Pub Date : 2015-09-04DOI: 10.4172/2153-0602.1000178
Ruchi Sethi Gutch, Kaushal Sharma, Aditi Tiwari
Fungi are versatile organisms; they exist on earth in all extremes of conditions. Fungi are sources of important chemical entities which may be both beneficial and deleterious. Biotechnology has helped to harness this potential of Fungi in a positive direction. The advancements in Genomics and Proteomics have opened up new horizon in research. Improved advanced Molecular Biological Technologies have given a boost to our understanding of genes and helped us to exploit the full potential of Fungi. Bioinformatics and Statistical sciences are indispensable in this regard. Databases are available, providing fast, efficient, meaningful interpretation and analysis of vast amounts of data generated in scientific laboratories.
{"title":"Review Paper: Data Mining of Fungal Secondary Metabolites Using Genomics and Proteomics","authors":"Ruchi Sethi Gutch, Kaushal Sharma, Aditi Tiwari","doi":"10.4172/2153-0602.1000178","DOIUrl":"https://doi.org/10.4172/2153-0602.1000178","url":null,"abstract":"Fungi are versatile organisms; they exist on earth in all extremes of conditions. Fungi are sources of important chemical entities which may be both beneficial and deleterious. Biotechnology has helped to harness this potential of Fungi in a positive direction. The advancements in Genomics and Proteomics have opened up new horizon in research. Improved advanced Molecular Biological Technologies have given a boost to our understanding of genes and helped us to exploit the full potential of Fungi. Bioinformatics and Statistical sciences are indispensable in this regard. Databases are available, providing fast, efficient, meaningful interpretation and analysis of vast amounts of data generated in scientific laboratories.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2015-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79059247","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 : 2015-08-21DOI: 10.4172/2153-0602.1000177
P. Jha, Sujit Ghosh, K. Mukhopadhyay, A. Sachan, A. S. Vidyarthi
The bituminous and sub-bituminous rank of coals is being produced from the Jharia basin of Jharkhand which is the largest producer of CBM in India. Although there have been many reports on methanogenesis from Jharia, the present study deals with the special emphasis on the syntrophic microbes which can act as catalyst for the hydrogenotrophic methanogenesis. Using the metagenomic approach followed by 454 pyro sequencing, the presence of syntrophic community has been deciphered for the first time from the formation water samples of Jharia coal bed basin. The taxonomic assignment of unassembled clean metagenomic sequences was performed using BLASTX against the GenBank database through MG-RAST server. The class clostridia revealed a sequence affiliation to family Syntrophomonadaceae and class Deltaproteobacteria to family Desulfobacteraceae, Pelobacteraceae, Syntrophaceae, and Syntrophobacteraceae. Results revealed the possibility of thermobiogenic methanogenesis in the coal bed due to the presence of syntrophs related to Syntrophothermus genus. The presence of such communities can aid in biotransformation of coal to methane leading to enhanced energy production
{"title":"Syntrophics Bridging the Gap of Methanogenesis in the Jharia Coal Bed Basin","authors":"P. Jha, Sujit Ghosh, K. Mukhopadhyay, A. Sachan, A. S. Vidyarthi","doi":"10.4172/2153-0602.1000177","DOIUrl":"https://doi.org/10.4172/2153-0602.1000177","url":null,"abstract":"The bituminous and sub-bituminous rank of coals is being produced from the Jharia basin of Jharkhand which is the largest producer of CBM in India. Although there have been many reports on methanogenesis from Jharia, the present study deals with the special emphasis on the syntrophic microbes which can act as catalyst for the hydrogenotrophic methanogenesis. Using the metagenomic approach followed by 454 pyro sequencing, the presence of syntrophic community has been deciphered for the first time from the formation water samples of Jharia coal bed basin. The taxonomic assignment of unassembled clean metagenomic sequences was performed using BLASTX against the GenBank database through MG-RAST server. The class clostridia revealed a sequence affiliation to family Syntrophomonadaceae and class Deltaproteobacteria to family Desulfobacteraceae, Pelobacteraceae, Syntrophaceae, and Syntrophobacteraceae. Results revealed the possibility of thermobiogenic methanogenesis in the coal bed due to the presence of syntrophs related to Syntrophothermus genus. The presence of such communities can aid in biotransformation of coal to methane leading to enhanced energy production","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"367 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2015-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74905512","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 : 2015-08-01Epub Date: 2013-07-31DOI: 10.4172/2153-0602.1000135
Curtis Davis, Karthik Kota, Venkat Baldhandapani, Wei Gong, Sahar Abubucker, Eric Becker, John Martin, Kristine M Wylie, Radhika Khetani, Matthew E Hudson, George M Weinstock, Makedonka Mitreva
Recent advances in next-generation sequencing technologies require alignment algorithms and software that can keep pace with the heightened data production. Standard algorithms, especially protein similarity searches, represent significant bottlenecks in analysis pipelines. For metagenomic approaches in particular, it is now often necessary to search hundreds of millions of sequence reads against large databases. Here we describe mBLAST, an accelerated search algorithm for translated and/or protein alignments to large datasets based on the Basic Local Alignment Search Tool (BLAST) and retaining the high sensitivity of BLAST. The mBLAST algorithms achieve substantial speed up over the National Center for Biotechnology Information (NCBI) programs BLASTX, TBLASTX and BLASTP for large datasets, allowing analysis within reasonable timeframes on standard computer architectures. In this article, the impact of mBLAST is demonstrated with sequences originating from the microbiota of healthy humans from the Human Microbiome Project. mBLAST is designed as a plug-in replacement for BLAST for any study that involves short-read sequences and includes high-throughput analysis. The mBLAST software is freely available to academic users at www.multicorewareinc.com.
新一代测序技术的最新进展要求校准算法和软件能够跟上数据生产的步伐。标准算法，特别是蛋白质相似性搜索，是分析管道中的重要瓶颈。特别是对于宏基因组方法，现在经常需要在大型数据库中搜索数以亿计的序列读取。mBLAST是一种基于基本局部比对搜索工具(Basic Local Alignment search Tool, BLAST)并保留BLAST的高灵敏度的大型数据集翻译和/或蛋白质比对的加速搜索算法。与国家生物技术信息中心(NCBI)项目BLASTX、TBLASTX和BLASTP相比，mBLAST算法实现了对大型数据集的大幅提速，允许在合理的时间框架内在标准计算机架构上进行分析。在这篇文章中，mBLAST的影响是通过来自人类微生物组计划的健康人类微生物群的序列来证明的。mBLAST被设计为BLAST的插件替代品，适用于任何涉及短读序列和高通量分析的研究。mBLAST软件免费提供给学术用户，网址是www.multicorewareinc.com。
{"title":"mBLAST: Keeping up with the sequencing explosion for (meta)genome analysis.","authors":"Curtis Davis, Karthik Kota, Venkat Baldhandapani, Wei Gong, Sahar Abubucker, Eric Becker, John Martin, Kristine M Wylie, Radhika Khetani, Matthew E Hudson, George M Weinstock, Makedonka Mitreva","doi":"10.4172/2153-0602.1000135","DOIUrl":"https://doi.org/10.4172/2153-0602.1000135","url":null,"abstract":"<p><p>Recent advances in next-generation sequencing technologies require alignment algorithms and software that can keep pace with the heightened data production. Standard algorithms, especially protein similarity searches, represent significant bottlenecks in analysis pipelines. For metagenomic approaches in particular, it is now often necessary to search hundreds of millions of sequence reads against large databases. Here we describe mBLAST, an accelerated search algorithm for translated and/or protein alignments to large datasets based on the Basic Local Alignment Search Tool (BLAST) and retaining the high sensitivity of BLAST. The mBLAST algorithms achieve substantial speed up over the National Center for Biotechnology Information (NCBI) programs BLASTX, TBLASTX and BLASTP for large datasets, allowing analysis within reasonable timeframes on standard computer architectures. In this article, the impact of mBLAST is demonstrated with sequences originating from the microbiota of healthy humans from the Human Microbiome Project. mBLAST is designed as a plug-in replacement for BLAST for any study that involves short-read sequences and includes high-throughput analysis. The mBLAST software is freely available to academic users at www.multicorewareinc.com.</p>","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4612494/pdf/nihms696431.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34117363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-23DOI: 10.4172/2153-0602.1000174
Guido Silva Francisco Altamirano, Walter Montenegro, Ricardo Silva
The relationship between human papillomavirus (HPV) and cervical cancer remains a topic of extensive research. This virus is responsible for mild and severe abnormalities that can slowly trigger some type of carcinoma with a strong association with sexual practice. Availability of new techniques for HPV tipification allow to better establish more common virus types associated to this neoplasia. The article presents prevalence and molecular epidemiology (PCR results) from 1000 female patients affiliated to the Ecuadorian Institute of Social Security (IESS), concurrent to Teodoro Maldonado Carbo Hospital in the city of Guayaquil, Ecuador, from July 2011 to August 2013. Results prove that the most prevalent types of HPV present are: HPV-16 (29, 77%); HPV-52 (16, 18%); HPV-51 (12, 30%); HPV-6 (9, 71%); and HPV-59 (8, 74%). Molecular epidemiology is quite distinct from that found in other parts of the world. Ecuador is importing Papillomavirus vaccines, and general idea from health authorities is that these vaccines offer protection against 75% of papilloma virus infections. Results presented in this study, suggest that this protection is less than 30% for women in the province of Guayas.
{"title":"Prevalence and Molecular Epidemiology of Human Papillomavirus in Ecuadorian Women with Cervical Cytological Abnormalities","authors":"Guido Silva Francisco Altamirano, Walter Montenegro, Ricardo Silva","doi":"10.4172/2153-0602.1000174","DOIUrl":"https://doi.org/10.4172/2153-0602.1000174","url":null,"abstract":"The relationship between human papillomavirus (HPV) and cervical cancer remains a topic of extensive research. This virus is responsible for mild and severe abnormalities that can slowly trigger some type of carcinoma with a strong association with sexual practice. Availability of new techniques for HPV tipification allow to better establish more common virus types associated to this neoplasia. The article presents prevalence and molecular epidemiology (PCR results) from 1000 female patients affiliated to the Ecuadorian Institute of Social Security (IESS), concurrent to Teodoro Maldonado Carbo Hospital in the city of Guayaquil, Ecuador, from July 2011 to August 2013. Results prove that the most prevalent types of HPV present are: HPV-16 (29, 77%); HPV-52 (16, 18%); HPV-51 (12, 30%); HPV-6 (9, 71%); and HPV-59 (8, 74%). Molecular epidemiology is quite distinct from that found in other parts of the world. Ecuador is importing Papillomavirus vaccines, and general idea from health authorities is that these vaccines offer protection against 75% of papilloma virus infections. Results presented in this study, suggest that this protection is less than 30% for women in the province of Guayas.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"330 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2015-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73859919","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 : 2015-07-16DOI: 10.4172/2153-0602.1000175
Yosuke Kondo, S. Miyazaki
So far, in order to predict important sites of a protein, many computational methods have been developed. In the era of big-data, it is required for improvements and sophistication of existing methods by integrating sequence data in the structural data. In this paper, we aim at two things: improving sequence-based methods and developing a new method using both sequence and structural data. Therefore, we developed an originally modified evolutionary trace method, in which we defined conservative grades calculated from a given multiple sequence alignment and a proximate grade in order to evaluate predicted active sites from a viewpoint of protein-ion, protein-ligand, protein-nucleic acid, proteinprotein interaction by use of three-dimensional structures. In other words, the proximate grade also can evaluate an amino acid residue. When we applied our method to translation elongation factor Tu/1A proteins, it showed that the conservative grades are evaluated accurately by the proximate grade. Consequently, our idea indicated two advantages. One is that we can take into account various cocrystal structures for evaluation. Another one is that, by calculating the fitness between the given conservative grade and the proximate grade, we can select the best conservative grade.
{"title":"Protein Functional Site Prediction Using a Conservative Grade and aProximate Grade","authors":"Yosuke Kondo, S. Miyazaki","doi":"10.4172/2153-0602.1000175","DOIUrl":"https://doi.org/10.4172/2153-0602.1000175","url":null,"abstract":"So far, in order to predict important sites of a protein, many computational methods have been developed. In the era of big-data, it is required for improvements and sophistication of existing methods by integrating sequence data in the structural data. In this paper, we aim at two things: improving sequence-based methods and developing a new method using both sequence and structural data. Therefore, we developed an originally modified evolutionary trace method, in which we defined conservative grades calculated from a given multiple sequence alignment and a proximate grade in order to evaluate predicted active sites from a viewpoint of protein-ion, protein-ligand, protein-nucleic acid, proteinprotein interaction by use of three-dimensional structures. In other words, the proximate grade also can evaluate an amino acid residue. When we applied our method to translation elongation factor Tu/1A proteins, it showed that the conservative grades are evaluated accurately by the proximate grade. Consequently, our idea indicated two advantages. One is that we can take into account various cocrystal structures for evaluation. Another one is that, by calculating the fitness between the given conservative grade and the proximate grade, we can select the best conservative grade.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"13 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2015-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84365596","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 : 2015-07-14DOI: 10.4172/2153-0602.1000173
A. Lisitsa, Elizabeth Stewart, E. Kolker
The concept of cognitive bioinformatics has been proposed for structuring of knowledge in the field of molecular biology. While cognitive science is considered as “thinking about the process of thinking”, cognitive bioinformatics strives to capture the process of thought and analysis as applied to the challenging intersection of diverse fields such as biology, informatics, and computer science collectively known as bioinformatics. Ten years ago cognitive bioinformatics was introduced as a model of the analysis performed by scientists working with molecular biology and biomedical web resources. At present, the concept of cognitive bioinformatics can be examined in the context of the opportunities represented by the information “data deluge” of life sciences technologies. The unbalanced nature of accumulating information along with some challenges poses currently intractable problems for researchers. The solutions to these problems at the micro-and macro-levels are considered with regards to the role of cognitive approaches in the field of bioinformatics.
{"title":"Is it Time for Cognitive Bioinformatics","authors":"A. Lisitsa, Elizabeth Stewart, E. Kolker","doi":"10.4172/2153-0602.1000173","DOIUrl":"https://doi.org/10.4172/2153-0602.1000173","url":null,"abstract":"The concept of cognitive bioinformatics has been proposed for structuring of knowledge in the field of molecular biology. While cognitive science is considered as “thinking about the process of thinking”, cognitive bioinformatics strives to capture the process of thought and analysis as applied to the challenging intersection of diverse fields such as biology, informatics, and computer science collectively known as bioinformatics. Ten years ago cognitive bioinformatics was introduced as a model of the analysis performed by scientists working with molecular biology and biomedical web resources. At present, the concept of cognitive bioinformatics can be examined in the context of the opportunities represented by the information “data deluge” of life sciences technologies. The unbalanced nature of accumulating information along with some challenges poses currently intractable problems for researchers. The solutions to these problems at the micro-and macro-levels are considered with regards to the role of cognitive approaches in the field of bioinformatics.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"100 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2015-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85794229","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 : 2015-07-09DOI: 10.4172/2153-0602.1000172
Wajdi Dhifli, Abdoulaye Baniré Diallo
Graph theory and graph mining constitute rich fields of computational techniques to study the structures, topologies and properties of graphs. These techniques constitute a good asset in bioinformatics if there exist efficient methods for transforming biological data into graphs. In this paper, we present Protein Graph Repository (PGR), a novel database of protein 3D-structures transformed into graphs allowing the use of the large repertoire of graph theory techniques in protein mining. This repository contains graph representations of all currently known protein 3D-structures described in the Protein Data Bank (PDB). PGR also provides an efficient online converter of protein 3Dstructures into graphs, biological and graph-based description, pre-computed protein graph attributes and statistics, visualization of each protein graph, as well as graph-based protein similarity search tool. Such repository presents an enrichment of existing online databases that will help bridging the gap between graph mining and protein structure analysis. PGR data and features are unique and not included in any other protein database. The repository is available at http://wjdi.bioinfo. uqam.ca/
{"title":"PGR: A Novel Graph Repository of Protein 3D-Structures","authors":"Wajdi Dhifli, Abdoulaye Baniré Diallo","doi":"10.4172/2153-0602.1000172","DOIUrl":"https://doi.org/10.4172/2153-0602.1000172","url":null,"abstract":"Graph theory and graph mining constitute rich fields of computational techniques to study the structures, topologies and properties of graphs. These techniques constitute a good asset in bioinformatics if there exist efficient methods for transforming biological data into graphs. In this paper, we present Protein Graph Repository (PGR), a novel database of protein 3D-structures transformed into graphs allowing the use of the large repertoire of graph theory techniques in protein mining. This repository contains graph representations of all currently known protein 3D-structures described in the Protein Data Bank (PDB). PGR also provides an efficient online converter of protein 3Dstructures into graphs, biological and graph-based description, pre-computed protein graph attributes and statistics, visualization of each protein graph, as well as graph-based protein similarity search tool. Such repository presents an enrichment of existing online databases that will help bridging the gap between graph mining and protein structure analysis. PGR data and features are unique and not included in any other protein database. The repository is available at http://wjdi.bioinfo. uqam.ca/","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"36 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81069342","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 : 2015-07-08DOI: 10.4172/2153-0602.1000171
Ahmad Maleki, Vahid Vaezinia, A. Fekri
Promoter is a part of the DNA sequence that comes before the gene and is key as a regulator of genes. Promoter prediction helps determine gene position and analyze gene expression. Hence, it is of great importance in the field of bioinformatics. In bioinformatics research, a number of machine learning approaches are applied to discover new meaningful knowledge from biological databases. In this study, two learning approaches, expectation maximization clustering and support vector machine classifier (EMSVM) are used to perform promoter detection. Expectation maximization (EM) algorithm is used to identify groups of samples that behave similarly and dissimilarly, such as the activity of promoters and non-promoters in the first stage, while the support vector machine (SVM) is used in the second stage to classify all the data into the correct class category. We have applied this method to datasets corresponding to σ24, σ32, σ38, σ70 promoters and its effectiveness was demonstrated on a range of different promoter regions. Furthermore, it was compared with other classification algorithms to indicate the appropriate performance of the proposed algorithm. Test results show that EMSVM performs better than other methods.
{"title":"Promoter Prediction in Bacterial DNA Sequences Using Expectation Maximization and Support Vector Machine Learning Approach","authors":"Ahmad Maleki, Vahid Vaezinia, A. Fekri","doi":"10.4172/2153-0602.1000171","DOIUrl":"https://doi.org/10.4172/2153-0602.1000171","url":null,"abstract":"Promoter is a part of the DNA sequence that comes before the gene and is key as a regulator of genes. Promoter prediction helps determine gene position and analyze gene expression. Hence, it is of great importance in the field of bioinformatics. In bioinformatics research, a number of machine learning approaches are applied to discover new meaningful knowledge from biological databases. In this study, two learning approaches, expectation maximization clustering and support vector machine classifier (EMSVM) are used to perform promoter detection. Expectation maximization (EM) algorithm is used to identify groups of samples that behave similarly and dissimilarly, such as the activity of promoters and non-promoters in the first stage, while the support vector machine (SVM) is used in the second stage to classify all the data into the correct class category. We have applied this method to datasets corresponding to σ24, σ32, σ38, σ70 promoters and its effectiveness was demonstrated on a range of different promoter regions. Furthermore, it was compared with other classification algorithms to indicate the appropriate performance of the proposed algorithm. Test results show that EMSVM performs better than other methods.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"36 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74748558","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 : 2015-07-02DOI: 10.4172/2153-0602.1000170
P. Sonika, Ey, M. Srivastava, M. Shahid, Vipul Kumar, A. Singh, Shubham Trivedi, Y. K. Srivastava
The main aim of this study was to analyze eight species of Trichoderma for cellulase enzyme production by solid state fermentation. Different carbon sources such as wheat bran, corn cob, sucrose, maltose and filter paper were used. Highest celluase enzyme production was achieved with T. harzianum on media supplemented with corn cob. The optimum pH, temperature and thermal stability of isolated enzymes were also analyzed. The best pH for enzyme production was found between 4-6. The optimum temperature range for cellulase production ranged between 30-40°C. Choosing the optimum pH, temperature and best carbon source are essential for the enzyme production. Compare to other fungal genera it has been found that Trichoderma spp. have the greater potential to synthesize cellulase enzyme.
{"title":"Trichoderma species Cellulases Produced by Solid State Fermentation","authors":"P. Sonika, Ey, M. Srivastava, M. Shahid, Vipul Kumar, A. Singh, Shubham Trivedi, Y. K. Srivastava","doi":"10.4172/2153-0602.1000170","DOIUrl":"https://doi.org/10.4172/2153-0602.1000170","url":null,"abstract":"The main aim of this study was to analyze eight species of Trichoderma for cellulase enzyme production by solid state fermentation. Different carbon sources such as wheat bran, corn cob, sucrose, maltose and filter paper were used. Highest celluase enzyme production was achieved with T. harzianum on media supplemented with corn cob. The optimum pH, temperature and thermal stability of isolated enzymes were also analyzed. The best pH for enzyme production was found between 4-6. The optimum temperature range for cellulase production ranged between 30-40°C. Choosing the optimum pH, temperature and best carbon source are essential for the enzyme production. Compare to other fungal genera it has been found that Trichoderma spp. have the greater potential to synthesize cellulase enzyme.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"12 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2015-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82434325","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 : 2015-03-30DOI: 10.4172/2153-0602.10000S1
S. MirajkarSJKalePBBangarS, Dipika A Padole
Flax (Linum usitatissimum L., 2n = 30) belongs to the family Linaceae and is a dual-purpose crop with utility as an oilseed (linseed) as well as stem fiber (linen). It is emerging as one of the key sources of phytochemicals in the functional food arena. It is clinically proven that consumption of flax seed reduces the risk of heart attack, inflammatory disorders, arthrosclerosis and inhibits growth of prostate and breast cancers. Flax seed is the richest agricultural source of the essential fatty acid, α-lenolenic acid (ALA) of omega-3 class and lignans along with high quality proteins, soluble fibers and phenolic compounds. Oil and lignans are important nutraceuticals that accumulate in endosperm and seed coat, respectively during seed development.In our study, high-throughput proteomics approach was employed to determine the expression profile and identity of hundreds of proteins during seed filling in flax. The proteins were analyzed at 4, 8, 12, 16, 22, 30 and 48 days after flowering using one dimensional SDS-PAGE as prefractionation method and nLC-ESI-MS/MS. Relative protein concentration was determined by spiking samples with 50 fmol of standard BSA tryptic digest. Spectral counting of standard BSA peptides was considered for relative quantification of unknown proteins. A database was developed from predicted gene models of flax whole genome sequence and raw data were searched to identify the proteins and confirmed by BLAST analysis. We identified 965 non-redundant proteins, which were classified into 14 major functional categories. The proteins involved in metabolism, protein destination and storage, metabolite transport and disease/defense were the most abundant. For each functional category, a composite expression profile has been presented to gain insight into seed physiology and the general regulation of proteins associated with each functional class. Using this approach, the metabolism-related proteins were found to decrease, while the proteins associated with destination and storage increased during seed filling.
{"title":"Nutrigenomics: Just another ??Omic??","authors":"S. MirajkarSJKalePBBangarS, Dipika A Padole","doi":"10.4172/2153-0602.10000S1","DOIUrl":"https://doi.org/10.4172/2153-0602.10000S1","url":null,"abstract":"Flax (Linum usitatissimum L., 2n = 30) belongs to the family Linaceae and is a dual-purpose crop with utility as an oilseed (linseed) as well as stem fiber (linen). It is emerging as one of the key sources of phytochemicals in the functional food arena. It is clinically proven that consumption of flax seed reduces the risk of heart attack, inflammatory disorders, arthrosclerosis and inhibits growth of prostate and breast cancers. Flax seed is the richest agricultural source of the essential fatty acid, α-lenolenic acid (ALA) of omega-3 class and lignans along with high quality proteins, soluble fibers and phenolic compounds. Oil and lignans are important nutraceuticals that accumulate in endosperm and seed coat, respectively during seed development.In our study, high-throughput proteomics approach was employed to determine the expression profile and identity of hundreds of proteins during seed filling in flax. The proteins were analyzed at 4, 8, 12, 16, 22, 30 and 48 days after flowering using one dimensional SDS-PAGE as prefractionation method and nLC-ESI-MS/MS. Relative protein concentration was determined by spiking samples with 50 fmol of standard BSA tryptic digest. Spectral counting of standard BSA peptides was considered for relative quantification of unknown proteins. A database was developed from predicted gene models of flax whole genome sequence and raw data were searched to identify the proteins and confirmed by BLAST analysis. We identified 965 non-redundant proteins, which were classified into 14 major functional categories. The proteins involved in metabolism, protein destination and storage, metabolite transport and disease/defense were the most abundant. For each functional category, a composite expression profile has been presented to gain insight into seed physiology and the general regulation of proteins associated with each functional class. Using this approach, the metabolism-related proteins were found to decrease, while the proteins associated with destination and storage increased during seed filling.","PeriodicalId":15630,"journal":{"name":"Journal of Data Mining in Genomics & Proteomics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72698721","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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