Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552132
Ashwani Kumar, T. Singh
Deciphering the underlying mechanisms of all complex interactions involved in different signaling pathways is a pivotal step in the dissection and study of network-based data. Heuristic statistical solutions are conventionally used across the world to derive a meaningful perspective of the network based data by identifying related biological networks. However, classical pathway analysis gives us elusive results by ignoring important aspects of biology. To overcome the limitations of the classical analysis, we have implemented systems biology approach which includes enrichment analysis of Alzheimer's disease (AD) gene set and topological enrichment analysis. Exploration of pathway ranking and regression analysis on the basis of XD-Score and Fisher-q value is also elucidated. Topology-based enrichment studies gave us insight on important parameters such as shortest path length, node betweenness, degree, clustering coefficient, eigenvector centrality and their association in AD based statistical score which turned out to be significantly high (5.062) at a significant threshold (0.74). A linear fit in regression plot and enrichment in associated gene pathways were observed.
{"title":"Systems biology approach for gene set enrichment and topological analysis of Alzheimer's disease pathway","authors":"Ashwani Kumar, T. Singh","doi":"10.1109/BSB.2016.7552132","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552132","url":null,"abstract":"Deciphering the underlying mechanisms of all complex interactions involved in different signaling pathways is a pivotal step in the dissection and study of network-based data. Heuristic statistical solutions are conventionally used across the world to derive a meaningful perspective of the network based data by identifying related biological networks. However, classical pathway analysis gives us elusive results by ignoring important aspects of biology. To overcome the limitations of the classical analysis, we have implemented systems biology approach which includes enrichment analysis of Alzheimer's disease (AD) gene set and topological enrichment analysis. Exploration of pathway ranking and regression analysis on the basis of XD-Score and Fisher-q value is also elucidated. Topology-based enrichment studies gave us insight on important parameters such as shortest path length, node betweenness, degree, clustering coefficient, eigenvector centrality and their association in AD based statistical score which turned out to be significantly high (5.062) at a significant threshold (0.74). A linear fit in regression plot and enrichment in associated gene pathways were observed.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123121086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552150
V. Antsiperov, A. Bugaev, I. V. Zabrosaev
The report is devoted to research and development of new ECG processing methods, based on Multiscale Correlation Analysis approach, in particular, the development of new PVC (premature ventricular complexes) and SPB (supraventricular premature beats) detection method. It is shown that in case of ECG, where a signal has a form of repeated wave pulses, the instruments previously developed by the authors and called the analytical spectra technique are highly effective. Computational algorithm based on this technique accurately estimates the heart rate. Heart rate estimation allows for building premature beats detection algorithm. The report provides first results received in this direction. The results of PVC/SPB detection for long ECG recordings from standard MIT-BIH NSRDB and SADB are briefly summarized in the appropriate section and shortly characterized in the conclusions.
{"title":"A new PVC/SPB detection method: Based on analytical spectra technique","authors":"V. Antsiperov, A. Bugaev, I. V. Zabrosaev","doi":"10.1109/BSB.2016.7552150","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552150","url":null,"abstract":"The report is devoted to research and development of new ECG processing methods, based on Multiscale Correlation Analysis approach, in particular, the development of new PVC (premature ventricular complexes) and SPB (supraventricular premature beats) detection method. It is shown that in case of ECG, where a signal has a form of repeated wave pulses, the instruments previously developed by the authors and called the analytical spectra technique are highly effective. Computational algorithm based on this technique accurately estimates the heart rate. Heart rate estimation allows for building premature beats detection algorithm. The report provides first results received in this direction. The results of PVC/SPB detection for long ECG recordings from standard MIT-BIH NSRDB and SADB are briefly summarized in the appropriate section and shortly characterized in the conclusions.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552151
Abhivyakti Srivastava, Mottadi Shiva, P. Kumari, Y. Hasija
Even before the onset of Human Genome Project in 1990, various research, analysis and experiments was going on human genome. In human genome, approximately 3 billion base pairs are structured in the form of 23 pairs of chromosomes. Chromosome 11 is a chromosome that is rich in disease and has a size of 134 million base pairs. From over 1000 genome web servers, the sequence data for the chromosome 11 has been taken as a (.bam) file. On Chromosome 11, approximately 45 different analyses were performed across various fields and categories, such as determining the sequence quality, over represented sequence, peak model, visualizing BAC end pairs, secondary structure prediction by analyses hydroxyl cleavage sites, GC percentage, studying phenotype and disease associated with respect to chromosome 11. Here, we have analysed the evolutionary relationship by isolating positively selected genes across 6 species; predicted the t-RNA and RNA secondary structures and aligned them discretely with the human genome chromosome 11. All these analyses was done primarily with the help of two tools, 1) Web based program-NEBULA and 2) UCSC genome browser.
{"title":"Comprehensive computational analysis of chromosome 11","authors":"Abhivyakti Srivastava, Mottadi Shiva, P. Kumari, Y. Hasija","doi":"10.1109/BSB.2016.7552151","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552151","url":null,"abstract":"Even before the onset of Human Genome Project in 1990, various research, analysis and experiments was going on human genome. In human genome, approximately 3 billion base pairs are structured in the form of 23 pairs of chromosomes. Chromosome 11 is a chromosome that is rich in disease and has a size of 134 million base pairs. From over 1000 genome web servers, the sequence data for the chromosome 11 has been taken as a (.bam) file. On Chromosome 11, approximately 45 different analyses were performed across various fields and categories, such as determining the sequence quality, over represented sequence, peak model, visualizing BAC end pairs, secondary structure prediction by analyses hydroxyl cleavage sites, GC percentage, studying phenotype and disease associated with respect to chromosome 11. Here, we have analysed the evolutionary relationship by isolating positively selected genes across 6 species; predicted the t-RNA and RNA secondary structures and aligned them discretely with the human genome chromosome 11. All these analyses was done primarily with the help of two tools, 1) Web based program-NEBULA and 2) UCSC genome browser.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134141546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552163
O. Sushkova, A. Morozov, A. Gabova
A method of analysis of EEG wave trains based on wavelets and nonparametric statistics is developed. The method is compared with standard methods based on Fourier spectra and complex Morlet wavelets by the example of Parkinson's disease experimental data. We demonstrate that these methods are complementary, that is, the standard methods and the wave train analysis method reveal sufficiently different effects in the EEG data.
{"title":"A method of analysis of EEG wave trains in early stages of Parkinson's disease","authors":"O. Sushkova, A. Morozov, A. Gabova","doi":"10.1109/BSB.2016.7552163","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552163","url":null,"abstract":"A method of analysis of EEG wave trains based on wavelets and nonparametric statistics is developed. The method is compared with standard methods based on Fourier spectra and complex Morlet wavelets by the example of Parkinson's disease experimental data. We demonstrate that these methods are complementary, that is, the standard methods and the wave train analysis method reveal sufficiently different effects in the EEG data.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123350185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552141
Aamna Lawrence, R. Shukla, Utkarsh Raj, Pritish Kumar Varadwaj
High Throughput Next Generation Sequencing (HT-NGS) technology has taken human and animal genome analysis and genomics researches to another level. From the analysis of the genomes by the aforementioned technologies, the most important modification to DNA, namely epigenetic modifications can be analyzed and studied to predict the gene expression and any disease onset in the future. By using the SRA (Serial Read Archive) toolkit, FastQC visualization tool and Bowtie aligner on ChIP-Seq (Chromatin Immunoprecipitation Sequenced) data, an estimate of the percentage epigenetic modifications or protein interactions found in the experimental human genome compared to those found normally in the genome of a healthy individual has been made. The results were used to predict whether the subject was at a risk of developing diseases due to mutations or epigenetic modifications like cancer.
{"title":"Estimating percentage epigenetic modifications in human genome using NGS data","authors":"Aamna Lawrence, R. Shukla, Utkarsh Raj, Pritish Kumar Varadwaj","doi":"10.1109/BSB.2016.7552141","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552141","url":null,"abstract":"High Throughput Next Generation Sequencing (HT-NGS) technology has taken human and animal genome analysis and genomics researches to another level. From the analysis of the genomes by the aforementioned technologies, the most important modification to DNA, namely epigenetic modifications can be analyzed and studied to predict the gene expression and any disease onset in the future. By using the SRA (Serial Read Archive) toolkit, FastQC visualization tool and Bowtie aligner on ChIP-Seq (Chromatin Immunoprecipitation Sequenced) data, an estimate of the percentage epigenetic modifications or protein interactions found in the experimental human genome compared to those found normally in the genome of a healthy individual has been made. The results were used to predict whether the subject was at a risk of developing diseases due to mutations or epigenetic modifications like cancer.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126758405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552154
Anju Gupta, P. Kumari, Mottadi Shiva, Y. Hasija
The miRNAs are 22-46 nucleotidelong, non-protein coding RNAsthat act as oncogenes. Their altered expression can cause diseases, includingcancer. Lung cancer is causing death of million peopleworldwide. Yet only few genetic biomarkers are known for detecting lung cancer. miRNAs are those non-coding RNAs that act as such biomarkers. They act as oncogenes and regulate many biological processesand cellular pathways. The miRNAs regulate gene expression of protein coding genes by generating either translation repression or RNAdegradation. When they bind with 3' UTR of newly formed miRNA transcripts along with other helper proteins, they inhibit their expression and function. This translation inhibition, affects tumor suppressor genes and leads to cancer.miRNAexpression deregulation found in various cancers such as Prostrate, Breast, Colonialand Lung canceretc. In this study insilico approach has been used to find out functional genetic variation in miRNA binding sites of genes responsible for causing lung cancer. As a result of this study we have found 7 SNPs involved in such cancers-rs6772, rs1036672, rs739442, rs1050700, rs3185695, rs12723035, rs3787030. These SNPs can act as candidate biomarkers for lung cancer.
{"title":"Identification of functional genetic variants in miRNA binding site in genes associated with lung cancer","authors":"Anju Gupta, P. Kumari, Mottadi Shiva, Y. Hasija","doi":"10.1109/BSB.2016.7552154","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552154","url":null,"abstract":"The miRNAs are 22-46 nucleotidelong, non-protein coding RNAsthat act as oncogenes. Their altered expression can cause diseases, includingcancer. Lung cancer is causing death of million peopleworldwide. Yet only few genetic biomarkers are known for detecting lung cancer. miRNAs are those non-coding RNAs that act as such biomarkers. They act as oncogenes and regulate many biological processesand cellular pathways. The miRNAs regulate gene expression of protein coding genes by generating either translation repression or RNAdegradation. When they bind with 3' UTR of newly formed miRNA transcripts along with other helper proteins, they inhibit their expression and function. This translation inhibition, affects tumor suppressor genes and leads to cancer.miRNAexpression deregulation found in various cancers such as Prostrate, Breast, Colonialand Lung canceretc. In this study insilico approach has been used to find out functional genetic variation in miRNA binding sites of genes responsible for causing lung cancer. As a result of this study we have found 7 SNPs involved in such cancers-rs6772, rs1036672, rs739442, rs1050700, rs3185695, rs12723035, rs3787030. These SNPs can act as candidate biomarkers for lung cancer.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122081530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552142
Aman Chandra Kaushik, Avinash Dhar, S. Sahi
Stem prediction has been a problem that has attracted the attention of bioinformaticians for a long time. With important role in central dogma of prokarryotes and eukaryotes alike along with known role in viral genome encapsidation, the importance of DNA/RNA stems cannot be neglected. Bacterial interspersed Mosaic Elements known as BIMEs play an important regulatory role in bacterial replication. Similarly, predicted DNA thermodynamic properties can help us in manipulating and understanding the dynamics of DNA denaturation and renaturation in a far better manner. Particle Swarm Optimization (PSO) is an important evolutionary algorithm based on swarm intelligence to solve NP optimization problems. This paper proposes a PSO based algorithm for predicting DNA/RNA stems, BIMEs and DNA thermodynamic properties.
{"title":"DrovePred: Server for DNA stem and BIME's prediction using Particle Swarm Optimization","authors":"Aman Chandra Kaushik, Avinash Dhar, S. Sahi","doi":"10.1109/BSB.2016.7552142","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552142","url":null,"abstract":"Stem prediction has been a problem that has attracted the attention of bioinformaticians for a long time. With important role in central dogma of prokarryotes and eukaryotes alike along with known role in viral genome encapsidation, the importance of DNA/RNA stems cannot be neglected. Bacterial interspersed Mosaic Elements known as BIMEs play an important regulatory role in bacterial replication. Similarly, predicted DNA thermodynamic properties can help us in manipulating and understanding the dynamics of DNA denaturation and renaturation in a far better manner. Particle Swarm Optimization (PSO) is an important evolutionary algorithm based on swarm intelligence to solve NP optimization problems. This paper proposes a PSO based algorithm for predicting DNA/RNA stems, BIMEs and DNA thermodynamic properties.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124724367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552149
V. Antsiperov, G. Mansurov, A. Polupanov, Basil Bonch-Bruevich
The report presents the latest results of developing a new method of noninvasive continuous blood pressure monitoring. This method is based on the principle of pulse wave compensation. It is shown that sensors for such a measurement should be not only smart, but also active. In this connection, a part of introduction is devoted to the issues of expanding the concept of smart sensors to the concept of active sensors. A significant part of the report describes the technical design of the active sensor for noninvasive pressure measurement. The results of its calibration and testing are under discussion. The main section of the report is devoted to the development of software for active sensor control - its intellectual stuffing. We describe and justify a new principle of active measurement of quasi-periodic processes - pulse wave compensation based on prediction patterns. The progress achieved in research and the ways for further investigation are outlined in the conclusions.
{"title":"Noninvasive arterial blood pressure monitoring: By active sensor based on the principle of pulse wave compensation","authors":"V. Antsiperov, G. Mansurov, A. Polupanov, Basil Bonch-Bruevich","doi":"10.1109/BSB.2016.7552149","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552149","url":null,"abstract":"The report presents the latest results of developing a new method of noninvasive continuous blood pressure monitoring. This method is based on the principle of pulse wave compensation. It is shown that sensors for such a measurement should be not only smart, but also active. In this connection, a part of introduction is devoted to the issues of expanding the concept of smart sensors to the concept of active sensors. A significant part of the report describes the technical design of the active sensor for noninvasive pressure measurement. The results of its calibration and testing are under discussion. The main section of the report is devoted to the development of software for active sensor control - its intellectual stuffing. We describe and justify a new principle of active measurement of quasi-periodic processes - pulse wave compensation based on prediction patterns. The progress achieved in research and the ways for further investigation are outlined in the conclusions.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126821210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552138
N. Mathivanan, G. Kiruthika, R. Subashree
Periodontal disease is an infectious disease caused by gram negative bacteria. The aim is to identify the potential drug target of Prevotella Intermedia strain 17. In silico analysis was performed to find the essential genes in Prevotella Intermedia strain 17. 470 protein sequences was retrieved from NCBI, 439 sequences passed the 90% threstold in CD-HIT, BLAST filtered 24 sequences as homolog to human and retained remaining 415 sequences. DEG identified 144 proteins as essential genes. Ezypred classified 57 essential genes as enzymes and 87 as non enzymes. Psortb identified the cellular component of the essential genes where most of the genes was found to be cytoplamic. KEGG identified the genes involved in the unique pathways of Prevotella Intermedia strain 17. Two enzymes 3-deoxy-D-manno-octulosonate cytidylyltransferase and 3-deoxy-D-manno-octulosonate 8-phosphate phosphatase were identified to be involved in Lipopolysaccharide Biosynthesis Pathway which is a unique pathway for gram negative bacteria.
{"title":"Insilico screening of Prevotella Intermedia 17 identifies Lipopolysaccharide Biosynthesis Pathway genes as potential drug targets","authors":"N. Mathivanan, G. Kiruthika, R. Subashree","doi":"10.1109/BSB.2016.7552138","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552138","url":null,"abstract":"Periodontal disease is an infectious disease caused by gram negative bacteria. The aim is to identify the potential drug target of Prevotella Intermedia strain 17. In silico analysis was performed to find the essential genes in Prevotella Intermedia strain 17. 470 protein sequences was retrieved from NCBI, 439 sequences passed the 90% threstold in CD-HIT, BLAST filtered 24 sequences as homolog to human and retained remaining 415 sequences. DEG identified 144 proteins as essential genes. Ezypred classified 57 essential genes as enzymes and 87 as non enzymes. Psortb identified the cellular component of the essential genes where most of the genes was found to be cytoplamic. KEGG identified the genes involved in the unique pathways of Prevotella Intermedia strain 17. Two enzymes 3-deoxy-D-manno-octulosonate cytidylyltransferase and 3-deoxy-D-manno-octulosonate 8-phosphate phosphatase were identified to be involved in Lipopolysaccharide Biosynthesis Pathway which is a unique pathway for gram negative bacteria.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"17 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125625917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-04DOI: 10.1109/BSB.2016.7552152
H. Shashirekha, A. Wani
The advancement in high-throughput microarray experiments has paved a way for several transcriptomic studies across the globe by several researchers in the area of functional genomics, molecular genetics, gene discovery, differentially expressed gene detection, diagnosis and prognosis etc. As a result, the tremendous amount of data that has been produced and accumulated in various public repositories such as Gene Expression Omnibus (GEO) and ArrayExpress, have been frequently used by researchers to readdress various biological goals. Since an independent study often comes with less sample size and limited statistical power, researchers are now relying on a more powerful technique called meta-analysis, an integrated analysis of existing data from different related independent studies. Meta-analysis is an active area in biomedical research which plays a vital role in increasing the statistical power to detect differentially expressed genes and to understand molecular and cellular aspects in a broader way. However, little efforts have been put to design user friendly tool/software to carry out meta-analysis automatically. In this paper, we present ShinyMDE, a user friendly tool to integrate different gene expression data for differentially expressed gene detection in an easy and efficient way. The tool handles processed and raw data generated from most widely used data platforms such as Affymetrix and Illumina. In addition, the tool provides user with an option of choosing the method of their choice from the list for meta-analysis. The tool is very simple to use and is developed with the aim of having an automated meta-analysis of gene expression data facilitating screening and downloading the results.
高通量微阵列实验技术的进步，为功能基因组学、分子遗传学、基因发现、差异表达基因检测、诊断和预后等领域的转录组学研究铺平了道路。因此，在各种公共存储库(如Gene Expression Omnibus (GEO)和ArrayExpress)中产生和积累的大量数据已被研究人员经常用于重新解决各种生物学目标。由于独立研究通常样本量较小，统计能力有限，研究人员现在依赖于一种更强大的技术，称为元分析，即对来自不同相关独立研究的现有数据进行综合分析。荟萃分析是生物医学研究中的一个活跃领域，它在提高统计能力以检测差异表达基因和更广泛地了解分子和细胞方面起着至关重要的作用。然而，很少有人努力设计用户友好的工具/软件来自动执行元分析。在本文中，我们提出了ShinyMDE，一个用户友好的工具，整合不同的基因表达数据，以一种简单有效的方式进行差异表达基因检测。该工具处理从最广泛使用的数据平台(如Affymetrix和Illumina)生成的已处理和原始数据。此外，该工具还为用户提供了从列表中选择他们选择的方法进行元分析的选项。该工具使用非常简单，开发的目的是对基因表达数据进行自动荟萃分析，方便筛选和下载结果。
{"title":"ShinyMDE: Shiny tool for microarray meta-analysis for differentially expressed gene detection","authors":"H. Shashirekha, A. Wani","doi":"10.1109/BSB.2016.7552152","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552152","url":null,"abstract":"The advancement in high-throughput microarray experiments has paved a way for several transcriptomic studies across the globe by several researchers in the area of functional genomics, molecular genetics, gene discovery, differentially expressed gene detection, diagnosis and prognosis etc. As a result, the tremendous amount of data that has been produced and accumulated in various public repositories such as Gene Expression Omnibus (GEO) and ArrayExpress, have been frequently used by researchers to readdress various biological goals. Since an independent study often comes with less sample size and limited statistical power, researchers are now relying on a more powerful technique called meta-analysis, an integrated analysis of existing data from different related independent studies. Meta-analysis is an active area in biomedical research which plays a vital role in increasing the statistical power to detect differentially expressed genes and to understand molecular and cellular aspects in a broader way. However, little efforts have been put to design user friendly tool/software to carry out meta-analysis automatically. In this paper, we present ShinyMDE, a user friendly tool to integrate different gene expression data for differentially expressed gene detection in an easy and efficient way. The tool handles processed and raw data generated from most widely used data platforms such as Affymetrix and Illumina. In addition, the tool provides user with an option of choosing the method of their choice from the list for meta-analysis. The tool is very simple to use and is developed with the aim of having an automated meta-analysis of gene expression data facilitating screening and downloading the results.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133610262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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