From Expression to Pathways Using Online Tools
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Microarray and RNA seq analysis using Online Tools Content: Microarray Types Microarray Vs RNA-Seq Transcriptomic Database Network Vs Enrichment Vs Pathway Connectivity Map GEO2Enrichr
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Qi liu 08.08.2014
This document discusses integrative omics analysis, which involves analyzing multiple types of molecular data together, such as genomics, transcriptomics, epigenomics, and proteomics data. It describes various data sources, methods, tools, and things to consider for integrative analysis. Methods discussed include sequential/overlap analysis, clustering, correlation analysis, linear regression, and network-based analysis. Tools mentioned are for clustering, correlation, linear regression, network analysis, visualization, and databases of omics data. Challenges of dimensionality and need for biological knowledge integration are noted.
Next generation sequencing by Muhammad Abbas
Next generation sequencing (NGS) allows for the massively parallel sequencing of DNA sequences. NGS technologies can sequence entire genomes in a single run and provide information useful for pathogen identification, outbreak investigation, and molecular diagnostics. NGS workflows involve sample preparation, sequencing using platforms such as Illumina or Ion Torrent, and bioinformatics analysis to assemble and interpret the large amounts of sequencing data produced. NGS has many applications including mutation discovery, microbial genome mapping, and metagenomics.
Impact_of_gene_length_on_DEG
1) The document discusses a study analyzing the impact of gene length on detecting differentially expressed genes using RNA-seq technology.
2) The study will first test the reproducibility of RNA-seq and the effect of normalization. It will then compare different statistical tests for identifying differentially expressed genes.
3) Finally, the study will specifically test how gene length impacts the likelihood of a gene being identified as differentially expressed, as longer genes are easier to map with short reads.
Bioinformatics as a tool for understanding clinically significant variations ...
Presentation made at Hellenic Association of Medical Geneticists describing bioinformatics applications to cancer genetics.
Bioinformatics tools for NGS data analysis
Bioinformatics tools are essential for analyzing next-generation sequencing (NGS) data. The summary describes the typical stages of NGS data analysis:
1. Primary analysis involves demultiplexing, base calling and quality control to produce fastq files.
2. Secondary analysis maps reads to a reference genome to produce SAM/BAM files and calls variants to produce VCF files.
3. Tertiary analysis annotates and filters variants to prioritize those relevant to disease.
5th RNA-Seq San Francisco Agenda
Discover new cases studies giving you unprecedented access to both the data and results of how RNA-Seq is being applied successfully from bench to bedside
Gain new insights into RNA-Seq for the study of toxicity, IO, host-viral interactions and more from companies such as BMS, Janssen, Pfizer, Merck, UCSC and Stanford
Bioinformatics
This document provides an overview and syllabus for a course on bioinformatics. It discusses the goals of learning about available bioinformatics programs and tools, and interpreting their outputs. The course will cover topics like sequence alignment, phylogenetics, genome comparison and using databases. Assessment will include homework, exams, a report, and participation. The document contrasts the "old" and "new" biology, noting how the new biology generates large datasets that require computational analysis to make sense of the data. It emphasizes that bioinformatics uses algorithms and databases to organize, analyze and interpret biological data at large scales.
High-Throughput Sequencing
Talk on High-Throughput Sequencing: Overview and Selected Applications for Masters students. Nov 9th 2011
Recommended
Qi liu 08.08.2014
This document discusses integrative omics analysis, which involves analyzing multiple types of molecular data together, such as genomics, transcriptomics, epigenomics, and proteomics data. It describes various data sources, methods, tools, and things to consider for integrative analysis. Methods discussed include sequential/overlap analysis, clustering, correlation analysis, linear regression, and network-based analysis. Tools mentioned are for clustering, correlation, linear regression, network analysis, visualization, and databases of omics data. Challenges of dimensionality and need for biological knowledge integration are noted.
Next generation sequencing by Muhammad Abbas
Next generation sequencing (NGS) allows for the massively parallel sequencing of DNA sequences. NGS technologies can sequence entire genomes in a single run and provide information useful for pathogen identification, outbreak investigation, and molecular diagnostics. NGS workflows involve sample preparation, sequencing using platforms such as Illumina or Ion Torrent, and bioinformatics analysis to assemble and interpret the large amounts of sequencing data produced. NGS has many applications including mutation discovery, microbial genome mapping, and metagenomics.
Impact_of_gene_length_on_DEG
1) The document discusses a study analyzing the impact of gene length on detecting differentially expressed genes using RNA-seq technology.
2) The study will first test the reproducibility of RNA-seq and the effect of normalization. It will then compare different statistical tests for identifying differentially expressed genes.
3) Finally, the study will specifically test how gene length impacts the likelihood of a gene being identified as differentially expressed, as longer genes are easier to map with short reads.
Bioinformatics as a tool for understanding clinically significant variations ...
Presentation made at Hellenic Association of Medical Geneticists describing bioinformatics applications to cancer genetics.
Bioinformatics tools for NGS data analysis
Bioinformatics tools are essential for analyzing next-generation sequencing (NGS) data. The summary describes the typical stages of NGS data analysis:
1. Primary analysis involves demultiplexing, base calling and quality control to produce fastq files.
2. Secondary analysis maps reads to a reference genome to produce SAM/BAM files and calls variants to produce VCF files.
3. Tertiary analysis annotates and filters variants to prioritize those relevant to disease.
5th RNA-Seq San Francisco Agenda
Discover new cases studies giving you unprecedented access to both the data and results of how RNA-Seq is being applied successfully from bench to bedside
Gain new insights into RNA-Seq for the study of toxicity, IO, host-viral interactions and more from companies such as BMS, Janssen, Pfizer, Merck, UCSC and Stanford
Bioinformatics
This document provides an overview and syllabus for a course on bioinformatics. It discusses the goals of learning about available bioinformatics programs and tools, and interpreting their outputs. The course will cover topics like sequence alignment, phylogenetics, genome comparison and using databases. Assessment will include homework, exams, a report, and participation. The document contrasts the "old" and "new" biology, noting how the new biology generates large datasets that require computational analysis to make sense of the data. It emphasizes that bioinformatics uses algorithms and databases to organize, analyze and interpret biological data at large scales.
High-Throughput Sequencing
Talk on High-Throughput Sequencing: Overview and Selected Applications for Masters students. Nov 9th 2011
Analytical Study of Hexapod miRNAs using Phylogenetic Methods
MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene expression.
Identification of total number of miRNAs even in completely sequenced organisms is still an
open problem. However, researchers have been using techniques that can predict limited
number of miRNA in an organism. In this paper, we have used homology based approach for
comparative analysis of miRNA of hexapoda group .We have used Apis mellifera, Bombyx
mori, Anopholes gambiae and Drosophila melanogaster miRNA datasets from miRBase
repository. We have done pair wise as well as multiple alignments for the available miRNAs in
the repository to identify and analyse conserved regions among related species. Unfortunately,
to the best of our knowledge, miRNA related literature does not provide in depth analysis of
hexapods. We have made an attempt to derive the commonality among the miRNAs and to
identify the conserved regions which are still not available in miRNA repositories. The results
are good approximation with a small number of mismatches. However, they are encouraging and may facilitate miRNA biogenesis for hexapods.
Bio Informatics
This document discusses bioinformatics and some of its key techniques and uses. It introduces bioinformatics as a field that merges biology, computer science, and information technology to manage and analyze biological data using advanced computing. Some techniques discussed include phylogenetics, proteomics, sequence analysis, structure determination, and gene expression analysis. Recombinant DNA technology and cloning are also summarized, including the process of recombining DNA from different species and the various types of human cloning.
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
RT-PCR and DNA microarray measurement of mRNA cell proliferation
For mRNA quantification, RT-PCR and DNA microarrays have been compared in few studies
(RT-PCR). Healing callus of adult and juvenile rats after femur injury was found to be rich in mRNA at
various stages of the healing process. We used both methods to examine ten samples and a total of 26 genes.
Internal DNA probes tagged with 32P were employed in reverse transcription-polymerase chain reaction
(RT-PCR) to identify genes (RT-PCR). Ten Affymetrix® Rat U34A cRNA microarrays were hybridized with
biotin-labeled cRNA generated from mRNA. There was a wide range of correlation coefficients (r) between
RT-PCR and microarray data for each gene. Meaning became genetically unique because of this diversity.
Relatively lowly expressed genes had the highest r values. The distance between PCR primers and
microarray probes was found to be higher than previously assumed, leading to a drop in agreement between
microarray calls and PCR outcomes. Microarray research showed that RT-PCR expression levels for two
genes had a "floor effect." As a result, PCR primers and microarray probes that overlap in mRNA expression
levels can provide good agreement between these two techniques.
SPIN Workshop Microbial Genomics @NIST
This document discusses NIST's work in developing genomic reference materials and methods to evaluate microbial genomics measurements. It describes three projects: 1) assessing genomic purity by detecting low levels of contaminants using sequencing and classification, 2) evaluating SNP calling methods using reference materials and replicates to establish confidence, and 3) developing characterized genomic reference materials for public health pathogens. The overall aim is to build an infrastructure to support genome-based characterization of microbial samples.
Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
"Development of FDA MicroDB: A Regulatory-Grade
Microbial Reference Database" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Heike Sichtig, PhD from the FDA and Luke Tallon from IGS UMSOM.
Poster ESHG
- The study provides a cost analysis of three next-generation sequencing applications: targeted gene panels (TGP), whole exome sequencing (WES), and whole genome sequencing (WGS). It finds per-sample costs of €333 for TGP, €792 for WES, and €1,669 for WGS.
- Costs are mainly driven by consumables such as sequencing and sample preparation. The estimated $1,000 genome has not been achieved, though it may be approached under best-case assumptions of long-term, efficient use of equipment and considerable cost reductions.
- The choice of sequencing approach in clinical practice should consider both costs and clinical effectiveness, not just costs alone.
Data analysis & integration challenges in genomics
Presentation given at the Genomics Today and Tomorrow event in Uppsala, Sweden, 19 March 2015. (http://connectuppsala.se/events/genomics-today-and-tomorrow/) Topics include APIs, "querying by data set", machine learning.
Next Generation Sequencing application in virology
Next Generation Sequencing (NGS) is a promising technique for virus diagnosis that provides several advantages over traditional Sanger sequencing. NGS workflows involve sample preparation, sequencing, and data analysis. NGS has various applications in virology including identifying viral quasispecies, detecting antiviral drug resistance mutations, discovering novel virus genotypes, and performing quality control of live vaccines. While NGS reduces costs and improves throughput over Sanger sequencing, analyzing large NGS datasets requires strong bioinformatics skills. Overall, NGS represents a significant improvement for virus research and diagnosis.
Bioinformatics Final Report
The document provides information about bioinformatics and BLAST (Basic Local Alignment Search Tool). It defines bioinformatics as the application of information technology to molecular biology. It describes what BLAST is and how it works to compare biological sequences and identify similar sequences in databases. It also lists different BLAST programs and databases that can be used depending on the type of sequence being searched.
Illumina-General-Overview-Q1-17
This document is a presentation from Illumina about genomics and their company. It discusses how genome sequencing can provide health insights, their progress in clinical adoption and research use, and their mission to improve human health through genomics. It provides details on Illumina's background, products, markets served, and goals to accelerate genomic adoption and establish new businesses in cancer screening and consumer genomics.
The Transforming Genetic Medicine Initiative (TGMI)
Presentation by Nazneen Rahman at the GRC workshop held at the 2016 Genome Informatics meeting at Hinxton, UK
Next Generation Sequencing in Big Data
A huge revolution has taken place in the area of Genomic science. Sequencing of millions of DNA strands in parallel and also getting a higher throughput reduces the need to implement fragment cloning methods, where extra copies of genes are produced. The methodology of sequencing a large number of DNA strands in parallel is known as Next Generation Sequencing technique. An overview of how different sequencing methods work is described. Selection of two sequencing methods, Sanger Sequencing method and Next generation sequencing method and analysis of the parameters used in both these techniques. A Comparative study of these two methods is carried out accordingly. An overview of when to use Sanger sequencing and when to use Next generation sequencing is described. Increase in the amount of genomic data has given rise to challenges like sharing, integrating and analyzing the genetic data. Therefore, application of one of the big data techniques known as Map Reduce model is used to sequence the genetic data. A flow chart of how genetic is processed using MapReduce model is also present. Next Generation Sequencing for analysis of huge amount of genetic data is very useful but it has few limitations such as scaling and efficiency. Fortunately recent researches have proven that these demerits of Next Generation Sequencing can be easily overcome by implementing big data methodologies. Chinmayee C | Amrita Nischal | C R Manjunath | Soumya K N"Next Generation Sequencing in Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12975.pdf http://www.ijtsrd.com/computer-science/bioinformatics/12975/next-generation-sequencing-in-big-data/chinmayee-c
Lrg and mane 16 oct 2018
The Locus Reference Genomic (LRG) project provides stable reference sequences for reporting clinically-relevant genetic variants. It aims to address challenges of keeping track of variants over time as genomes and transcripts change. LRG records are manually curated and contain genomic, transcript, and protein sequences selected by experts that do not change versions. They are used to report variants in a standardized way compatible with HGVS nomenclature. While the LRG project is not scalable due to being fully manual, there is potential convergence with the automated but expert-guided MANE Plus project to cover more transcripts. The LRG project also works independently of the GRCh38 human reference genome, creating records for genes where an alternate sequence is preferred.
Developing tools & Methodologies for the NExt Generation of Genomics & Bio In...
This document discusses computational challenges in analyzing next generation DNA sequencing data and implications for diagnostics and therapeutics. It notes that sequencing one genome now takes just a few days but analysis is the bottleneck. The author's organization, Genomeon, has developed a high performance computing system called SHADOWFAX to analyze over 7,700 genomes. Genomeon is focusing on analyzing repetitive DNA sequences called microsatellites that were previously understudied. They have identified patterns of informative microsatellites that differentiate cancer types like breast cancer from healthy genomes with high sensitivity and specificity. These findings could enable new cancer risk assessment, companion diagnostics, clinical trial stratification, drug targets, and non-cancer applications.
Next generation sequencing in pharmacogenomics
The document discusses the growth of genomic sequence data due to falling costs of DNA sequencing. This creates both a "big data" problem of how to store and analyze large amounts of data, as well as a "diminishing discovery" problem as it becomes harder to find new discoveries within the data. The document proposes several solutions to these problems including pre-competitive collaboration between organizations to share data and analytics platforms. It provides examples of existing data sharing platforms like tranSMART and describes how next generation sequencing is revealing different types of human genetic variation including single nucleotide polymorphisms and their role in pharmacogenomics.
Molecular insight into Gene Expression Using Digital RNAseq: Digital RNAseq W...
Gene expression profiling is the key to understanding biological pathways and complex cellular systems. In this webinar we will discuss the challenges of targeted RNA-seq data analysis and present the solutions provided by the QIAGEN automated online data analysis tools. Using raw sequencing data from targeted sequencing, the output of the QIAseq primary data analysis tool and the options in QIAseq secondary analysis, such as normalization strategies, will be described. The use of Ingenuity Pathway Analysis (IPA) to unlock the molecular insights buried in experimental data by quickly identifying relationships, mechanisms, functions, and pathways of relevance will be shown with an example.
10.1.1.80.2149
This document discusses optimal tiling algorithms for selecting genomic DNA fragments for applications such as microarray design and homology searching. It defines several tiling problems involving finding the maximum weighted set of tiles (sequence fragments) within certain size bounds from a given genomic sequence. Typical parameter values are provided for applications involving sequencing lengths up to 3.4GB, tile sizes from 200bp to 1.5kb, and allowing overlaps of up to 100bp for homology searching. Efficient algorithms are sought with linear or near-linear runtimes to solve these tiling problems.
Ngs part ii 2013
This document discusses next-generation sequencing (NGS) and its role in cancer research. It begins with an introduction to NGS technology and applications. The speaker then discusses how NGS can help with cancer research by identifying new cancer genes and mutations, assisting with diagnosis and targeted therapies, and enabling patient stratification. The need for NGS is explained by discussing cancer genetics, statistics on cancer incidence, and the need to identify driver mutations and develop personalized treatments. Qiagen offers an end-to-end NGS workflow and solutions including sample preparation, target enrichment, library preparation, sequencing, and data analysis. Their targeted gene panels and analysis software provide a streamlined sample-to-result experience for cancer research.
Sophie F. summer Poster Final
This study demonstrates the utility of using Next Generation Sequencing (NGS) technology and DNA analysis to identify and analyze closely related insect species and populations. The researchers sequenced DNA from two mitochondrial genes and a nuclear gene from individuals of two closely related fly species, Bactrocera philippinensis and B. occipitalis. They obtained overlapping sequences from these genes that could be assembled into full gene sequences. Their goal is to ultimately sequence the entire genome of multiple individuals to better characterize populations and species through comparative genomic analysis. DNA-based methods provide advantages over traditional taxonomy by requiring less material and being consistent across life stages.
Rna seq
This document provides an overview of RNA-seq analysis using the T-BioInfo platform. It describes analyzing RNA-seq data from breast cancer patient-derived xenograft models to identify differences between cancer subtypes and mouse models. The analysis includes mapping reads, quantifying gene and isoform expression, normalizing data, performing PCA, and identifying biomarker genes for breast cancer subtypes using factor regression analysis. The goal is to gain insights into cancer biology and identify diagnostic or therapeutic targets.
Next generation sequencing
This document discusses next generation sequencing technologies. It provides details on several massively parallel sequencing platforms and describes their advantages over traditional Sanger sequencing such as higher throughput, lower costs, and ability to process millions of reads in parallel. It then outlines several applications of next generation sequencing like mutation discovery, transcriptome analysis, metagenomics, epigenetics research and discovery of non-coding RNAs.
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Analytical Study of Hexapod miRNAs using Phylogenetic Methods
MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene expression.
Identification of total number of miRNAs even in completely sequenced organisms is still an
open problem. However, researchers have been using techniques that can predict limited
number of miRNA in an organism. In this paper, we have used homology based approach for
comparative analysis of miRNA of hexapoda group .We have used Apis mellifera, Bombyx
mori, Anopholes gambiae and Drosophila melanogaster miRNA datasets from miRBase
repository. We have done pair wise as well as multiple alignments for the available miRNAs in
the repository to identify and analyse conserved regions among related species. Unfortunately,
to the best of our knowledge, miRNA related literature does not provide in depth analysis of
hexapods. We have made an attempt to derive the commonality among the miRNAs and to
identify the conserved regions which are still not available in miRNA repositories. The results
are good approximation with a small number of mismatches. However, they are encouraging and may facilitate miRNA biogenesis for hexapods.
Bio Informatics
This document discusses bioinformatics and some of its key techniques and uses. It introduces bioinformatics as a field that merges biology, computer science, and information technology to manage and analyze biological data using advanced computing. Some techniques discussed include phylogenetics, proteomics, sequence analysis, structure determination, and gene expression analysis. Recombinant DNA technology and cloning are also summarized, including the process of recombining DNA from different species and the various types of human cloning.
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
RT-PCR and DNA microarray measurement of mRNA cell proliferation
For mRNA quantification, RT-PCR and DNA microarrays have been compared in few studies
(RT-PCR). Healing callus of adult and juvenile rats after femur injury was found to be rich in mRNA at
various stages of the healing process. We used both methods to examine ten samples and a total of 26 genes.
Internal DNA probes tagged with 32P were employed in reverse transcription-polymerase chain reaction
(RT-PCR) to identify genes (RT-PCR). Ten Affymetrix® Rat U34A cRNA microarrays were hybridized with
biotin-labeled cRNA generated from mRNA. There was a wide range of correlation coefficients (r) between
RT-PCR and microarray data for each gene. Meaning became genetically unique because of this diversity.
Relatively lowly expressed genes had the highest r values. The distance between PCR primers and
microarray probes was found to be higher than previously assumed, leading to a drop in agreement between
microarray calls and PCR outcomes. Microarray research showed that RT-PCR expression levels for two
genes had a "floor effect." As a result, PCR primers and microarray probes that overlap in mRNA expression
levels can provide good agreement between these two techniques.
SPIN Workshop Microbial Genomics @NIST
This document discusses NIST's work in developing genomic reference materials and methods to evaluate microbial genomics measurements. It describes three projects: 1) assessing genomic purity by detecting low levels of contaminants using sequencing and classification, 2) evaluating SNP calling methods using reference materials and replicates to establish confidence, and 3) developing characterized genomic reference materials for public health pathogens. The overall aim is to build an infrastructure to support genome-based characterization of microbial samples.
Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
"Development of FDA MicroDB: A Regulatory-Grade
Microbial Reference Database" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Heike Sichtig, PhD from the FDA and Luke Tallon from IGS UMSOM.
Poster ESHG
- The study provides a cost analysis of three next-generation sequencing applications: targeted gene panels (TGP), whole exome sequencing (WES), and whole genome sequencing (WGS). It finds per-sample costs of €333 for TGP, €792 for WES, and €1,669 for WGS.
- Costs are mainly driven by consumables such as sequencing and sample preparation. The estimated $1,000 genome has not been achieved, though it may be approached under best-case assumptions of long-term, efficient use of equipment and considerable cost reductions.
- The choice of sequencing approach in clinical practice should consider both costs and clinical effectiveness, not just costs alone.
Data analysis & integration challenges in genomics
Presentation given at the Genomics Today and Tomorrow event in Uppsala, Sweden, 19 March 2015. (http://connectuppsala.se/events/genomics-today-and-tomorrow/) Topics include APIs, "querying by data set", machine learning.
Next Generation Sequencing application in virology
Next Generation Sequencing (NGS) is a promising technique for virus diagnosis that provides several advantages over traditional Sanger sequencing. NGS workflows involve sample preparation, sequencing, and data analysis. NGS has various applications in virology including identifying viral quasispecies, detecting antiviral drug resistance mutations, discovering novel virus genotypes, and performing quality control of live vaccines. While NGS reduces costs and improves throughput over Sanger sequencing, analyzing large NGS datasets requires strong bioinformatics skills. Overall, NGS represents a significant improvement for virus research and diagnosis.
Bioinformatics Final Report
The document provides information about bioinformatics and BLAST (Basic Local Alignment Search Tool). It defines bioinformatics as the application of information technology to molecular biology. It describes what BLAST is and how it works to compare biological sequences and identify similar sequences in databases. It also lists different BLAST programs and databases that can be used depending on the type of sequence being searched.
Illumina-General-Overview-Q1-17
This document is a presentation from Illumina about genomics and their company. It discusses how genome sequencing can provide health insights, their progress in clinical adoption and research use, and their mission to improve human health through genomics. It provides details on Illumina's background, products, markets served, and goals to accelerate genomic adoption and establish new businesses in cancer screening and consumer genomics.
The Transforming Genetic Medicine Initiative (TGMI)
Presentation by Nazneen Rahman at the GRC workshop held at the 2016 Genome Informatics meeting at Hinxton, UK
Next Generation Sequencing in Big Data
A huge revolution has taken place in the area of Genomic science. Sequencing of millions of DNA strands in parallel and also getting a higher throughput reduces the need to implement fragment cloning methods, where extra copies of genes are produced. The methodology of sequencing a large number of DNA strands in parallel is known as Next Generation Sequencing technique. An overview of how different sequencing methods work is described. Selection of two sequencing methods, Sanger Sequencing method and Next generation sequencing method and analysis of the parameters used in both these techniques. A Comparative study of these two methods is carried out accordingly. An overview of when to use Sanger sequencing and when to use Next generation sequencing is described. Increase in the amount of genomic data has given rise to challenges like sharing, integrating and analyzing the genetic data. Therefore, application of one of the big data techniques known as Map Reduce model is used to sequence the genetic data. A flow chart of how genetic is processed using MapReduce model is also present. Next Generation Sequencing for analysis of huge amount of genetic data is very useful but it has few limitations such as scaling and efficiency. Fortunately recent researches have proven that these demerits of Next Generation Sequencing can be easily overcome by implementing big data methodologies. Chinmayee C | Amrita Nischal | C R Manjunath | Soumya K N"Next Generation Sequencing in Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12975.pdf http://www.ijtsrd.com/computer-science/bioinformatics/12975/next-generation-sequencing-in-big-data/chinmayee-c
Lrg and mane 16 oct 2018
The Locus Reference Genomic (LRG) project provides stable reference sequences for reporting clinically-relevant genetic variants. It aims to address challenges of keeping track of variants over time as genomes and transcripts change. LRG records are manually curated and contain genomic, transcript, and protein sequences selected by experts that do not change versions. They are used to report variants in a standardized way compatible with HGVS nomenclature. While the LRG project is not scalable due to being fully manual, there is potential convergence with the automated but expert-guided MANE Plus project to cover more transcripts. The LRG project also works independently of the GRCh38 human reference genome, creating records for genes where an alternate sequence is preferred.
Developing tools & Methodologies for the NExt Generation of Genomics & Bio In...
This document discusses computational challenges in analyzing next generation DNA sequencing data and implications for diagnostics and therapeutics. It notes that sequencing one genome now takes just a few days but analysis is the bottleneck. The author's organization, Genomeon, has developed a high performance computing system called SHADOWFAX to analyze over 7,700 genomes. Genomeon is focusing on analyzing repetitive DNA sequences called microsatellites that were previously understudied. They have identified patterns of informative microsatellites that differentiate cancer types like breast cancer from healthy genomes with high sensitivity and specificity. These findings could enable new cancer risk assessment, companion diagnostics, clinical trial stratification, drug targets, and non-cancer applications.
Next generation sequencing in pharmacogenomics
The document discusses the growth of genomic sequence data due to falling costs of DNA sequencing. This creates both a "big data" problem of how to store and analyze large amounts of data, as well as a "diminishing discovery" problem as it becomes harder to find new discoveries within the data. The document proposes several solutions to these problems including pre-competitive collaboration between organizations to share data and analytics platforms. It provides examples of existing data sharing platforms like tranSMART and describes how next generation sequencing is revealing different types of human genetic variation including single nucleotide polymorphisms and their role in pharmacogenomics.
Molecular insight into Gene Expression Using Digital RNAseq: Digital RNAseq W...
Gene expression profiling is the key to understanding biological pathways and complex cellular systems. In this webinar we will discuss the challenges of targeted RNA-seq data analysis and present the solutions provided by the QIAGEN automated online data analysis tools. Using raw sequencing data from targeted sequencing, the output of the QIAseq primary data analysis tool and the options in QIAseq secondary analysis, such as normalization strategies, will be described. The use of Ingenuity Pathway Analysis (IPA) to unlock the molecular insights buried in experimental data by quickly identifying relationships, mechanisms, functions, and pathways of relevance will be shown with an example.
10.1.1.80.2149
This document discusses optimal tiling algorithms for selecting genomic DNA fragments for applications such as microarray design and homology searching. It defines several tiling problems involving finding the maximum weighted set of tiles (sequence fragments) within certain size bounds from a given genomic sequence. Typical parameter values are provided for applications involving sequencing lengths up to 3.4GB, tile sizes from 200bp to 1.5kb, and allowing overlaps of up to 100bp for homology searching. Efficient algorithms are sought with linear or near-linear runtimes to solve these tiling problems.
Ngs part ii 2013
This document discusses next-generation sequencing (NGS) and its role in cancer research. It begins with an introduction to NGS technology and applications. The speaker then discusses how NGS can help with cancer research by identifying new cancer genes and mutations, assisting with diagnosis and targeted therapies, and enabling patient stratification. The need for NGS is explained by discussing cancer genetics, statistics on cancer incidence, and the need to identify driver mutations and develop personalized treatments. Qiagen offers an end-to-end NGS workflow and solutions including sample preparation, target enrichment, library preparation, sequencing, and data analysis. Their targeted gene panels and analysis software provide a streamlined sample-to-result experience for cancer research.
Sophie F. summer Poster Final
This study demonstrates the utility of using Next Generation Sequencing (NGS) technology and DNA analysis to identify and analyze closely related insect species and populations. The researchers sequenced DNA from two mitochondrial genes and a nuclear gene from individuals of two closely related fly species, Bactrocera philippinensis and B. occipitalis. They obtained overlapping sequences from these genes that could be assembled into full gene sequences. Their goal is to ultimately sequence the entire genome of multiple individuals to better characterize populations and species through comparative genomic analysis. DNA-based methods provide advantages over traditional taxonomy by requiring less material and being consistent across life stages.
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RT-PCR and DNA microarray measurement of mRNA cell proliferation
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Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
Data analysis & integration challenges in genomics
Data analysis & integration challenges in genomics
Next Generation Sequencing application in virology
Next Generation Sequencing application in virology
The Transforming Genetic Medicine Initiative (TGMI)
The Transforming Genetic Medicine Initiative (TGMI)
Developing tools & Methodologies for the NExt Generation of Genomics & Bio In...
Developing tools & Methodologies for the NExt Generation of Genomics & Bio In...
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Molecular insight into Gene Expression Using Digital RNAseq: Digital RNAseq W...
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This document provides an overview of RNA-seq analysis using the T-BioInfo platform. It describes analyzing RNA-seq data from breast cancer patient-derived xenograft models to identify differences between cancer subtypes and mouse models. The analysis includes mapping reads, quantifying gene and isoform expression, normalizing data, performing PCA, and identifying biomarker genes for breast cancer subtypes using factor regression analysis. The goal is to gain insights into cancer biology and identify diagnostic or therapeutic targets.
Next generation sequencing
This document discusses next generation sequencing technologies. It provides details on several massively parallel sequencing platforms and describes their advantages over traditional Sanger sequencing such as higher throughput, lower costs, and ability to process millions of reads in parallel. It then outlines several applications of next generation sequencing like mutation discovery, transcriptome analysis, metagenomics, epigenetics research and discovery of non-coding RNAs.
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Clinical Analysis of Long Non-coding RNA (LncRNA): Therapeutic Targeting of T...
Clinical Analysis of Long Non-coding RNA (LncRNA): Therapeutic Targeting of T...SSR Institute of International Journal of Life Sciences
ABSTRACT- Long non-coding RNAs (lncRNAs) are a group of longer than 200 nucleotides which are the largest and more diverse transcripts in the cells. After study from Functional Annotation of Mammalian cDNA, lncRNAs demonstrated some special characteristics such as lower quantity, higher tissue-specificity, higher stage specificity and higher cell subtype specificity. The current evidence from tumor diseases suggests that lncRNAs are an important regulatory RNA present at tumor cells, and therefore their alterations are associated with tumorigenesis and tumor diseases. Here we presented a clinical landscape of lncRNA including detection of lncRNA and their clinical application such as diagnosis biomarkers and therapeutic targets. We also discussed the challenges and resolving strategies for these clinical applications.
Key-words- Long non-coding RNA (lncRNA), Transcripts, sampling, Tumor and tumorigenesis
Transcriptomics approaches
The study of the complete set of RNAs (transcriptome) encoded by the genome of a specific cell or organism at a specific time or under a specific set of conditions is called Transcriptomics.
Transcriptomics aims:
I. To catalogue all species of transcripts, including mRNAs, noncoding RNAs and small RNAs.
II. To determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications.
III. To quantify the changing expression levels of each transcript during development and under different conditions.
Total RNA Discovery for RNA Biomarker Development Webinar
Precision medicine offers to transform patient care by targeting treatment to those with most to gain. To date the most significant advances have been at the level of DNA, for example, the use of somatic DNA alterations as diagnostic indicators of disease and for prediction of pharmacodynamic response. Development of RNA expression signatures as biomarkers has been more problematic. While RNA expression analysis has yielded valuable insights into the biological mechanisms of disease, RNA is a more unstable molecule than DNA, and more easily damaged or degraded during sample collection and isolation. In addition, RNA levels are inherently dynamic and gene expression signatures are extraordinarily complex. Recently, much progress has been made in identifying key changes in gene expression in cancer and other diseases, as well as identifying expression signatures in circulating nucleic acid that have the potential to be developed into diagnostic and prognostic indicators.
Targeted RNAseq for Gene Expression Using Unique Molecular Indexes (UMIs): In...
Traditional RNA sequencing (RNA-Seq) is a powerful tool for expression profiling, but is hindered by PCR amplification bias and inaccuracy at low expressing genes. QIAseq RNA is a flexible and precise tool developed for mitigating these complications, allowing digital gene expression analysis. This in-depth webinar will cover sample requirements, experimental design, NGS platform-specific challenges and workflow for gene enrichment, library prep and sequencing. The applications of QIASeq RNA Panels in cancer research, stem cell differentiation and elucidating the effects small molecules on signaling pathways will be highlighted.
RNA Sequencing Research
Single cell RNA-seq was performed on 18 mouse bone marrow dendritic cells. 982 genes were found to be differentially expressed between two cells, while the majority of genes showed similar expression levels. Future work will analyze the functions of differentially expressed genes to better understand heterogeneity between cells and potential roles in disease.
Towards Precision Medicine: Tute Genomics, a cloud-based application for anal...
Tute Genomics is cloud-based software that can rapidly analyze entire human genomes. The cost of whole genome sequencing is dropping rapidly and we are in the middle of a genomic revolution. Tute is opening a new door for personalized medicine by helping researchers & healthcare organizations analyze human genomes.
Bacterial rna sequencing
CD Genomics provides a fast, one-stop bacterial RNA sequencing solution from the quality control of sample to comprehensive data analysis. Please contact us for more information and a detailed quote.
Dna microarray mehran- u of toronto
DNA microarrays allow for the high-throughput analysis of differential gene expression. They work by hybridizing fluorescently-labeled cDNA from experimental and control RNA samples to a large number of gene sequences spotted on a glass slide. After hybridization, scanned images are analyzed to determine differences in gene expression levels between the two samples. While a powerful tool, microarray results often require confirmation through low-throughput methods like quantitative RT-PCR due to the risk of false positives. Studies have used microarrays to identify genes involved in atherosclerosis, response to oxidized LDL, and effects of shear stress on endothelial cells.
Microarray @ujjwal sirohi
A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface.
The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs.
ASHG 2015 - Redundant Annotations in Tertiary Analysis
After obtaining genetic variants from next generation sequencing data, a precursory step in tertiary analysis is to annotate each variant with available relevant information. There is no standardized compendium for this purpose; researchers instead are required to compile data from a motley of annotation tools and public datasets. These sources for annotation are independently maintained, and accordingly there is limited concordance between their reported contents. The choice of annotation datasets thus has a direct and significant impact on the results of the analysis.
R Gene Expression and Transcription Profiling
R Gene expression and transcription profiling in plants. Incorporating gene resistance and its transcription database.
Silencing RNA techniques
This document discusses RNA interference (RNAi) techniques such as silencing RNA and CRISPR/Cas9. It explains that double-stranded RNA is cut by the enzyme Dicer into short interfering RNAs (siRNAs) that can degrade mRNA strands in a highly specific process. RNAi is involved in regulating 30% of the human genome and acts as a defense mechanism against viruses and transposons. The document also discusses selecting effective siRNAs, considerations for species variation and secondary RNA structures, and strategies for gene knockdown screening using shRNA and CRISPR/Cas9.
Acc 2002 mehran for print
This document discusses the use of DNA microarrays in researching vulnerable plaque. DNA microarrays allow high-throughput analysis of gene expression and have opened doors to exploring unknown molecular mechanisms. The author's research group is conducting genomic and proteomic experiments on human atherosclerotic plaques to shed light on the molecular mechanisms involved in atherosclerosis development and vulnerability. Proteomic analysis provides insights not available through genomics alone. Understanding these molecular processes could lead to better understanding of vulnerable plaque development and complications.
Acc 2002 mehran for print
This document discusses the use of DNA microarrays in studying vulnerable atherosclerotic plaques. It provides background on atherosclerosis and plaque rupture. DNA microarrays allow high-throughput analysis of gene and protein expression, which can provide insights into molecular mechanisms underlying plaque vulnerability. One study used microarrays to analyze gene expression differences between ruptured and stable plaques, identifying perilipin as upregulated in ruptured plaques. However, microarray analysis of atherosclerosis is still in its early stages with many technical challenges to address.
Dna microarray application in vp research mehran
This document discusses the use of DNA microarrays in researching vulnerable plaque. DNA microarrays allow high-throughput analysis of gene expression and have opened doors to exploring unknown molecular mechanisms. The author's research group is conducting genomic and proteomic experiments on human atherosclerotic plaques to shed light on the molecular mechanisms involved in atherosclerosis development and vulnerability. They are examining differential gene and protein expression between ruptured and stable plaques using various techniques including laser capture microdissection. The goal is to gain a better understanding of the molecular processes leading to vulnerable plaques and their complications.
PROKARYOTIC TRANSCRIPTOMICS AND METAGENOMICS
After billions of years of evolution, prokaryotes have developed a huge diversity of regulatory mechanisms, many of which are probably uncharacterized. Now that the powerful tool of whole-transcriptome analysis can be used to study the RNA of bacteria and archaea, a new set of un expected RNA-based regulatory strategies might be revealed.
Metagenomics, together with in vitro evolution and high-throughput screening technologies, provides industry with an unprecedented chance to bring biomolecules into industrial application.
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Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
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From Expression to Pathways Using Online Tools
- 1. From Expression to Pathways & Drugs Using Online Tools Ali Kishk Research Assistant, Nile University
- 3. Content 1. Microarray Types 2. Microarray Vs RNA-Seq 3. Transcriptomic Database 4. Network Vs Enrichment Vs Pathway 5. Connectivity Map 6. GEO2Enrichr
- 4. Why Transcriptomics? 1. Why Transcription? 2. Why OMICs?
- 6. Microarray Types Type Application Gene expression profiling Expression Level SNP Array Population SNPs Exon Array Alternative Splicing Chromosomal microarray Copy Number Vraiation (CNV) Fusion genes microarray Cancer
- 7. RNA-Seq Microarray Cost High Low Noise Low High Standards Experimental Established Data Size In GBs In MBs New Organisms Yes No Novel transcripts Yes No Low Abundance Sensitivity Yes No RNA-Seq Vs Expression Microarray
- 8. RNA-Seq in 3 words - Sensitivity - Reproducibility - Discovery
- 9. Microarray Databases ● GEO Datasets ● GEO Profiles RNA-Seq Databases ● GEO Datasets ● GEO Profiles ● SRA
- 11. Microarray Platform Record Stable GEO accession number (GPLxxx). Important for Pathway Analysis Platform ID Platform Title for DAVID
- 12. Network Vs Enrichment Vs Pathway Network Analysis Enrichment Analysis Pathway Analysis Databases - Protein Protein Interaction (STRING) - Co-Expression - Pathways (GO, Reactome, WikiPathway) - MicroRNA (TargetScan) - Protein Families (Pfam) - Protein Motifs (MEME) -Pathway Question What are the Hub genes / proteins in my gene list? What are the important MicroRNA that affects my gene list? What are the Pathways MicroRNA that affects my gene list?
- 13. Practical 1: Microarray Pathway Analysis Paper: Pezzulo, Alejandro A., et al. "HSP90 inhibitor geldanamycin reverts IL-13– and IL-17–induced airway goblet cell metaplasia." The Journal of clinical investigation 129.2 (2019). Tools: GEO2Enrichr : Microarray Analysis DAVID : Enrichment Analysis
- 26. Platform Title
- 43. LINCS: Library of Integrated Network-based Cellular Signatures For ~20 K drugs on ~77 cancer cell line Total: ~1.3 Million transcriptomic profiles.
- 44. Why do we need Connectivity Map * Many drugs * Many genes / transcripts Assumption: ~1000 genes predicts ~20,000 expression.
- 45. L1000 Vs RNA-Seq 3,176 Samples from GTeX were applied to L1000 Result : 86% correlation RNA-Seq L1000 Cost ~1k USD 2 USD for eagents Transcripts ~ 20 k ~ 1k Computational Analysis intensive simple
- 46. LINCs data types 1- Drug Perturbation 2- Down-regulation: shRNA 3- Over-expression: cDNA 4- Knockout: CRISPR
- 47. Disease-Drug Image source: doi: 10.1016/j.cell.2015.05.011.
- 48. Target-Drug Discovery of a selective CSNK1A1 inhibitor: CSNK1A1 mutation in : ● myelodysplastic syndrome ● acute myeloid leukemia No selective inhibitor Solution: ● shRNA ● A candidate drug , strong enzymatic activity
- 49. Other applications Side effect prediction Repurposing FDA approved drugs (Polypharmacology)
- 50. Practical 2: Drug Expression Similarity Tools: L1000CDS2
- 54. RNA-Seq Analysis Steps 1- Quality Control 2- Filtration & Trimming 3- Merging for Paired End 4- Alignment 5- Differential Expression Analysis 6- Network / Pathway Analysis There other pipelines
- 55. RNA-Seq File Formats FastQ files Differential Expression TSV, CSV, TXT
- 56. GTeX Genotype-Tissue Expression Expression profiles + SNPs: All tissues in ~2000 post-mortem patients Image source: doi:10.1038/nature24277
- 74. Conclusion 1. Pathway & Enrichment are great tools for Biological Interpretation 1. Connectivity map is the biggest transcriptomic database 1. Connectivity map can be used in drug repurposing, side effect prediction. 1. Connectivity map can extend its prediction on other organisms.