This document provides a summary of the Scalable Genome Analysis with ADAM project. ADAM is an open-source, high-performance, distributed platform for genomic analysis that defines a data schema, data layout on disk, and programming interface for distributed processing of genomic data using Spark and Scala. The goal of ADAM is to integrate across terabyte and petabyte-scale datasets to enable the discovery of low frequency genetic variants linked to traits and diseases.
BioBankCloud: Machine Learning on Genomics + GA4GH @ Med at ScaleAndy Petrella
A talk given at the BioBankCloud conference in Feb 2015 about distributed computing in the contexts of genomics and health.
In this one, we exposed what results we obtained exploring the 1000genomes data using ADAM, followed by an introduction to our scalable GA4GH server implementation built using ADAM, Apache Spark and Play Framework 2.
Spark Summit Europe: Share and analyse genomic data at scaleAndy Petrella
Share and analyse genomic data
at scale with Spark, Adam, Tachyon & the Spark Notebook
Sharp intro to Genomics data
What are the Challenges
Distributed Machine Learning to the rescue
Projects: Distributed teams
Research: Long process
Towards Maximum Share for efficiency
Managing Genomes At Scale: What We Learned - StampedeCon 2014StampedeCon
At StampedeCon 2014, Rob Long (Monstanto) presented "Managing Genomes At Scale: What We Learned."
Monsanto generates large amounts of genomic sequence data every year. Agronomists and other scientists use this data as input for predictive analytics to aid breeding and the discovery of new traits such as disease or drought resistance. In order to enable the broadest use possible of this valuable data, scientists would like to query genomic data by species, chromosome, position, and myriad other categories. We present our solutions to these problems, as realized on top of HBase here at Monsanto.We will be discussing our particular learnings around: flat/wide vs tall/narrow HBase schema design, preprocessing and caching windows of data for use in web based visualizations, approaches to complex multi-join queries across deep data sets, and distributed indexing via SolrCloud.
Data Enthusiasts London: Scalable and Interoperable data services. Applied to...Andy Petrella
Data science requires so many skills, people and time before the results can be accessed. Moreover, these results cannot be static anymore. And finally, the Big Data comes to the plate and the whole tool chain needs to change.
In this talk Data Fellas introduces Shar3, a tool kit aiming to bridged the gaps to build a interactive distributed data processing pipeline, or loop!
Then the talk covers genomics nowadays problems including data types, processing, discovery by introducing the GA4GH initiative and its implementation using Shar3.
BioBankCloud: Machine Learning on Genomics + GA4GH @ Med at ScaleAndy Petrella
A talk given at the BioBankCloud conference in Feb 2015 about distributed computing in the contexts of genomics and health.
In this one, we exposed what results we obtained exploring the 1000genomes data using ADAM, followed by an introduction to our scalable GA4GH server implementation built using ADAM, Apache Spark and Play Framework 2.
Spark Summit Europe: Share and analyse genomic data at scaleAndy Petrella
Share and analyse genomic data
at scale with Spark, Adam, Tachyon & the Spark Notebook
Sharp intro to Genomics data
What are the Challenges
Distributed Machine Learning to the rescue
Projects: Distributed teams
Research: Long process
Towards Maximum Share for efficiency
Managing Genomes At Scale: What We Learned - StampedeCon 2014StampedeCon
At StampedeCon 2014, Rob Long (Monstanto) presented "Managing Genomes At Scale: What We Learned."
Monsanto generates large amounts of genomic sequence data every year. Agronomists and other scientists use this data as input for predictive analytics to aid breeding and the discovery of new traits such as disease or drought resistance. In order to enable the broadest use possible of this valuable data, scientists would like to query genomic data by species, chromosome, position, and myriad other categories. We present our solutions to these problems, as realized on top of HBase here at Monsanto.We will be discussing our particular learnings around: flat/wide vs tall/narrow HBase schema design, preprocessing and caching windows of data for use in web based visualizations, approaches to complex multi-join queries across deep data sets, and distributed indexing via SolrCloud.
Data Enthusiasts London: Scalable and Interoperable data services. Applied to...Andy Petrella
Data science requires so many skills, people and time before the results can be accessed. Moreover, these results cannot be static anymore. And finally, the Big Data comes to the plate and the whole tool chain needs to change.
In this talk Data Fellas introduces Shar3, a tool kit aiming to bridged the gaps to build a interactive distributed data processing pipeline, or loop!
Then the talk covers genomics nowadays problems including data types, processing, discovery by introducing the GA4GH initiative and its implementation using Shar3.
Lightning fast genomics with Spark, Adam and ScalaAndy Petrella
We are at a time where biotech allow us to get personal genomes for $1000. Tremendous progress since the 70s in DNA sequencing have been done, e.g. more samples in an experiment, more genomic coverages at higher speeds. Genomic analysis standards that have been developed over the years weren't designed with scalability and adaptability in mind. In this talk, we’ll present a game changing technology in this area, ADAM, initiated by the AMPLab at Berkeley. ADAM is framework based on Apache Spark and the Parquet storage. We’ll see how it can speed up a sequence reconstruction to a factor 150.
Spark meetup london share and analyse genomic data at scale with spark, adam...Andy Petrella
Genomics and Health data is nowadays one of the hot topics requiring lots of computations and specially machine learning. This helps science with a very relevant societal impact to get even better outcome. That is why Apache Spark and its ADAM library is a must have.
This talk will be twofold.
First, we'll show how Apache Spark, MLlib and ADAM can be plugged all together to extract information from even huge and wide genomics dataset. Everything will be packed into examples from the Spark Notebook, showing how bio-scientists can work interactively with such a system.
Second, we'll explain how these methodologies and even the datasets themselves can be shared at very large scale between remote entities like hospitals or laboratories using micro services leveraging Apache Spark, ADAM, Play Framework 2, Avro and Tachyon.
"Spark, Deep Learning and Life Sciences, Systems Biology in the Big Data Age"...Dataconomy Media
"Spark, DeepLearning and Life Sciences, Systems Biology in the Big Data age" Dev Lakhani, Founder of Batch Insights
YouTube Link: https://www.youtube.com/watch?v=z6aTv0ZKndQ
Watch more from Data Natives 2015 here: http://bit.ly/1OVkK2J
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the author:
Dev Lakhani has a background in Software Engineering and Computational Statistics and is a founder of Batch Insights, a Big Data consultancy that has worked on numerous Big Data architectures and data science projects in Tier 1 banking, global telecoms, retail, media and fashion. Dev has been actively working with the Hadoop infrastructure since it’s inception and is currently researching and contributing to the Apache Spark and Tachyon community.
DNA sequencing is producing a wave of data which will change the way that drugs are developed, patients diagnosed, and our understanding of human biology. To fulfill this promise, however, the tools for interpretation and analysis must scale to match the quantity and diversity of "big data genomics."
ADAM is an open-source genomics processing engine, built using Spark, Apache Avro, and Parquet. This talk will discuss some of the advantages that the Spark platform brings to genomics, the benefits of using technologies like Parquet in conjunction with Spark, and the challenges of adapting new technologies for existing tools in bioinformatics.
These are slides for a talk given at the Apache Spark Meetup in Boston on October 20, 2014.
Hadoop for Bioinformatics: Building a Scalable Variant StoreUri Laserson
Talk at Mount Sinai School of Medicine. Introduction to the Hadoop ecosystem, problems in bioinformatics data analytics, and a specific use case of building a genome variant store backed by Cloudera Impala.
Slides presented at the Spark Summit East 2015 (http://spark-summit.org/east). Video should be available through their site, at some point in the future.
(Some of these slides were adapted from an earlier talk "Why is Bioinformatics a Good Fit for Spark?", given to a Spark meetup audience.)
Presentation from Strata-Hadoop 2015 (http://strataconf.com/big-data-conference-ny-2015/public/schedule/speaker/197575) -- a brief introduction to genomics followed by an overview of approaches to bioinformatics coding using Spark. Pretty high-level.
Population-scale high-throughput sequencing data analysisDenis C. Bauer
Unprecedented computational capabilities and high-throughput data collection methods promise a new era of personalised, evidence-based healthcare, utilising individual genomic profiles to tailor health management as demonstrated by recent successes in rare genetic disorders or stratified cancer treatments. However, processing genomic information at a scale relevant for the health-system remains challenging due to high demands on data reproducibility and data provenance. Furthermore, the necessary computational requirements requires a large investment associated with compute hardware and IT personnel, which is a barrier to entry for small laboratories and difficult to maintain at peak times for larger institutes. This hampers the creation of time-reliable production informatics environments for clinical genomics. Commercial cloud computing frameworks, like Amazon Web Services (AWS) provide an economical alternative to in-house compute clusters as they allow outsourcing of computation to third-party providers, while retaining the software and compute flexibility.
To cater for this resource-hungry, fast pace yet sensitive environment of personalized medicine, we developed NGSANE, a Linux-based, HPC-enabled framework that minimises overhead for set up and processing of new projects yet maintains full flexibility of custom scripting and data provenance when processing raw sequencing data either on a local cluster or Amazon’s Elastic Compute Cloud (EC2).
A talk presented at an NSF Workshop on Data-Intensive Computing, July 30, 2009.
Extreme scripting and other adventures in data-intensive computing
Data analysis in many scientific laboratories is performed via a mix of standalone analysis programs, often written in languages such as Matlab or R, and shell scripts, used to coordinate multiple invocations of these programs. These programs and scripts all run against a shared file system that is used to store both experimental data and computational results.
While superficially messy, the flexibility and simplicity of this approach makes it highly popular and surprisingly effective. However, continued exponential growth in data volumes is leading to a crisis of sorts in many laboratories. Workstations and file servers, even local clusters and storage arrays, are no longer adequate. Users also struggle with the logistical challenges of managing growing numbers of files and computational tasks. In other words, they face the need to engage in data-intensive computing.
We describe the Swift project, an approach to this problem that seeks not to replace the scripting approach but to scale it, from the desktop to larger clusters and ultimately to supercomputers. Motivated by applications in the physical, biological, and social sciences, we have developed methods that allow for the specification of parallel scripts that operate on large amounts of data, and the efficient and reliable execution of those scripts on different computing systems. A particular focus of this work is on methods for implementing, in an efficient and scalable manner, the Posix file system semantics that underpin scripting applications. These methods have allowed us to run applications unchanged on workstations, clusters, infrastructure as a service ("cloud") systems, and supercomputers, and to scale applications from a single workstation to a 160,000-core supercomputer.
Swift is one of a variety of projects in the Computation Institute that seek individually and collectively to develop and apply software architectures and methods for data-intensive computing. Our investigations seek to treat data management and analysis as an end-to-end problem. Because interesting data often has its origins in multiple organizations, a full treatment must encompass not only data analysis but also issues of data discovery, access, and integration. Depending on context, data-intensive applications may have to compute on data at its source, move data to computing, operate on streaming data, or adopt some hybrid of these and other approaches.
Thus, our projects span a wide range, from software technologies (e.g., Swift, the Nimbus infrastructure as a service system, the GridFTP and DataKoa data movement and management systems, the Globus tools for service oriented science, the PVFS parallel file system) to application-oriented projects (e.g., text analysis in the biological sciences, metagenomic analysis, image analysis in neuroscience, information integration for health care applications, management of experimental data from X-ray sources, diffusion tensor imaging for computer aided diagnosis), and the creation and operation of national-scale infrastructures, including the Earth System Grid (ESG), cancer Biomedical Informatics Grid (caBIG), Biomedical Informatics Research Network (BIRN), TeraGrid, and Open Science Grid (OSG).
For more information, please see www.ci.uchicago/swift.
Algorithms and Tools for Genomic Analysis on Spark: Spark Summit East talk by...Spark Summit
Hammer Lab has built several tools for analyzing genomic data on Spark, as well as libraries for more general computations using RDDs; I’ll discuss some of the most interesting applications and algorithms therein:
Guacamole (https://github.com/hammerlab/guacamole) is a somatic variant caller built on Spark; it identifies mutations in cancer genomes in a fraction of the time that comparable tools take.
Pageant (https://github.com/hammerlab/pageant) contains miscellaneous other genomic analyses and a few interesting and novel algorithms for massively-parallel Burrows-Wheeler-Transform and FM-Index construction.
Magic RDDs (https://github.com/hammerlab/magic-rdds) contains some yet more interesting general-purpose algorithms implemented on RDDs.
Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 67...GigaScience, BGI Hong Kong
Lisa Johnson's talk at the #ICG13 GigaScience Prize Track: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Shenzhen, 26th October 2018
MongoDB and the Connectivity Map: Making Connections Between Genetics and Dis...MongoDB
The Broad Institute has developed a novel high-throughput gene-expression profiling technology and has used it to build an open-source catalog of over a million profiles that captures the functional states of cells when treated with drugs and other types of perturbations. Referred to as the Connectivity Map (or CMap), these data when paired with pattern matching algorithms, facilitate the discovery of connections between drugs, genes and diseases. We wished to expose this resource to scientists around the world via an API that is easily accessible to programmers and biologists alike. We required a database solution that could handle a variety of data types and handle frequent changes to the schema. We realized that a relational database did not fit our needs, and gravitated towards MongoDB for its ease of use, support for dynamic schema, complex data structures and expressive query syntax. In this talk, we’ll walk through how we built the CMap library. We’ll discuss why we chose MongoDB, the various schema design iterations and tradeoffs we’ve made, how people are using the API, and what we’re planning for the next generation of biomedical data.
Lightning fast genomics with Spark, Adam and ScalaAndy Petrella
We are at a time where biotech allow us to get personal genomes for $1000. Tremendous progress since the 70s in DNA sequencing have been done, e.g. more samples in an experiment, more genomic coverages at higher speeds. Genomic analysis standards that have been developed over the years weren't designed with scalability and adaptability in mind. In this talk, we’ll present a game changing technology in this area, ADAM, initiated by the AMPLab at Berkeley. ADAM is framework based on Apache Spark and the Parquet storage. We’ll see how it can speed up a sequence reconstruction to a factor 150.
Spark meetup london share and analyse genomic data at scale with spark, adam...Andy Petrella
Genomics and Health data is nowadays one of the hot topics requiring lots of computations and specially machine learning. This helps science with a very relevant societal impact to get even better outcome. That is why Apache Spark and its ADAM library is a must have.
This talk will be twofold.
First, we'll show how Apache Spark, MLlib and ADAM can be plugged all together to extract information from even huge and wide genomics dataset. Everything will be packed into examples from the Spark Notebook, showing how bio-scientists can work interactively with such a system.
Second, we'll explain how these methodologies and even the datasets themselves can be shared at very large scale between remote entities like hospitals or laboratories using micro services leveraging Apache Spark, ADAM, Play Framework 2, Avro and Tachyon.
"Spark, Deep Learning and Life Sciences, Systems Biology in the Big Data Age"...Dataconomy Media
"Spark, DeepLearning and Life Sciences, Systems Biology in the Big Data age" Dev Lakhani, Founder of Batch Insights
YouTube Link: https://www.youtube.com/watch?v=z6aTv0ZKndQ
Watch more from Data Natives 2015 here: http://bit.ly/1OVkK2J
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the author:
Dev Lakhani has a background in Software Engineering and Computational Statistics and is a founder of Batch Insights, a Big Data consultancy that has worked on numerous Big Data architectures and data science projects in Tier 1 banking, global telecoms, retail, media and fashion. Dev has been actively working with the Hadoop infrastructure since it’s inception and is currently researching and contributing to the Apache Spark and Tachyon community.
DNA sequencing is producing a wave of data which will change the way that drugs are developed, patients diagnosed, and our understanding of human biology. To fulfill this promise, however, the tools for interpretation and analysis must scale to match the quantity and diversity of "big data genomics."
ADAM is an open-source genomics processing engine, built using Spark, Apache Avro, and Parquet. This talk will discuss some of the advantages that the Spark platform brings to genomics, the benefits of using technologies like Parquet in conjunction with Spark, and the challenges of adapting new technologies for existing tools in bioinformatics.
These are slides for a talk given at the Apache Spark Meetup in Boston on October 20, 2014.
Hadoop for Bioinformatics: Building a Scalable Variant StoreUri Laserson
Talk at Mount Sinai School of Medicine. Introduction to the Hadoop ecosystem, problems in bioinformatics data analytics, and a specific use case of building a genome variant store backed by Cloudera Impala.
Slides presented at the Spark Summit East 2015 (http://spark-summit.org/east). Video should be available through their site, at some point in the future.
(Some of these slides were adapted from an earlier talk "Why is Bioinformatics a Good Fit for Spark?", given to a Spark meetup audience.)
Presentation from Strata-Hadoop 2015 (http://strataconf.com/big-data-conference-ny-2015/public/schedule/speaker/197575) -- a brief introduction to genomics followed by an overview of approaches to bioinformatics coding using Spark. Pretty high-level.
Population-scale high-throughput sequencing data analysisDenis C. Bauer
Unprecedented computational capabilities and high-throughput data collection methods promise a new era of personalised, evidence-based healthcare, utilising individual genomic profiles to tailor health management as demonstrated by recent successes in rare genetic disorders or stratified cancer treatments. However, processing genomic information at a scale relevant for the health-system remains challenging due to high demands on data reproducibility and data provenance. Furthermore, the necessary computational requirements requires a large investment associated with compute hardware and IT personnel, which is a barrier to entry for small laboratories and difficult to maintain at peak times for larger institutes. This hampers the creation of time-reliable production informatics environments for clinical genomics. Commercial cloud computing frameworks, like Amazon Web Services (AWS) provide an economical alternative to in-house compute clusters as they allow outsourcing of computation to third-party providers, while retaining the software and compute flexibility.
To cater for this resource-hungry, fast pace yet sensitive environment of personalized medicine, we developed NGSANE, a Linux-based, HPC-enabled framework that minimises overhead for set up and processing of new projects yet maintains full flexibility of custom scripting and data provenance when processing raw sequencing data either on a local cluster or Amazon’s Elastic Compute Cloud (EC2).
A talk presented at an NSF Workshop on Data-Intensive Computing, July 30, 2009.
Extreme scripting and other adventures in data-intensive computing
Data analysis in many scientific laboratories is performed via a mix of standalone analysis programs, often written in languages such as Matlab or R, and shell scripts, used to coordinate multiple invocations of these programs. These programs and scripts all run against a shared file system that is used to store both experimental data and computational results.
While superficially messy, the flexibility and simplicity of this approach makes it highly popular and surprisingly effective. However, continued exponential growth in data volumes is leading to a crisis of sorts in many laboratories. Workstations and file servers, even local clusters and storage arrays, are no longer adequate. Users also struggle with the logistical challenges of managing growing numbers of files and computational tasks. In other words, they face the need to engage in data-intensive computing.
We describe the Swift project, an approach to this problem that seeks not to replace the scripting approach but to scale it, from the desktop to larger clusters and ultimately to supercomputers. Motivated by applications in the physical, biological, and social sciences, we have developed methods that allow for the specification of parallel scripts that operate on large amounts of data, and the efficient and reliable execution of those scripts on different computing systems. A particular focus of this work is on methods for implementing, in an efficient and scalable manner, the Posix file system semantics that underpin scripting applications. These methods have allowed us to run applications unchanged on workstations, clusters, infrastructure as a service ("cloud") systems, and supercomputers, and to scale applications from a single workstation to a 160,000-core supercomputer.
Swift is one of a variety of projects in the Computation Institute that seek individually and collectively to develop and apply software architectures and methods for data-intensive computing. Our investigations seek to treat data management and analysis as an end-to-end problem. Because interesting data often has its origins in multiple organizations, a full treatment must encompass not only data analysis but also issues of data discovery, access, and integration. Depending on context, data-intensive applications may have to compute on data at its source, move data to computing, operate on streaming data, or adopt some hybrid of these and other approaches.
Thus, our projects span a wide range, from software technologies (e.g., Swift, the Nimbus infrastructure as a service system, the GridFTP and DataKoa data movement and management systems, the Globus tools for service oriented science, the PVFS parallel file system) to application-oriented projects (e.g., text analysis in the biological sciences, metagenomic analysis, image analysis in neuroscience, information integration for health care applications, management of experimental data from X-ray sources, diffusion tensor imaging for computer aided diagnosis), and the creation and operation of national-scale infrastructures, including the Earth System Grid (ESG), cancer Biomedical Informatics Grid (caBIG), Biomedical Informatics Research Network (BIRN), TeraGrid, and Open Science Grid (OSG).
For more information, please see www.ci.uchicago/swift.
Algorithms and Tools for Genomic Analysis on Spark: Spark Summit East talk by...Spark Summit
Hammer Lab has built several tools for analyzing genomic data on Spark, as well as libraries for more general computations using RDDs; I’ll discuss some of the most interesting applications and algorithms therein:
Guacamole (https://github.com/hammerlab/guacamole) is a somatic variant caller built on Spark; it identifies mutations in cancer genomes in a fraction of the time that comparable tools take.
Pageant (https://github.com/hammerlab/pageant) contains miscellaneous other genomic analyses and a few interesting and novel algorithms for massively-parallel Burrows-Wheeler-Transform and FM-Index construction.
Magic RDDs (https://github.com/hammerlab/magic-rdds) contains some yet more interesting general-purpose algorithms implemented on RDDs.
Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 67...GigaScience, BGI Hong Kong
Lisa Johnson's talk at the #ICG13 GigaScience Prize Track: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Shenzhen, 26th October 2018
MongoDB and the Connectivity Map: Making Connections Between Genetics and Dis...MongoDB
The Broad Institute has developed a novel high-throughput gene-expression profiling technology and has used it to build an open-source catalog of over a million profiles that captures the functional states of cells when treated with drugs and other types of perturbations. Referred to as the Connectivity Map (or CMap), these data when paired with pattern matching algorithms, facilitate the discovery of connections between drugs, genes and diseases. We wished to expose this resource to scientists around the world via an API that is easily accessible to programmers and biologists alike. We required a database solution that could handle a variety of data types and handle frequent changes to the schema. We realized that a relational database did not fit our needs, and gravitated towards MongoDB for its ease of use, support for dynamic schema, complex data structures and expressive query syntax. In this talk, we’ll walk through how we built the CMap library. We’ll discuss why we chose MongoDB, the various schema design iterations and tradeoffs we’ve made, how people are using the API, and what we’re planning for the next generation of biomedical data.
GRC Workshop at Churchill College on Sep 21, 2014. This is Michael Schatz's talk on the theory and practice of representing population data in graph structures.
Lecture on the annotation of transposable elementsfmaumus
Lecture on the annotation of transposable elements at the CNRS school "BioinfoTE" in 2020 (Fréjus, France). https://bioinfote.sciencesconf.org/
ORGANIZING COMITEE
Emmanuelle Lerat (LBBE – CNRS Université Lyon 1),
Anna-Sophie Fiston-Lavier (ISEM – Université de Montpellier)
Florian Maumus (URGI – INRAe Versailles)
François Sabot (DIADE – IRD Montpellier)
GRC Workshop held at Churchill College on Sep 21, 2014. Talk by Bronwen Aken discussing the Ensembl approach to annotating the complete human reference assembly.
whole genome analysis
history
needs
steps involved
human genome data
NGS
pyrosequencing
illumina
SOLiD
Ion torrent
PacBio
applications
problems
benefits
Similar to Scalable Genome Analysis With ADAM (20)
Rethinking Data-Intensive Science Using Scalable Analytics Systems fnothaft
Presentation from SIGMOD 2015. With Matt Massie, Timothy Danford, Zhao Zhang, Uri Laserson, Carl Yeksigian, Jey Kottalam, Arun Ahuja, Jeff Hammerbacher, Michael Linderman, Michael J. Franklin, Anthony D. Joseph, David A. Patterson. Paper at http://dl.acm.org/citation.cfm?id=2742787.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
1. Scalable Genome Analysis
With ADAM
Frank Austin Nothaft, UC Berkeley AMPLab
fnothaft@berkeley.edu, @fnothaft
4/24/2015
2. Analyzing genomes:
What is our goal?
• Genomes are the “source” code for life:
• The human genome is a 3.2B character
“program”, split across 46 “files”
• Within a species, genomes are ~99.9% similar
• The 0.1% variance gives rise to diverse traits, as
well as diseases
3. The Sequencing Abstraction
It was the best of times, it was the worst of times…
Metaphor borrowed from Michael Schatz
It was the
the best of
times, it was
the worst of
worst of times
best of times
was the worst
• Sequencing is a Poission-substring sampling process
• For $1,000, we can sequence a 30x copy of your genome
4. My focus:
Genome Resequencing
• The Human Genome Project identified the “average”
genome from 20 individuals at $1B cost
• To make this process cheaper, we use our knowledge
of the “average” genome to calculate a diff
• Two problems:
• How do we compute this diff?
• How do we make sense of the differences?
5. The Alignment Abstraction
It was the best of times, it was the worst of times…
It was the
the best of
times, it was
the worst of
worst of times
best of times was the worst
It was the
the best of
times, it was
the worst of
worst of times
best of times
was the worst
6. Sequence Assembly
It was the best of times, it was the worst of times…
It was the
the best of
times, it was
the worst of
worst of times
best of times
was the worst
It was the
the best of
times, it was
the worst of
worst of times
best of times
was the worst
7. Data Intensive Genomics
• “Data intensive science”: by collecting large datasets,
we can statistically generate hypotheses
• New population-scale experiments will sequence
10-100k samples
• 100k samples @ 60x WGS will generate ~20PB of
read data and ~300TB of genotype data
• These large datasets allow us to identify low frequency
variants, and link these variants with diseases
8. Our building block: ADAM
• ADAM is an open source, high performance, distributed
platform for genomic analysis
• ADAM defines a:
1. Data schema and layout on disk*
2. Programming interface for distributed processing of
genomic data using Spark + Scala**
• ADAM is designed with the goal of integrating across terabyte/
petabyte scale datasets to find low frequency variants
* Via Parquet and Avro
** Work on Python integration is underway
9. BDG: ADAM’s Ecosystem
!
ADAM:!
Core API +
CLIs
bdg-
formats:!
Data
schemas
RNAdam:!
RNA analysis
on ADAM
avocado:!
Distributed local
assembler
fig:!
Variant
annotation
eggo:!
Datasets
10. What are the challenges?
• Variant Detection:
• For accurate variant discovery, we want to
reassemble variants, but reassembly is expensive
• We need to statistically integrate over a large
collection of samples to discover low frequency
variants
• Variant Analysis:
• Variants don’t always have straightforward
explanations
11. Variant Detection
• The sequencing process is noisy:
• 2% of bases are mis-sequenced
• If we have a large edit, string alignment may
have errors
• We algorithmically “clean” the reads and apply a
statistical model to reconstruct the genome
12. avocado performs efficient
de Bruijn reassembly
ACACTGCACT
ACA
CAC
ACT
CTG
TGC
GCA
CAC
ACT
ACA CAC ACT
CTGTGCGCA
• Several high accuracy variant callers (GATK, Platypus,
Scalpel) reassemble reads aligned at genomic regions
• Typically use a de Bruijn graph: nodes are k-mers, and
edges represent observed transitions between k-mers
13. Efficient Local Reassembly
• Current methods elaborate all paths through the graph, perform O(hn)
realignments at O(lrlh) cost, score O(h2
) haplotype pairs
• Instead, identify “bubbles” and emit statistics directly from the graph:
• Eliminate expensive realignment!
• Variant alleles are provably canonical.
ACA CAC ACT
CTGTGCGCA
CTTTTCTCA
Reference:
CTGA
Bubble:
CTTA
h: number of haplotypes (paths), n: number of reads, lr: read length, lh: haplotype length
Proofs that alleles are canonical are too long for slides; will gladly share offline.
17. Genotyping
• Use sliding “window” traversal of genome to bucket
sites
• Currently use a likelihood model that assumes site
independence, run EM per site to estimate allele
frequency
A CA C C T C T G T C
A C C C T C T G T C
A CA C C C C T G T C
A CA C C C C T G T C
A C C C T C TT G T C
18. Making Sense of Variation
• Variation in the genome can affect biology in
several ways:
• A variant can modify or break a protein
• A variant can modify how much of a protein is
created
• The subset of your genome that encodes proteins
is the exome. This is ~1% of your genome!
19. Mutations in AML
There is a big “long tail”, including people who have
cancer, but who have no “modified” genes!
20. Looking Outside
of the Exome
• We analyze mutations
in the exome using the
grammar for protein
creation
• Can we apply a similar
approach outside of
the exome?
• Let’s use the grammar
for regulation instead!
S. Weingarten-Gabbay and E. Segal, Human Genetics, 2014.
21. You Can Help!
• Detecting variants requires good tools for
identifying patterns and edits in text
• Understanding variants requires ways to
understand the underlying grammar of biology
• All of our projects are open source software:
• https://www.github.com/bigdatagenomics
• Apache 2 licensed
22. Acknowledgements
• UC Berkeley: Matt Massie, Timothy Danford, André Schumacher, Jey
Kottalam, Karen Feng, Eric Tu, Niranjan Kumar, Ananth Pallaseni, Anthony
Joseph, Dave Patterson!
• Mt. Sinai: Arun Ahuja, Neal Sidhwaney, Ryan Williams, Michael Linderman,
Jeff Hammerbacher!
• GenomeBridge: Carl Yeksigian!
• Cloudera: Uri Laserson, Tom White!
• Microsoft Research: Ravi Pandya, Bill Bolosky!
• UC Santa Cruz: Benedict Paten, David Haussler, Hannes Schmidt, Beau
Norgeot!
• And many other open source contributors, especially Michael Heuer, Neil
Ferguson, Andy Petrella, Xavier Tordior!
• Total of 40 contributors to ADAM/BDG from >12 institutions