The document summarizes integrating genome and proteome data from Francisella tularensis in RDF. It discusses integrating data from multiple sources, including genome annotations, proteomics experiments, and transcriptomics data. Semantic data integration across "omes" data silos is demonstrated using RDF and the open source Sesame framework. Reifying biological statements, such as identified peptides and abundances, allows more complex queries across the integrated data.
The document summarizes several recent biotechnology innovations, including using oil-eating bacteria to clean up oil spills, using a protein called GDF 11 to improve aging brains and muscles in mice, developing advanced biofuels from cellulosic biomass, using 3D x-ray filming to study insect movements, discovering anti-psychotic drugs that kill brain cancer, developing affordable genome sequencing technology, engineering immune cells to attack cancer, creating RNA detection probes without harming cells, and assessing monoclonal antibody therapies using ADCC reporter assays.
Here are the festivals that complete the fragments:
1. La Tomatina (Tomato Festival)
2. Día de los Muertos (Day of the Dead)
3. Battaglia delle Arance (Orange Battle)
4. Songkran (Thai New Year)
How Waves of Innovation in Biotechnology Shaped a Small Business VentureChristopher Kemp
I recently had the opportunity to visit a multi-cultural STEM high school this past December. Here is my talk/deck for the student body of PRISMS (Princeton International School of Mathematics and Science).
Innovation and entrepreneurship in biotechnology an intl perspective - d. h...sanguru1977
This document provides an introduction to innovation and entrepreneurship in biotechnology. It defines innovation as new products, services, processes or ideas that are novel to an organization. Innovation can take various forms, including technological versus non-technological, and product versus process innovation. The document scopes the focus of the book, which is on entrepreneurship and innovation processes that are important for new biotechnology firms. It emphasizes the importance of innovation and entrepreneurship for competitiveness in the biotechnology industry.
The document discusses interval position analysis (IPA), a method for analyzing DNA and RNA sequences. IPA calculates characteristics such as V, G, and g values based on the distances between similar elements in a sequence. The values of IPA characteristics are sensitive to the order of elements in a sequence. IPA can be used to construct phylogenetic trees and analyze local profiles of RNA sequences. Heap's law and rank distribution models are also discussed in relation to evaluating DNA segmentation.
The document discusses DNA sequencing software. It describes a fast and accurate DNA sequencing assembly software for Windows that can assemble DNA sequences into contigs and directly compare trace data to nucleotide data. It handles over 100,000 samples from various sequence and file formats and accelerates proofreading and comparing nucleotides to trace peaks. Several other related DNA sequencing software are also mentioned such as DNA DYNAMO, DNA MASTER, and Mesto DNA program starter.
The document summarizes several recent biotechnology innovations, including using oil-eating bacteria to clean up oil spills, using a protein called GDF 11 to improve aging brains and muscles in mice, developing advanced biofuels from cellulosic biomass, using 3D x-ray filming to study insect movements, discovering anti-psychotic drugs that kill brain cancer, developing affordable genome sequencing technology, engineering immune cells to attack cancer, creating RNA detection probes without harming cells, and assessing monoclonal antibody therapies using ADCC reporter assays.
Here are the festivals that complete the fragments:
1. La Tomatina (Tomato Festival)
2. Día de los Muertos (Day of the Dead)
3. Battaglia delle Arance (Orange Battle)
4. Songkran (Thai New Year)
How Waves of Innovation in Biotechnology Shaped a Small Business VentureChristopher Kemp
I recently had the opportunity to visit a multi-cultural STEM high school this past December. Here is my talk/deck for the student body of PRISMS (Princeton International School of Mathematics and Science).
Innovation and entrepreneurship in biotechnology an intl perspective - d. h...sanguru1977
This document provides an introduction to innovation and entrepreneurship in biotechnology. It defines innovation as new products, services, processes or ideas that are novel to an organization. Innovation can take various forms, including technological versus non-technological, and product versus process innovation. The document scopes the focus of the book, which is on entrepreneurship and innovation processes that are important for new biotechnology firms. It emphasizes the importance of innovation and entrepreneurship for competitiveness in the biotechnology industry.
The document discusses interval position analysis (IPA), a method for analyzing DNA and RNA sequences. IPA calculates characteristics such as V, G, and g values based on the distances between similar elements in a sequence. The values of IPA characteristics are sensitive to the order of elements in a sequence. IPA can be used to construct phylogenetic trees and analyze local profiles of RNA sequences. Heap's law and rank distribution models are also discussed in relation to evaluating DNA segmentation.
The document discusses DNA sequencing software. It describes a fast and accurate DNA sequencing assembly software for Windows that can assemble DNA sequences into contigs and directly compare trace data to nucleotide data. It handles over 100,000 samples from various sequence and file formats and accelerates proofreading and comparing nucleotides to trace peaks. Several other related DNA sequencing software are also mentioned such as DNA DYNAMO, DNA MASTER, and Mesto DNA program starter.
The document discusses two bioinformatics software tools: DNA Baser and Darwin. DNA Baser is a tool for manual and automatic DNA sequence assembly, analysis, editing, and more. It allows for automation of sequence assembly through functions like end trimming, vector removal, and batch assembly of thousands of sequences. Darwin is an interpreted computer language for research in bioscience that provides libraries and functions for tasks like sequence comparison, alignment, phylogenetics, and more.
this is the project regarding the detection and analysis of DNA sequences,it provide the fascility to find the repets from the hudge data set.we can find tha all repeats which is occured in human body.
Genome assembly: then and now (with notes) — v1.2Keith Bradnam
This was a talk given on 2014-09-17 for the Genome Center’s Bioinformatics Core as part of a 1 week workshop. It concerns the Assemblathon projects as well as other aspects relating to genome assembly.
A version of this talk is also available on Slideshare without notes.
Note, this is an evolving talk. There are older and newer versions of the talk also available on slideshare.
DNA testing has become the "gold standard" of forensics, but linking an item of evidence to a person of interest isn't always clear cut. New open source tools allow DNA analysts to give statistical weight to evidentiary profiles that were previously unusable, letting juries weigh the evidence for themselves. This talk will discuss the Lab Retriever software package for probabilistic genotyping.
The document discusses analysis of DNA microarray data using various techniques including gene-based, gene set, and functional group approaches. It describes preprocessing methods, platforms like Affymetrix, and tools for analysis including LIMMA and GSEA. Applications mentioned include biomarker discovery, clinical outcomes, and regulatory network analysis.
A I Macan Markar & Co is a chartered accountancy firm founded in 1946 in Sri Lanka. It has 4 partners and provides auditing, taxation, management consulting, and IT services to clients across various industries. The firm also has associated companies that offer secretarial services, management consulting, and legal advice.
ubio is starting a series of biology tutorials aimed at introducing biology, biotechnology and bioinformatics to computer engineers. The first part of the presentation is essentially a biochemistry tutorial that introduces molecular biochemistry.
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...CIAT
The document summarizes efforts to develop common bean varieties in Rwanda resistant to Bean Common Mosaic Necrotic Virus (BCMNV) using Marker Assisted Selection (MAS). Researchers screened 219 bean varieties and identified genes conferring resistance. They developed 86 breeding lines by crossing donor lines containing resistance genes with local varieties. These lines were selected using linked markers and for resistance to BCMNV and other diseases. Participatory plant breeding involved farmers in selection. The integration of conventional breeding and MAS was successful in pyramiding resistance genes and developing lines adapted to Rwanda.
- DNA molecules are very long and consist of millions of base pairs. To study their structure, restriction enzymes are used to cut the DNA molecules into smaller, easier to analyze fragments at specific recognition sites.
- The fragments produced can be separated by gel electrophoresis based on their size, with shorter fragments traveling farther through the gel. This produces a pattern called a genetic fingerprint that can be used for applications like genetic profiling in criminal cases.
- The human genome project aimed to map all human genes by determining the full DNA sequence. While about 3% of human DNA codes for proteins, other non-coding "junk DNA" may have undiscovered functions and contains regions of repeated sequences that vary between individuals.
Now days Biotech Era, What is application of biotechnology in Agriculture, Plantation and fertilizer. If we want to Improve qualitative and quantitative of Agri & Plantation then we definitely need of applying Biotechnological application.
Back to Basics: Fundamental Concepts and Special Considerations in gDNA Isola...QIAGEN
This document provides an overview of genomic DNA (gDNA) isolation. It discusses key considerations for gDNA isolation including sample stabilization, disruption, and storage. Common isolation technologies like silica membrane and magnetic bead kits are described. The document reviews measuring gDNA concentration and quality via UV spectroscopy and gel electrophoresis. It also provides guidance on selecting appropriate QIAGEN gDNA isolation kits based on sample type.
This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.
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.
I. The document outlines a proteogenomics course at EMBL-EBI, discussing integrating proteomics and genomics data.
II. It discusses what proteogenomics is, using multi-omics approaches to correlate genomic and proteomic sequence events like mutations and modifications.
III. The talk will cover integrating proteomics data into Ensembl and UCSC trackhubs, as well as tools for proteogenomics analysis.
Introduction to Protein Families and DatabasesRohit Satyam
The presentation highlights the Protein Families concept, methods used to predict them, and some automated servers for annotation of Hypothetical Proteins
This document provides an overview of bioinformatics. It defines bioinformatics as the science of collecting, analyzing and conceptualizing biological data through computational techniques. It discusses that bioinformatics involves managing, organizing and processing biological information from databases, as well as analyzing, visualizing and sharing biological data over the internet. It also outlines some of the goals of bioinformatics like organizing the human and mouse genomes, as well as some applications like genomic and protein sequence analysis, protein structure prediction, and characterizing genomes.
Presentation pathway extensions using knowledge integration and network approaches presented at the Systems Biology Institute in Luxembourg on November 28 2012.
1) AbstractDB & ProteinComplexDB are databases that contain protein complexes extracted from PubMed abstracts along with the abstracts themselves.
2) The databases were developed using a Bayesian classifier to rank abstracts by their relevance to protein complexes based on the frequency of discriminatory words.
3) The databases allow users to validate extracted protein complexes by searching against known complex databases and enable scientists to evaluate and revise the data.
Bioinformatics combines biology, chemistry, statistics, and computer science to analyze and interpret biological data. It uses algorithms and techniques of computer science to solve complex biological problems. Some key areas of bioinformatics include organizing biological knowledge, performing sequence analysis, predicting protein structure, genome annotation, and comparative genomics. Bioinformatics is essential for applications like pharmaceutical research, gene therapy, forensic analysis, and understanding biological pathways and networks in systems biology.
The document discusses two bioinformatics software tools: DNA Baser and Darwin. DNA Baser is a tool for manual and automatic DNA sequence assembly, analysis, editing, and more. It allows for automation of sequence assembly through functions like end trimming, vector removal, and batch assembly of thousands of sequences. Darwin is an interpreted computer language for research in bioscience that provides libraries and functions for tasks like sequence comparison, alignment, phylogenetics, and more.
this is the project regarding the detection and analysis of DNA sequences,it provide the fascility to find the repets from the hudge data set.we can find tha all repeats which is occured in human body.
Genome assembly: then and now (with notes) — v1.2Keith Bradnam
This was a talk given on 2014-09-17 for the Genome Center’s Bioinformatics Core as part of a 1 week workshop. It concerns the Assemblathon projects as well as other aspects relating to genome assembly.
A version of this talk is also available on Slideshare without notes.
Note, this is an evolving talk. There are older and newer versions of the talk also available on slideshare.
DNA testing has become the "gold standard" of forensics, but linking an item of evidence to a person of interest isn't always clear cut. New open source tools allow DNA analysts to give statistical weight to evidentiary profiles that were previously unusable, letting juries weigh the evidence for themselves. This talk will discuss the Lab Retriever software package for probabilistic genotyping.
The document discusses analysis of DNA microarray data using various techniques including gene-based, gene set, and functional group approaches. It describes preprocessing methods, platforms like Affymetrix, and tools for analysis including LIMMA and GSEA. Applications mentioned include biomarker discovery, clinical outcomes, and regulatory network analysis.
A I Macan Markar & Co is a chartered accountancy firm founded in 1946 in Sri Lanka. It has 4 partners and provides auditing, taxation, management consulting, and IT services to clients across various industries. The firm also has associated companies that offer secretarial services, management consulting, and legal advice.
ubio is starting a series of biology tutorials aimed at introducing biology, biotechnology and bioinformatics to computer engineers. The first part of the presentation is essentially a biochemistry tutorial that introduces molecular biochemistry.
Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...CIAT
The document summarizes efforts to develop common bean varieties in Rwanda resistant to Bean Common Mosaic Necrotic Virus (BCMNV) using Marker Assisted Selection (MAS). Researchers screened 219 bean varieties and identified genes conferring resistance. They developed 86 breeding lines by crossing donor lines containing resistance genes with local varieties. These lines were selected using linked markers and for resistance to BCMNV and other diseases. Participatory plant breeding involved farmers in selection. The integration of conventional breeding and MAS was successful in pyramiding resistance genes and developing lines adapted to Rwanda.
- DNA molecules are very long and consist of millions of base pairs. To study their structure, restriction enzymes are used to cut the DNA molecules into smaller, easier to analyze fragments at specific recognition sites.
- The fragments produced can be separated by gel electrophoresis based on their size, with shorter fragments traveling farther through the gel. This produces a pattern called a genetic fingerprint that can be used for applications like genetic profiling in criminal cases.
- The human genome project aimed to map all human genes by determining the full DNA sequence. While about 3% of human DNA codes for proteins, other non-coding "junk DNA" may have undiscovered functions and contains regions of repeated sequences that vary between individuals.
Now days Biotech Era, What is application of biotechnology in Agriculture, Plantation and fertilizer. If we want to Improve qualitative and quantitative of Agri & Plantation then we definitely need of applying Biotechnological application.
Back to Basics: Fundamental Concepts and Special Considerations in gDNA Isola...QIAGEN
This document provides an overview of genomic DNA (gDNA) isolation. It discusses key considerations for gDNA isolation including sample stabilization, disruption, and storage. Common isolation technologies like silica membrane and magnetic bead kits are described. The document reviews measuring gDNA concentration and quality via UV spectroscopy and gel electrophoresis. It also provides guidance on selecting appropriate QIAGEN gDNA isolation kits based on sample type.
This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.
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.
I. The document outlines a proteogenomics course at EMBL-EBI, discussing integrating proteomics and genomics data.
II. It discusses what proteogenomics is, using multi-omics approaches to correlate genomic and proteomic sequence events like mutations and modifications.
III. The talk will cover integrating proteomics data into Ensembl and UCSC trackhubs, as well as tools for proteogenomics analysis.
Introduction to Protein Families and DatabasesRohit Satyam
The presentation highlights the Protein Families concept, methods used to predict them, and some automated servers for annotation of Hypothetical Proteins
This document provides an overview of bioinformatics. It defines bioinformatics as the science of collecting, analyzing and conceptualizing biological data through computational techniques. It discusses that bioinformatics involves managing, organizing and processing biological information from databases, as well as analyzing, visualizing and sharing biological data over the internet. It also outlines some of the goals of bioinformatics like organizing the human and mouse genomes, as well as some applications like genomic and protein sequence analysis, protein structure prediction, and characterizing genomes.
Presentation pathway extensions using knowledge integration and network approaches presented at the Systems Biology Institute in Luxembourg on November 28 2012.
1) AbstractDB & ProteinComplexDB are databases that contain protein complexes extracted from PubMed abstracts along with the abstracts themselves.
2) The databases were developed using a Bayesian classifier to rank abstracts by their relevance to protein complexes based on the frequency of discriminatory words.
3) The databases allow users to validate extracted protein complexes by searching against known complex databases and enable scientists to evaluate and revise the data.
Bioinformatics combines biology, chemistry, statistics, and computer science to analyze and interpret biological data. It uses algorithms and techniques of computer science to solve complex biological problems. Some key areas of bioinformatics include organizing biological knowledge, performing sequence analysis, predicting protein structure, genome annotation, and comparative genomics. Bioinformatics is essential for applications like pharmaceutical research, gene therapy, forensic analysis, and understanding biological pathways and networks in systems biology.
Biological data is widely distributed over the web and can be retrieved using search engines like Google or data retrieval tools. Dedicated data retrieval tools for molecular biologists include Entrez, DBGET, and SRS which allow text searching of linked databases and sequence searching. Entrez, developed by NCBI, integrates information from databases including GenBank, PubMed, and OMIM. DBGET covers databases like GenBank, EMBL, and PDB. SRS, developed by EBI, integrates over 80 molecular biology databases.
This document discusses the importance and benefits of exposing data as linked data. It provides examples of how to link different datasets by assigning common identifiers, unifying classes and properties. Creating unified views of linked data from multiple schemas can make the data easier to query while still maintaining the advantages of linked data. Linked data allows for more powerful queries by connecting related information across different sources.
Using ontologies to do integrative systems biologyChris Evelo
The document discusses using ontologies to integrate systems biology data. It describes typical steps in systems biology studies such as finding studies, processing data, integrating data, and combining data from multiple sources. Ontologies can help link information from different analysis techniques and combine data from many studies by capturing study metadata. The document advocates using standards like ISA-TAB and MAGE-TAB to capture study data and proposes using a generic study capture framework with modular components to integrate different types of 'omics data. Ontologies are needed for collaboration and to provide controlled vocabularies for annotation.
Unison: Enabling easy, rapid, and comprehensive proteomic miningReece Hart
Unison is an online database and data integration platform that aggregates proteomic and genomic data from multiple sources and provides over 200 million precomputed predictions on protein sequences, domains, structures, and more. It aims to enable easy, rapid, and comprehensive proteomic mining through semantic integration of distinct data types and automated querying of predictions. Custom data mining projects using Unison have led to discoveries about proteins like Bcl-2 that regulate apoptosis.
The document discusses using proteomics to develop vaccines. It describes how proteomics can help understand protein interactions for vaccine development. The document then focuses on developing a vaccine for Lassa fever. It outlines computational methods used to analyze the Lassa virus glycoprotein, including determining its structure, domains, and interactions within cells. The goal is to use this analysis to develop a stabilized vaccine candidate against Lassa virus that can protect humans.
IRJET- Disease Identification using Proteins Values and Regulatory ModulesIRJET Journal
1) The document proposes developing a common knowledge base for genomic and proteomic analysis to identify genetic disorders using regulatory modules.
2) It involves using collaborative filtering and depth first search to cluster gene ontology terms and regulatory modules for each gene expression.
3) Finally, a Bayesian rose tree is used to represent the taxonomy for a particular gene ID and identify associated diseases.
High throughput approaches to understanding gene function and mapping archite...Tintumann
The document describes high-throughput approaches used to map gene and protein interactions in bacteria. It discusses reverse genetic techniques like loss-of-function and gain-of-function screens to understand gene phenotypes. Protein-protein interaction mapping methods like bacterial two-hybrid systems, bimolecular fluorescence complementation, and microarrays are described. As an example, a bacterial two-hybrid system was used to map 284 interactions between 155 proteins in Rickettsia sibirica by functionally shotgun sequencing its genome. Key virulence genes were identified through their interactions, providing insight into the pathogen's mechanism.
FANS is a bioinformatics tool that predicts the functional effects of novel single nucleotide polymorphisms (SNPs) and point mutations in humans and mice. It classifies variants into risk levels - very high, high, medium, and very low - based on their potential impact on coding regions, splicing sites, protein domains and protein function. It integrates data from multiple databases and algorithms to analyze the functional consequences of variants.
Research report (alternative splicing, protein structure; retinitis pigmentosa)avalgar
This presentation explains the two major scientific projects I have been involved in.
It extends way further than a CV, but shorter than an actual scientific paper.
Similar to Genome and Proteome data integration in RDF (20)
Spontaneous Bacterial Peritonitis - Pathogenesis , Clinical Features & Manage...Jim Jacob Roy
In this presentation , SBP ( spontaneous bacterial peritonitis ) , which is a common complication in patients with cirrhosis and ascites is described in detail.
The reference for this presentation is Sleisenger and Fordtran's Gastrointestinal and Liver Disease Textbook ( 11th edition ).
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
The biomechanics of running involves the study of the mechanical principles underlying running movements. It includes the analysis of the running gait cycle, which consists of the stance phase (foot contact to push-off) and the swing phase (foot lift-off to next contact). Key aspects include kinematics (joint angles and movements, stride length and frequency) and kinetics (forces involved in running, including ground reaction and muscle forces). Understanding these factors helps in improving running performance, optimizing technique, and preventing injuries.
Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
Are you looking for a long-lasting solution to your missing tooth?
Dental implants are the most common type of method for replacing the missing tooth. Unlike dentures or bridges, implants are surgically placed in the jawbone. In layman’s terms, a dental implant is similar to the natural root of the tooth. It offers a stable foundation for the artificial tooth giving it the look, feel, and function similar to the natural tooth.
Debunking Nutrition Myths: Separating Fact from Fiction"AlexandraDiaz101
In a world overflowing with diet trends and conflicting nutrition advice, it’s easy to get lost in misinformation. This article cuts through the noise to debunk common nutrition myths that may be sabotaging your health goals. From the truth about carbohydrates and fats to the real effects of sugar and artificial sweeteners, we break down what science actually says. Equip yourself with knowledge to make informed decisions about your diet, and learn how to navigate the complexities of modern nutrition with confidence. Say goodbye to food confusion and hello to a healthier you!
Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
The Children are very vulnerable to get affected with respiratory disease.
In our country, the respiratory Disease conditions are consider as major cause for mortality and Morbidity in Child.
1. Semantic Web Applications and Tools for Life Sciences
November 2008
Genome and Proteome data integration in RDF
Nadia Anwar, Ela Hunt, Walter Kolch and Andy Pitt
e Me
ts
tab
nom
Pr
rip
e olit
ot
G es
sc
ein
an
s
Tr
Data Discovery
2. Outline
• Data Integration in Bioinformatics.
• Semantic data integration
• Francisella
• Integrating genome annotations with experimental proteomics data in RDF
• Further work
7. Bioterrorism
• Francisella tularensis is a very successful intracellular pathogen that causes
severe disease (respiratory tulareamia is the most acute form of the disease)
• low infectious dose (10-50 bacterium compared to anthrax which requires
8,000-15,000 spores)
• weaponisation fears
20. Data Integration
Adding new experiments
Experiment Public
2
Experiment domain data
1
PSN rdfs:seeAlso
PSNV2 rdfs:seeAlso
PSNV3 rdfs:seeAlso
FTN
rdfs:seeAlso
Experiment
3 DDBID
Experiment
4
GO AC No. EC
21. Data integration
Sesame
NadiaAnwar:~ nadia$ openrdf-sesame-2.1/bin/console.sh
Connected to default data directory
Commands end with '.' at the end of a line
Type 'help.' for help
> connect http://127.0.0.1:8080/openrdf-sesame/.
Disconnecting from default data directory
Connected to http://127.0.0.1:8080/openrdf-sesame/
> show r.
+----------
|SYSTEM ("System configuration repository")
|ftnRepoNative ("Francisella Test")
|FrancisellaNative ("FrancisellaTestStore")
|FrancisellaReified ("Native store with RDF Schema inferencing")
|FrancisellaReified_index2 ("Native store with RDF Schema inferencing")
|Francisella ("Native store with RDF Schema inferencing")
+----------
> open FrancisellaReified_index2.
Opened repository 'FrancisellaReified_index2'
23. Data Integration
Mgla data (ftnRepoNative)
analysis
Identified Peptide mgla:poson
abundance PSN rdfs:seeAlso
PSNV2 rdfs:seeAlso
PSNV3 rdfs:seeAlso
FTN
rdfs:seeAlso
Experiment
DDBID
Peptide
sequence
SELECT psn, ftn, ec FROM
{ftn} rdfs:seeAlso {ec},
GO SP EC
{psn} rdfs:seeAlso {ftn},
{analysis} mgla:poson {psn}
WHERE ec LIKE “*[EC:*”
USING NAMESPACE
mgla =<http://www.francisella.org/novicida/schema/fnu112/experiments/mgla/>
24. Data Integration
Mgla data (ftnRepoNative)
analysis
rdf:about
Identified Peptide mgla:poson
mgla:sequence
mgla:experiment
abundance PSN rdfs:seeAlso
PSNV2 rdfs:seeAlso
PSNV3 rdfs:seeAlso
FTN
Peptide
sequence rdfs:seeAlso
DDBID
SELECT abundance, psn, ec, ftn FROM
{ftn} rdfs:seeAlso {ec},
{psn} rdfs:seeAlso {ftn}, GO SP EC
{analysis} mgla:poson {psn},
{analysis} mgla:experiment {abundance},
WHERE ec LIKE “*[EC:*”
USING NAMESPACE
mgla =<http://www.francisella.org/novicida/schema/fnu112/experiments/mgla/>
25. Really easy, But....
• Simple excel to RDF conversion does not enable all queries
• Not a simple conversion - Data needs to be “modelled”
analysis
rdf:about
Identified Peptide mgla:poson
mgla:sequence
mgla:experiment
abundance PSN
identifiedIn Experiment
Peptide Peptide Sequence
Replicate
{
sequence
hasAbundance
abundance
26. Data Integration
Reified statements
rdf:type
analysis Identified Peptide
Peptide
sequence
mgla:poson
PSN rdfs:seeAlso
PSNV2 rdfs:seeAlso
PSNV3 rdfs:seeAlso
FTN
Experiment
Replicate
rdfs:seeAlso
t
jec
rd f:ob DDBID
analysis data
rdf:type rdf:Statement
rd
rdf:s
f:
pr
ubje
ct GO SP EC
ed
analysis data
ica
mgla:PeptideAbundance
te
InExperimentReplicate
abundance
31. Reified statements
• Reified mgla data are much bigger (4 more statements/abundance)
• The really interesting queries return Java out of memory error (-Xms-1024M -
Xmx 1536M)
identifiedIn Experiment
Peptide Sequence
Replicate
{
• Haven’t yet tested shortcut path expression
hasAbundance
{ {reifSubj} reifPred {reifObj} } pred {obj}
abundance
{ {seq} identifiedIn {ExpRep} } hasAbundance {abd}
<#WholeCell_Lvl7_02.12> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/1999/02/22-rdf-syntax-ns#Statement>.
<#WholeCell_Lvl7_02.12> <http://www.w3.org/1999/02/22-rdf-syntax-ns#subject> <http:/www.francisella.org/novicida/schema/fnu112/experiments/mgla/WholeCell_Lvl7_02.1>.
<#WholeCell_Lvl7_02.12> <http://www.w3.org/1999/02/22-rdf-syntax-ns#predicate> <http:/www.francisella.org/novicida/schema/fnu112/experiments/mgla/InExperimentReplicate>.
<#WholeCell_Lvl7_02.12> <http://www.w3.org/1999/02/22-rdf-syntax-ns#object> <http:/www.francisella.org/novicida/schema/fnu112/experiments/mgla/wildtype/01_wc_01>.
<#WholeCell_Lvl7_02.12> <http:/www.francisella.org/novicida/schema/fnu112/experiments/mgla/PeptideAbundance> "2594".
32. Comparison of integrated experimental data
Distinct and overlapping posons identified within each biological fraction (>20000)
171 146
185
mem sol
mem MINUS sol sol MINUS mem
select distinct psn from select distinct psn from
{x} fns:poson {psn}, {x} fns:poson {psn},
{x} fn:InExperimentReplicate {experiment}, {x} fn:InExperimentReplicate {experiment},
{analysis} rdf:subject {x}, {analysis} rdf:subject {x},
{analysis} rdf:object {exp}, INTERSECT {analysis} rdf:object {exp},
{analysis} fn:PeptideAbundance {abundance} {analysis} fn:PeptideAbundance {abundance}
select distinct psn from
where xsd:integer(abundance) > 20000 where xsd:integer(abundance) > 20000
{x} fns:poson {psn},
and experiment LIKE "*mem*" and experiment LIKE "*sol*"
{x} fn:InExperimentReplicate {experiment},
MINUS MINUS
{analysis} rdf:subject {x},
select distinct psn from select distinct psn from
{analysis} rdf:object {exp},
{x} fns:poson {psn}, {x} fns:poson {psn},
{analysis} fn:PeptideAbundance {abundance}
{x} fn:InExperimentReplicate {experiment}, {x} fn:InExperimentReplicate {experiment},
where xsd:integer(abundance) > 20000
{analysis} rdf:subject {x}, {analysis} rdf:subject {x},
and experiment LIKE "*sol*"
{analysis} rdf:object {exp}, {analysis} rdf:object {exp},
INTERSECT
{analysis} fn:PeptideAbundance {abundance} {analysis} fn:PeptideAbundance {abundance}
select distinct psn from
where xsd:integer(abundance) > 20000 where xsd:integer(abundance) > 20000
{x} fns:poson {psn},
and experiment LIKE "*sol*" and experiment LIKE "*mem*"
{x} fn:InExperimentReplicate {experiment},
using namespace using namespace
{analysis} rdf:subject {x},
{analysis} rdf:object {exp},
{analysis} fn:PeptideAbundance {abundance}
where xsd:integer(abundance) > 20000
and experiment LIKE "*mem*"
using namespace
33. Comparison of integrated experimental data
Distinct and overlapping posons identified within each biological fraction (<5000)
219 125
245
mem sol
mem MINUS sol sol MINUS mem
select distinct psn from select distinct psn from
{x} fns:poson {psn}, {x} fns:poson {psn},
{x} fn:InExperimentReplicate {experiment}, {x} fn:InExperimentReplicate {experiment},
{analysis} rdf:subject {x}, {analysis} rdf:subject {x},
{analysis} rdf:object {exp}, INTERSECT {analysis} rdf:object {exp},
{analysis} fn:PeptideAbundance {abundance} {analysis} fn:PeptideAbundance {abundance}
select distinct psn from
where xsd:integer(abundance) < 5000 where xsd:integer(abundance) < 5000
{x} fns:poson {psn},
and experiment LIKE "*mem*" and experiment LIKE "*sol*"
{x} fn:InExperimentReplicate {experiment},
MINUS MINUS
{analysis} rdf:subject {x},
select distinct psn from select distinct psn from
{analysis} rdf:object {exp},
{x} fns:poson {psn}, {x} fns:poson {psn},
{analysis} fn:PeptideAbundance {abundance}
{x} fn:InExperimentReplicate {experiment}, {x} fn:InExperimentReplicate {experiment},
where xsd:integer(abundance) < 5000
{analysis} rdf:subject {x}, {analysis} rdf:subject {x},
and experiment LIKE "*sol*"
{analysis} rdf:object {exp}, {analysis} rdf:object {exp},
INTERSECT
{analysis} fn:PeptideAbundance {abundance} {analysis} fn:PeptideAbundance {abundance}
select distinct psn from
where xsd:integer(abundance) < 5000 where xsd:integer(abundance) < 5000
{x} fns:poson {psn},
and experiment LIKE "*sol*" and experiment LIKE "*mem*"
{x} fn:InExperimentReplicate {experiment},
using namespace using namespace
{analysis} rdf:subject {x},
{analysis} rdf:object {exp},
{analysis} fn:PeptideAbundance {abundance}
where xsd:integer(abundance) < 5000
and experiment LIKE "*mem*"
using namespace
34. Further work
• Queries are slow in the native repository, database repositories are probably
faster.
• Adding transcriptomic experiment:
Wt Vs mglA mutant
GEO AC GSE5468
• RDF-S inferencing?
35. Acknowledgements
• Funding: BBSRC -Radical Solutions for Researching the Proteome
• University of Glasgow, Glasgow
• Prof. Walter Kolch
• Dr Andy Pitt
• University of Strathclyde, Glasgow
• Dr Ela Hunt (Scientific Advisor)
• University of Washington, Seattle
• Prof. Dave Goodlett (Scientific Advisor)
• Dr Mitch Brittnacher, Mathew Radey, Laurence Rohmer
• Dr Tina Guina (MglA experiment)
36. Abundance thresholds....
• SeRQL aggregate functions would be nice to have
• Queries to find low and high abundance values:
• WHERE abundance BETWEEN MEDIAN(abundance) AND
MAX(abundance)
• WHERE abundance BETWEEN MIN(abundance) and MEDIAN(abundance)