Top 25 Indian Bioinformatics Companies
1. Accelrys Software Solution Pvt Ltd. 12th Floor, Discover, ITPL, White Field, Bangalore-65. www.accelrys.com 2. Apticraft Systems (P) Ltd. 142, Electronics Complex, Pardeshipura, Indore – 452010 (M.P.), India www.apticraft.com3. Aptuit Informatics Plot No. 100-103, Export Promotion Industrial Park, White Field, Bangalore-560066 www.aptiuit.com4. Bigtec J. K. Towers, 8th Block, Sangam Circle,46th Cross, Bangalore-560082. www.bigtec.org 5. Bijam Biosciences Private Limited Nagarjuna Hills, Hyderabad 500 082, India www.nagarjunagroup.com 6. Bio Base Databases India Pvt Ltd. Crescent Towers, 4th Floor, No : 32/1, Crescent Road, Bnagalore - 560 001 www.biobase-international.com7. BioImagene India Pvt. Ltd. 4th floor, C-Wing, Godrej Eternia, Shivajinagar, Pune-411005 www.bioimagene.com8. BioInformatics Institute Of India - Noida C-56 A/28, Sector -62, Noida - 201 301 www.bii.in9. CLC bio India Pvt Ltd #Plot No. 51, H.No. 8-3-214/51, Srinivasa Nagar (West) Ameerpet Hyderabad - 500 038 www.clcbio.com/india 10. CytoGenomics India (P) Ltd. #3004, 12A Main HAL 2nd Stage, Bangalore 560008 www.silicocyte.com 11. Genotypic Technology 211, 6th Cross, 80ft Road, RMV II Stage, Bangalore 560094 www.genotypic.co.in 12. Genvea Biosciences Dr. D. T. Singh, CSO, 53, Craig Rd. #04-01, Singapore-089691 www.genvea.com 13. Helix Info Systems 132 A, II Floor, Sterling Towers, IV Cross Street, Sterling Road, Nungambakkam, Chennai. www.helixinfosystems.com 14. Jalaja Technologies Pvt. Ltd., 21/1,Victoria Layout, Victoria Road, Bangalore-47 www.jalaja.com 15. Jubilant Biosys Ltd #96, Industrial Subrub, 2nd Stage, Yeshwanthpur, Bangalore- 560022 Jubilant Organosys Ltd. 1A, Sector 16A, Noida - 201 301 (India) www.jubl.com 16. Kshema Technologies #1, Global Village, Mylasandra, Mysore Road, Bangalore-560 059. www.mphasis.com 17. LabNetworx B-704, Gitanjali Apartments, Vikas Marg Extension, New Delhi - 110 092 www.labnetworx.com
Monday, November 24, 2008
Tuesday, November 4, 2008
Sequencing Softwares
Sequencing Softwares
Bioinformatics software plays a variety of roles in the general field of sequencing, including assembling genomes and identifying genes and regulatory elements. Software also helps investigators analyze similarities and differences between genes and organisms. Several dozen companies—including DNASTAR, InforMax, and Nonlinear Dynamics—create software for manipulating genes and DNA sequences.
DNASTAR, for instance, makes the Lasergene suite. According to John Schroeder, vice president of research and development at DNASTAR, this software performs many tasks: sequence assembly and finishing, primer design, gene discovery and annotation, sequence pair and family alignment with phylogeny, restriction site analysis and mapping, and protein structure analysis. “Basically, Lasergene provides a wide range of functionality,” Schroeder says. He adds that more than three thousand research articles mention using this software.
A variety of features makes this package so widely used. First, it works with most of the major file formats. Schroeder says, “Our priority is ease of use, and we want this to be as convenient a package as possible.” For example, this package lets a user drag-and-drop whole folders of sequences.
DNASTAR offers other products, too. GenVision—a DNASTAR plug-in for Adobe Illustrator—helps scientists visualize expression data, functional comparisons, genome presentations, and more. StarBlast, on the other hand, stores data and can be used to publish a sequence online.
Bioinformatics software plays a variety of roles in the general field of sequencing, including assembling genomes and identifying genes and regulatory elements. Software also helps investigators analyze similarities and differences between genes and organisms. Several dozen companies—including DNASTAR, InforMax, and Nonlinear Dynamics—create software for manipulating genes and DNA sequences.
DNASTAR, for instance, makes the Lasergene suite. According to John Schroeder, vice president of research and development at DNASTAR, this software performs many tasks: sequence assembly and finishing, primer design, gene discovery and annotation, sequence pair and family alignment with phylogeny, restriction site analysis and mapping, and protein structure analysis. “Basically, Lasergene provides a wide range of functionality,” Schroeder says. He adds that more than three thousand research articles mention using this software.
A variety of features makes this package so widely used. First, it works with most of the major file formats. Schroeder says, “Our priority is ease of use, and we want this to be as convenient a package as possible.” For example, this package lets a user drag-and-drop whole folders of sequences.
DNASTAR offers other products, too. GenVision—a DNASTAR plug-in for Adobe Illustrator—helps scientists visualize expression data, functional comparisons, genome presentations, and more. StarBlast, on the other hand, stores data and can be used to publish a sequence online.
The Future of Pharmaceuticals
The Future of Pharmaceuticals
Drug discovery companies, like Celera Genomics and Millennium, use genomic and proteomic data for developing new pharmaceuticals. Ellen Beasley, director of bioinformatics at Celera, says, “Bioinformatics is especially useful in early stages of target identification and the drug-target validation process.”
In general, Celera takes three approaches to drug discovery. One involves proteomics. For example, Beasley says, “We look for differential expression of cell surface proteins related to cancer.” Celera scientists also use genetics for drug discovery. Investigators from Celera and Celera Diagnostics, for instance, recently resequenced 80 percent to 90 percent of the human genes in 39 individuals and identified over 40,000 new functional single nucleotide polymorphisms (SNPs). “This unique SNP resource then becomes the basis for seeking associations between genes and disease,” Beasley says. “The genes identified in these association studies may also be therapeutic targets.” Third, Celera acquired Axys Pharmaceuticals to develop drugs against protease targets.
Overall, reaching the full potential of bioinformatics demands collaborations. Companies must work together—teaming up directly or simply making their tools compatible across corporate lines—to give research scientists all of the experimental and analytical power available. Such goodwill could lead to prosperity for companies and researchers alike.
Drug discovery companies, like Celera Genomics and Millennium, use genomic and proteomic data for developing new pharmaceuticals. Ellen Beasley, director of bioinformatics at Celera, says, “Bioinformatics is especially useful in early stages of target identification and the drug-target validation process.”
In general, Celera takes three approaches to drug discovery. One involves proteomics. For example, Beasley says, “We look for differential expression of cell surface proteins related to cancer.” Celera scientists also use genetics for drug discovery. Investigators from Celera and Celera Diagnostics, for instance, recently resequenced 80 percent to 90 percent of the human genes in 39 individuals and identified over 40,000 new functional single nucleotide polymorphisms (SNPs). “This unique SNP resource then becomes the basis for seeking associations between genes and disease,” Beasley says. “The genes identified in these association studies may also be therapeutic targets.” Third, Celera acquired Axys Pharmaceuticals to develop drugs against protease targets.
Overall, reaching the full potential of bioinformatics demands collaborations. Companies must work together—teaming up directly or simply making their tools compatible across corporate lines—to give research scientists all of the experimental and analytical power available. Such goodwill could lead to prosperity for companies and researchers alike.
Grid Computing
Grid Computing
The wide collection of ‘omics’—from genomics to, well, who knows what—generated very high expectations, too high according to Liz From, a global life science business strategist at Sun Microsystems. “After you get past the hype,” From says, “you must pay the piper. With so much data and so many different kinds of data, trying to draw conclusions poses a major challenge. This is where information technology can play a significant role by transforming that data into knowledge that will drive new advancements in the industry.” One solution for handling and analyzing so much disparate data comes from grid computing, which connects many computers within and between institutions with a software system, such as the Sun One Grid Engine.
Research at Sun shows the need for grids. Loralyn Mears, Sun’s market segment manager for the life sciences, says, “We did a study and found that the average biotech company has five months worth of projects, and most companies had to run those serially. Worse yet, the average company used only 22 percent to 25 percent of the available compute cycles.” With Sun’s Grid Engine technology, Mears says, those companies could use 99 percent of the available cycles, without buying one more computer or taking up any additional space. She adds that the Sun One Grid Engine software scales up easily from a dozen computers to a thousand.
“If someone is reasonably information-technology savvy,” Mears says, “he or she can configure a grid in one day with the Sun One Grid Engine. And it can connect machines from Sun, IBM, Silicon Graphics, most anything.” In addition, an open-source version of this software is free. Nevertheless, a company that wants to assign policy restrictions to a grid will need the Enterprise edition, which must be purchased. So far, about eight thousand installations of Sun’s software run grids around the world, and From says, “Life scientists are the biggest consumer group.” For example, Oxford GlycoSciences struggled with BLAST searches that took up to three months to complete, but after installing Sun One Grid Engine software the searches now take about a week.
The wide collection of ‘omics’—from genomics to, well, who knows what—generated very high expectations, too high according to Liz From, a global life science business strategist at Sun Microsystems. “After you get past the hype,” From says, “you must pay the piper. With so much data and so many different kinds of data, trying to draw conclusions poses a major challenge. This is where information technology can play a significant role by transforming that data into knowledge that will drive new advancements in the industry.” One solution for handling and analyzing so much disparate data comes from grid computing, which connects many computers within and between institutions with a software system, such as the Sun One Grid Engine.
Research at Sun shows the need for grids. Loralyn Mears, Sun’s market segment manager for the life sciences, says, “We did a study and found that the average biotech company has five months worth of projects, and most companies had to run those serially. Worse yet, the average company used only 22 percent to 25 percent of the available compute cycles.” With Sun’s Grid Engine technology, Mears says, those companies could use 99 percent of the available cycles, without buying one more computer or taking up any additional space. She adds that the Sun One Grid Engine software scales up easily from a dozen computers to a thousand.
“If someone is reasonably information-technology savvy,” Mears says, “he or she can configure a grid in one day with the Sun One Grid Engine. And it can connect machines from Sun, IBM, Silicon Graphics, most anything.” In addition, an open-source version of this software is free. Nevertheless, a company that wants to assign policy restrictions to a grid will need the Enterprise edition, which must be purchased. So far, about eight thousand installations of Sun’s software run grids around the world, and From says, “Life scientists are the biggest consumer group.” For example, Oxford GlycoSciences struggled with BLAST searches that took up to three months to complete, but after installing Sun One Grid Engine software the searches now take about a week.
Computing in Clusters
Computing in Clusters
Computing plays two fundamental roles in bioinformatics and computational biology, according to Royyuru. First, computers participate in data analysis, ranging from accessing high throughput data and sequencing single nucleotide polymorphisms to analyzing microarrays and experiments in proteomics. “The biggest challenge,” says Royyuru, “is reducing the dimensionality of these data so that scientists can understand them.” To do that, computing should combine data mining with biological insight. Second, computers can run in silico models that test biological theories. “You can ask questions like: What happens to a cell system when you knock out a certain gene or administer a specific drug?” Royyuru explains. “In silico modeling must capture homeostatic behavior of cells, systems, and organisms. Then, scientists can explore what happens during disease or other perturbations.” Reaching higher levels of modeling demands increased computing capabilities, and all major information technology vendors—including Apple, IBM, Hewlett-Packard, and Sun Microsystems—offer solutions that address this market.
One computing advance relies on tightly coupled clusters of processors. In other words, many processors can be connected—with high levels of communication between them—to work as a team, such as the IBM P series of supercomputers and the Sun Fire 15K server with Sun Cluster software. Tightly coupled clusters work very well for many applications, including simulating molecular biology, chemical kinetics, protein folding, and so on.
The question for the future is: How many processors can be packed into a reasonable space? Today’s machines use a few thousand processors to churn out a few tens of teraflops, or trillion floating point operations per second (flops). “You can do a good amount of science on these,” says Royyuru, “but there is more science to get to that is not possible with these machines.” He thinks it will take petaflops (1,000 trillion flops) machines to simulate the behavior of proteins, for example. To get that kind of power, computer scientists could simply put more boxes in a room or rethink how they put together processors.
Royyuru points out that putting more boxes in a room will eventually become increasingly difficult, because even 2,000 processors in a traditional design occupy the floor space of a basketball court. Scaling up to petaflops machines would grow too large and take too much power. Instead, IBM looked for new approaches to computer architecture. In project Blue Gene, for example, investigators at IBM and the Lawrence Livermore National Laboratory converged on so-called cellular architecture, which mimics the way biological structures are composed. Blue Gene should crank out 6 teraflops with a single rack of equipment—about one thousand processors. This year, scientists at IBM’s Watson Research Laboratory hope to put together half a rack of Blue Gene chips. “We learn as we go along,” Royyuru says, “and we intend to make this hardware relevant to biological research.”
Computing plays two fundamental roles in bioinformatics and computational biology, according to Royyuru. First, computers participate in data analysis, ranging from accessing high throughput data and sequencing single nucleotide polymorphisms to analyzing microarrays and experiments in proteomics. “The biggest challenge,” says Royyuru, “is reducing the dimensionality of these data so that scientists can understand them.” To do that, computing should combine data mining with biological insight. Second, computers can run in silico models that test biological theories. “You can ask questions like: What happens to a cell system when you knock out a certain gene or administer a specific drug?” Royyuru explains. “In silico modeling must capture homeostatic behavior of cells, systems, and organisms. Then, scientists can explore what happens during disease or other perturbations.” Reaching higher levels of modeling demands increased computing capabilities, and all major information technology vendors—including Apple, IBM, Hewlett-Packard, and Sun Microsystems—offer solutions that address this market.
One computing advance relies on tightly coupled clusters of processors. In other words, many processors can be connected—with high levels of communication between them—to work as a team, such as the IBM P series of supercomputers and the Sun Fire 15K server with Sun Cluster software. Tightly coupled clusters work very well for many applications, including simulating molecular biology, chemical kinetics, protein folding, and so on.
The question for the future is: How many processors can be packed into a reasonable space? Today’s machines use a few thousand processors to churn out a few tens of teraflops, or trillion floating point operations per second (flops). “You can do a good amount of science on these,” says Royyuru, “but there is more science to get to that is not possible with these machines.” He thinks it will take petaflops (1,000 trillion flops) machines to simulate the behavior of proteins, for example. To get that kind of power, computer scientists could simply put more boxes in a room or rethink how they put together processors.
Royyuru points out that putting more boxes in a room will eventually become increasingly difficult, because even 2,000 processors in a traditional design occupy the floor space of a basketball court. Scaling up to petaflops machines would grow too large and take too much power. Instead, IBM looked for new approaches to computer architecture. In project Blue Gene, for example, investigators at IBM and the Lawrence Livermore National Laboratory converged on so-called cellular architecture, which mimics the way biological structures are composed. Blue Gene should crank out 6 teraflops with a single rack of equipment—about one thousand processors. This year, scientists at IBM’s Watson Research Laboratory hope to put together half a rack of Blue Gene chips. “We learn as we go along,” Royyuru says, “and we intend to make this hardware relevant to biological research.”
Clinical bioinformatics: advancing genomic medicine with informatics methods and tools
Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools.
Knaup P, Ammenwerth E, Brandner R, Brigl B, Fischer G, Garde S, Lang E, Pilgram R, Ruderich F, Singer R, Wolff AC, Haux R, Kulikowski C.
University of Heidelberg, Department of Medical Informatics, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany. petra_knaup@med.uni-heidelberg.de
OBJECTIVES: To summarize the challenges facing clinical applications in the light of growing research results in genomic medicine and bioinformatics. METHODS: Analysis of the contents of the Yearbook of Medical Informatics 2004 of the International Medical Informatics Association (IMIA).
RESULTS: The Yearbook of Medical Informatics 2004 includes 32 articles selected from 22 peer-reviewed scientific journals. A special section on clinical bioinformatics highlights recent developments in this field. Several guest editors review the promises and limitations of available methods and resources from biomedical informatics that are relevant to clinical medicine. Integrated data and knowledge resources are generally regarded to be central and key issues for clinical bioinformatics. Further review papers deal with public health implications of bioinformatics, knowledge management and trends in health care education. The Yearbook includes for the first time a section on the history of medical informatics, where the significant impact of the Reisensburg protocol 1973 on international health and medical informatics education is examined.
CONCLUSIONS: Close collaboration between bioinformatics and medical informatics researchers can contribute to new insights in genomic medicine and contribute towards the more efficient and effective use of genomic data to advance clinical care.
Knaup P, Ammenwerth E, Brandner R, Brigl B, Fischer G, Garde S, Lang E, Pilgram R, Ruderich F, Singer R, Wolff AC, Haux R, Kulikowski C.
University of Heidelberg, Department of Medical Informatics, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany. petra_knaup@med.uni-heidelberg.de
OBJECTIVES: To summarize the challenges facing clinical applications in the light of growing research results in genomic medicine and bioinformatics. METHODS: Analysis of the contents of the Yearbook of Medical Informatics 2004 of the International Medical Informatics Association (IMIA).
RESULTS: The Yearbook of Medical Informatics 2004 includes 32 articles selected from 22 peer-reviewed scientific journals. A special section on clinical bioinformatics highlights recent developments in this field. Several guest editors review the promises and limitations of available methods and resources from biomedical informatics that are relevant to clinical medicine. Integrated data and knowledge resources are generally regarded to be central and key issues for clinical bioinformatics. Further review papers deal with public health implications of bioinformatics, knowledge management and trends in health care education. The Yearbook includes for the first time a section on the history of medical informatics, where the significant impact of the Reisensburg protocol 1973 on international health and medical informatics education is examined.
CONCLUSIONS: Close collaboration between bioinformatics and medical informatics researchers can contribute to new insights in genomic medicine and contribute towards the more efficient and effective use of genomic data to advance clinical care.
Bioinformatics and genomic medicine
Bioinformatics and genomic medicine.
Kim JH.
Children's Hospital Informatics Program, Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational science. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolution in both bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high-throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever, in much the same way that biochemistry did a generation ago. This paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine-learning algorithms are discussed. Use of integrative biochip informatics technologies, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and the integrated management of biomolecular databases, are also discussed.
Kim JH.
Children's Hospital Informatics Program, Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational science. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolution in both bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high-throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever, in much the same way that biochemistry did a generation ago. This paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine-learning algorithms are discussed. Use of integrative biochip informatics technologies, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and the integrated management of biomolecular databases, are also discussed.
Bioinformatics in the post-sequence era
Bioinformatics in the post-sequence era.
Kanehisa M, Bork P.
Bioinformatics Center, Kyoto University, Uji, Kyoto 611-0011, Japan. kanehisa@kuicr.kyoto-u.ac.jp
In the past decade, bioinformatics has become an integral part of research and development in the biomedical sciences. Bioinformatics now has an essential role both in deciphering genomic, transcriptomic and proteomic data generated by high-throughput experimental technologies and in organizing information gathered from traditional biology. Sequence-based methods of analyzing individual genes or proteins have been elaborated and expanded, and methods have been developed for analyzing large numbers of genes or proteins simultaneously, such as in the identification of clusters of related genes and networks of interacting proteins. With the complete genome sequences for an increasing number of organisms at hand, bioinformatics is beginning to provide both conceptual bases and practical methods for detecting systemic functional behaviors of the cell and the organism.
Kanehisa M, Bork P.
Bioinformatics Center, Kyoto University, Uji, Kyoto 611-0011, Japan. kanehisa@kuicr.kyoto-u.ac.jp
In the past decade, bioinformatics has become an integral part of research and development in the biomedical sciences. Bioinformatics now has an essential role both in deciphering genomic, transcriptomic and proteomic data generated by high-throughput experimental technologies and in organizing information gathered from traditional biology. Sequence-based methods of analyzing individual genes or proteins have been elaborated and expanded, and methods have been developed for analyzing large numbers of genes or proteins simultaneously, such as in the identification of clusters of related genes and networks of interacting proteins. With the complete genome sequences for an increasing number of organisms at hand, bioinformatics is beginning to provide both conceptual bases and practical methods for detecting systemic functional behaviors of the cell and the organism.
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