Overview of three areas where the ENCODE DCC is facilitating the integration of diverse datasets: (1) defining a metadata standard (2) using ontologies for annotation (3) creating a RESTful interface for data access
Findable Accessable Interoperable Reusable < data |models | SOPs | samples | articles| * >. FAIR is a mantra; a meme; a myth; a mystery; a moan. For the past 15 years I have been working on FAIR in a bunch of projects and initiatives in Life Science projects. Some are top-down like Life Science European Research Infrastructures ELIXIR and ISBE, and some are bottom-up, supporting research projects in Systems and Synthetic Biology (FAIRDOM), Biodiversity (BioVel), and Pharmacology (open PHACTS), for example. Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. Some have happy endings. Who are the villains and who are the heroes? What are the morals we can draw from these stories?
Research Objects: more than the sum of the partsCarole Goble
Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA
https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
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
Cross-linked metadata standards, repositories and the data policies - The Bio...Peter McQuilton
A 20 minute presentation given in Denver (CO) on the 17th September as part of the Biosharing Registry WG, Metadata Standards Catalog WG, and Publishing Data Workflows WG joint session at the Research Data Alliance 8th Plenary (part of International Data Week).
This presentation covers the explosion of metadata standards and databases in the life, biomedical and environmental sciences and how BioSharing is helping to understand this landscape, both in terms of the relationship between standards and other standards and databases, and the life cycle and evolution of each resource. BioSharing also links these resources to the data policies that recommend them (for example, from funding agencies or journal publishers), enabling an understanding of the entire data cycle, from conception to publishing and storage.
Implementation of GPU-based bioinformatic tools at the ENCODE DCCENCODE-DCC
An overview of the assays performed and distributed by the ENCODE DCC as well as a summary of the uniform processing pipelines that are being implemented by the ENCODE Consortium. Here, we talk about the impact using GPUs has on speed of running the ChIP-seq pipeline.
Findable Accessable Interoperable Reusable < data |models | SOPs | samples | articles| * >. FAIR is a mantra; a meme; a myth; a mystery; a moan. For the past 15 years I have been working on FAIR in a bunch of projects and initiatives in Life Science projects. Some are top-down like Life Science European Research Infrastructures ELIXIR and ISBE, and some are bottom-up, supporting research projects in Systems and Synthetic Biology (FAIRDOM), Biodiversity (BioVel), and Pharmacology (open PHACTS), for example. Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. Some have happy endings. Who are the villains and who are the heroes? What are the morals we can draw from these stories?
Research Objects: more than the sum of the partsCarole Goble
Workshop on Managing Digital Research Objects in an Expanding Science Ecosystem, 15 Nov 2017, Bethesda, USA
https://www.rd-alliance.org/managing-digital-research-objects-expanding-science-ecosystem
Research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
A first step is to think of Digital Research Objects as a broadening out to embrace these artefacts or assets of research. The next is to recognise that investigations use multiple, interlinked, evolving artefacts. Multiple datasets and multiple models support a study; each model is associated with datasets for construction, validation and prediction; an analytic pipeline has multiple codes and may be made up of nested sub-pipelines, and so on. Research Objects (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
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
Cross-linked metadata standards, repositories and the data policies - The Bio...Peter McQuilton
A 20 minute presentation given in Denver (CO) on the 17th September as part of the Biosharing Registry WG, Metadata Standards Catalog WG, and Publishing Data Workflows WG joint session at the Research Data Alliance 8th Plenary (part of International Data Week).
This presentation covers the explosion of metadata standards and databases in the life, biomedical and environmental sciences and how BioSharing is helping to understand this landscape, both in terms of the relationship between standards and other standards and databases, and the life cycle and evolution of each resource. BioSharing also links these resources to the data policies that recommend them (for example, from funding agencies or journal publishers), enabling an understanding of the entire data cycle, from conception to publishing and storage.
Implementation of GPU-based bioinformatic tools at the ENCODE DCCENCODE-DCC
An overview of the assays performed and distributed by the ENCODE DCC as well as a summary of the uniform processing pipelines that are being implemented by the ENCODE Consortium. Here, we talk about the impact using GPUs has on speed of running the ChIP-seq pipeline.
I have evidence that using git and GitHub for documentation and community doc techniques can give us 300 doc changes in a month. I’ve bet my career on these methods and I want to share with you.
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.
Rafael C Jimenez presents the Omics Discovery Index | OSFair2017 Workshop
Workshop title: How FAIR friendly is your data catalogue?
Workshop overview:
This workshop will build upon the work planned by the EOSCpilot data interoperability task and the BlueBridge workshop held on April 3 at the RDA meeting. We will investigate common mechanisms for interoperation of data catalogues that preserve established community standards, norms and resources, while simplifying the process of being/becoming FAIR. Can we have a simple interoperability architecture based on a common set of metadata types? What are the minimum metadata requirements to expose FAIR data to EOSC services and EOSC users?
DAY 3 - PARALLEL SESSION 6 & 7
DeepBlue epigenomic data server: programmatic data retrieval and analysis of ...Felipe Albrecht
Short description and updates about DeepBlue Epigenomic Data Server that I presented during the last Blueprint (http://www.blueprint-epigenome.eu/) Jamboree in Madrid (June 2016)
How to make your published data findable, accessible, interoperable and reusablePhoenix Bioinformatics
Seminar Presentation for PMB Department, UC Berkeley for Love Data Week. Subject is how to prepare publications and associated data sets for maximum reuse.
Open innovation contributions from RSC resulting from the Open Phacts projectKen Karapetyan
The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.
The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.
KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools developed at Rothamsted Research for integrating and visualising large biological datasets in order to accelerate gene discovery. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes or publications. The aim is to provide leads for scientists who are investigating the molecular basis for a particular trait or ways of improving the organism’s performance in some way
KnetMiner provides an easy to use web interface to visualisation and data mining tools for the discovery and evaluation of candidate genes from large scale integrations of public and private data sets. It addresses the needs of scientists who generally lack the time and technical expertise to review all relevant information available in the literature, from key model species and from a potentially wide range of related biological databases. We have previously developed genome-scale knowledge networks (GSKNs) for multiple crop and animal species (Hassani-Pak et al. 2016). The KnetMiner web server searches and evaluates millions of relations and concepts within the GSKNs in real-time to determine if direct or indirect links between genes and trait-based keywords can be established. KnetMiner accepts as user inputs: search terms in combination with a gene list and/or genomic regions. It produces a table of ranked candidate genes and allows users to explore the output in interactive genome and network map visualisation tools that have been optimised for web use on desktop and mobile devices. The KnetMiner web server and the GSKNs provide a step-forward towards systematic and evidence-based gene discovery.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
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.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
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.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
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 .
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
What is greenhouse gasses and how many gasses are there to affect the Earth.
ENCODE-DCC-metadata-standard-Biocurator 2014
1. The
ENCODE
metadata
standard
to
integrate
diverse
experimental
data
sets
Eurie
L.
Hong,
Ph.D.
(@elhong)
Project
Manager,
ENCODE
DCC
Department
of
GeneFcs
•
Stanford
University
School
of
Medicine
Intro
to
the
DCC
Metadata
definiFon
Using
ontologies
Accessing
metadata
2. 2
Not
pictured:
Tim
Dreszer,
Jorge
Garcia,
Donna
Karolchik,
Katrina
Learned,
Forrest
Tanaka,
Marcus
Ho
ENCODE
DCC
Galt
Barber,
Morgan
Maddren,
Nikhil
Podduturi,
Greg
Roe,
Kate
Rosenbloom,
Laurence
Rowe
Esther
Chan,
Venkat
Malladi,
Cricket
Sloan,
Seth
StraWan
Eurie
Hong,
Mike
Cherry
(PI),
Jim
Kent
(co-‐PI),
Ben
Hitz
Brian
Lee,
Stuart
Miyasato,
MaW
Simison,
Zhenhua
Wang
@encodedcc
encode-‐help@lists.stanford.edu
Data
Wranglers
So]ware
engineers
QA,
sysadmins,
admin
hWps://github.com/ENCODE-‐DCC/encoded
3. ProducFon
labs
Analysis
groups
Role:
Data
genera?on
Data
organiza?on
Data
access
Tasks:
Perform
assays
Data
processing
&
validaFon
Web-‐based
searches
Perform
analyses
Data
file
storage
Data
downloads
Validate
data
Metadata
curaFon
Submit
data
files
Submit
metadata
Genome
Browser
ENCODE
portal
(DCC)
Role
of
the
Data
CoordinaFon
Center
Data
files
Metadata
DCC
DCC
Integrative
websites!
Scientific!
community!
4. Challenge:
How
do
you
define
a
metadata
standard
for
diverse
assays
in
mulFple
species?
Modified
from
PLoS
Biol
9-‐e1001046,2011
(M.
Pazin)
5. Principles
driving
metadata
definiFon
• Provide
transparency
about
how
experiments
were
performed
• Capture
data
provenance
during
analyses
• Communicate
key
experimental
variables
of
an
experiment
• Communicate
quality
metrics
about
the
data
•
Help
analyze
and
interpret
the
data
•
Help
organize
and
find
the
data
6. Capture
the
experimental
design
Biological
replicate
1
Technical
replicate
1
Technical
replicate
2
Biological
replicate
2
Technical
replicate
1
Technical
replicate
2
Control
1
Control
2
Data
file
Technical
replicate
1
Data
file
Results
file
Experiment
Experiment
7. IdenFfy
reusable
experimental
variables
Biosamples
• Type
(e.g.
Fssue,
cell
line)
• Ontology
term
name
• Source,
product
id,
lot
id
• Treatments
• Knockdown
• Fusion
construct
informaFon
• Donor
or
strain
informaFon
• Dates
(e.g.
growth,
harvest,
procurement)
• Passage
number
• StarFng
amount
• Lab
assigned
IDs
AnFbodies
• Source,
product
id,
lot
id
• Isotype
• AnFgen
• Host
• PurificaFon
method
• ValidaFon
status
• NHGRI
approval
status
• Target
• Species
• Dbxrefs
Libraries
• Library
preparaFon
protocol
• Strand
specificity
• Size
selecFon
method
• ValidaFon
document
• Lysis
method
• SonicaFon
method
• ExtracFon
method
• Nucleic
acid
type
• Nucleic
acid
size
range
+
Files
Peak
calls
• Reference
genome
version
• Alignment
so]ware
• So]ware
parameters
• So]ware
version
• Quality
metrics
(e.g.
NRF,
FRiP)
Alignment
(selected
subset
of
all
metadata)
Experiment
with
replicates
8. Accession
them
Biosamples
• Type
(e.g.
Fssue,
cell
line)
• Ontology
term
name
• Source,
product
id,
lot
id
• Treatments
• Knockdown
• Fusion
construct
informaFon
• Donor
or
strain
informaFon
• Dates
(e.g.
growth,
harvest,
procurement)
• Passage
number
• StarFng
amount
• Lab
assigned
IDs
AnFbodies
• Source,
product
id,
lot
id
• Isotype
• AnFgen
• Host
• PurificaFon
method
• ValidaFon
status
• NHGRI
approval
status
• Target
• Species
• DBxrefs
Libraries
• Library
preparaFon
protocol
• Strand
specificity
• Size
selecFon
method
• ValidaFon
document
• Lysis
method
• SonicaFon
method
• ExtracFon
method
• Nucleic
acid
type
• Nucleic
acid
size
range
+
Files
Peak
calls
• Reference
genome
version
• Alignment
so]ware
• So]ware
parameters
• So]ware
version
• Quality
metrics
(e.g.
NRF,
FRiP)
Alignment
(selected
subset
of
all
metadata)
Experiment
with
replicates
(ENCSR000DRY)
ENCBS095DKV
(biosample)
ENCDO826IFN
(donors)
ENCAB964IAU
ENCLB239KAN
ENCFF254TDA
9. Define
their
relaFonship
to
each
other
Biosample
AnFbodies
Libraries
+
Files
Donor
Biosample
Replicate
has
has
has
has
has
has
Experiment
has
10. Challenge:
Find
common
biosamples
from
data
generated
by
two
consorFa
356
terms
hWp://encodeproject.org/ENCODE/cellTypes.html
Projects
are
internally
consistent…..
314
terms
GEO
characterisFcs:
common_name,
Fssue_type,
cell_type,
lines
11. 360
terms
Cell
type
…
but
only
3
biosample
names
match
exactly
between
projects
314
terms
GEO
IMR90
PBMC
Th17
12. Challenge:
Find
all
heart-‐related
Fssues?
Heart_OC
HCF
HCFaa
HCM
Others?
Fetal
Heart
Heart
Right
Atrium
Right
Ventricle
Others?
15. Metadata
database
Metadata
in
JSON-‐LD
Metadata
viewed
as
web
page
Scripts
Query
using
REST
API
commands:
GET,
PATCH,
POST
DCC
Challenge:
Provide
user-‐friendly
*AND*
programmaFc
access
to
the
data
Genome
Browser
17. Future
direcFons
• Metadata
definiFon:
Finalize
so]ware
and
file
provenance
• Ontology-‐based
searches:
Implement
searches
for
ChIP-‐seq
targets
using
GO
annotaFons
• ProgrammaFc
access:
Implement
addiFonal
validaFons
upon
data
submission
18. Intro
to
the
DCC
Metadata
definiFon
Using
ontologies
Accessing
metadata
We
developed
a
single
data
model
that
reflects
the
experimental
process
to
store
the
30+
assays
done
by
the
ENCODE
producFon
labs
Using
ontologies
to
annotate
metadata
provides
instant
interoperability
with
other
datasets
&
search
funcFonality
ApplicaFon
built
on
a
REST
API
&
JSON-‐LD
supports
programmaFc
querying
across
other
scienFfic
resources
Conclusions
19. 19
Acknowledgements
Brian
Lee,
Nikhil
Podduturi,
Greg
Roe,
Laurence
Rowe
Esther
Chan,
Venkat
Malladi,
Cricket
Sloan,
Seth
StraWan
Eurie
Hong,
Mike
Cherry
(PI),
Jim
Kent
(co-‐PI),
Ben
Hitz
@encodedcc
encode-‐help@lists.stanford.edu