VariantSpark a customer Apache Spark library for genomic data. Customer wide random forest machine learning algorithm, designed for workloads with millions of features.
deck from talk at YOW Data in Sydney, covers VariantSpark, custom Apache Spark Machine Learning library and also GT-Scan2 using AWS Lambda architecture for bioinformatics
Going Server-less for Web-Services that need to Crunch Large Volumes of DataDenis C. Bauer
AgileIndia Breakout session on serverless applications. This talk covers how AWS serverless infrastructure can be used for a wide range of applications, such as compute intensive tasks (GT-Scan), tasks requiring continuous learning (CryptoBreeder), data intensive tasks (PhenGen Database).
How novel compute technology transforms life science researchDenis C. Bauer
AgileIndia 2018 Keynote. This talk covers how ‘Datafication’ will make data ‘wider’ (more features describing a data point), which represents a paradigm shift for Machine Learning applications. It also covers serverless architecture, which can cater for even compute-intensive tasks. It concludes by stating that business and life-science research are not that different: so let’s build a community together!
Customer Case Study: How Novel Compute Technology Transforms Medical and Life...Amazon Web Services
This session outlines how to deal with “big” (many samples) and “wide” (many features per sample) data on Apache Spark, how to keep runtime constant by using instantaneously scalable micro services (AWS Lambda), and how AWS technology has enabled inspirational real-world research use cases at CSIRO.
Speaker: Denis Bauer, Transformational Bioinformatics Team Leader, CSIRO
Level: 200
Accelerating Time to Science: Transforming Research in the CloudJamie Kinney
Researchers working on projects ranging from work at individual labs to some of the world's largest scientific investigations are using AWS to accelerate the pace of scientific discovery and ask questions that were previously impossible to explore. This talk explains why scientists are using Amazon Web Services and showcases a range of real-word examples.
deck from talk at YOW Data in Sydney, covers VariantSpark, custom Apache Spark Machine Learning library and also GT-Scan2 using AWS Lambda architecture for bioinformatics
Going Server-less for Web-Services that need to Crunch Large Volumes of DataDenis C. Bauer
AgileIndia Breakout session on serverless applications. This talk covers how AWS serverless infrastructure can be used for a wide range of applications, such as compute intensive tasks (GT-Scan), tasks requiring continuous learning (CryptoBreeder), data intensive tasks (PhenGen Database).
How novel compute technology transforms life science researchDenis C. Bauer
AgileIndia 2018 Keynote. This talk covers how ‘Datafication’ will make data ‘wider’ (more features describing a data point), which represents a paradigm shift for Machine Learning applications. It also covers serverless architecture, which can cater for even compute-intensive tasks. It concludes by stating that business and life-science research are not that different: so let’s build a community together!
Customer Case Study: How Novel Compute Technology Transforms Medical and Life...Amazon Web Services
This session outlines how to deal with “big” (many samples) and “wide” (many features per sample) data on Apache Spark, how to keep runtime constant by using instantaneously scalable micro services (AWS Lambda), and how AWS technology has enabled inspirational real-world research use cases at CSIRO.
Speaker: Denis Bauer, Transformational Bioinformatics Team Leader, CSIRO
Level: 200
Accelerating Time to Science: Transforming Research in the CloudJamie Kinney
Researchers working on projects ranging from work at individual labs to some of the world's largest scientific investigations are using AWS to accelerate the pace of scientific discovery and ask questions that were previously impossible to explore. This talk explains why scientists are using Amazon Web Services and showcases a range of real-word examples.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
Laurie Goodman: Sharing and Reusing Cell Image Data, ASCB/EMBO 2017 Subgroup ...GigaScience, BGI Hong Kong
Laurie Goodman's pre-prepared slides for the Subgroup S Sharing and Reusing Cell Image Data session at the 2017 ASCB│EMBO meeting in Philadelphia. December 2017
Reusable Software and Open Data To Optimize AgricultureDavid LeBauer
Abstract:
Humans need a secure and sustainable food supply, and science can help. We have an opportunity to transform agriculture by combining knowledge of organisms and ecosystems to engineer ecosystems that sustainably produce food, fuel, and other services. The challenge is that the information we have. Measurements, theories, and laws found in publications, notebooks, measurements, software, and human brains are difficult to combine. We homogenize, encode, and automate the synthesis of data and mechanistic understanding in a way that links understanding at different scales and across domains. This allows extrapolation, prediction, and assessment. Reusable components allow automated construction of new knowledge that can be used to assess, predict, and optimize agro-ecosystems.
Developing reusable software and open-access databases is hard, and examples will illustrate how we use the Predictive Ecosystem Analyzer (PEcAn, pecanproject.org), the Biofuel Ecophysiological Traits and Yields database (BETYdb, betydb.org), and ecophysiological crop models to predict crop yield, decide which crops to plant, and which traits can be selected for the next generation of data driven crop improvement. A next step is to automate the use of sensors mounted on robots, drones, and tractors to assess plants in the field. The TERRA Reference Phenotyping Platform (TERRA-Ref, terraref.github.io) will provide an open access database and computing platform on which researchers can use and develop tools that use sensor data to assess and manage agricultural and other terrestrial ecosystems.
TERRA-Ref will adopt existing standards and develop modular software components and common interfaces, in collaboration with researchers from iPlant, NEON, AgMIP, USDA, rOpenSci, ARPA-E, many scientists and industry partners. Our goal is to advance science by enabling efficient use, reuse, exchange, and creation of knowledge.
---
Invited talk for the "Informatics for Reproducibility in Earth and Environmental Science Research" session at the American Geophysical Union Fall Meeting, Dec 17 2015.
Plenary talk at the international Synchrotron Radiation Instrumentation conference in Taiwan, on work with great colleagues Ben Blaiszik, Ryan Chard, Logan Ward, and others.
Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. I present here three projects that use cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. I draw conclusions about best practices for building next-generation data automation systems for future light sources.
DCSF 19 Towards Reproducable Climate ResearchDocker, Inc.
Aparna Radhakrishnan, Engility
NOAA/GFDL was founded in 1955 and is still in the forefront of climate research, contributing to the numerous policies and decisions undertaken in this world of evolving responses with respect to climate, which in turn creates an avalanche of effects in various sectors, e.g agriculture, health, GDP. The scale and magnitude of computing and data have proven to increase significantly in the last decade, thus making data delivery methods to the world a herculean research problem by itself. In addition to this, the time and efforts invested by a user in analyzing and peer-reviewing a research article is very laborious. Literature shows numerous outstanding climate studies published in International climate assessment reports, such as the Intergovernmental Panel on Climate Change (IPCC), the United Nations body for assessing the science related to climate change. The need to verify the research and make it reproducible and transparent before it gets translated into major decisions is, now more than ever, one of our most critical challenges. In this presentation, we will paint a picture of the history of climate computing and analytics with significant transformations applied in order to make meaningful, quantifiable, credible, interoperable, accessible and reusable climate research. In other words, we will draw a path towards reproducible research using Docker containers for massive data publishing and climate analytics. This paper will also discuss some of the pioneering efforts from collaborators from other laboratories and organizations (such as ESGF, Google, NASA JPL, Columbia University, PMEL, etc.) in the area of Docker containers in computing and analysis on and off the cloud.
Democratizing Machine Learning: Perspective from a scikit-learn CreatorDatabricks
<p>Once an obscure branch of applied mathematics, machine learning is now the darling of tech. I will talk about lessons learned democratizing machine learning. How libraries like scikit-learn were designed to empower users: simplifying but avoiding ambiguous behaviors. How the Python data ecosystem was built from scientific computing tools: the importance of good numerics. How some machine-learning patterns easily provide value to real-world situations. I will also discuss remain challenges to address and the progresses that we are making. Scaling up brings different bottlenecks to numerics. Integrating data in the statistical models, a hurdle to data-science practice requires to rethink data cleaning pipelines.</p><p>This talk will drawn from my experience as a scikit-learn developer, but also as a researcher in machine learning and applications.</p>
In this slidecast, Jason Stowe from Cycle Computing describes the company's recent record-breaking Petascale CycleCloud HPC production run.
"For this big workload, a 156,314-core CycleCloud behemoth spanning 8 AWS regions, totaling 1.21 petaFLOPS (RPeak, not RMax) of aggregate compute power, to simulate 205,000 materials, crunched 264 compute years in only 18 hours. Thanks to Cycle's software and Amazon's Spot Instances, a supercomputing environment worth $68M if you had bought it, ran 2.3 Million hours of material science, approximately 264 compute-years, of simulation in only 18 hours, cost only $33,000, or $0.16 per molecule."
Learn more: http://blog.cyclecomputing.com/2013/11/back-to-the-future-121-petaflopsrpeak-156000-core-cyclecloud-hpc-runs-264-years-of-materials-science.html
Watch the video presentation: http://wp.me/p3RLHQ-aO9
My recent presentation about what is Big Data, Why so much Hype now, Startling Facts, Opportunity, History, Important Research Papers such as GFS, Map-Reduce , Technology Platforms and Organizations , Hadoop, Cassandra, Introduction to Hadoop, Contribution of Indians to various Big Data technologies working in Google, Cloudera, Hortonworks, Yahoo, Facebook, Aadhar - "All your answers lie in data - @Sameer Sawhney"
Big Data Day LA 2016/ Big Data Track - Twitter Heron @ Scale - Karthik Ramasa...Data Con LA
Twitter generates billions and billions of events per day. Analyzing these events in real time presents a massive challenge. Twitter designed and deployed a new streaming system called Heron. Heron has been in production nearly 2 years and is widely used by several teams for diverse use cases. This talk looks at Twitter's operating experiences and challenges of running Heron at scale and the approaches taken to solve those challenges.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
(BDT311) MegaRun: Behind the 156,000 Core HPC Run on AWS and Experience of On...Amazon Web Services
"Not only did the 156,000+ core run (nicknamed the MegaRun) on Amazon EC2 break industry records for size, scale, and power, but it also delivered real-world results. The University of Southern California ran the high-performance computing job in the cloud to evaluate over 220,000 compounds and build a better organic solar cell. In this session, USC provides an update on the six promising compounds that we have found and is now synthesizing in laboratories for a clean energy project. We discuss the implementation of and lessons learned in running a cluster in eight AWS regions worldwide, with highlights from Cycle Computing's project Jupiter, a low-overhead cloud scheduler and workload manager. This session also looks at how the MegaRun was financially achievable using the Amazon EC2 Spot Instance market, including an in-depth discussion on leveraging Spot Instances, a strategy to deal with the variability of Spot pricing, and a template to avoid compromising workflow integrity, security, or management.
After a year of production workloads on AWS, HGST, a Western Digital Company, has zeroed in on understanding how to create on-demand clusters to maximize value on AWS. HGST will outline the company's successes in addressing the company's changes in operations, culture, and behavior to this new vision of on-demand clusters. In addition, the session will provide insights into leveraging Amazon EC2 Spot Instances to reduce costs and maximize value, while maintaining the needed flexibility, and agility that AWS is known for.andquot;
"
How novel compute technology transforms life science researchDenis C. Bauer
Unprecedented data volumes and pressure on turnaround time driven by commercial applications require bioinformatics solutions to evolve to meed these new demands. New compute paradigms and cloud-based IT solutions enable this transition. Here I present two solution capable of meeting these demands for genomic variant analysis, VariantSpark, as well as genome engineering applications, GT-Scan2.
VariantSpark classifies 3000 individuals with 80 Million genomic variants each in under 30 minutes. This Hadoop/Spark solution for machine learning application on genomic data is hence capable to scale up to population size cohorts.
GT-Scan2, identifies CRISPR target sites by minimizing off-target effects and maximizing on-target efficiency. This optimization is powered by AWS Lambda functions, which offer an “always-on” web service that can instantaneously recruit enough compute resources keep runtime stable even for queries with several thousand of potential target sites.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
Laurie Goodman: Sharing and Reusing Cell Image Data, ASCB/EMBO 2017 Subgroup ...GigaScience, BGI Hong Kong
Laurie Goodman's pre-prepared slides for the Subgroup S Sharing and Reusing Cell Image Data session at the 2017 ASCB│EMBO meeting in Philadelphia. December 2017
Reusable Software and Open Data To Optimize AgricultureDavid LeBauer
Abstract:
Humans need a secure and sustainable food supply, and science can help. We have an opportunity to transform agriculture by combining knowledge of organisms and ecosystems to engineer ecosystems that sustainably produce food, fuel, and other services. The challenge is that the information we have. Measurements, theories, and laws found in publications, notebooks, measurements, software, and human brains are difficult to combine. We homogenize, encode, and automate the synthesis of data and mechanistic understanding in a way that links understanding at different scales and across domains. This allows extrapolation, prediction, and assessment. Reusable components allow automated construction of new knowledge that can be used to assess, predict, and optimize agro-ecosystems.
Developing reusable software and open-access databases is hard, and examples will illustrate how we use the Predictive Ecosystem Analyzer (PEcAn, pecanproject.org), the Biofuel Ecophysiological Traits and Yields database (BETYdb, betydb.org), and ecophysiological crop models to predict crop yield, decide which crops to plant, and which traits can be selected for the next generation of data driven crop improvement. A next step is to automate the use of sensors mounted on robots, drones, and tractors to assess plants in the field. The TERRA Reference Phenotyping Platform (TERRA-Ref, terraref.github.io) will provide an open access database and computing platform on which researchers can use and develop tools that use sensor data to assess and manage agricultural and other terrestrial ecosystems.
TERRA-Ref will adopt existing standards and develop modular software components and common interfaces, in collaboration with researchers from iPlant, NEON, AgMIP, USDA, rOpenSci, ARPA-E, many scientists and industry partners. Our goal is to advance science by enabling efficient use, reuse, exchange, and creation of knowledge.
---
Invited talk for the "Informatics for Reproducibility in Earth and Environmental Science Research" session at the American Geophysical Union Fall Meeting, Dec 17 2015.
Plenary talk at the international Synchrotron Radiation Instrumentation conference in Taiwan, on work with great colleagues Ben Blaiszik, Ryan Chard, Logan Ward, and others.
Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. I present here three projects that use cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. I draw conclusions about best practices for building next-generation data automation systems for future light sources.
DCSF 19 Towards Reproducable Climate ResearchDocker, Inc.
Aparna Radhakrishnan, Engility
NOAA/GFDL was founded in 1955 and is still in the forefront of climate research, contributing to the numerous policies and decisions undertaken in this world of evolving responses with respect to climate, which in turn creates an avalanche of effects in various sectors, e.g agriculture, health, GDP. The scale and magnitude of computing and data have proven to increase significantly in the last decade, thus making data delivery methods to the world a herculean research problem by itself. In addition to this, the time and efforts invested by a user in analyzing and peer-reviewing a research article is very laborious. Literature shows numerous outstanding climate studies published in International climate assessment reports, such as the Intergovernmental Panel on Climate Change (IPCC), the United Nations body for assessing the science related to climate change. The need to verify the research and make it reproducible and transparent before it gets translated into major decisions is, now more than ever, one of our most critical challenges. In this presentation, we will paint a picture of the history of climate computing and analytics with significant transformations applied in order to make meaningful, quantifiable, credible, interoperable, accessible and reusable climate research. In other words, we will draw a path towards reproducible research using Docker containers for massive data publishing and climate analytics. This paper will also discuss some of the pioneering efforts from collaborators from other laboratories and organizations (such as ESGF, Google, NASA JPL, Columbia University, PMEL, etc.) in the area of Docker containers in computing and analysis on and off the cloud.
Democratizing Machine Learning: Perspective from a scikit-learn CreatorDatabricks
<p>Once an obscure branch of applied mathematics, machine learning is now the darling of tech. I will talk about lessons learned democratizing machine learning. How libraries like scikit-learn were designed to empower users: simplifying but avoiding ambiguous behaviors. How the Python data ecosystem was built from scientific computing tools: the importance of good numerics. How some machine-learning patterns easily provide value to real-world situations. I will also discuss remain challenges to address and the progresses that we are making. Scaling up brings different bottlenecks to numerics. Integrating data in the statistical models, a hurdle to data-science practice requires to rethink data cleaning pipelines.</p><p>This talk will drawn from my experience as a scikit-learn developer, but also as a researcher in machine learning and applications.</p>
In this slidecast, Jason Stowe from Cycle Computing describes the company's recent record-breaking Petascale CycleCloud HPC production run.
"For this big workload, a 156,314-core CycleCloud behemoth spanning 8 AWS regions, totaling 1.21 petaFLOPS (RPeak, not RMax) of aggregate compute power, to simulate 205,000 materials, crunched 264 compute years in only 18 hours. Thanks to Cycle's software and Amazon's Spot Instances, a supercomputing environment worth $68M if you had bought it, ran 2.3 Million hours of material science, approximately 264 compute-years, of simulation in only 18 hours, cost only $33,000, or $0.16 per molecule."
Learn more: http://blog.cyclecomputing.com/2013/11/back-to-the-future-121-petaflopsrpeak-156000-core-cyclecloud-hpc-runs-264-years-of-materials-science.html
Watch the video presentation: http://wp.me/p3RLHQ-aO9
My recent presentation about what is Big Data, Why so much Hype now, Startling Facts, Opportunity, History, Important Research Papers such as GFS, Map-Reduce , Technology Platforms and Organizations , Hadoop, Cassandra, Introduction to Hadoop, Contribution of Indians to various Big Data technologies working in Google, Cloudera, Hortonworks, Yahoo, Facebook, Aadhar - "All your answers lie in data - @Sameer Sawhney"
Big Data Day LA 2016/ Big Data Track - Twitter Heron @ Scale - Karthik Ramasa...Data Con LA
Twitter generates billions and billions of events per day. Analyzing these events in real time presents a massive challenge. Twitter designed and deployed a new streaming system called Heron. Heron has been in production nearly 2 years and is widely used by several teams for diverse use cases. This talk looks at Twitter's operating experiences and challenges of running Heron at scale and the approaches taken to solve those challenges.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
(BDT311) MegaRun: Behind the 156,000 Core HPC Run on AWS and Experience of On...Amazon Web Services
"Not only did the 156,000+ core run (nicknamed the MegaRun) on Amazon EC2 break industry records for size, scale, and power, but it also delivered real-world results. The University of Southern California ran the high-performance computing job in the cloud to evaluate over 220,000 compounds and build a better organic solar cell. In this session, USC provides an update on the six promising compounds that we have found and is now synthesizing in laboratories for a clean energy project. We discuss the implementation of and lessons learned in running a cluster in eight AWS regions worldwide, with highlights from Cycle Computing's project Jupiter, a low-overhead cloud scheduler and workload manager. This session also looks at how the MegaRun was financially achievable using the Amazon EC2 Spot Instance market, including an in-depth discussion on leveraging Spot Instances, a strategy to deal with the variability of Spot pricing, and a template to avoid compromising workflow integrity, security, or management.
After a year of production workloads on AWS, HGST, a Western Digital Company, has zeroed in on understanding how to create on-demand clusters to maximize value on AWS. HGST will outline the company's successes in addressing the company's changes in operations, culture, and behavior to this new vision of on-demand clusters. In addition, the session will provide insights into leveraging Amazon EC2 Spot Instances to reduce costs and maximize value, while maintaining the needed flexibility, and agility that AWS is known for.andquot;
"
How novel compute technology transforms life science researchDenis C. Bauer
Unprecedented data volumes and pressure on turnaround time driven by commercial applications require bioinformatics solutions to evolve to meed these new demands. New compute paradigms and cloud-based IT solutions enable this transition. Here I present two solution capable of meeting these demands for genomic variant analysis, VariantSpark, as well as genome engineering applications, GT-Scan2.
VariantSpark classifies 3000 individuals with 80 Million genomic variants each in under 30 minutes. This Hadoop/Spark solution for machine learning application on genomic data is hence capable to scale up to population size cohorts.
GT-Scan2, identifies CRISPR target sites by minimizing off-target effects and maximizing on-target efficiency. This optimization is powered by AWS Lambda functions, which offer an “always-on” web service that can instantaneously recruit enough compute resources keep runtime stable even for queries with several thousand of potential target sites.
VariantSpark: applying Spark-based machine learning methods to genomic inform...Denis C. Bauer
Genomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. Here we introduce VariantSpark, which utilizes Hadoop/Spark along with its machine learning library, MLlib, providing the means of parallelisation for population-scale bioinformatics tasks. VariantSpark is the interface to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results.
To demonstrate the capabilities of VariantSpark, we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VariantSpark is 80% faster than the Spark-based genome clustering approach, ADAM, the comparable implementation using Hadoop/Mahout, as well as Admixture, a commonly used tool for determining individual ancestries. It is over 90% faster than traditional implementations using R and Python. These benefits of speed, resource consumption and scalability enables VariantSpark to open up the usage of advanced, efficient machine learning algorithms to genomic data.
The package is written in Scala and available at https://github.com/BauerLab/VariantSpark.
AWS Public Sector Symposium 2014 Canberra | Big Data in the Cloud: Accelerati...Amazon Web Services
The cloud not only helps organizations do things better, cheaper, and faster; it also drives breakthroughs that transform mission delivery. This session will feature a panel of international government and university leaders who are using the cloud to take on big data challenges, and innovating in the “white space” between data silos to deliver impact.
Time to Science/Time to Results: Transforming Research in the CloudAmazon Web Services
This session demonstrates how cloud can accelerate breakthroughs in scientific research by providing on-demand access to powerful computing. You will gain insight into how scientific researchers are using the cloud to solve complex science, engineering, and business problems that require high bandwidth, low latency networking and very high compute capabilities. You will hear how leveraging the cloud reduces the costs and time to conduct large scale, worldwide collaborative research. Researchers can then access computational power, data storage, and supercomputing resources, and data sharing capabilities in a cost-efficient manner without implementation delays. Disease research can be accomplished in a fraction of the time, and innovative researchers in small schools or distant corners of the world have access to the same computing power as those at major research institutions by leveraging Amazon EC2, Amazon S3, optimizing C3 instances and more to increase collaboration. This session will provide best practices and insight from UC Berkeley AMP Lab on the services used to connect disparate sets of data to drive meaningful new insight and impact.
This is a talk titled "Cloud-Based Services For Large Scale Analysis of Sequence & Expression Data: Lessons from Cistrack" that I gave at CAMDA 2009 on October 6, 2009.
Cloud-native machine learning - Transforming bioinformatics research Denis C. Bauer
Cloud computing and artificial intelligence transforms bioinformatics research
Denis Bauer, Transformational Bioinformatics Team
Genomic data is outpacing traditional Big Data disciplines, producing more information than Astronomy, twitter, and YouTube combined. As such, Genomic research has leapfrogged to the forefront of Big Data and Cloud solutions. We developed software platforms using the latest in cloud architecture, artificial intelligence and machine learning to support every aspect genome medicine; from disease gene detection through to validation and personalized medicine.
This talk outlines how we find disease genes for complex genetic diseases, such as ALS, using VariantSpark, which is a custom machine learning implementation capable of dealing with Whole Genome Sequencing data of 80 million common and rare variants. To support disease gene validation, we created GT-Scan, which is an innovative web application, which we think of it as the “search engine for the genome”. It enables researchers to identify the optimal editing spot to create animal models efficiently. The talk concludes by demonstrating how cloud-based software distribution channels (digital Marketplaces) can be harnessed to share bioinformatics tools internationally and make research more reproducible.
The Transformation of Systems Biology Into A Large Data ScienceRobert Grossman
This is a talk I gave at the Institute for Genomics & System Biology (IGSB) on December 7, 2009. The talk looks at the role of cloud computing platforms, including private clouds, for managing the large data produced by next generation sequencing platforms.
Scott Edmunds talk on GigaScience Big-Data, Data Citation and future data handling at the International Conference of Genomics on the 15th November 2011.
A Data Ecosystem to Support Machine Learning in Materials ScienceGlobus
This presentation was given at the 2019 GlobusWorld Conference in Chicago, IL by Ben Blaiszik from University of Chicago and Argonne National Laboratory Data Science and Learning Division.
Translating genomics into clinical practice - 2018 AWS summit keynoteDenis C. Bauer
CSIRO's part of the co-presented Keynote at the AWS Public Sector Summit in Canberra on genomics health care. Three key messages: 1) We need a shift from treatment towards prevention 2) Once you go serverless you never go back 3) DevOps 2.0: Hypothesis-driven architecture evolution
Finding Needles in Genomic Haystacks with “Wide” Random Forest: Spark Summit ...Spark Summit
Recent advances in genome sequencing technologies and bioinformatics have enabled whole-genomes to be studied at population-level rather then for small number of individuals. This provides new power to whole genome association studies (WGAS
), which now seek to identify the multi-gene causes of common complex diseases like diabetes or cancer.
As WGAS involve studying thousands of genomes, they pose both technological and methodological challenges. The volume of data is significant, for example the dataset from 1000 Genomes project with genomes of 2504 individuals includes nearly 85M genomic variants with raw data size of 0.8 TB. The number of features is enormous and greatly exceeds the number of samples, which makes it challenging to apply traditional statistical approaches.
Random forest is one of the methods that was found to be useful in this context, both because of its potential for parallelization and its robustness. Although there is a number of big data implementations available (including Spark ML) they are tuned for typical dataset with large number of samples and relatively small number of variables, and either fail or are inefficient in the GWAS context especially, that a costly data preprocessing is usually required.
To address these problems, we have developed the RandomForestHD – a Spark based implementation optimized for highly dimensional data sets. We have successfully RandomForestHD applied it to datasets beyond the reach of other tools and for smaller datasets found its performance superior. We are currently applying RandomForestHD, released as part of the VariantSpark toolkit, to a number of WGAS studies.
In the presentation we will introduce the domain of WGAS and related challenges, present RandomForestHD with its design principles and implementation details with regards to Spark, compare its performance with other tools, and finally showcase the results of a few WGAS applications.
Scott Edmunds talk in the "Policies and Standards for Reproducible Research" session on Revolutionizing Data Dissemination: GigaScience, at the Genomic Standards Consortium meeting at Shenzhen. 6th March 2012
Similar to VariantSpark - a Spark library for genomics (20)
Core deck for developer audience, explaining origins, mission, activities and goals of 'Teaching Kids Programming non-profit - courseware for teachers to teach kids core computational concepts with a customized version of Java
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.
This pdf is about the Schizophrenia.
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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.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
3. Natalie Twine
Transformational Bioinformatics Team
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
Denis Bauer Oscar Luo Rob Dunne Piotr SzulAidan O’BrienLaurence Wilson
Adrian White
Mia Champion
Gaetan Burgio
Collaborators
David Levy
News
Software
Dan Andrews
Kaitao Lai
Kaylene Simpson
Iva Nikolic
Ian Blair
Kelly Williams
5. Unsupervised ML : K-Means
www.cloudaccess.eu
1000 x 40 Million variants
Matrix *
k-means
Predict super
population
4
14 ethnic groups and
s u p e r
populations
VariantSpark | Denis C. Bauer @allPowerde
* VariantSpark can also process phase 3 data: 3000 individuals and 80 million variants
9. Performance – Faster and More Accurate
VariantSpark is the only method to scale to 100% of the genome
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
10. Scaling to 50 M variables and 10 K samples
Transformational Bioinformatics | Denis C. Bauer | @allPowerde
100K trees: 5 – 50h
AWS: ~$215.50
100K trees: 200 – 2000h
AWS: ~ $ 8620.00
• Yarn Cluster (12 workers)
• 16 x Intel Xeon E5-2660@2.20GHz CPU
• 128 GB of RAM
• Spark 1.6.1 on YARN
• 128 executors
• 6GB / executor (0.75TB)
• Synthetic dataset (mtry = 0.25)
Whole Genome
Range
GWAS Range