Pub Date : 2016-01-01DOI: 10.4172/2153-0602.1000129
A. Roy, J. Shao, J. Hartung, W. Schneider, R. Brlansky
The advent of innovative sequencing technology referred to as “Next-Generation” Sequencing (NGS), provides a new approach to identify the ‘unknown known’ and ‘unknown unknown’ viral pathogens without a priori knowledge. The genomes of plant viruses can be rapidly determined even when occurring at extremely low titers in the infected host. The method is based on massively parallel sequencing of the population of small RNA molecules 18-35 nucleotides in length produced by RNA silencing host defense. Improvements in chemistries, bioinformatic tools and advances in engineering has reduced the costs of NGS, increased its accessibility, and enabled its application in the field of plant virology. In this review, we discuss the utilization of the Illumina GA IIX platform combined with the application of molecular biology and bioinformatic tools for the discovery of a novel cytoplasmic Citrus leprosis virus (CiLV). This new virus produced symptoms typical of CiLV but was not detected with either serological or PCR-based assays for the previously described virus. The new viral genome was also present in low titer in sweet orange (Citrus sinensis), an important horticultural crop with incomplete genomic resources. This is a common situation in horticultural research and provides an example of the broader utility of this approach. In addition to the discovery of novel viruses, the sequence data may be useful for studies of viral evolution and ecology and the interactions between viral and host transcriptomes.
被称为“下一代”测序(NGS)的创新测序技术的出现，提供了一种在没有先验知识的情况下识别“未知已知”和“未知未知”病毒病原体的新方法。植物病毒的基因组可以快速确定，即使在感染宿主中以极低的滴度发生。该方法基于对RNA沉默宿主防御产生的长度为18-35个核苷酸的小RNA分子群体进行大规模平行测序。化学、生物信息学工具的改进和工程技术的进步降低了NGS的成本，增加了其可及性，并使其在植物病毒学领域的应用成为可能。在这篇综述中，我们讨论了利用Illumina GA IIX平台结合应用分子生物学和生物信息学工具发现一种新的细胞质型柑橘型麻风病毒(CiLV)。这种新病毒产生了CiLV的典型症状，但对先前描述的病毒进行血清学或基于pcr的检测均未检测到。在基因组资源不完整的重要园艺作物甜橙(Citrus sinensis)中也存在低滴度的新病毒基因组。这是园艺研究中常见的情况，并提供了这种方法更广泛应用的一个例子。除了发现新病毒外，序列数据还可用于研究病毒进化和生态学以及病毒与宿主转录组之间的相互作用。
{"title":"A Case Study on Discovery of Novel Citrus Leprosis Virus Cytoplasmic Type 2 Utilizing Small RNA Libraries by Next Generation Sequencing and Bioinformatic Analyses","authors":"A. Roy, J. Shao, J. Hartung, W. Schneider, R. Brlansky","doi":"10.4172/2153-0602.1000129","DOIUrl":"https://doi.org/10.4172/2153-0602.1000129","url":null,"abstract":"The advent of innovative sequencing technology referred to as “Next-Generation” Sequencing (NGS), provides a new approach to identify the ‘unknown known’ and ‘unknown unknown’ viral pathogens without a priori knowledge. The genomes of plant viruses can be rapidly determined even when occurring at extremely low titers in the infected host. The method is based on massively parallel sequencing of the population of small RNA molecules 18-35 nucleotides in length produced by RNA silencing host defense. Improvements in chemistries, bioinformatic tools and advances in engineering has reduced the costs of NGS, increased its accessibility, and enabled its application in the field of plant virology. In this review, we discuss the utilization of the Illumina GA IIX platform combined with the application of molecular biology and bioinformatic tools for the discovery of a novel cytoplasmic Citrus leprosis virus (CiLV). This new virus produced symptoms typical of CiLV but was not detected with either serological or PCR-based assays for the previously described virus. The new viral genome was also present in low titer in sweet orange (Citrus sinensis), an important horticultural crop with incomplete genomic resources. This is a common situation in horticultural research and provides an example of the broader utility of this approach. In addition to the discovery of novel viruses, the sequence data may be useful for studies of viral evolution and ecology and the interactions between viral and host transcriptomes.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"19 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76838281","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 : 2016-01-01DOI: 10.4172/2153-0602.1000183
Qihua Tan, Martin Tepel, Hans Christian Beck, Lars Melholt Rasmussen, Jacob v. B. Hjelmborg
J Data Mining Genomics Proteomics ISSN: 2153-0602 JDMGP, an open access journal Volume 7 • Issue 1 • 1000183 As a popular measure of association, the Pearson’s correlation coefficient has been frequently used in omics data analysis e.g. in feature selection process during prediction model building using high dimensional gene expression data [1] and proteomics data [2]. However, Pearson’s correlation coefficient captures only linear relationships which greatly limit its use in situations of nonlinear association. Statistical modeling for dealing with nonlinear patterns can be complicated [3] and requires intensive computation in case of high dimensional data such as microarray data or genome sequence data. In the analysis of omics data, high dimension means that there can be diverse patterns of dependence not limited to linearity. In this situation, the generalized measures of association more adequate than the Pearson’s correlation and capable of capturing both linear and nonlinear correlations are needed. Recently, generalized correlation coefficients have been frequently discussed [4] and their application to large scale genomic data illustrated through microarray gene expression time-course analysis [5].
{"title":"Generalized Measure of Dependency for Analysis of Omics Data","authors":"Qihua Tan, Martin Tepel, Hans Christian Beck, Lars Melholt Rasmussen, Jacob v. B. Hjelmborg","doi":"10.4172/2153-0602.1000183","DOIUrl":"https://doi.org/10.4172/2153-0602.1000183","url":null,"abstract":"J Data Mining Genomics Proteomics ISSN: 2153-0602 JDMGP, an open access journal Volume 7 • Issue 1 • 1000183 As a popular measure of association, the Pearson’s correlation coefficient has been frequently used in omics data analysis e.g. in feature selection process during prediction model building using high dimensional gene expression data [1] and proteomics data [2]. However, Pearson’s correlation coefficient captures only linear relationships which greatly limit its use in situations of nonlinear association. Statistical modeling for dealing with nonlinear patterns can be complicated [3] and requires intensive computation in case of high dimensional data such as microarray data or genome sequence data. In the analysis of omics data, high dimension means that there can be diverse patterns of dependence not limited to linearity. In this situation, the generalized measures of association more adequate than the Pearson’s correlation and capable of capturing both linear and nonlinear correlations are needed. Recently, generalized correlation coefficients have been frequently discussed [4] and their application to large scale genomic data illustrated through microarray gene expression time-course analysis [5].","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"11 1","pages":"1000183"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84274963","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 : 2016-01-01DOI: 10.4172/2153-0602.1000e126
M. Simonian
Proteomics is the identification of proteins in a tissue or cell, and the determination of their function, structure and modifications [1,2]. The term proteome was coined by Marc Wilkins to describe all the proteins expressed by a genome [1]. It is considered to be the next step in modern biology. Proteomics is dynamic compared to genomics because it changes constantly to reflect the cell’s environment. The main objectives in the field of proteomics are: (i) Identify all proteins; (ii) Analyse differential protein expression in different samples; (iii) Characterise proteins by identifying and studying their function and cellular localisation; and (iv) Understand protein interaction networks.
{"title":"Proteomics in Medicine","authors":"M. Simonian","doi":"10.4172/2153-0602.1000e126","DOIUrl":"https://doi.org/10.4172/2153-0602.1000e126","url":null,"abstract":"Proteomics is the identification of proteins in a tissue or cell, and the determination of their function, structure and modifications [1,2]. The term proteome was coined by Marc Wilkins to describe all the proteins expressed by a genome [1]. It is considered to be the next step in modern biology. Proteomics is dynamic compared to genomics because it changes constantly to reflect the cell’s environment. The main objectives in the field of proteomics are: (i) Identify all proteins; (ii) Analyse differential protein expression in different samples; (iii) Characterise proteins by identifying and studying their function and cellular localisation; and (iv) Understand protein interaction networks.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"20 1","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83940121","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 : 2016-01-01DOI: 10.4172/2153-0602.1000E121
G. Nava, Yajaira Esquivel-Hern, Ez
Worldwide, Salmonella enterica remains an important health threat. A recent study by the World Health Organization estimated that nontyphoidal S. enterica causes ∼ 230,000 deaths annually [1]. The biology of this pathogen has been studied for almost a century [2]; until recently, however, we have started to elucidate genomic features of adaptation to its hosts. Now, it is know that S. enterica has evolved to establish sympatric (generalists) and allopatric (specialists) and associations with its host. For example, S. enterica subspecies enterica, serotypes Choleraesuis, Dublin and Gallinarum have established allopatric association with porcine, bovine and avian species, respectively. In contrast, serotype Enteritidis and Typhimurium have adapted a sympatric strategy to colonize the intestinal tract of a broad number of avian and mammalian species [3]. This progress in knowledge has been accomplished with the aid of molecular microbiology.
{"title":"Comparative Genomics of Salmonella Could Reveal Key Features of Adaptation","authors":"G. Nava, Yajaira Esquivel-Hern, Ez","doi":"10.4172/2153-0602.1000E121","DOIUrl":"https://doi.org/10.4172/2153-0602.1000E121","url":null,"abstract":"Worldwide, Salmonella enterica remains an important health threat. A recent study by the World Health Organization estimated that nontyphoidal S. enterica causes ∼ 230,000 deaths annually [1]. The biology of this pathogen has been studied for almost a century [2]; until recently, however, we have started to elucidate genomic features of adaptation to its hosts. Now, it is know that S. enterica has evolved to establish sympatric (generalists) and allopatric (specialists) and associations with its host. For example, S. enterica subspecies enterica, serotypes Choleraesuis, Dublin and Gallinarum have established allopatric association with porcine, bovine and avian species, respectively. In contrast, serotype Enteritidis and Typhimurium have adapted a sympatric strategy to colonize the intestinal tract of a broad number of avian and mammalian species [3]. This progress in knowledge has been accomplished with the aid of molecular microbiology.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"37 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82616188","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 : 2016-01-01DOI: 10.4172/2153-0602.1000184
R. Shouval, A. Nagler, M. Labopin, R. Unger
J Data Mining Genomics Proteomics ISSN: 2153-0602 JDMGP, an open access journal Volume 7 • Issue 1 • 1000184 Allogeneic (allo) hematopoietic stem transplantation (HSCT) is a potentially curative procedure for selected patients with hematological disease. Despite a reduction in transplant risk in recent years, morbidity and mortality remains substantial, making the decision of whom, how and when to transplant, of great importance [1].
{"title":"Interpretable Boosted Decision Trees for Prediction of Mortality Following Allogeneic Hematopoietic Stem Cell Transplantation","authors":"R. Shouval, A. Nagler, M. Labopin, R. Unger","doi":"10.4172/2153-0602.1000184","DOIUrl":"https://doi.org/10.4172/2153-0602.1000184","url":null,"abstract":"J Data Mining Genomics Proteomics ISSN: 2153-0602 JDMGP, an open access journal Volume 7 • Issue 1 • 1000184 Allogeneic (allo) hematopoietic stem transplantation (HSCT) is a potentially curative procedure for selected patients with hematological disease. Despite a reduction in transplant risk in recent years, morbidity and mortality remains substantial, making the decision of whom, how and when to transplant, of great importance [1].","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"30 1 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77359203","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 : 2015-12-17DOI: 10.4172/2153-0602.C1.001
Luis Sousa
{"title":"Data Mining and Geo-Engineering","authors":"Luis Sousa","doi":"10.4172/2153-0602.C1.001","DOIUrl":"https://doi.org/10.4172/2153-0602.C1.001","url":null,"abstract":"","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73874438","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 : 2015-10-31DOI: 10.4172/2153-0602.1000E120
J. Mao, H. Weier
{"title":"In the Race towards a Better Diagnosis and Prognostication of Cancer Patients Long Non-Coding Intergenic RNA's (lincrna's) have found their Place","authors":"J. Mao, H. Weier","doi":"10.4172/2153-0602.1000E120","DOIUrl":"https://doi.org/10.4172/2153-0602.1000E120","url":null,"abstract":"","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"16 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2015-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81077021","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 : 2015-10-25DOI: 10.4172/2153-0602.1000181
K. Shaukat, N. Masood, S. Mehreen, Ulya Azmeen
Dengue is a threatening disease caused by female mosquitos. It is typically found in widespread hot regions. From long periods of time, Experts are trying to find out some of features on Dengue disease so that they can rightly categorize patients because different patients require different types of treatment. Pakistan has been target of Dengue disease from last few years. Dengue fever is used in classification techniques to evaluate and compare their performance. The dataset was collected from District Headquarter Hospital (DHQ) Jhelum. For properly categorizing our dataset, different classification techniques are used. These techniques are Naive Bayesian, REP Tree, Random tree, J48 and SMO. WEKA was used as Data mining tool for classification of data. Firstly we will evaluate the performance of all the techniques separately with the help of tables and graphs depending upon dataset and secondly we will compare the performance of all the techniques.
{"title":"Dengue Fever Prediction: A Data Mining Problem","authors":"K. Shaukat, N. Masood, S. Mehreen, Ulya Azmeen","doi":"10.4172/2153-0602.1000181","DOIUrl":"https://doi.org/10.4172/2153-0602.1000181","url":null,"abstract":"Dengue is a threatening disease caused by female mosquitos. It is typically found in widespread hot regions. From long periods of time, Experts are trying to find out some of features on Dengue disease so that they can rightly categorize patients because different patients require different types of treatment. Pakistan has been target of Dengue disease from last few years. Dengue fever is used in classification techniques to evaluate and compare their performance. The dataset was collected from District Headquarter Hospital (DHQ) Jhelum. For properly categorizing our dataset, different classification techniques are used. These techniques are Naive Bayesian, REP Tree, Random tree, J48 and SMO. WEKA was used as Data mining tool for classification of data. Firstly we will evaluate the performance of all the techniques separately with the help of tables and graphs depending upon dataset and secondly we will compare the performance of all the techniques.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"132 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76152379","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 : 2015-10-18DOI: 10.4172/2153-0602.1000180
N. Suárez, J. Bonacina, E. Hebert, L. Saavedra
Among 151 bacterial isolates from nine artisanal cheeses, Enterococcus faecium CRL 1879 showed antibacterial activity against the food-borne pathogen Listeria monocytogenes. The isolate produced a proteinase K-sensitive compound in the cell free supernatant. Genome analysis demonstrated the presence of enterocin A, enterocin B, enterocin P, enterocin SE-K4-like and enterocin X biosynthetic gene clusters. Nucleotide sequences encoding for a putative two-component bacteriocin were detected using bioinformatics tools, here named enterocin CRL1879αβ. A transcriptional analysis of all bacteriocin genes by quantitative real time PCR analysis (qRT-PCR) revealed the transcription of each enterocin gene at different levels. Finally, analysis of bacteriocin genes distribution in 251 E. faecium bioprojects was performed and compared to those identify in E. faecium CRL1879. The discriminative analysis demonstrated that bacteriocin genes are widely distributed among Enterococcus, independently of the origin of the strain. The results presented in this paper represent a unique finding since this is the first demonstration of an E. faecium strain isolated from an artisanal cheese with the complete genetic machinery to produce six classes II and one class III bacteriocins.
{"title":"Genome Mining and Transcriptional Analysis of Bacteriocin Genes in Enterococcus faecium CRL1879","authors":"N. Suárez, J. Bonacina, E. Hebert, L. Saavedra","doi":"10.4172/2153-0602.1000180","DOIUrl":"https://doi.org/10.4172/2153-0602.1000180","url":null,"abstract":"Among 151 bacterial isolates from nine artisanal cheeses, Enterococcus faecium CRL 1879 showed antibacterial activity against the food-borne pathogen Listeria monocytogenes. The isolate produced a proteinase K-sensitive compound in the cell free supernatant. Genome analysis demonstrated the presence of enterocin A, enterocin B, enterocin P, enterocin SE-K4-like and enterocin X biosynthetic gene clusters. Nucleotide sequences encoding for a putative two-component bacteriocin were detected using bioinformatics tools, here named enterocin CRL1879αβ. A transcriptional analysis of all bacteriocin genes by quantitative real time PCR analysis (qRT-PCR) revealed the transcription of each enterocin gene at different levels. Finally, analysis of bacteriocin genes distribution in 251 E. faecium bioprojects was performed and compared to those identify in E. faecium CRL1879. The discriminative analysis demonstrated that bacteriocin genes are widely distributed among Enterococcus, independently of the origin of the strain. \u0000The results presented in this paper represent a unique finding since this is the first demonstration of an E. faecium strain isolated from an artisanal cheese with the complete genetic machinery to produce six classes II and one class III bacteriocins.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"32 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2015-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84794603","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 : 2015-09-09DOI: 10.4172/2153-0602.1000179
A. Motsinger-Reif, Daniel M. Rotroff
Lymphoblastoid cell lines (LCL) are becoming popular tools for modeling drug response. LCLs, and other in vitro assays, offer the ability to test many drugs, doses, and biological samples relatively quickly and inexpensively. In addition, a unique advantage to LCLs is that they are available from a large cohort of individuals, providing the capability to test for genetic variability on a scale not readily available in other in vitro systems. Since oftentimes the genotype data is publically available, the experimental costs can be limited to the cost of the drug response phenotyping. Here we describe several advantages and limitations of LCLs. In addition we review several important aspects of LCL experimental design and statistical analysis. Lastly, we present an example of LCLs being successfully used to identify candidate single nucleotide polymorphisms and genes for variability in response to a chemotherapeutic used to treat chronic myeloid leukemia.
{"title":"Leveraging Lymphoblastoid Cell Lines for Drug Response Modeling","authors":"A. Motsinger-Reif, Daniel M. Rotroff","doi":"10.4172/2153-0602.1000179","DOIUrl":"https://doi.org/10.4172/2153-0602.1000179","url":null,"abstract":"Lymphoblastoid cell lines (LCL) are becoming popular tools for modeling drug response. LCLs, and other in vitro assays, offer the ability to test many drugs, doses, and biological samples relatively quickly and inexpensively. In addition, a unique advantage to LCLs is that they are available from a large cohort of individuals, providing the capability to test for genetic variability on a scale not readily available in other in vitro systems. Since oftentimes the genotype data is publically available, the experimental costs can be limited to the cost of the drug response phenotyping. Here we describe several advantages and limitations of LCLs. In addition we review several important aspects of LCL experimental design and statistical analysis. Lastly, we present an example of LCLs being successfully used to identify candidate single nucleotide polymorphisms and genes for variability in response to a chemotherapeutic used to treat chronic myeloid leukemia.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"62 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2015-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90251801","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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