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3rd International Conference on Transcriptomics, will be organized around the theme “Exploring Pathways Towards Novel Research”
Transcriptomics 2017 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Transcriptomics 2017
Submit your abstract to any of the mentioned tracks.
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The transcriptome is a collection of all RNA present in a cell or a population of cells at any given moment. The transcriptome is dynamic, as the levels of RNA transcripts vary during different developmental stages or in response to certain conditions.
Transcriptome sequencing or RNA-Seq is a next-generation sequencing (NGS)-based approach to profiling and analysing RNA. This technique delivers unbiased information without the need for prior knowledge of the genome or transcriptome. Transcriptome sequencing is often the method of choice for analysis of differentially expressed genes, as well as for RNA editing and profiling of allele-specific gene expression. RNA-seq can also be used to investigate splicing patterns, splicing variants, gene isoforms, single nucleotide polymorphisms and post transcriptional modifications.
- Track 1-1Scope & Methods of Construction
- Track 1-2mRNA & RNA-seq Analysis
- Track 1-3Applications of Different Transcriptomes
Data emanating from RNA-Seq studies have greatly improved the coverage of Transcriptomics and Proteomics of Plants. This technology further compounded transcriptome analysis by making it possible to identify differentially spliced transcripts etc. In the research, either microarray or RNAseq based datasets is used for Transcriptome Analysis of Agricultural Plants. These datasets are stored as Transcriptome sequence Databases. To clarify the phylogeny of green plants, sequences from the plastid genome are used which is termed as Plant Phylogenomics
Proteomics of Plant Study has been a major boon for many companies, to name a few being Accelrys, ActivX Biosciences, Beyond Genomics, Biomax Informatics AG and Biovation. The research as gained a lot of applications in industry have gained up as a major study in too in top schools as of International Plant Proteomics Organization, Murdoch University, University of Nottingham, University of Salford Manchester and University Of Nebraska–Lincoln.
- Track 2-1Transcriptomic and Proteomic Analysis of Bacterial and Fungal Biofilms
- Track 2-2Large scale Analysis of Complex Bacterial Communities
- Track 2-3Bacterial Physiology and Adaptations
- Track 2-4System-level approaches of studying bacterial physiology and metabolism
- Track 2-5Transcriptomics in response to viral infections
- Track 2-6Transcriptomic and Proteomic Profiling of Viruses
For an International conference on Transcriptomics, an overview on Transcriptome analysis and Gene Expression is the first and the essential most topics to be discussed. While going in depth of the subject, it is necessary to understand Transcriptome as Key Players in Gene Expression. For that we should know the basics knowledge of how the central dogma works. This can be achieved by gaining proper knowledge about functioning of mRNA, tRNA and rRNA. Gene expression analysis experiments can focus on a subset of relevant target genes. The location of gene and relative distances between genes on a chromosome can be determined through Sequence mapping. Even in the absence of the reference genome, transcriptome can be created using de novo transcriptome assembly method. Globally around thousands of Universities and institutes are carrying research on gene expression and transcriptome analysis. Being specific are the University of Leeds, Case Western Reserve University, Arizona State University, Tempe. Institutes like The Genome Institute – St. Louis, Missouri and NIH - National Human Genome Research Institute are working tremendously towards the same where researchers are having a database of over 40,000 gene sequences that they can use for this purpose
- Track 3-1Transcriptomes
- Track 3-2Transcriptome Profiling
- Track 3-3Functioning of mRNA, tRNA and rRNA
- Track 3-4de novo transcriptome assembly
- Track 3-5Transcription of gene
Next Generation Sequencing (NGS) Technologies enable a wide variety of methods, allowing researchers to ask virtually any question related to genome, transcriptome, or epigenome of any organism. Sequencing methods differ primarily by how the DNA or RNA samples are obtained and by the data analysis used. The numbers of methods is constantly growing. The most common are small RNA-Seq: Whole Transcriptome Shotgun Sequencing, Exome Sequencing, De Novo Full-Length Transcriptome Analysis and Hybrid Sequencing Approach. Various NGS platforms in the market such as Illumina, SOLiD, and Roche, offer unprecedented ability to apply massively parallel sequencing of transcriptomics (RNA) and genomic (DNA) samples to understand disease and health. The enormous Differential Splicing and RNA Sequencing Data pose a fundamental problem of management and analysis. Various data analysis solutions eliminate the next-generation sequencing data management. ABRF Next-Generation Sequencing (ABRF-NGS) is the most effective technique for accurate sequencing data. For Ribonucleic acid, Single-cell RNA sequencing (scRNA-seq) is carried out which examines the RNA sequence information from individual cells with optimized next generation sequencing (NGS) technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment.
- Track 4-1RNA-Seq: Whole Transcriptome Shotgun Sequencing
- Track 4-2ABRF Next-Generation Sequencing (ABRF-NGS)
- Track 4-3Transcriptome Profiling by RNA-Seq
- Track 4-4Transcriptome Databases
- Track 4-5Microarray Gene Expression
- Track 4-6Differential Splicing and RNA Sequencing Data
- Track 4-7Exome Sequencing
- Track 4-8De Novo Full-Length Transcriptome Analysis
- Track 4-9Hybrid Sequencing Approach
- Track 4-10Software and Platforms
Proteins provide most of the molecular machinery of cells. Many are enzymes or subunits of enzymes. Other proteins play structural or mechanical roles, such as those that form the struts and joints of the cytoskeleton. Each protein is linear polymers built of amino acids.
- Track 5-1Bio and Chemical Synthesis
- Track 5-2Cellular Functions
- Track 5-3Proteomics
- Track 5-4Bio-Molecules & Metabolism
- Track 5-5Plant Biochemistry
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques.
Bioinformatics is both an umbrella term for the body of biological studies that use computer programming as part of their methodology, as well as a reference to specific analysis "pipelines" that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences, called proteomics.
- Track 6-1Sequence analysis
- Track 6-2Gene and protein expression
- Track 6-3Network and systems biology
- Track 6-4Structural bioinformatics & Analysis of cellular organization
Gene expression profiling simultaneously compares the expression levels of many genes between two or more sample types. This analysis can help scientists identify the molecular basis for phenotypic differences and select gene expression targets for in-depth study using other technologies. Gene expression profiling provides valuable insight into the role of differential gene expression in normal biological and disease processes. There are many techniques used for gene expression profiling. One of the major techniques for expression profiling includes Measuring Relative Activity by SAGE and Super SAGE, where SAGE stands for serial analysis of gene expression technology for the analysis of expressed genes. The other technique includes Gene annotation which provides functional and other information, for example the location of each gene within a particular chromosome. Some functional annotations are more reliable than others; some are absent. Gene annotation databases change regularly, and various databases refer to the same protein by different names, reflecting a changing understanding of protein function. Having identified some set of regulated genes, the next step in expression profiling involves looking for patterns within the regulated set for categorizing regulated genes. This can be done by finding similarities between the functioning of the proteins produced from different cell. Further categorization is done on the basis of relationship between two genes and their products by finding patterns among regulated genes. This is analysed by the fact that what these regulated genes actually are and what they do. Compared to Proteomics, the human genome contains on the order of 25,000 genes which work in concert to produce on the order of 1,000,000 distinct proteins. Knowledge of the precise proteins a cell makes, is more relevant than knowing how much messenger RNA is made from each gene, gene expression profiling provides the most global picture possible in a single experiment.
Gene Expression profiling has a growing research being utilized by major companies like Wafergen Biosystems, Afymetrix, SBI, Luminex, Asper Biotech, Genentech San Francisco to name a few. The research has been supported as a course by more than 470 universities and gains international funding too.
- Track 7-1SAGE and SuperSAGE
- Track 7-2Gene annotation
- Track 7-3Categorizing Regulated Genes
- Track 7-4Finding Patterns among Regulated Genes
- Track 7-5Comparison to Proteomics
Oncogenomics is a relatively new sub-field of genomics that applies high throughput technologies to characterize genes associated with cancer. The study involves the research in to the Bioinformatics and functional analysis of oncogenes, which refers to the gene that has the potential to cause cancer. In tumor cells, they are often mutated or expressed at high levels.
Cancer genome sequencing requires the development of new techniques utilizing Genomics and bioinformatics tools for target assessment, including both experimental protocols and data analysis algorithms, to enable a deeper understanding of complex biological systems. Databases for Cancer Research have been developed which primarily study the Mutations in Mitochondrial DNA and Cancer to come up with effective and informative databases. It involves the development of Tools for integrative meta-analysis, 3c-based data integration and application of Networks and OMICS data, mathematical modeling and computational simulation techniques to the study of Integrative eqtl-based analyses, High performance genomics data visualization and Laboratory information management system to come up with Potential Diagnostic Applications.
Computational Genomics research has grown after the increased research in Genomics with major universities like Iowa State University, University Of California, and The George Washington University Concentrating on the growing topic. The Bisti Consortium has even launched the NIH and Government Programs and Initiatives in Biomedical Informatics and Computational Biology (BICB) with a list of programs concentrating on Computational Biology Research.
- Track 8-1Introduction to Oncogenomics
- Track 8-2Cancer Biomarkers
- Track 8-3Potential Diagnostic Applications
- Track 8-4Mutations in Mitochondrial DNA and Cancer
- Track 8-5Advances from Oncogenomics
- Track 8-6Databases for Cancer Research
- Track 8-7Comparative Oncogenomics
- Track 8-8Bioinformatics and functional analysis of oncogenes
- Track 8-9Cancer Transcriptomes
- Track 8-10Cancer genome sequencing
- Track 8-11P53 mediated Transcriptomics
Epigenetic modulation of gene expression is responsible for tissue specific and temporal changes across growth and development. The most widely studied of these epigenetic modifications is DNA methylation of 5-methylcytosine at CpG dinucleotides and histone modification. This DNA methylation can be caused by small factors like smoking tobaccos. Aberrations of DNA methylation are associated with a range of diseases, including imprinting disorders and cancer. Recent advances in technologies have made it possible to study the epigenetic changes associated with these diseases using robust genome-wide technologies including the Infinium HumanMethylation450 BeadChip (henceforward denoted the 450 k array; These intensities are then used to calculate DNA methylation levels, with advantageous throughput, cost, coverage and technical consistency. Gene silencing is also used for epigenetic regulation of gene expression. To study the modification on the genetic material of the cell called epigenome, Epigenomics comes into action. For all aspects of epigenetic principles and mechanisms in relation to human disease like diabetes and cancer, diagnosis and therapy Clinical Epigenetics is taken into consideration. It focuses on Clinical trials and research in disease model organisms. Currently, Pfizer is a major company carrying its research and development on Epigenetics.
Ulrich Mahlknecht is currently a Professor of medicine at the University of Heidelberg, and Head of the Department of Hematology/Oncology at St. Lukas Clinic in Solingen, Germany. His research focuses on novel targeted immunological and epigenetic concepts in the treatment of solid tumors and hematological malignancies.
- Track 9-1DNA Methylation
- Track 9-2Histone Modification
- Track 9-3Gene Silencing
- Track 9-4Epigenomics
- Track 9-5Clinical Epigenetics
Transcriptional Regulation and Transcriptional Attenuation deals with Regulation of Transcription by which a cell regulates the conversion of DNA to RNA and attenuation which is a regulatory feature causing premature termination of transcription. There are various classes of attenuators according to the type of molecule which induces the change in RNA structure. It can be a Small-Molecule-Mediated Attenuation: Introduction to Riboswitches, in which Riboswitch sequences (in the mRNA leader transcript) bind micro and macro molecules, which cause a conformational change in the mRNA. The other type of attenuation includes Protein-Mediated Attenuation and Ribosome-Mediated Attenuation. This phenomenon of transcriptional attenuation is most prominent in the trp Operon found throughout Archaea and Bacteria.
Major Universities as of University of Southampton, University of Utah, MBL, University Of Colorado Boulder, University of Manitoba and University of Michigan have bought up courses dealing exclusively with Transcriptome Regulation and Attenuation encouraging and attracting specific research worldwide. The topic attracts major research funding too from INRA, NIH, Laboratory of Genome Regulation and Universitat Pompeu Fabra to name a few.
- Track 10-1Regulation of Transcription
- Track 10-2Protein-Mediated Attenuation
- Track 10-3Ribosome-Mediated Attenuation
- Track 10-4The trp Operon
- Track 10-5Gene Annotation
- Track 10-6Gene Regulation
Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. mRNA gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to provide a better understanding of cellular biology
- Track 11-1Analytical technologies
- Track 11-2Statistical methods
- Track 11-3Key applications
- Track 11-4Functional Genomics
- Track 11-5Cancer Therapeutic Approaches
- Track 11-6Transcriptomics & Metabolic pathways
A multiplatform approach in next-generation sequencing (NGS) involves the combined analysis of sequencing reads generated on two or more different sequencing platforms. Most often researchers that require very long read lengths with very low error rates opt for this sequencing strategy. Having physical access to a range of sequencing instruments and the technical knowledge to handle various sequencing technologies are both key elements in properly applying a hybrid sequencing approach.
- Track 12-1Novel transcription
- Track 12-2Micro-biome analysis
- Track 12-3Virus Trafficking
- Track 12-4Applications
Whole-genome sequencing (WGS) is the sequencing of all DNA present in the genome of an organism, including chromosomal DNA, mitochondrial DNA and chloroplast DNA if in plants. Two types of WGS approaches are available that mainly differ in their dependency on a reference genome sequence.
- Track 13-1Current Technologies & Research
- Track 13-2Comparative Genomics
- Track 13-3Ethical Concerns
Single cell sequencing examines the sequence information from individual cells with optimized next generation sequencing (NGS) technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. Single cell DNA genome sequencing involves isolating a single cell, performing whole-genome-amplification (WGA), constructing sequencing libraries and then sequencing the DNA using a next-generation sequencer (ex. Ion Torrent, Illumina). It can be used in metagenomics studies and when sequencing the first time from novel species. In addition, it can be united with high throughput cell sorting of microorganisms and cancer. One popular method used for single cell genome sequencing is multiple displacement amplification and this enables research into various areas such as microbial genetics, ecology and infectious diseases. Furthermore, data obtained from microorganisms might establish processes for culturing in the future.
- Track 14-1Single cell genome (DNA) sequencing
- Track 14-2Ethical Considerations
- Track 14-3Single-cell RNA sequencing (scRNA-seq)
- Track 14-4Applications
Biostatistics is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch of this is medical biostatistics, which is exclusively concerned with medicine and health.
Systems biology is the computational and mathematical modeling of complex biological systems. An emerging engineering approach applied to biological scientific research, systems biology is a biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.
- Track 15-1Bioinformatics and data analysis
- Track 15-2Case Reports
- Track 15-3Network Biology & Synthetic Biology