This document describes an automated robotic biorepository system developed by researchers at the University of Virginia to store and retrieve genomic DNA samples on a large scale. The system integrates three primary devices - a robotic arm, liquid handling robot, and microplate storage system. It is capable of automatically processing, analyzing, storing, and retrieving up to 250,000 genomic DNA samples in microplates held at -80°C. The system uses barcoded microplates and tubes to track samples and a database to store associated demographic and analysis data. Its main functions are to create master plates from samples, generate daughter plates for storage and retrieval, and assemble send-out plates of selected samples for distribution.
Articulo escrito por Hector Sánchez Villeda.
Hector Sánchez ha desarrollado tecnologías de la información para las ciencias biológicas por más de 20 años y actualmente es Fundador y Director de Desarrollo de IT de G2 Apps una empresa de innovación tecnológica basada en Querétaro, México.
G2 APPS se dedica a la implementación de LIMS (Laboratory Information Management Systems) utilizando un enfoque multidisciplinario que desde luego incluye un alto nivel de conocimientos en las ciencias de la vida para llevar a cabo una facil implementación.
JEVBase: An Interactive Resource for Protein Annotationof JE VirusCSCJournals
Databases containing proteome ic information have become indispensable for virology related studies. Rajendra Memorial Research Institute of Medical Sciences (RMRIMS) has compiled and maintained a functional and molecular annotation database (http://www.jevbase.biomedinformri.org) commonly referred to as JEVBase. This database facilitates significant relationship between molecular analysis, cleavage sites, possible protein functional families assigned to different proteins of Japanese encephalitis virus (JEV). Identification of different protein functions and molecular analysis facilitates a mechanistic understanding of (JEV) infection and opens novel means for drug development. JEVBase database aims to be a resource for scientists working on JE virus
Bioinformatics combines computer science, statistics, mathematics, and engineering to analyze biological data. Major bioinformatics databases and resources include NCBI, EMBL-EBI, and ExPASy. NCBI was established in 1988 as part of the National Library of Medicine and contains databases like PubMed, OMIM, and PubChem. EMBL-EBI was established in 1980 and provides DNA sequences and additional biological information through tools like Webin and SRS. ExPASy was established in 1993 by the Swiss Institute of Bioinformatics and contains protein databases like Swiss-Prot, TrEMBL, and InterPro.
1) The document describes a feature extraction program developed to analyze gene expression data from the Gene Expression Omnibus (GEO) database.
2) The program was tested on human transcription factor expression data sets and was able to successfully extract gene expression information.
3) Analyzing specific gene sets from GEO files had previously been a labor-intensive task, but the object-oriented program streamlines this process using C and Perl programming languages.
This document discusses biological databases and their classifications. It introduces bioinformatics and the need for biological databases to store and communicate large datasets. It describes different types of biological data that can be stored in databases, including nucleotide sequences, protein sequences, 3D protein structures, and gene expression data. The document also covers different ways databases can be classified, such as by the type of data, whether they contain primary or analyzed data, how the data is linked, and access restrictions.
This document provides information on biological databases, including their history, features, and classifications. It notes that the first protein sequenced was insulin in 1965, and the first genome sequenced was of a virus in 1995. Key features of biological databases discussed include their heterogeneity, high volume of data, uncertainty, data curation, integration, sharing, and dynamic nature as new data is added. Biological databases can be classified by data type, maintainer status, data access, source, design, and organism covered. The purpose of biological databases is to systematically organize and make available vast amounts of complex biological data.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
Publicly available tools and open resources in BioinformaticsArindam Ghosh
This document provides an overview of several freely available bioinformatics tools and open resources. It describes the purpose and function of popular tools for database searching (BLAST, FASTA), sequence alignment (Clustal Omega), visualization (RasMol), molecular dynamics simulation (GROMACS), genome browsing (Ensembl), and protein modeling (MODELLER). These tools are commonly used for tasks like extracting meaningful biological information from databases, comparing sequences, understanding phylogenetic relationships, displaying molecular structures, simulating protein dynamics, and building protein models. The document provides links to each tool's homepage for further information.
Articulo escrito por Hector Sánchez Villeda.
Hector Sánchez ha desarrollado tecnologías de la información para las ciencias biológicas por más de 20 años y actualmente es Fundador y Director de Desarrollo de IT de G2 Apps una empresa de innovación tecnológica basada en Querétaro, México.
G2 APPS se dedica a la implementación de LIMS (Laboratory Information Management Systems) utilizando un enfoque multidisciplinario que desde luego incluye un alto nivel de conocimientos en las ciencias de la vida para llevar a cabo una facil implementación.
JEVBase: An Interactive Resource for Protein Annotationof JE VirusCSCJournals
Databases containing proteome ic information have become indispensable for virology related studies. Rajendra Memorial Research Institute of Medical Sciences (RMRIMS) has compiled and maintained a functional and molecular annotation database (http://www.jevbase.biomedinformri.org) commonly referred to as JEVBase. This database facilitates significant relationship between molecular analysis, cleavage sites, possible protein functional families assigned to different proteins of Japanese encephalitis virus (JEV). Identification of different protein functions and molecular analysis facilitates a mechanistic understanding of (JEV) infection and opens novel means for drug development. JEVBase database aims to be a resource for scientists working on JE virus
Bioinformatics combines computer science, statistics, mathematics, and engineering to analyze biological data. Major bioinformatics databases and resources include NCBI, EMBL-EBI, and ExPASy. NCBI was established in 1988 as part of the National Library of Medicine and contains databases like PubMed, OMIM, and PubChem. EMBL-EBI was established in 1980 and provides DNA sequences and additional biological information through tools like Webin and SRS. ExPASy was established in 1993 by the Swiss Institute of Bioinformatics and contains protein databases like Swiss-Prot, TrEMBL, and InterPro.
1) The document describes a feature extraction program developed to analyze gene expression data from the Gene Expression Omnibus (GEO) database.
2) The program was tested on human transcription factor expression data sets and was able to successfully extract gene expression information.
3) Analyzing specific gene sets from GEO files had previously been a labor-intensive task, but the object-oriented program streamlines this process using C and Perl programming languages.
This document discusses biological databases and their classifications. It introduces bioinformatics and the need for biological databases to store and communicate large datasets. It describes different types of biological data that can be stored in databases, including nucleotide sequences, protein sequences, 3D protein structures, and gene expression data. The document also covers different ways databases can be classified, such as by the type of data, whether they contain primary or analyzed data, how the data is linked, and access restrictions.
This document provides information on biological databases, including their history, features, and classifications. It notes that the first protein sequenced was insulin in 1965, and the first genome sequenced was of a virus in 1995. Key features of biological databases discussed include their heterogeneity, high volume of data, uncertainty, data curation, integration, sharing, and dynamic nature as new data is added. Biological databases can be classified by data type, maintainer status, data access, source, design, and organism covered. The purpose of biological databases is to systematically organize and make available vast amounts of complex biological data.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
Publicly available tools and open resources in BioinformaticsArindam Ghosh
This document provides an overview of several freely available bioinformatics tools and open resources. It describes the purpose and function of popular tools for database searching (BLAST, FASTA), sequence alignment (Clustal Omega), visualization (RasMol), molecular dynamics simulation (GROMACS), genome browsing (Ensembl), and protein modeling (MODELLER). These tools are commonly used for tasks like extracting meaningful biological information from databases, comparing sequences, understanding phylogenetic relationships, displaying molecular structures, simulating protein dynamics, and building protein models. The document provides links to each tool's homepage for further information.
Pharmacoinformatics is an emerging field that draws from bioinformatics and cheminformatics. It deals with using technology in drug discovery and monitoring patients. The scope includes jobs with drug and clinical research companies. Training is currently limited to a few postgraduate programs in India. While the field is emerging, placements are unclear as most companies are still evaluating how to apply pharmacoinformatics.
The DNA Data Bank of Japan (DDBJ) is a biological database located in Japan that collects and stores nucleotide sequence data. It began operations in 1986 and exchanges data daily with the European Nucleotide Archive and GenBank to form the International Nucleotide Sequence Database Collaboration (INSDC). DDBJ accepts sequence submissions from researchers worldwide and assigns unique identification numbers to published sequences to recognize intellectual property rights. It also provides search and analysis tools and supercomputing resources to support genomic research.
This document discusses biological databases. It defines biological databases as structured, searchable collections of biological data that are periodically updated and cross-referenced. It notes that biological databases store data electronically and systematize, make available, and allow analysis of computed biological data. The document then describes some key features of biological databases, including data heterogeneity, high data volumes, uncertainty, data curation, integration, sharing, and dynamic nature. It also provides examples of different types of biological databases classified by data type, maintainer, access, source, design, and organism covered.
This document discusses applications and trends in data mining for biological data analysis. It outlines how the explosive growth of genomics, proteomics, and biomedical research has led to the need for data mining techniques. Some key applications of data mining discussed include semantic integration of heterogeneous biological databases, sequence alignment and analysis, discovery of genetic networks and protein pathways, association analysis to identify co-occurring genes, and visualization tools. Biological data mining has become essential for research in bioinformatics.
This document lists various protein databases that can be categorized into protein sequence databases, proteomics databases, protein structure databases, secondary databases, protein model databases, and protein-protein interaction databases. Some of the major protein sequence databases included are UniProt, Swiss-Prot, Pfam, and PROSITE. The Protein Data Bank (PDB) is a central repository for 3D protein structure data. Other databases like SCOP and CATH provide structural classification of proteins. Databases such as IntAct and STRING contain information on known and predicted protein-protein interactions.
Bioinformatics is the combination of biology and information technology. It uses computational tools and methods to analyze and manage large volumes of biological data. Bioinformatics deals with DNA, RNA, protein sequences, gene expression profiles, and other types of biological data. It involves creating databases to store biological data, developing algorithms and statistics to analyze relationships within large datasets, and interpreting the results of data analyses. Some applications of bioinformatics include sequence mapping, gene identification, structure prediction, molecular modeling, drug design, and systems modeling.
This document provides an overview of protein databases. It discusses the importance of protein databases for storing and analyzing protein sequence, structure, and functional data generated by modern biology. It summarizes several major public protein databases, including UniProt, NCBI RefSeq, PDB, InterPro, and Pfam, which contain protein sequences, structures, families, domains, and functional annotations. Searching and comparing sequences in these databases is an important first step in studying new proteins.
This document outlines the course content for a bioinformatics course covering 4 units:
Unit 1 introduces basic concepts of bioinformatics including proteins, DNA, RNA, and sequence, structure, and function.
Unit 2 covers major bioinformatics databases including those for nucleotide sequences, protein sequences, sequence motifs, protein structures, and other relevant databases.
Unit 3 discusses topics like single and pairwise sequence alignment, scoring matrices, and multiple sequence alignments.
Unit 4 covers the human genome project, gene and genomic databases, genomic data mining, and microarray techniques.
The EcoCyc database is a freely accessible, comprehensive database that combines information about the genome and metabolism of Escherichia coli K-12. It describes the known genes of E. coli, the enzymes encoded by these genes, and how these enzymes catalyze reactions organized into metabolic pathways. The EcoCyc database is jointly developed and curated by researchers at SRI International and the Marine Biological Laboratory based on experimental literature. It provides graphical tools for visualizing and exploring genomic and biochemical data through its user interface, facilitating analysis of high-throughput data and metabolic modeling.
An Introduction to "Bioinformatics & Internet"Asar Khan
This document provides an introduction to computers and bioinformatics. It defines key concepts like what a computer is, computer hardware, software, programming languages, computer networks, the internet, bioinformatics, and important bioinformatics databases and tools. Specifically, it discusses how computers accept data as input, process it, and provide information as output. It also explains how bioinformatics applies information technology to biological data to receive, analyze and retrieve biological information. Important databases mentioned include NCBI, EMBL, SRS and tools like Entrez.
Bioinformatics n bio-bio-1_uoda_workshop_4_july_2013_v1.0Fokhruz Zaman
This document discusses bioinformatics and provides an overview of the topic. It defines bioinformatics as finding patterns in molecular biological data in order to characterize biological processes and predict properties. The document outlines different types of molecular biology data that are analyzed using bioinformatics, such as DNA sequences, protein structures, gene expression data, and phenotypes. It also discusses related fields like computational biology and various "-omics" disciplines. The role of bioinformatics in applications like drug discovery, agriculture, and human health is covered at a high level. Finally, the document encourages learning bioinformatics and lists some important websites in the field.
This document provides an overview of the field of bioinformatics. It defines bioinformatics as the intersection of biology and computer science, using computational tools to analyze and distribute biological information like DNA, RNA, and proteins. The goals of bioinformatics are to better understand cells at the molecular level by analyzing sequence and structure data. Key applications include drug design, DNA analysis, and agricultural biotechnology. The document also describes different types of biological databases like primary databases that contain raw sequence data, and secondary databases that provide additional annotation and analysis of sequences.
introduction,history scope and applications of
relation to other fields , bioinformatics,biological databases,computers internet,sequence development, and
introduction to sequence development and alignment
This document discusses databases in bioinformatics. It begins by explaining that bioinformatics concerns the creation and maintenance of biological databases to allow researchers to access existing information and submit new entries. The aims of bioinformatics are to organize data, develop analysis tools, and use these tools to analyze data and interpret results in a biologically meaningful way. Several important biological databases are described, including nucleotide sequence databases like NCBI and protein sequence databases. GenBank is also discussed as the annotated collection of all publicly available DNA sequences. Biological databases make large datasets available to researchers and are important for biological research infrastructure.
DNA sequencing is a technique that provides a detailed analysis of the structure of DNA and consists of a set of techniques and biochemical methods that allow us to determine the sequence of nucleotides (A, C, G, and T) analysis is DNA.
In the mid-1970s happened a revolution in technology for identifying DNA sequence. In 1977 was published the complete nucleotide sequence of a viral genome (φ X174, 5375 nucleotides long). This milestone in molecular biology occurred in the laboratory of Frederick Sanger, who identified the amino acid sequence of the polypeptide (insulin) 25 years earlier.
Bioinformatics is the application of computer technology to information in molecular biology, encompassing aspects of the acquisition, processing, distribution, analysis, interpretation and integration of biological information. There are several databases that organize information and they are often used, which are presented in the following bioinformatics centers: GenBank (NCBI) and BOLD Systems
The NCBI database (established in 1988) has a public database, with three components. Creating databases (store biological data), development of algorithms and statistics to determine relationships between databases, and use these tools to analyze and interpret various types of biological data (sequences of DNA, RNA, protein, protein structure, gene expression, biochemical pathways)
The Barcode of Life Data Systems (BOLD) is an informatics workbench aiding the acquisition, storage, analysis, and publication of DNA barcode records. By assembling molecular, morphological, and distributional data, it bridges a traditional bioinformatics chasm. BOLD is freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances.
The document describes EXPASY (Expert Protein Analysis System), a web server that provides access to databases and analytical tools for proteins and proteomics. It contains Swiss-Prot, Trembl, Swiss-2DPAGE, Prosite, Enzyme, and Swiss-Model Repository databases. Analysis tools are available for tasks like similarity searches, pattern recognition, structure prediction, and sequence alignment. EXPASY was created in 1993 as one of the first biological web servers and has since been expanded and maintained by the SIB Swiss Institute of Bioinformatics.
A full picture of -omics cellular networks of regulation brings researchers closer to a realistic and reliable understanding of complex conditions. For more information, please visit: http://tbioinfopb.pine-biotech.com/
Bioinformatics databases: Current Trends and Future PerspectivesUniversity of Malaya
Data is the most powerful resource in any field or subject of study. In Biology, data comes from scientists and their actions, while any institution that makes sense of the data collected, will be in the forefront in their respective research field. In the beginning of any data collection endeavour, it is critical to find proper management techniques to store data and to maximise its utilisation. This presentation reflects upon the current trends and techniques of data modeling, architecture with a highlight on the uses of database, focusing on Bioinformatics examples and case studies. Finally, the future of bioinformatics databases is highlighted to give an overview of the modeling techniques to accommodate the biological data escalation in coming years.
Bioinformatics combines computer science, statistics, mathematics, and biology to study and process biological data on a large scale. The document discusses several applications of bioinformatics including information search and retrieval, sequence comparison for genetics, phylogenetic analysis, genome annotation, proteomics, pharmacogenomics, and drug discovery. Tools are provided for various applications such as linkage analysis, phylogenetic analysis, genome annotation, and protein identification.
This document discusses the potential for biomass-solar hybrid power generation in India. It begins by noting the large amount of biomass produced annually in India but challenges in obtaining a consistent fuel supply due to seasonal variability and infrastructure problems. A biomass-solar hybrid model is proposed to overcome these barriers by having two renewable resources complement each other. The document then provides details on different solar and biomass technologies that could be used in a hybrid system. It describes how combining solar thermal with biomass combustion can provide continuous power by generating steam from both sources. The author proposes studying a pilot hybrid project at one of their existing biomass plants in India to validate the technical feasibility and economics of this approach.
Employees & Self Employees Co Op Society Projects DetailsSanthosh Kumar
M and M Bangalore Pvt Ltd presents residential society projects in North, East Bangalore and Mysore. They have developed projects for various housing societies. They are developing projects called Krishna Greens in Midlake Phase II in Doddaballapur Road, IVC Road in Devanahalli, East Wind in Devangundi, and West Mist in Mysore. The projects offer sites of various dimensions from 20x30 to 50x80 feet. There are details on location, payment plans in installments, amenities and booking details provided. For more details on booking, contact the listed representative.
Pharmacoinformatics is an emerging field that draws from bioinformatics and cheminformatics. It deals with using technology in drug discovery and monitoring patients. The scope includes jobs with drug and clinical research companies. Training is currently limited to a few postgraduate programs in India. While the field is emerging, placements are unclear as most companies are still evaluating how to apply pharmacoinformatics.
The DNA Data Bank of Japan (DDBJ) is a biological database located in Japan that collects and stores nucleotide sequence data. It began operations in 1986 and exchanges data daily with the European Nucleotide Archive and GenBank to form the International Nucleotide Sequence Database Collaboration (INSDC). DDBJ accepts sequence submissions from researchers worldwide and assigns unique identification numbers to published sequences to recognize intellectual property rights. It also provides search and analysis tools and supercomputing resources to support genomic research.
This document discusses biological databases. It defines biological databases as structured, searchable collections of biological data that are periodically updated and cross-referenced. It notes that biological databases store data electronically and systematize, make available, and allow analysis of computed biological data. The document then describes some key features of biological databases, including data heterogeneity, high data volumes, uncertainty, data curation, integration, sharing, and dynamic nature. It also provides examples of different types of biological databases classified by data type, maintainer, access, source, design, and organism covered.
This document discusses applications and trends in data mining for biological data analysis. It outlines how the explosive growth of genomics, proteomics, and biomedical research has led to the need for data mining techniques. Some key applications of data mining discussed include semantic integration of heterogeneous biological databases, sequence alignment and analysis, discovery of genetic networks and protein pathways, association analysis to identify co-occurring genes, and visualization tools. Biological data mining has become essential for research in bioinformatics.
This document lists various protein databases that can be categorized into protein sequence databases, proteomics databases, protein structure databases, secondary databases, protein model databases, and protein-protein interaction databases. Some of the major protein sequence databases included are UniProt, Swiss-Prot, Pfam, and PROSITE. The Protein Data Bank (PDB) is a central repository for 3D protein structure data. Other databases like SCOP and CATH provide structural classification of proteins. Databases such as IntAct and STRING contain information on known and predicted protein-protein interactions.
Bioinformatics is the combination of biology and information technology. It uses computational tools and methods to analyze and manage large volumes of biological data. Bioinformatics deals with DNA, RNA, protein sequences, gene expression profiles, and other types of biological data. It involves creating databases to store biological data, developing algorithms and statistics to analyze relationships within large datasets, and interpreting the results of data analyses. Some applications of bioinformatics include sequence mapping, gene identification, structure prediction, molecular modeling, drug design, and systems modeling.
This document provides an overview of protein databases. It discusses the importance of protein databases for storing and analyzing protein sequence, structure, and functional data generated by modern biology. It summarizes several major public protein databases, including UniProt, NCBI RefSeq, PDB, InterPro, and Pfam, which contain protein sequences, structures, families, domains, and functional annotations. Searching and comparing sequences in these databases is an important first step in studying new proteins.
This document outlines the course content for a bioinformatics course covering 4 units:
Unit 1 introduces basic concepts of bioinformatics including proteins, DNA, RNA, and sequence, structure, and function.
Unit 2 covers major bioinformatics databases including those for nucleotide sequences, protein sequences, sequence motifs, protein structures, and other relevant databases.
Unit 3 discusses topics like single and pairwise sequence alignment, scoring matrices, and multiple sequence alignments.
Unit 4 covers the human genome project, gene and genomic databases, genomic data mining, and microarray techniques.
The EcoCyc database is a freely accessible, comprehensive database that combines information about the genome and metabolism of Escherichia coli K-12. It describes the known genes of E. coli, the enzymes encoded by these genes, and how these enzymes catalyze reactions organized into metabolic pathways. The EcoCyc database is jointly developed and curated by researchers at SRI International and the Marine Biological Laboratory based on experimental literature. It provides graphical tools for visualizing and exploring genomic and biochemical data through its user interface, facilitating analysis of high-throughput data and metabolic modeling.
An Introduction to "Bioinformatics & Internet"Asar Khan
This document provides an introduction to computers and bioinformatics. It defines key concepts like what a computer is, computer hardware, software, programming languages, computer networks, the internet, bioinformatics, and important bioinformatics databases and tools. Specifically, it discusses how computers accept data as input, process it, and provide information as output. It also explains how bioinformatics applies information technology to biological data to receive, analyze and retrieve biological information. Important databases mentioned include NCBI, EMBL, SRS and tools like Entrez.
Bioinformatics n bio-bio-1_uoda_workshop_4_july_2013_v1.0Fokhruz Zaman
This document discusses bioinformatics and provides an overview of the topic. It defines bioinformatics as finding patterns in molecular biological data in order to characterize biological processes and predict properties. The document outlines different types of molecular biology data that are analyzed using bioinformatics, such as DNA sequences, protein structures, gene expression data, and phenotypes. It also discusses related fields like computational biology and various "-omics" disciplines. The role of bioinformatics in applications like drug discovery, agriculture, and human health is covered at a high level. Finally, the document encourages learning bioinformatics and lists some important websites in the field.
This document provides an overview of the field of bioinformatics. It defines bioinformatics as the intersection of biology and computer science, using computational tools to analyze and distribute biological information like DNA, RNA, and proteins. The goals of bioinformatics are to better understand cells at the molecular level by analyzing sequence and structure data. Key applications include drug design, DNA analysis, and agricultural biotechnology. The document also describes different types of biological databases like primary databases that contain raw sequence data, and secondary databases that provide additional annotation and analysis of sequences.
introduction,history scope and applications of
relation to other fields , bioinformatics,biological databases,computers internet,sequence development, and
introduction to sequence development and alignment
This document discusses databases in bioinformatics. It begins by explaining that bioinformatics concerns the creation and maintenance of biological databases to allow researchers to access existing information and submit new entries. The aims of bioinformatics are to organize data, develop analysis tools, and use these tools to analyze data and interpret results in a biologically meaningful way. Several important biological databases are described, including nucleotide sequence databases like NCBI and protein sequence databases. GenBank is also discussed as the annotated collection of all publicly available DNA sequences. Biological databases make large datasets available to researchers and are important for biological research infrastructure.
DNA sequencing is a technique that provides a detailed analysis of the structure of DNA and consists of a set of techniques and biochemical methods that allow us to determine the sequence of nucleotides (A, C, G, and T) analysis is DNA.
In the mid-1970s happened a revolution in technology for identifying DNA sequence. In 1977 was published the complete nucleotide sequence of a viral genome (φ X174, 5375 nucleotides long). This milestone in molecular biology occurred in the laboratory of Frederick Sanger, who identified the amino acid sequence of the polypeptide (insulin) 25 years earlier.
Bioinformatics is the application of computer technology to information in molecular biology, encompassing aspects of the acquisition, processing, distribution, analysis, interpretation and integration of biological information. There are several databases that organize information and they are often used, which are presented in the following bioinformatics centers: GenBank (NCBI) and BOLD Systems
The NCBI database (established in 1988) has a public database, with three components. Creating databases (store biological data), development of algorithms and statistics to determine relationships between databases, and use these tools to analyze and interpret various types of biological data (sequences of DNA, RNA, protein, protein structure, gene expression, biochemical pathways)
The Barcode of Life Data Systems (BOLD) is an informatics workbench aiding the acquisition, storage, analysis, and publication of DNA barcode records. By assembling molecular, morphological, and distributional data, it bridges a traditional bioinformatics chasm. BOLD is freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances.
The document describes EXPASY (Expert Protein Analysis System), a web server that provides access to databases and analytical tools for proteins and proteomics. It contains Swiss-Prot, Trembl, Swiss-2DPAGE, Prosite, Enzyme, and Swiss-Model Repository databases. Analysis tools are available for tasks like similarity searches, pattern recognition, structure prediction, and sequence alignment. EXPASY was created in 1993 as one of the first biological web servers and has since been expanded and maintained by the SIB Swiss Institute of Bioinformatics.
A full picture of -omics cellular networks of regulation brings researchers closer to a realistic and reliable understanding of complex conditions. For more information, please visit: http://tbioinfopb.pine-biotech.com/
Bioinformatics databases: Current Trends and Future PerspectivesUniversity of Malaya
Data is the most powerful resource in any field or subject of study. In Biology, data comes from scientists and their actions, while any institution that makes sense of the data collected, will be in the forefront in their respective research field. In the beginning of any data collection endeavour, it is critical to find proper management techniques to store data and to maximise its utilisation. This presentation reflects upon the current trends and techniques of data modeling, architecture with a highlight on the uses of database, focusing on Bioinformatics examples and case studies. Finally, the future of bioinformatics databases is highlighted to give an overview of the modeling techniques to accommodate the biological data escalation in coming years.
Bioinformatics combines computer science, statistics, mathematics, and biology to study and process biological data on a large scale. The document discusses several applications of bioinformatics including information search and retrieval, sequence comparison for genetics, phylogenetic analysis, genome annotation, proteomics, pharmacogenomics, and drug discovery. Tools are provided for various applications such as linkage analysis, phylogenetic analysis, genome annotation, and protein identification.
This document discusses the potential for biomass-solar hybrid power generation in India. It begins by noting the large amount of biomass produced annually in India but challenges in obtaining a consistent fuel supply due to seasonal variability and infrastructure problems. A biomass-solar hybrid model is proposed to overcome these barriers by having two renewable resources complement each other. The document then provides details on different solar and biomass technologies that could be used in a hybrid system. It describes how combining solar thermal with biomass combustion can provide continuous power by generating steam from both sources. The author proposes studying a pilot hybrid project at one of their existing biomass plants in India to validate the technical feasibility and economics of this approach.
Employees & Self Employees Co Op Society Projects DetailsSanthosh Kumar
M and M Bangalore Pvt Ltd presents residential society projects in North, East Bangalore and Mysore. They have developed projects for various housing societies. They are developing projects called Krishna Greens in Midlake Phase II in Doddaballapur Road, IVC Road in Devanahalli, East Wind in Devangundi, and West Mist in Mysore. The projects offer sites of various dimensions from 20x30 to 50x80 feet. There are details on location, payment plans in installments, amenities and booking details provided. For more details on booking, contact the listed representative.
Najati Al Hammouri is a Jordanian national seeking a position in a professional environment where he can apply his knowledge and skills. He has over 5 years of experience in construction project management, including supervising sites, scheduling deliveries, managing inventory, and ensuring work meets customer needs and standards. He is skilled in AutoCAD, Microsoft Office, communication, and managing diverse teams.
This document provides an overview of the November 2000 issue of JALA (Journal of Analytical Laboratories Automation). It describes the development of a novel robotic system for the New York Cancer Project biorepository in collaboration with the Medical Automation Research Center. The biorepository receives 50-100 blood samples per day which are processed robotically to extract, quantify, aliquot and store DNA, plasma and RNA to be accessible to investigators. The robotic system aims to provide rapid random access to the hundreds of thousands of DNA samples stored for high-throughput analysis in studies of gene-environment interactions and cancer risk.
Periféricos de procesamiento y Multimediasaul del orbe
Este documento describe varios componentes internos fundamentales de las computadoras como la memoria, las tarjetas, la CPU, los puertos, el bus de datos y la placa base. Explica brevemente el funcionamiento y evolución de estos componentes a través de las generaciones, así como los diferentes tipos de buses y fuentes de alimentación. También define conceptos clave como GHz, MHz y proporciona detalles sobre periféricos multimedia.
The document provides information on various jobsites using SENNEBOGEN material handlers and cranes. It describes 9 different material handlers performing tasks like loading logs, utility poles, scrap recycling, and port operations. It also describes 3 duty cycle cranes used for tasks like digging sand and gravel and installing diaphragm walls. The document contains details on the models used, capabilities, applications, and benefits over previous equipment at each jobsite. It promotes SENNEBOGEN's products and expertise in handling a wide range of materials.
This document discusses cost-based pricing strategies and compares traditional costing approaches to activity-based costing. Traditional costing allocates overhead expenses arbitrarily, which can reduce customer value. Activity-based costing links resource expenses to the variety and complexity of goods and services, yielding more accurate cost information. However, when setting prices, customers care more about the value a product or service provides to them rather than the production costs to the firm.
There are many characteristics of biological data. All these characteristics make the management of biological information a particularly challenging problem. Here mainly we will focus on characteristics of biological information and multidisciplinary field called bioinformatics. Bioinformatics, now a days has emerged with graduate degree programs in several universities.
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
It is widely agreed that complex diseases are typically caused by joint effects of multiple genetic variations, rather than a single genetic variation. Multi-SNP interactions, also known as epistatic interactions, have the potential to provide information about causes of complex diseases, and build on GWAS studies that look at associations between single SNPs and phenotypes. However, epistatic analysis methods are both computationally expensive, and have limited accessibility for biologists wanting to analyse GWAS datasets due to being command line based. Here we present APPistatic, a prototype desktop version of a pipeline for epistatic analysis of GWAS datasets. his application combines ease-of-use, via a GUI, with accelerated implementation of BOOST and FaST-LMM epistatic analysis methods.
This document summarizes computational analysis methods for determining expectation values commonly used in bioinformatics databases. It discusses tools like BLAST, FASTA, and databases like NCBI that allow querying and analyzing sequences. The expectation value provides the probability that a match could occur by chance, with lower values indicating higher quality matches. These tools and databases facilitate customizable extraction of data from sequences to enable further analysis and knowledge discovery in bioinformatics.
The document discusses recent advances in accelerating synthetic biology through computational and hardware methods. It describes developing biological programming languages to specify combinatorial DNA libraries and using microfluidic devices to build these libraries at a desktop scale. It also discusses using machine learning for data analysis to speed up computational simulations in synthetic biology.
The suite of free software tools created within the OpenCB (Open Computational Biology – https://github.com/opencb) initiative makes possible to efficiently manage large genomic databases.
These tools are not widely used, since there is quite a steep learning curve for their adoption, thanks to the complexity of the software stack, but they may be really cost-effective for hospitals, research institutions etcetera.
The objective of the talk is showing the potential of the OpenCB suite, the information to start using it and the advantages for the end users. BioDec is currently deploying a large OpenCGA installation for the Genetic Unit of one of the main Italian Hospitals, where data in the order of the hundreds of TBs will be managed and analyzed by bioinformaticians.
This document compares two solutions for filtering hierarchical data sets: Solution A uses MySQL and Python, while Solution B uses MongoDB and C++. Both solutions were tested on a 2011 MeSH data set using various filtering methods and thresholds. Solution A generally had faster execution times at lower thresholds, while Solution B scaled better to higher thresholds. However, the document concludes that neither solution is clearly superior, and further study is needed to evaluate their performance for real-world human users.
Presentation in the "Whole genome sequencing for clinical microbiology:Translation into routine applications" Symposium , Basel , Switzerland, 2 Sep 2017
Grid computing is a distributed computing model that enables transparent sharing and aggregation of computing, storage, and network resources across dynamic and geographically dispersed organizations. Key characteristics include distributing computational resources among multiple and widely separated sources and users, providing a means for using distributed resources to solve large problems, and making resources appear as a single virtual machine with powerful capabilities. Example applications discussed include scientific computing, business applications, and volunteer computing projects.
8 A Cellular Neural Network based system for cell counting in culture of biol...Cristian Randieri PhD
A Cellular Neural Network based system for cell counting in culture of biological cells - Proceedings of the 1998 IEEE International Conference on Control Applications, Trieste (Italy) 1-4 September 1998, Vol. 1, pp. 341-345.
di L. Bertucco, G. Nunnari, C. Randieri
Abstract
Cell counting methods are important tools in molecular biology as well as clinical medicine. It is not always technically possible to measure quantitatively the events of cellular growth and fission. When it can be done, the procedures are neither so simple nor without excessive tedium as to lend themselves practically to the necessary replication of observations with large number of individual cells. In this paper, we describe a CNN based system that uses a CNN simulator for counting cells. The performances of the proposed system are illustrated by a simple cell counting experiment using a Petroff- Hauser based counter system.
This document discusses best practices for managing the analysis of high-throughput sequencing data. It recommends:
1) Systematically collecting comprehensive metadata before data processing begins.
2) Establishing a unique identification system for samples to prevent errors.
3) Organizing data and results in a structured, hierarchical manner reflecting the analysis process.
4) Automating data processing as much as possible through scalable, parallelized, and modular code.
5) Thoroughly documenting all aspects of the analysis to ensure reproducibility.
6) Developing interactive web applications to empower users to access and analyze processed data independently.
This presentation discusses pathology informatics and laboratory information systems. It begins by defining pathology informatics and the pathologist's role as an information officer. It then describes common health care information systems, focusing on the laboratory information system. The key features of an LIS including hardware, software, specialized functions for anatomical and clinical pathology, and interfaces between information systems are explained. Finally, communication standards and the future of informatics are briefly discussed.
This document describes a microfluidic bioreactor system developed to provide controlled spatial and temporal concentration gradients of multiple molecular factors to 3D cultures of human pluripotent stem cells. The bioreactor contains rows of microwells connected by microchannels that generate stable concentration gradients when different factors are flowed through the lateral channels. Human embryonic and induced pluripotent stem cells were cultured as embryoid bodies in the bioreactor and exposed to gradients of mesoderm-inducing morphogens. Gene expression analysis showed the system could evaluate the initiation of mesodermal induction in a controlled manner. The bioreactor aims to provide a more in vivo-like model for studying stem cell development and differentiation.
In this deck from the 2014 HPC User Forum in Seattle, Jack Collins from the National Cancer Institute presents: Genomes to Structures to Function: The Role of HPC.
Watch the video presentation: http://wp.me/p3RLHQ-d28
Artículo escrito por el MC Hector Sánchez VIlleda acerca de su participación en el desarrollo, diseño e implementacion de un Sistema de Administración de la Información para Laboratorios en la Universidad de Missouri.
Hector Sánchez Villeda ha trabajado por más de 25 años en el desarrollo de TI para las ciencias biologicas y es fundador y Director de Desarrollo de IT en G2 Apps, una compañia de inovación tecnológica basada en la ciudad de Querétaro, Mexico
T-Bioinfo is a comprehensive bioinformatics platform that allows the user to navigate NGS, Mass-Spec and Structural Biology data analysis pipelines using consistent interface. Analysis and integration of such data allows for better and faster discovery and optimization of personalized and precision treatment of complex diseases and understanding of medical conditions. For more information, go to pine-biotech.com
This document provides an overview of bioinformatics and discusses key concepts like:
- Bioinformatics combines biology, computer science, and information technology to analyze large amounts of biological data.
- High-throughput DNA sequencing has generated vast genomic data that requires bioinformatics tools and databases accessible via the internet to analyze and share.
- Popular sequence alignment tools like BLAST, FASTA, and ClustalW are used to search databases and compare sequences, helping researchers analyze genes and genomes.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
1. A
vailability of the draft ver-
sion of the human genome
provides investigational op-
portunities to comprehen-
sively study new genes and the cor-
responding control of cellular
reactions.1
In order to support these
studies, there will be a parallel need
for purified human genomic DNA
samples from characterized popula-
tions of subjects as starting materi-
als. Moreover, biographical/demo-
graphic information describing each
sample will be essential for the com-
plete understanding of the complex
interactions of the human genome
with its environment. Although these
samples can be obtained using sev-
eral methods, one efficient approach
would be to automate both speci-
men management and collection of
the specimen’s demographic infor-
mation. This streamlined approach
contrasts with the large majority of
biorepositories that manually pro-
cess, store, and retrieve samples and
manually store and retrieve speci-
men information from a database.
The creation of the automated
biorepository can therefore be con-
sidered a direct consequence of the
increased need for specimens and
information in a timely, cost-effec-
tive manner.
Many of the individual laboratory
tasks associated with sample han-
dling, such as analysis, dilution, aspi-
ration and delivery, and archival stor-
age and retrieval, have already been
automated. The challenge is to inte-
grate these laboratory activities into
a seamless operation that delivers
walkaway automation yet permits
user input at key points in the sample
processing cycle. In collaboration
with the North Shore University Hos-
pital and AMDeC (Academic Medical
Development Corp., New York, NY,
www.amdec.org), the Medical Au-
tomation Research Center (MARC)
at the University of Virginia (Char-
FEATURE ARTICLE
Theodore E. Mifflin, B. Sean Graves, and Robin A. Felder
A Large-Scale Robotic Storage and Retrieval
System (Biorepository) Capable of Fully
Automated Operation
Abstract
A robotic system to process, analyze, store, and archivally retrieve genomic samples (DNA and plasma) has been devel-
oped. Its primary function is the automated storage and retrieval of selected samples for distribution according to sam-
ple attributes. The robotic biorepository integrates the operation of three primary devices (track robotic arm, three-axis
pipetting workstation, and large-capacity microplate storage system) into a seamless operation that performs a series of
tasks in a walkaway manner. A user-friendly interface (Visual Basic [Microsoft, Redmond, WA]) allows for operator selec-
tion of tasks and provides for real-time monitoring of events. The system’s operation is synchronized by two PCs, one to
control its tasks, and the other to store demographic and other sample-specific information for later access using Mi-
crosoft SQL. The system is developed around the bar-coded microplate as a common means of storage, analysis, and
distribution with a maximum capacity of approx. 2500 microplates or 250,000 samples. Additional functionality such as
thermal cycling and DNA array spotting, which would be coupled to the biorepository, are modules to be developed.
Handling and storage of other sample types such as RNA, cell suspension, and tissues are also being considered.
Key Words
Biorepository, robotic, automation, liquid handling, cherry picking, microplate, genomic DNA, analysis
The authors are with the University of Virginia, Department of Pathology, Medical Automation Research Center
(MARC), P.O. Box 800214, Charlottesville, VA 22908, U.S.A.; tel.: 804-924-8215; fax: 804-924-5718; e-mail:
tem6h@virginia.edu. The authors wish to recognize the many contributions made by the following members of MARC
during the design, development, and construction of the biorepository: Dr. Glen Wasson, Mr. Jim Gunderson, Mr.
Steve Kell, Ms. Sarah Woods, and Ms. Catherine Piche. Ms. Elle Kovarikova provided the animation and rendered im -
ages used in Figure 1. The authors also wish to recognize the extensive collaboration of persons at the project’s sponsor
(AMDeC): Dr. Peter Gregersen, Mr. Robert Lundsten, and Mr. Houman Khalili. This project would not have been com -
pleted without their efforts. Several hardware vendors are also acknowledged, including TECAN Instruments U.S.
(Durham, NC) and CRS Robotics (Toronto, Canada), for their substantial assistance regarding both the hardware
and software issues that arose during the biorepository’s development.
2. BIOREPOSITORY continued
lottesville, VA) has designed and con-
structed a large-scale biorepository
that maximizes the use of robotics
and automation to achieve this objec-
tive (see marc.med.virginia.edu and
Ref. 2). In its current form, the
robotic biorepository has the capac-
ity to automatically accession, dis-
pense, analyze, store, and randomly
access up to 250,000 genomic DNA
samples. Additional capability is now
being developed to manipulate hu-
man blood plasma as well as RNA
and cell suspensions.
Hardware
The core components of the ba-
sic biorepository design are three
automated devices: an articulated
robot arm mounted on a 3-m track
(T265, CRS Robotics, Toronto, Ca-
nada), a three-axis liquid handing
robot (Genesis 150 RSP, TECAN In -
struments U.S., Durham, NC), and
a tiered microplate storage system
(MolBank [GIRA], TECAN Instru -
ments ) capable of holding up to
2500 standard microplates at 4 °C.
Additional functionality is provided
by several secondary devices that
are connected to the primary de-
vices either mechanically or through
electronic means (e.g., serial ports).
These secondary devices include
microplate heat sealer, balance, mi-
croplate chiller, and a microplate
reader capable of quantifying UV
and fluorescence measurements.
They are arranged on the track
robot’s table in a manner that makes
them accessible to the track robot
arm (Figure 1). The track robot has
a five-axis arm with a gripper capa-
ble of holding both tubes and mi-
croplates, depending on the grip
width of the two sets of fingers
attached to the gripper itself. Two
interconnected computers (Dell
Computer , Inc., Austin, TX) are
used for operating the biorepository
and are run on Microsoft Windows
NT. One PC (the controller) orches-
trates the operation of the various
pieces of hardware, while the sec-
ond PC (the database) functions as
a host for the database (Microsoft
server) to store demographic infor-
mation. A series of uninterruptable
power supplies maintain constant
power into the biorepository.
Software
Similar to the hardware, the soft-
ware operating system was designed
using a mixture of application pack-
ages.3
Several of these packages were
provided with the core components,
such as the operating system for the
robotic arm (CROSnt) (CRS Ro -
botics ) and the operating system for
“THE ROBOTIC BIOREPOSI-
TORY INTEGRATES THE
OPERATION OF THREE
PRIMARY DEVICES INTO A
SEAMLESS OPERATION THAT
PERFORMS A SERIES OF
TASKS IN A WALKAWAY
MANNER.
”
Figure 1 Illustration of robotic biorepository. The robotic arm’s 3-m track tra -
verses the length of the table and ends at the MolBank (right side) while the Gen -
esis is positioned so that it can be accessed by the arm (middle of track). Other
accessories are positioned around the table within reach of the track robot. Sam -
ples (in 50-mL tubes) are placed near the MolBank in four racks of 24
samples/rack (foreground of table). Microplates are loaded into the carousel op -
posite the Genesis (far corner) next to the microplate sealer.
Figure 2 Software organization on the robotic biorepository. The user interface
and the CROS system software reside on the controller PC, whereas the SQL re -
sides on the server PC. The controller PC communicates with most of the acces -
sory devices via their serial ports, while the MolBank is connected via a custom-
written interface protocol. The arrows show the data flow direction.
3. the Genesis pipetting station (Gem-
ini). Connectivity to the remaining ac-
cessories was accomplished using se-
rial ports on the accessories and
coupled to the operational PC via a
single communication system (Cy-
clades-Z, Cycleades Corp., Fre-
mont, CA). The entire biorepository
is interconnected using a network
based on POLARA™ (CRS Robot -
ics ). The biorepository’s operation is
normally controlled using a custom-
written user interface (UI) and oper-
ating system based on Visual Basic
(VB) and RAPL-3 (Robotic Application
Programming Language-3, CRS
Robotics ) (Figure 2), but may also be
controlled via an Internet connection
using PC anywhere. In addition to an
integrated operation mode, several of
the accessory devices (e.g., Spec-
traFluor Plus microplate analyzer
[TECAN-US, Research Triangle Park,
NC] and Sartorius [Edgewood, NJ]
balance) can be operated in an inde-
pendent mode. This standalone opera-
tion offers flexibility for the bioreposi-
tory operators to develop other
applications, while using existing
hardware resources more efficiently.
Consumables
The robotic biorepository uses
five different disposables for pro-
cessing, analyzing, storing, and re-
trieving genomic DNA (Figure 3).
Their characteristics (summarized in
Table 1) illustrate the diverse prop-
erties needed for the successful op-
eration of the biorepository. For ex-
ample, the storage microplates
(items 1, 2, and 5) are constructed
from polypropylene to withstand
low temperatures (to –80 °C) and are
sterile to prevent degradation of ge-
nomic DNA from stray nucleases.
For the protein-based human
plasma, a sixth type of storage sys-
tem has been incorporated that uses
a two-dimensional matrix code
(TrakMate™, Matrix Corp., Hud-
son, NH) on individual tubes (racks
of 96 matrix-coded plasma tubes are
also liner bar-coded). Individual mi-
croplates (and brands) are selected
for the attributes that best match
their role in the biorepository’s oper-
ation (Table 1). All consumables
contain linear bar codes for perma-
nent identification.
Biorepository operation
summary
The robotic biorepository is an in-
tegrated system that consists of a
group of hardware items, software
packages, and procedures that per-
form a concise menu of tasks. The
biorepository stores genomic DNA in
a compacted format with the major-
ity (>98%) of each sample frozen at
–80 °C. Most (~80%) of the genomic
DNA is contained in five deep-well
master plates (Table 1), while a
lesser fraction (2–18%) resides frozen
in up to nine daughter plates (Table
1). Only about 2% of the genomic
DNA is immediately available for
continual access (one daughter
plate) at 4 °C. Sendout plates contain
the collection of specimens identi-
fied from a search of the database for
specimens that match particular cri-
teria. Aliquots from these specimens
are removed from selected daughter
plates and are delivered into the
sendout plate for delivery to an in-
vestigator (Figure 4).
There are four main tasks of the
biorepository: 1) Create master
plates, 2) create daughter plates, 3)
create sendout plates, and 4) create
master plasma tubes. Each of these
tasks can be selected separately
from the primary UI (Figure 5). Ad-
ditional custom-designed UIs have
“THE DNA CONCENTRA-
TION FROM THE FLUO-
RESCENCE QUANTITATION
IS PREFERRED, SINCE THIS
METHOD IS MORE SENSITIVE
AND IS NOT SUBJECT TO
INTERFERENCE BY PROTEINS
OR OTHER SUBSTANCES.
”
Figure 3 Six disposable (sterile) microplates used for the robotic biorepository.
Shown counterclockwise from lower left are: 1) master plate, 2) daughter plate
with pierceable mat, 3) UV spectra microplate, 4) black (fluorescent quantita -
tion) microplate, 5) sendout plate sealed with peelable film, and 6) rack of
plasma tubes with 2-D matrix-coded tubes.
Table 1
Biorepository consumable items and their key attributes
Item Material No. of Sterile Storage Sealed
no. Item stored wells (Y/N) Composition temp. (Y/N)
1 Master plate Genomic DNA 96 Y Polypropylene –80 °C Y
2 Daughter plate Genomic DNA 96 Y Polypropylene –80 °C Y
3 UV analysis None 96 N Polystyrene/? — N
4 Fluor. quant. None 384 N Polystyrene — N
5 Sendout plate Genomic DNA 95 Y Polypropylene to Y
–80 °C
6 Plasma master Plasma 96 Y Polypropylene –80 °C *
tubes
*Tubes are sealed individually using sterile strip caps
4. BIOREPOSITORY continued
been developed for each task so
that the operator can immediately
recognize the task selected and
then interpret its specific informa-
tion. In addition, as the task is com-
pleted, indicators on the UI signal
progress as well real-time errors. A
brief summary of the major tasks is
provided below:
1. Task 1: Create master plate.
This task transfers genomic DNA
samples derived from the laboratory
extraction process into robotic-
friendly plasticware that is space ef-
ficient for storage. The process be-
gins by the Create Master Plate UI
that appears, and the operator se-
lects the number of genomic DNA
samples to be processed (1–96). The
purified genomic DNA samples (~15
mL) in 50-mL centrifuge tubes are
placed on the deck of the bioreposi-
tory. A checklist prompts the user to
review the status of various consum-
ables needed for creating master
plates, and the operator then initi-
ates processing. Each sample of ge-
nomic DNA (previously bar coded)
is first scanned, weighed, and placed
onto the deck of the Genesis pipet-
ting station. Six 2.0-mL aliquots are
sequentially removed and distrib-
uted into six prebar-coded polypro-
pylene deep-well microplates (DWP)
(volume/well = 2.2 mL). During the
dispensing phase, a seventh aliquot
is delivered from each sample tube
into a dilution microplate for spec-
troscopic measurements. An aliquot
from each well in the dilution mi-
croplate is transferred into a UV-
compatible microplate so that ab-
sorbance values at 260-, 280-, and
320-nm wavelengths can be ob-
tained. From these absorbances, an
A260/A280 ratio is calculated to deter-
mine each DNA sample’s relative
purity based on this ratio’s value (de-
sired value range 1.8–2.0). In addi-
tion, another aliquot from each well
is transferred into a black 384-well
microplate and mixed with a dilute
fluorescent reagent (PicoGreen,
Molecular Probes, Eugene, OR) to
quantify every sample’s genomic
DNA. The DNA concentration from
the fluorescence quantitation is pre-
ferred, since this method is more
sensitive and is not subject to inter-
ference by proteins or other sub-
stances. These two sample-specific
values are then transferred to the
database for later acquisition and
are also used for immediate quality
control rerun activities. When all of
the genomic DNA samples in the 50-
mL tubes have been processed, the
filled DWPs are transferred via the
robotic arm to the thermal film mi-
croplate sealer (ALPS-100, ABGene,
Rochester, NY), which applies a pee-
lable film to each deep-well mi-
croplate. All six DWPs are then sent
to storage at –80 °C.
2. Task 2: Create daughter plates.
A single master plate is used to cre-
ate 10 daughter plates (Table 1). Nine
of the daughter plates are robotically
sealed using peelable heat-seal film
and are stored frozen at –80 °C. The
tenth daughter plate is manually
sealed using a flexible silicone mat
that can be pierced repeatedly by the
probes on the pipetting station. All of
the daughter plates are sterile to pre-
vent degradation of the genomic
DNA during storage. A daughter
plate is an exact replica of a corre-
sponding master plate, except that it
holds only 150–200 µL/well.
3. Task 3: Create sendout plates.
A sendout plate is a
standard prebar-
coded 96-well mi-
croplate that can con-
tain up to 96 separate
genomic DNA sam-
ples from selected
daughter plates. The
operator creates a
sendout by sending a
list of samples with
desired attributes to
the database match-
ing routine. The data-
base matching rou-
tine then creates a
worklist containing a
set of sample ID num-
Figure 5 Primary UI for the robotic biorepository. The
main tasks are listed as buttons that can be selected by
the operator in an independent manner.
Figure 4 Main tasks 1–3 shown in a sequential manner. The majority of every
sample is stored at –80 °C (below the dotted line) until it is needed. Once a mas -
ter or daughter plate is consumed, another is thawed and placed into service as
indicated. Each daughter plate in the MolBank (4 °C environment) has a pierce -
able mat that allows robotic access while preventing evaporation during refriger -
ated storage.
“THE ROBOTIC BIOREPOSI-
TORY USES FIVE DIFFERENT
DISPOSABLES FOR PROCESS-
ING, ANALYZING, STORING,
AND RETRIEVING GENOMIC
DNA.
”
5. bers corresponding to the list of sam-
ples. The sendout plate routine con-
sults the worklist to instruct the
biorepository to remove those daugh-
ter plates in the MolBank microplate
storage system, which contains the
desired samples. Individual daughter
plates are sequentially moved by the
robotic arm from the storage system
to the pipetting station, which then
cherry picks aliquots from specific
samples matching the worklist. Since
the refrigerated daughter plates have
pierceable mats, the pipetting station
can then remove aliquots from indi-
vidual samples without the potential
for evaporative loss, since the mats
automatically reseal after aliquot re-
moval. The 15-µL aliquots are deliv-
ered into specific wells of the sendout
plate and are diluted with 135 µL of
sterile, deionized water. After each re-
frigerated daughter plate is pro-
cessed, it is automatically returned to
the microplate storage system by the
robotic arm. When all samples on the
worklist have been delivered into the
sendout plate, the plate is transferred
to the ALPS plate sealer and a peel-
able film is robotically applied. While
the sendout plate is being sealed, the
server PC creates a map of the send-
out plate that depicts the location and
identity of each sample along with its
corresponding concentration.
Summary
The automated biorepository de-
scribed here supports a variety of
needs for samples, including scien-
tific studies in which efficient and
rapid access to selected samples is
essential. A principal attribute is the
capability of the biorepository to
store up to 250,000 samples in a com-
pletely robot-friendly manner. All of
the major tasks are accessible and
controlled from a series of custom
user interfaces that permit real-time
information about completion as
well as error tracking and notifica-
tion. On the horizon are several other
modules that could also be inter-
faced, such as thermal cyclers and
DNA array spotting machines. It is
anticipated that the robotic biorepos-
itory will continue to evolve toward
smaller platforms and storage for-
mats. There is also the possibility of a
nanotechnology-based biorepository.
This automated biorepository
was installed at North Shore Univer-
sity Hospital in mid-May 2001 and
will begin processing genomic DNA
samples in the near future after final
optimization trials are completed.
References
1. Venter JC, Adams MD, Myers EW, et al.
The sequence of the human genome.
Science 2001; 291:1304–51.
2. Gregersen P, Felder RA. Searching for
gene-environment interactions in can-
cer:biorepository support for the New
York cancer project. J Assoc Lab Auto
2000; 5:37–9.
3. Graves BS, Mifflin TE, Gunderson J,
Geddy S, Kell S, Felder RA. Software
implementation of biological reposi-
tory for human genomic material. J As-
soc Lab Auto 2000; 6:106–8. AG/PT