PMR database is a community resource for deposition and analysis of metabolomics data and related transcriptomics data. PMR currently houses metabolomics data from over 25 species of eukaryotes. In this talk, we introduce PMRs RESTful web APIs for data sharing, and demonstrate its applications in research using Araport to provide Arabidopsis metabolomics data.
Tripal within the Arabidopsis Information Portal - PAG XXIIIVivek Krishnakumar
Araport plans to implement a Chado-backed data warehouse, fronted by Tripal, serving as as our core database, used to track multiple versions of genome annotation (TAIR10, Araport11, etc.), evidentiary data (used by our annotation update pipeline), metadata such as publications collated from multiple sources like TAIR, NCBI PubMed and UniProtKB (curated and unreviewed) and stock/germplasm data linked to AGI loci via their associated polymorphisms.
HRGRN: enabling graph search and integrative analysis of Arabidopsis signalin...Araport
The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only genes, RNA, protein and compounds but also the complicated interactions among them. Yet, even in the most thoroughly studied model plant Arabidopsis thaliana, the knowledge regarding these interactions are scattered throughout literatures and various public databases. Thus, new scientific discovery by exploring these complex and heterogeneous data remains a challenge task for biologists.
We developed a graph-search empowered platform named HRGRN to search known and, more importantly, discover the novel relationships among genes in Arabidopsis biological networks. The HRGRN includes over 51,000 “nodes” that represent very large sets of genes, proteins, small RNAs, and compounds and approximately 150,000 “edges” that are classified into nine types of interactions (interactions between proteins, compounds and proteins, transcription factors (TFs) and their downstream target genes, small RNAs and their target genes, kinases and downstream target genes, transporters and substrates, substrate/product compounds and enzymes, as well as gene pairs with similar expression patterns to provide deep insight into gene-gene relationships) to comprehensively model and represent the complex interactions between nodes. .
The HRGRN allows users to discover novel interactions between genes and/or pathways, and build sub-networks from user-specified seed nodes by searching the comprehensive collections of interactions stored in its back-end graph databases using graph traversal algorithms. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/. Currently, we are collaborating the Araport team to develop REST-like web services and provide the HRGRN’s graph search functions to Araport system.
Tripal within the Arabidopsis Information Portal - PAG XXIIIVivek Krishnakumar
Araport plans to implement a Chado-backed data warehouse, fronted by Tripal, serving as as our core database, used to track multiple versions of genome annotation (TAIR10, Araport11, etc.), evidentiary data (used by our annotation update pipeline), metadata such as publications collated from multiple sources like TAIR, NCBI PubMed and UniProtKB (curated and unreviewed) and stock/germplasm data linked to AGI loci via their associated polymorphisms.
HRGRN: enabling graph search and integrative analysis of Arabidopsis signalin...Araport
The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only genes, RNA, protein and compounds but also the complicated interactions among them. Yet, even in the most thoroughly studied model plant Arabidopsis thaliana, the knowledge regarding these interactions are scattered throughout literatures and various public databases. Thus, new scientific discovery by exploring these complex and heterogeneous data remains a challenge task for biologists.
We developed a graph-search empowered platform named HRGRN to search known and, more importantly, discover the novel relationships among genes in Arabidopsis biological networks. The HRGRN includes over 51,000 “nodes” that represent very large sets of genes, proteins, small RNAs, and compounds and approximately 150,000 “edges” that are classified into nine types of interactions (interactions between proteins, compounds and proteins, transcription factors (TFs) and their downstream target genes, small RNAs and their target genes, kinases and downstream target genes, transporters and substrates, substrate/product compounds and enzymes, as well as gene pairs with similar expression patterns to provide deep insight into gene-gene relationships) to comprehensively model and represent the complex interactions between nodes. .
The HRGRN allows users to discover novel interactions between genes and/or pathways, and build sub-networks from user-specified seed nodes by searching the comprehensive collections of interactions stored in its back-end graph databases using graph traversal algorithms. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/. Currently, we are collaborating the Araport team to develop REST-like web services and provide the HRGRN’s graph search functions to Araport system.
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Agnes Chan (J. Craig Venter Institute)
A Guided Tour of Araport
ICAR 2015
Plenary session (MONDAY, JULY 6, 2015, 10:15-10:30 AM)
Chris Town (J. Craig Venter Institute)
Araport: your one-stop-shop for Arabidopsis data in the 21st century
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Blake Meyers (University of Delaware)
A Community Collaborator Perspective: Case study 2 - Small RNA DBs
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Nick Provart (University of Toronto)
A Community Collaborator Perspective: Case study 1 - BioAnalytic Resource
Metagenomic Data Provenance and Management using the ISA infrastructure --- o...Alejandra Gonzalez-Beltran
Metagenomic Data Provenance and Management using the ISA infrastructure - overview, implementation patterns & software tools
Slides presented at EBI Metagenomics Bioinformatics course: http://www.ebi.ac.uk/training/course/metagenomics2014
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Yes, data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. And the multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Sharing data with lightweight data standards, such as schema.org and bioschemas. The Knetminer case, an application for the agrifood domain and molecular biology.
Presented at Open Data Sicilia (#ODS2021)
Written and presented by Tom Ingraham (F1000), at the Reproducible and Citable Data and Model Workshop, in Warnemünde, Germany. September 14th -16th 2015.
Being Reproducible: SSBSS Summer School 2017Carole Goble
Lecture 2:
Being Reproducible: Models, Research Objects and R* Brouhaha
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange.
In this talk I will explore these issues in more depth using the FAIRDOM Platform and its support for reproducible modelling. The talk will cover initiatives and technical issues, and raise social and cultural challenges.
Tripal v3, the Collaborative Online Database Platform Supporting an Internati...Bradford Condon
Talk given by Dr. Bradford Condon at the NSRP10 session of the Plant and Animal Genomes conference (PAG) 2019. Covers the basics of the biological database toolkit Tripal, and how Tripal enables FAIR data.
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
JBrowse within the Arabidopsis Information Portal - PAG XXIIIVivek Krishnakumar
Araport integrates JBrowse visualization software from GMOD. In order to support diverse sets of locally and remotely sourced tracks, the “ComboTrackSelector” JBrowse plugin was developed to enable the capability to partition metadata rich tracks in the “Faceted” selector while using the default “Hierarchical” selector for everything else.
A dynamic sequence viewer add-on, “SeqLighter”, was developed using the BioJS framework (http://biojs.net/), configured offer end-users with the capability to view the genomic sequence underlying the gene models (genic regions plus customizable flanking regions), highlight sub-features (like UTRs, exons, introns, start/stop codons) and export the annotated output in various formats (SVG, PNG, JPEG).
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Agnes Chan (J. Craig Venter Institute)
A Guided Tour of Araport
ICAR 2015
Plenary session (MONDAY, JULY 6, 2015, 10:15-10:30 AM)
Chris Town (J. Craig Venter Institute)
Araport: your one-stop-shop for Arabidopsis data in the 21st century
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Blake Meyers (University of Delaware)
A Community Collaborator Perspective: Case study 2 - Small RNA DBs
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Nick Provart (University of Toronto)
A Community Collaborator Perspective: Case study 1 - BioAnalytic Resource
Metagenomic Data Provenance and Management using the ISA infrastructure --- o...Alejandra Gonzalez-Beltran
Metagenomic Data Provenance and Management using the ISA infrastructure - overview, implementation patterns & software tools
Slides presented at EBI Metagenomics Bioinformatics course: http://www.ebi.ac.uk/training/course/metagenomics2014
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Yes, data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. And the multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Sharing data with lightweight data standards, such as schema.org and bioschemas. The Knetminer case, an application for the agrifood domain and molecular biology.
Presented at Open Data Sicilia (#ODS2021)
Written and presented by Tom Ingraham (F1000), at the Reproducible and Citable Data and Model Workshop, in Warnemünde, Germany. September 14th -16th 2015.
Being Reproducible: SSBSS Summer School 2017Carole Goble
Lecture 2:
Being Reproducible: Models, Research Objects and R* Brouhaha
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange.
In this talk I will explore these issues in more depth using the FAIRDOM Platform and its support for reproducible modelling. The talk will cover initiatives and technical issues, and raise social and cultural challenges.
Tripal v3, the Collaborative Online Database Platform Supporting an Internati...Bradford Condon
Talk given by Dr. Bradford Condon at the NSRP10 session of the Plant and Animal Genomes conference (PAG) 2019. Covers the basics of the biological database toolkit Tripal, and how Tripal enables FAIR data.
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
JBrowse within the Arabidopsis Information Portal - PAG XXIIIVivek Krishnakumar
Araport integrates JBrowse visualization software from GMOD. In order to support diverse sets of locally and remotely sourced tracks, the “ComboTrackSelector” JBrowse plugin was developed to enable the capability to partition metadata rich tracks in the “Faceted” selector while using the default “Hierarchical” selector for everything else.
A dynamic sequence viewer add-on, “SeqLighter”, was developed using the BioJS framework (http://biojs.net/), configured offer end-users with the capability to view the genomic sequence underlying the gene models (genic regions plus customizable flanking regions), highlight sub-features (like UTRs, exons, introns, start/stop codons) and export the annotated output in various formats (SVG, PNG, JPEG).
ICAR 2015
Workshop 10 (TUESDAY, JULY 7, 2015, 4:30-6:00 PM)
The Arabidopsis information portal for users and developers
Matt Vaughn (Texas Advanced Computing Center)
Developing Apps: Exposing your data through Araport
iDiffIR: Identifying differential intron retention from RNA-seqAraport
iDiffIR is a method for identifying differential intron retention from RNA-seq. For more information, please visit http://combi.cs.colostate.edu/idiffir/
BioThings API: Building a FAIR API Ecosystem for Biomedical KnowledgeChunlei Wu
My talk about BioThings API project at ISMB 2018 Chicago, as part of BD2K special session. BioThings API project provides a collection of high-performance APIs (MyGene.info, MyVariant.info, MyChem.info), an SDK for building a new biomedical API (BioThings SDK), and a JSON-LD and OpenAPI based solution for across-API interoperability and knowledge exploration.
Current advances to bridge the usability-expressivity gap in biomedical seman...Maulik Kamdar
I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search
Presented by Richard Kidd at "The Future Information Needs of Pharmaceutical & Medicinal Chemistry", Monday 28 November 2011 at The Linnean Society, Burlington Square, London run by the RSC CICAG group.
WikiPathways: how open source and open data can make omics technology more us...Chris Evelo
Presentation about collaborative development of open source pathway analysis code and pathways and about usage in analytical software distributed with analytical machines like mass spectrophotometers.
Using publicly available resources to build a comprehensive knowledgebase of ...Valery Tkachenko
There is a variety of public resources on the Internet which contain information about various aspects of chemical, biological and pharmaceutical domains. The quality, maturity, hosting organizations, team sizes behind these data resources vary wildly and as a consequence content cannot be always trusted and the effort of extracting information and preparing it for reuse is repeated again and again at various levels. This problem is especially serious in applications for QSAR, QSPR and QNAR modeling. On the other hand authors of this poster believe, based on their own extensive experience building various types of chemical, analytical and biological databases for decades, that the process of building such knowledgebase can be systematically described and automated. This poster will outline the work performed on text and data-mining various public resources on the Web, data curation process and making this information publicly available through a portal and a RESTful API. We will also demonstrate how such knowledgebase can be used for real-time QSAR and QSPR predictions.
BioThings API: Building a FAIR API Ecosystem for Biomedical KnowledgeChunlei Wu
My talk at NCI's CBIIT speaker series:
https://wiki.nci.nih.gov/display/CBIITSpeakers/2019/01/02/Jan+16%2C+Chunlei+Wu%2C+BioThings+API
A companion blog post: https://ncip.nci.nih.gov/blog/the-network-of-biothings/
See more details about BioThings project at http://biothings.io.
Finding knowledge, data and answers on the Semantic Webebiquity
Web search engines like Google have made us all smarter by providing ready access to the world's knowledge whenever we need to look up a fact, learn about a topic or evaluate opinions. The W3C's Semantic Web effort aims to make such knowledge more accessible to computer programs by publishing it in machine understandable form.
<p>
As the volume of Semantic Web data grows software agents will need their own search engines to help them find the relevant and trustworthy knowledge they need to perform their tasks. We will discuss the general issues underlying the indexing and retrieval of RDF based information and describe Swoogle, a crawler based search engine whose index contains information on over a million RDF documents.
<p>
We will illustrate its use in several Semantic Web related research projects at UMBC including a distributed platform for constructing end-to-end use cases that demonstrate the semantic web’s utility for integrating scientific data. We describe ELVIS (the Ecosystem Location Visualization and Information System), a suite of tools for constructing food webs for a given location, and Triple Shop, a SPARQL query interface which searches the Semantic Web for data relevant to a given query ELVIS functionality is exposed as a collection of web services, and all input and output data is expressed in OWL, thereby enabling its integration with Triple Shop and other semantic web resources.
The application of cloud computing to royal society of chemistry data platformsValery Tkachenko
Cloud computing offers significant advantages for the hosting of RSC chemistry databases in terms of reliability, performance and access to large scale computational power. The ChemSpider database contains almost 30 million unique chemical compounds and access to compute power to regenerate properties and add new properties is essential for efficient delivery on a manageable timescale. The use of cloud-based facilities reduces the needs for internal infrastructure and enhances performance generally at the cost of significant recoding of the platforms. This presentation will review our move of our ChemSpider related projects to the cloud, the associated challenges and both the obvious and unforeseen benefits. We will also discuss our use of parallelization technologies for mass calculation using Hadoop.
In this talk at the CECAM 2015 Workshop on Future Technologies in Automated Atomistic Simulations, I will discuss the Materials Project Ecosystem, an initiative to develop a comprehensive set of open-source software and data tools for materials informatics. The Materials Project is a US Department of Energy-funded initiative to make the computed properties of all known inorganic materials publicly available to all materials researchers to accelerate materials innovation. Today, the Materials Project database boasts more than 58,000 materials, covering a broad range of properties, including energetic properties (e.g., phase and aqueous stability, reaction energies), electronic structure (bandstructures, DOSs) and structural and mechanical properties (e.g., elastic constants).
A linchpin of the Materials Project is its robust data and software infrastructure, built on best open-source software development practices such as continuous testing and integration, and comprehensive documentation. I will provide an overview of the open-source software modules that have been developed for materials analysis (Python Materials Genomics), error handling (Custodian) and scientific workflow management (FireWorks), as well as the Materials API, a first-of-its-kind interface for accessing materials data based on REpresentational State Transfer (REST) principles. I will show a materials researcher may use and build on these software and data tools for materials informatics as well as to accelerate his own research.
The Role of Metadata in Reproducible Computational ResearchJeremy Leipzig
Reproducible computational research (RCR) provides the keystone to the scientific method, packaging the transformation of raw data to published results in a manner than can be communicated to others. Developing RCR standards has been a growing concern of statisticians, data scientists, and informatics professionals. Metadata provides context and provenance to raw data, and is essential to both discovery and validation RCR. This presentation will give an overview for emerging metadata standards in data, analysis, pipelines tools, and publications.
As part of our efforts to develop a public platform to provide access to predictive models we have attempted to disentangle the influence of the quality versus quantity of data available to develop and validate QSAR models. Using a thorough manual review of the data underlying the well-known EPI Suite software, we developed automated processes for the validation of the data using a KNIME workflow. This includes: approaches to validate different chemical structure representations (e.g. molfile and SMILES), identifiers (chemical names and registry numbers), and methods to standardize the data into QSAR-consumable formats for modeling. Our efforts to quantify and segregate data into various quality categories has allowed us to thoroughly investigate the resulting models developed from these data slices, as well as allowing us to examine whether or not efforts into the development of large high-quality datasets has the expected pay-off in terms of prediction performance. Machine-learning approaches have been applied to create a series of models that have been used to generate predicted physicochemical and environmental parameters for over 700,000 chemicals. These data are available online via the EPA’s iCSS Chemistry Dashboard. This abstract does not reflect U.S. EPA policy.
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
Arabidopsis Information Portal: A Community-Extensible Platform for Open DataMatthew Vaughn
Araport is an innovative model organism database resource that offers users the ability to bring their own visualizations, data sets, algorithms, and genome browser tracks and share them with their colleagues.
Presentation pathway extensions using knowledge integration and network approaches presented at the Systems Biology Institute in Luxembourg on November 28 2012.
An Overview of the iMicrobe Project and available tools in the iPlant Cyberinfrastructure. This talk was given at a workshop at ASLO in Granada, Spain focused on applications in Oceanography and Limnology.
Similar to PMR metabolomics and transcriptomics database and its RESTful web APIs: A data sharing resource (20)
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
PMR metabolomics and transcriptomics database and its RESTful web APIs: A data sharing resource
1. 1/19/2016
1
Manhoi Hur (mhhur@iastate.edu)
Iowa State University
Plant & Animal Genomes Conference
(PAG) 2016
PMR metabolomics and
transcriptomics database and its
RESTful web APIs:
A data sharing resource
API= ApplicationProgram Interface
Proteomics
Metabolomics
Fluxomics
Transcriptomics
Genomics & Transcriptomics:
Sequencing and assembly
Draft Metabolic and regulatory model
(Reaction set with regulators and
transporters)
Reconstructed biological network
Annotation
Experiments to
test validity and
accuracy of
model
Communitydatasets
Omics datasets as constraint
Overall vision
Rai A, Saito K. Omics data input for metabolic modeling.
Current Opinion in Biotechnology. 2016 Feb 29;37:127-34.
Proteomics
Metabolomics
Fluxomics
Transcriptomics
Genomics & Transcriptomics:
Sequencing and assembly
Draft Metabolic and regulatory model
(Reaction set with regulators and
transporters)
Reconstructed biological network
Annotation
Experiments to
test validity and
accuracy of
model
Communitydatasets
Omics datasets as constraint
Overall vision
Absence of available community
resources for metabolomics data
Rai A, Saito K. Omics data input for metabolic modeling.
Current Opinion in Biotechnology. 2016 Feb 29;37:127-34.
Community-based needs
• Repository for growing collections of defined metabolite
abundance data and associated metadata
• Clear, consistent formats
• Comprehensive metadata
• Easy access and download
• Computational tools to integrate genome system-scale
datasets
Hur, et al. "A global approach to analysis and interpretation of metabolic data for plant natural product
discovery." Natural Product Reports (2013)
PMR: Plant/Eukaryotic and Microbial Systems Resource
• Data repository
• Metabolomics data and transcriptomics data
• Metadata
• Statistical analysis and visualization
• Data integration
• Integrated metabolomics and transcriptomics
• Data sharing
• Download data directly
• Retrieve data and its plots through RESTful web APIs
Hur, et al. "A global approach to analysis and interpretation of metabolic data for plant natural product
discovery." Natural Product Reports (2013)
Data repository
Stat analysis Data sharing
Data integration
Technical basis for PMR Platform
• PMR database and its web-application
• Back-end: NoSQL database (MongoDB 3.x and Neo4J)
• Flexibility and scalability
• Front-end: PHP, jQuery, HighChart JS, and Plotly.js
• Apache HTTP Server
• pmr-RESTful web APIs
• Microsoft ASP.NET Web API 2.x
• C#, Plotly.js ,and Htmlwidgets.js
• Microsoft IIS Web Server
2. 1/19/2016
2
PMR website
http://metnetdb.org/PMR/ Species and
Experiments list
Star means samples also have
transcriptomicsdata
Experiments available for
selected species
Sample Names
Reproducibility test – Spearman correlations
Experimental Metadata
Platform Metadata
Metabolite list
3. 1/19/2016
3
Metabolite list
Metabolite data as Boxplot
Volcano plot:
Comparing data between samples
Metabolomics data empowers accurate computational
modeling and hypothesis development
• Hur et al. Natural Product Reports (2013)
• Quanbeck et al. Frontiers in Plant Science (2012)
• Yeo et al. JBC (2012)
• Ngaki et al Nature (2012)
• Atsushi et al Plant Physiology (2014)
• Vu et al Plant Signaling & Behavior (2015)
• Li et al BMC Genomics (2015)
pmr-RESTful web APIs to share PMR data and functionality
with diverse users
• Undergraduate teaching- 6-week Genetics lab unit
to determine function of yeast genes
(https://db.nanobio.illinois.edu)
• Araport- Arabidopsis information portal
(https://www.araport.org)
Near Future:
• Soybase (http://soybase.org)
• MaizeGDB (http://www.maizegdb.org)
• Sol Genomics Network (http://www.sgn.cornell.edu)
Genetics lab: Yeast: Let's Mess it up!
pmr-RESTful web APIs simply designed for use
by other resources
• Based on HTTP requests; detail encoded directly in URL path
• Output format: JSON (JavaScript Object Notation)
http(s)://BaseURL + Input parameters
• Example URL:
http://pmr-webapi-
gdcb.iastate.edu/pmrWebApi/api/v1
/experiments/list?species=Arabidopsis_thaliana
Retrieved data
{
"species": "Arabidopsis_thaliana",
"expName": "At2010-KO5",
"expId": 110
},
{
"species": "Arabidopsis_thaliana",
"expName": "At2010-KO6",
"expId": 111
},
{
"species": "Arabidopsis_thaliana",
"expName": "At2010-KO7",
"expId": 199
},
{
"species": "Arabidopsis_thaliana",
"expName": "At2010-KO8",
"expId": 198
},
{
"species": "Arabidopsis_thaliana",
"expName": "At2010-KO9",
"expId": 197
}
Example: PMR API for sharing
metabolite data
http://pmr-webapi-
gdcb.iastate.edu/pmrWebApi/api/v1
/metabolites/list?species=Arabidospis_thali
ana&expId=106&pId=84&dataVersion=1.0/
Retrieved data
[
{
"mId": 4348,
"metaboliteName":"16-Hydroxyhexadecanoic acid"
},
{
"mId": 4349,
"metaboliteName":"18-hydroxyoctadeca-9,12-dienoic acid"
},
{
"mId": 4350,
"metaboliteName":"18-hydroxyoctadec-9-enoicacid"
},
{
"mId": 4353,
"metaboliteName":"2-hydroxyhexadecanoic acid"
},
{
"mId": 4354,
"metaboliteName":"2-hydroxyeicosanoicacid"
},
{
"mId": 4355,
"metaboliteName":"2-hydroxydocosanoic acid"
},
{
"mId": 4356,
"metaboliteName":"2-hydroxytetracosanoicacid"
},
{
"mId": 4357,
"metaboliteName":"2-hydroxytetracos-2-enoicacid"
},
{
"mId": 4358,
"metaboliteName":"2-hydroxyhexacosanoic acid"
},
4. 1/19/2016
4
Example of PMR API for visualization (Box Plot)
JSON format
http://pmr-webapi-gdcb.iastate.edu/pmrWebApi/api/v1
/boxplot/list?species=Arabidopsis_thaliana&expId=106&
omicsType=valMetabolomics&pId=84&mId=4348&dataV
ersion=1.0&minLod=true&legendColor=chemicalgroup/
Htmlwidgets.js
Boxplot
Plotly.js
Htmlwidgets.js - http://www.htmlwidgets.org/
Plotly.js- https://plot.ly/
JavaScript applications
Showing actual plotsRetrieved data
Bringing PMR data and functions into ARAPORT
Example of Apps on ARAPORT – PMR_Plotter
Boxplot on PMR Boxplot on ARAPORT
Metabolite: 18-hydroxyoctadeca-9,12-dienoicacid
https://www.araport.org/apps/jmiller/pmrplotter
Conclusions and Future Directions
• PMR is a systems biology platform to support
the community
• Developed RESTful web APIs for PMR
• Integrate additional PMR metabolomics data
and functionalities with ARAPORT
• PMR will make well-designed documentation
for a workflow that can be used within the
ARAPORT science apps and other community
resources.
Data repository
Stat analysis Data sharing
Data integration
Acknowledgments
EEC-0813570
MCB-0951170
Medicinal Plant Consortium
GM092521
Dr. Jason MillerDr. Chris Town Irina Belyaeva Erik Ferlanti
DESC0014038
ARAPORT team at J. Craig Venter Institute
Iowa State University
Center
for
Metabolic
Biology
Dr. Basil J Nikolau Dr. Ling Li
PI: Eve Syrkin Wurtele
PMR team at Iowa State University
Zebulon Arendsee