adipose phenotypes. We showed that genes enriched in significantly up-regulated profiles were associated with phosphorylation and angiogenesis. In contrast, genes enriched in significantly down-regulated profiles were related to cell cycle and cytoskeleton organization, suggesting an important role for these biological processes in adipose growth and development. These results provide a resource for studying adipose development and promote the pig as a model organism for researching the development of human obesity, as well as being used in the pig industry.

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