scholarly journals Managing Evidence from Multiple Gene Finding Resources via an XML-based Integration Architecture

2005 ◽  
Vol 2 (1) ◽  
pp. 74-83
Author(s):  
Andigoni Malousi ◽  
Vassilis Koutkias ◽  
Nicos Maglaveras
Keyword(s):  
Gene Expression ◽  
Gene Prediction ◽  
Evaluation Studies ◽  
Gene Finding ◽  
Gene Assembly ◽  
Multiple Gene ◽  
Information Handling ◽  
Signal Features ◽  
Integration Architecture ◽  
Probable Gene

SummaryWhile biological processes underlying gene expression are still under experimental research, computational gene prediction techniques have reached high level of sophistication with the employment of efficient intrinsic and extrinsic methods that identify protein-coding regions within query genomic sequences. Their ability though to delineate the exact exon boundaries is characterized by a trade off between sensitivity and specificity and still is prone to alternations in gene regulation during transcription and splicing and to inherent complexities introduced by the implemented methodology. Evaluation studies have shown that combinatorial approaches exhibit improved accuracy levels through the integration of evidence data from multiple resources that are further assessed in order to end up with the most probable gene assembly.In this work, we present an integration and information handling architecture that exploits evidence derived from multiple gene finding resources, in order to generate machine-readable representations of optimal/suboptimal gene structure predictions, signal features identification and high scoring similarity matches. Unlike most combinatorial techniques, which end up with the most probable gene assembly, the objective of this architecture is to support advanced information handling mechanisms that may give more in depth insights on the underlying gene expression machinery and the alternations that may occur. Technically, XML was adopted to build and interchange structured data among the architecture’s components together with relevant technologies offering graphical representations and queries formulation/execution over single/multiple information sources.

scholarly journals Artificial complementary chromatic acclimation gene expression system in Escherichia coli

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Dwi Ariyanti ◽  
Kazunori Ikebukuro ◽  
Koji Sode
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Light Exposure ◽  
Expression System ◽  
Red Light ◽  
Green Light ◽  
Light Illumination ◽  
Multiple Gene ◽  
Gene Expression System ◽  
Chromatic Acclimation

Abstract Background The development of multiple gene expression systems, especially those based on the physical signals, such as multiple color light irradiations, is challenging. Complementary chromatic acclimation (CCA), a photoreversible process that facilitates the control of cellular expression using light of different wavelengths in cyanobacteria, is one example. In this study, an artificial CCA systems, inspired by type III CCA light-regulated gene expression, was designed by employing a single photosensor system, the CcaS/CcaR green light gene expression system derived from Synechocystis sp. PCC6803, combined with G-box (the regulator recognized by activated CcaR), the cognate cpcG2 promoter, and the constitutively transcribed promoter, the PtrcΔLacO promoter. Results One G-box was inserted upstream of the cpcG2 promoter and a reporter gene, the rfp gene (green light-induced gene expression), and the other G-box was inserted between the PtrcΔLacO promoter and a reporter gene, the bfp gene (red light-induced gene expression). The Escherichia coli transformants with plasmid-encoded genes were evaluated at the transcriptional and translational levels under red or green light illumination. Under green light illumination, the transcription and translation of the rfp gene were observed, whereas the expression of the bfp gene was repressed. Under red light illumination, the transcription and translation of the bfp gene were observed, whereas the expression of the rfp gene was repressed. During the red and green light exposure cycles at every 6 h, BFP expression increased under red light exposure while RFP expression was repressed, and RFP expression increased under green light exposure while BFP expression was repressed. Conclusion An artificial CCA system was developed to realize a multiple gene expression system, which was regulated by two colors, red and green lights, using a single photosensor system, the CcaS/CcaR system derived from Synechocystis sp. PCC6803, in E. coli. The artificial CCA system functioned repeatedly during red and green light exposure cycles. These results demonstrate the potential application of this CCA gene expression system for the production of multiple metabolites in a variety of microorganisms, such as cyanobacteria.

Antioxidants increase number of progenitor endothelial cells through multiple gene expression pathways

2008 ◽  
Vol 42 (8) ◽  
pp. 754-762 ◽  
Author(s):  
Carmela Fiorito ◽  
Monica Rienzo ◽  
Ettore Crimi ◽  
Raffaele Rossiello ◽  
Maria Luisa Balestrieri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Multiple Gene ◽  
Expression Pathways

scholarly journals Hierarchical assembly and disassembly of a transcriptionally active RAG locus in CD4+CD8+ thymocytes

2018 ◽  
Vol 216 (1) ◽  
pp. 231-243 ◽  
Author(s):  
Abani Kanta Naik ◽  
Aaron T. Byrd ◽  
Aaron C.K. Lucander ◽  
Michael S. Krangel
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Molecular Mechanisms ◽  
Promoter Activation ◽  
Active Chromatin ◽  
Gene Assembly ◽  
Down Regulation ◽  
Hierarchical Assembly ◽  
Rag2 Gene

Expression of Rag1 and Rag2 is tightly regulated in developing T cells to mediate TCR gene assembly. Here we have investigated the molecular mechanisms governing the assembly and disassembly of a transcriptionally active RAG locus chromatin hub in CD4+CD8+ thymocytes. Rag1 and Rag2 gene expression in CD4+CD8+ thymocytes depends on Rag1 and Rag2 promoter activation by a distant antisilencer element (ASE). We identify GATA3 and E2A as critical regulators of the ASE, and Runx1 and E2A as critical regulators of the Rag1 promoter. We reveal hierarchical assembly of a transcriptionally active chromatin hub containing the ASE and RAG promoters, with Rag2 recruitment and expression dependent on assembly of a functional ASE–Rag1 framework. Finally, we show that signal-dependent down-regulation of RAG gene expression in CD4+CD8+ thymocytes depends on Ikaros and occurs with disassembly of the RAG locus chromatin hub. Our results provide important new insights into the molecular mechanisms that orchestrate RAG gene expression in developing T cells.

scholarly journals Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5285 ◽  
Author(s):  
Mei Sze Tan ◽  
Siow-Wee Chang ◽  
Phaik Leng Cheah ◽  
Hwa Jen Yap
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cervical Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
Multiple Gene ◽  
Cervical Cancers ◽  
Learning Analysis

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

scholarly journals Development of Beet necrotic yellow vein virus ‐based vectors for multiple‐gene expression and guide RNA delivery in plant genome editing

2019 ◽  
Vol 17 (7) ◽  
pp. 1302-1315 ◽  
Author(s):  
Ning Jiang ◽  
Chao Zhang ◽  
Jun‐Ying Liu ◽  
Zhi‐Hong Guo ◽  
Zong‐Ying Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genome Editing ◽  
Plant Genome ◽  
Yellow Vein ◽  
Multiple Gene ◽  
Guide Rna

scholarly journals A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

2021 ◽  
Author(s):  
Taguchi Y-h. ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Learning Strategies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Prior Learning ◽  
Multiple Gene ◽  
Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

Sign in / Sign up

Export Citation Format

Share Document