This EMC Isilon sizing and performance guideline White Paper reviews the Key Performance Indicators (KPIs) that most strongly impact the production processes for the storage of data from Next-Generation Sequencing (NGS) workflows.
White Paper: Next-Generation Genome Sequencing Using EMC Isilon Scale-Out NAS...EMC
This EMC Isilon sizing and performance guideline White Paper reviews the Key Performance Indicators (KPIs) that most strongly impact the production processes for the storage of data from Next-Generation Sequencing (NGS) workflows.
Neuroscience core lecture given at the Icahn school of medicine at Mount Sinai. This is the version 2 of the same topic. I have made some modifications to give a more gentle introduction and add a new example for ngs.plot.
Next-generation sequencing data format and visualization with ngs.plot 2015Li Shen
An introduction to the commonly used formats for the next-generation sequencing data. ngs.plot is a popular tool for the visualization and data mining of the NGS data.
LUGM-Update of the Illumina Analysis PipelineHai-Wei Yen
Illumina VariantStudio is a powerful annotation tool for analyzing and interpreting variants from NGS data. It allows users to import VCF or gVCF files, annotate variants using various databases, filter variants, classify variants, and generate customizable reports. VariantStudio streamlines the analysis workflow from raw data to meaningful biological insights.
my students use ideas from my class on business models to develop a business model for ion proton's DNA sequencer. This sequencer uses semiconductor technology to read an organism's DNA sequence and is faster and cheaper than existing sequencers. This presentation describes the value proposition, customer selection, method of value capture and other aspects of a business model for Ion Proton's DNA sequencer
The field of next-generation sequencing (NGS) has been experiencing explosive growth over the past several years and shows little sign of slowing down. The increasing capabilities and dramatically lowered costs have expanded NGS's reach beyond that of the human genome into nearly every corner of biological research. An overview of the platforms on the market today, including an assessment of their relative strengths and weaknesses, will be presented. The presentation will conclude with a peek into where the technology is going and what will be available in the future.
Galaxy dna-seq-variant calling-presentationandpractical_gent_april-2016Prof. Wim Van Criekinge
This document provides an overview of variant analysis from next-generation sequencing data. It begins with introductions to the CCA-Drylab@VUmc, TraIT, and Galaxy projects. The focus of the lecture is explained to be variant analysis from NGS data using interactive demos in Galaxy. Background is provided on Illumina sequencing technology and properties of sequencing reads. Key steps in variant analysis are outlined, including quality control and read mapping, variant calling and annotation using tools like FastQC, BWA, FreeBayes, and SnpEff. Formats for storing sequencing data and variants are also introduced, such as FASTQ, SAM/BAM, and VCF.
White Paper: Next-Generation Genome Sequencing Using EMC Isilon Scale-Out NAS...EMC
This EMC Isilon sizing and performance guideline White Paper reviews the Key Performance Indicators (KPIs) that most strongly impact the production processes for the storage of data from Next-Generation Sequencing (NGS) workflows.
Neuroscience core lecture given at the Icahn school of medicine at Mount Sinai. This is the version 2 of the same topic. I have made some modifications to give a more gentle introduction and add a new example for ngs.plot.
Next-generation sequencing data format and visualization with ngs.plot 2015Li Shen
An introduction to the commonly used formats for the next-generation sequencing data. ngs.plot is a popular tool for the visualization and data mining of the NGS data.
LUGM-Update of the Illumina Analysis PipelineHai-Wei Yen
Illumina VariantStudio is a powerful annotation tool for analyzing and interpreting variants from NGS data. It allows users to import VCF or gVCF files, annotate variants using various databases, filter variants, classify variants, and generate customizable reports. VariantStudio streamlines the analysis workflow from raw data to meaningful biological insights.
my students use ideas from my class on business models to develop a business model for ion proton's DNA sequencer. This sequencer uses semiconductor technology to read an organism's DNA sequence and is faster and cheaper than existing sequencers. This presentation describes the value proposition, customer selection, method of value capture and other aspects of a business model for Ion Proton's DNA sequencer
The field of next-generation sequencing (NGS) has been experiencing explosive growth over the past several years and shows little sign of slowing down. The increasing capabilities and dramatically lowered costs have expanded NGS's reach beyond that of the human genome into nearly every corner of biological research. An overview of the platforms on the market today, including an assessment of their relative strengths and weaknesses, will be presented. The presentation will conclude with a peek into where the technology is going and what will be available in the future.
Galaxy dna-seq-variant calling-presentationandpractical_gent_april-2016Prof. Wim Van Criekinge
This document provides an overview of variant analysis from next-generation sequencing data. It begins with introductions to the CCA-Drylab@VUmc, TraIT, and Galaxy projects. The focus of the lecture is explained to be variant analysis from NGS data using interactive demos in Galaxy. Background is provided on Illumina sequencing technology and properties of sequencing reads. Key steps in variant analysis are outlined, including quality control and read mapping, variant calling and annotation using tools like FastQC, BWA, FreeBayes, and SnpEff. Formats for storing sequencing data and variants are also introduced, such as FASTQ, SAM/BAM, and VCF.
This document discusses RNA-Seq data analysis using Babelomics 5 software. It describes the typical RNA-Seq data analysis pipeline which includes sequence preprocessing, mapping, quantification, normalization, differential expression analysis, and functional profiling. It also discusses common file formats used like Fastq, BAM, and count matrices. Normalization methods like RPKM and TMM are explained. The document provides an overview of the RNA-Seq data analysis capabilities in Babelomics 5.
RNASeq - Analysis Pipeline for Differential ExpressionJatinder Singh
RNA-Seq is a technique that uses next generation sequencing to sequence RNA transcripts and quantify gene expression levels. It can be used to estimate transcript abundance, detect alternative splicing, and compare gene expression profiles between healthy and diseased tissue. Computational challenges include read mapping due to exon-exon junctions and normalization of read counts. Key steps in RNA-Seq analysis include read mapping, transcript assembly, counting and normalizing reads, and detecting differentially expressed genes.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.3- Introduction to NGS Variant Calling Analysis.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Phred/Phrap/Consed is a widely used package for DNA sequencing data analysis including:
- Reading trace files and assigning quality scores to bases
- Identifying vector and repeat sequences
- Assembling sequences into contigs
- Visualizing and editing assemblies
- Finishing genomes through automatic and manual processes
PerkinElmer provides end-to-end next generation sequencing (NGS) services from sample intake to data analysis. Their CLIA-certified sequencing laboratory is staffed by expert scientists with decades of experience in genomics who deliver consistently high quality sequencing results. PerkinElmer offers sequencing, library preparation, capture, bioinformatics analysis, and professional consulting services to build customized NGS solutions that meet customers' specific needs and requirements.
How to sequence a large eukaryotic genome - and how we sequenced the cod genome. A seminar I gave for the Computational Life Science (Univ. of Oslo) seminar series, September 28, 2011
Next-generation sequencing format and visualization with ngs.plotLi Shen
Lecture given at the department of neuroscience, Icahn school of medicine at Mount Sinai. ngs.plot has been published in BMC genomics. Link: http://www.biomedcentral.com/1471-2164/15/284
A workshop is intended for those who are interested in and are in the planning stages of conducting an RNA-Seq experiment. Topics to be discussed will include:
* Experimental Design of RNA-Seq experiment
* Sample preparation, best practices
* High throughput sequencing basics and choices
* Cost estimation
* Differential Gene Expression Analysis
* Data cleanup and quality assurance
* Mapping your data
* Assigning reads to genes and counting
* Analysis of differentially expressed genes
* Downstream analysis/visualizations and tables
This presentation gives an introduction to analysing ChIP-seq data and is part of a bioinformatics workshop. The accompanying websites are available at http://sschmeier.github.io/bioinf-workshop/#!galaxy-chipseq/
A Tovchigrechko - MGTAXA: a toolkit and webserver for predicting taxonomy of ...Jan Aerts
Talk by A Tovchigrechko at BOSC2012: "MGTAXA: a toolkit and webserver for predicting taxonomy of the metagenomic sequences with Galaxy frontend and parallel computational backend"
Next Generation Sequencing (NGS) Is A Modern And Cost Effective Sequencing Technology Which Enables Scientists To Sequence Nucleic Acids At Much Faster Rate. In This Presentation, You Will Learn About What is NGS, Idea Behind NGS, Methodology And Protocol, Widely Adapted NGS Protocols, Applications And References For Further Study.
Overview of methods for variant calling from next-generation sequence dataThomas Keane
This document provides an overview of methods for variant calling from next-generation sequencing data. It discusses data formats and workflows, including SNP calling, short indels, and structural variation. The document describes alignment, BAM improvement through realignment and base quality recalibration, library merging, and duplicate removal. It also reviews software tools for these processes and introduces the variant call format (VCF) standard.
The document discusses exome sequencing and compares the performance of the xGen Exome Research Panel to other commercial exome sequencing panels. Key points:
1) An independent study directly compared the xGen panel to 3 other commercial exome panels and found that the xGen panel had a higher on-target rate and more uniform coverage than the other panels.
2) When deeply sequenced, the xGen panel was able to achieve greater than 20x coverage of over 94% of bases in the RefSeq database with only 40 million reads, which is 2.5-4 times fewer reads than the other panels tested.
3) The coverage profile produced by the xGen panel more closely resembled whole genome sequencing
The document discusses genome assembly and finishing processes. It begins by outlining typical project goals of completely restoring the genome and producing a high-quality consensus sequence. It then describes the evolution of sequencing technologies from Sanger to newer platforms and their impact on draft assemblies. Key steps in the assembly and finishing process include library preparation, assembly, identifying gaps, and improving consensus quality.
This document provides an overview of analyzing RNA-Seq data using the Tuxedo protocol in Galaxy. It describes experimental design considerations, quality control of sequencing data using FastQC, mapping reads to a reference genome using Tophat, determining differential expression with Cuffdiff, and visualizing results using IGV and CummeRbund. The tutorial walks through an example analysis on Drosophila melanogaster RNA-Seq data, covering topics such as setting file formats, running alignment and expression tools, extracting workflows, and useful Galaxy resources.
Next generation sequencing: research opportunities and bioinformatic challenges. A seminar I gave for the Computational Life Science (Univ. of Oslo) seminar series, March 2, 2011
This document provides an overview of next generation sequencing analysis for ChIP-seq experiments. It describes the basic ChIP-seq workflow including performing ChIP, preparing libraries, sequencing, alignment, filtering duplicates, peak calling, and downstream analysis. It also reviews several common peak finding tools and demonstrates how to perform ChIP-seq analysis using the USeq toolset, including running commands and visualizing results.
This document discusses the bioinformatics analysis of ChIP-seq data. It begins with an overview of ChIP-seq experiments and the major steps in processing and analyzing the sequencing data, including quality control, alignment, peak calling, and downstream analyses. Pipelines for automated analysis are described, such as Cluster Flow and Nextflow. The talk emphasizes that there is no single correct approach and the analysis depends on the biological question and experimental design.
The document discusses different allocation methods such as first come first serve, rationing, and auctions. It then explains the law of demand, how lowering price increases quantity demanded, and how businesses can use demand to predict revenue. The key points are that auctions allocate resources to those willing to pay the most, the law of demand states that as price decreases quantity demanded increases, and businesses care about demand because it allows them to predict revenue from price and quantity sold.
This document discusses RNA-Seq data analysis using Babelomics 5 software. It describes the typical RNA-Seq data analysis pipeline which includes sequence preprocessing, mapping, quantification, normalization, differential expression analysis, and functional profiling. It also discusses common file formats used like Fastq, BAM, and count matrices. Normalization methods like RPKM and TMM are explained. The document provides an overview of the RNA-Seq data analysis capabilities in Babelomics 5.
RNASeq - Analysis Pipeline for Differential ExpressionJatinder Singh
RNA-Seq is a technique that uses next generation sequencing to sequence RNA transcripts and quantify gene expression levels. It can be used to estimate transcript abundance, detect alternative splicing, and compare gene expression profiles between healthy and diseased tissue. Computational challenges include read mapping due to exon-exon junctions and normalization of read counts. Key steps in RNA-Seq analysis include read mapping, transcript assembly, counting and normalizing reads, and detecting differentially expressed genes.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.3- Introduction to NGS Variant Calling Analysis.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Phred/Phrap/Consed is a widely used package for DNA sequencing data analysis including:
- Reading trace files and assigning quality scores to bases
- Identifying vector and repeat sequences
- Assembling sequences into contigs
- Visualizing and editing assemblies
- Finishing genomes through automatic and manual processes
PerkinElmer provides end-to-end next generation sequencing (NGS) services from sample intake to data analysis. Their CLIA-certified sequencing laboratory is staffed by expert scientists with decades of experience in genomics who deliver consistently high quality sequencing results. PerkinElmer offers sequencing, library preparation, capture, bioinformatics analysis, and professional consulting services to build customized NGS solutions that meet customers' specific needs and requirements.
How to sequence a large eukaryotic genome - and how we sequenced the cod genome. A seminar I gave for the Computational Life Science (Univ. of Oslo) seminar series, September 28, 2011
Next-generation sequencing format and visualization with ngs.plotLi Shen
Lecture given at the department of neuroscience, Icahn school of medicine at Mount Sinai. ngs.plot has been published in BMC genomics. Link: http://www.biomedcentral.com/1471-2164/15/284
A workshop is intended for those who are interested in and are in the planning stages of conducting an RNA-Seq experiment. Topics to be discussed will include:
* Experimental Design of RNA-Seq experiment
* Sample preparation, best practices
* High throughput sequencing basics and choices
* Cost estimation
* Differential Gene Expression Analysis
* Data cleanup and quality assurance
* Mapping your data
* Assigning reads to genes and counting
* Analysis of differentially expressed genes
* Downstream analysis/visualizations and tables
This presentation gives an introduction to analysing ChIP-seq data and is part of a bioinformatics workshop. The accompanying websites are available at http://sschmeier.github.io/bioinf-workshop/#!galaxy-chipseq/
A Tovchigrechko - MGTAXA: a toolkit and webserver for predicting taxonomy of ...Jan Aerts
Talk by A Tovchigrechko at BOSC2012: "MGTAXA: a toolkit and webserver for predicting taxonomy of the metagenomic sequences with Galaxy frontend and parallel computational backend"
Next Generation Sequencing (NGS) Is A Modern And Cost Effective Sequencing Technology Which Enables Scientists To Sequence Nucleic Acids At Much Faster Rate. In This Presentation, You Will Learn About What is NGS, Idea Behind NGS, Methodology And Protocol, Widely Adapted NGS Protocols, Applications And References For Further Study.
Overview of methods for variant calling from next-generation sequence dataThomas Keane
This document provides an overview of methods for variant calling from next-generation sequencing data. It discusses data formats and workflows, including SNP calling, short indels, and structural variation. The document describes alignment, BAM improvement through realignment and base quality recalibration, library merging, and duplicate removal. It also reviews software tools for these processes and introduces the variant call format (VCF) standard.
The document discusses exome sequencing and compares the performance of the xGen Exome Research Panel to other commercial exome sequencing panels. Key points:
1) An independent study directly compared the xGen panel to 3 other commercial exome panels and found that the xGen panel had a higher on-target rate and more uniform coverage than the other panels.
2) When deeply sequenced, the xGen panel was able to achieve greater than 20x coverage of over 94% of bases in the RefSeq database with only 40 million reads, which is 2.5-4 times fewer reads than the other panels tested.
3) The coverage profile produced by the xGen panel more closely resembled whole genome sequencing
The document discusses genome assembly and finishing processes. It begins by outlining typical project goals of completely restoring the genome and producing a high-quality consensus sequence. It then describes the evolution of sequencing technologies from Sanger to newer platforms and their impact on draft assemblies. Key steps in the assembly and finishing process include library preparation, assembly, identifying gaps, and improving consensus quality.
This document provides an overview of analyzing RNA-Seq data using the Tuxedo protocol in Galaxy. It describes experimental design considerations, quality control of sequencing data using FastQC, mapping reads to a reference genome using Tophat, determining differential expression with Cuffdiff, and visualizing results using IGV and CummeRbund. The tutorial walks through an example analysis on Drosophila melanogaster RNA-Seq data, covering topics such as setting file formats, running alignment and expression tools, extracting workflows, and useful Galaxy resources.
Next generation sequencing: research opportunities and bioinformatic challenges. A seminar I gave for the Computational Life Science (Univ. of Oslo) seminar series, March 2, 2011
This document provides an overview of next generation sequencing analysis for ChIP-seq experiments. It describes the basic ChIP-seq workflow including performing ChIP, preparing libraries, sequencing, alignment, filtering duplicates, peak calling, and downstream analysis. It also reviews several common peak finding tools and demonstrates how to perform ChIP-seq analysis using the USeq toolset, including running commands and visualizing results.
This document discusses the bioinformatics analysis of ChIP-seq data. It begins with an overview of ChIP-seq experiments and the major steps in processing and analyzing the sequencing data, including quality control, alignment, peak calling, and downstream analyses. Pipelines for automated analysis are described, such as Cluster Flow and Nextflow. The talk emphasizes that there is no single correct approach and the analysis depends on the biological question and experimental design.
The document discusses different allocation methods such as first come first serve, rationing, and auctions. It then explains the law of demand, how lowering price increases quantity demanded, and how businesses can use demand to predict revenue. The key points are that auctions allocate resources to those willing to pay the most, the law of demand states that as price decreases quantity demanded increases, and businesses care about demand because it allows them to predict revenue from price and quantity sold.
Based on a map of languages in Europe, students should identify three countries where Spanish-like languages are spoken, such as Italy, Portugal, France, and three countries where German-like languages are spoken, such as Germany, Austria, Switzerland. The document instructs students to compare this map to a modern map of Europe to identify 14 countries that were part of the Roman Empire and explain why Spanish and Italian would be mutually intelligible, as well as write a paragraph about how modern US society has borrowed from Roman culture.
The document discusses different types of research:
- Exploratory research is flexible and informal, used to gain background on a problem. It does not provide conclusive evidence but informs subsequent research.
- Descriptive research describes characteristics but not causes. It can profile populations through cross-sectional or longitudinal studies.
- Correlational research determines the relationship between variables but cannot prove causation. The correlation coefficient indicates the strength and direction of relationships.
- Explanatory research aims to understand relationships between independent and dependent variables to explain phenomena rather than just report observations. It tests and advances theoretical explanations.
This document provides an overview of the syllabus and schedule for a US History summer school class from June 2-13 at Mountain View/Marana High School. It outlines the daily schedule, assignments, topics to be covered including what defines an American and early colonial history. Students will analyze primary sources, create maps, discuss bias in images, and write a letter to the NFL commissioner arguing for or against changing the Redskins team name. The class uses interactive group activities and aims to make history relevant through discussion of why students should care about different time periods and events.
Oral lichen planus is a chronic inflammatory condition that affects the mucous membranes inside the mouth. It causes white lace-like patches, red swollen tissue, open sores, and a burning or metallic taste. While the exact cause is unknown, it may be triggered by hepatitis C, other liver diseases, allergies or medications. It most commonly occurs on the tongue and inner cheeks. Treatment involves topical corticosteroids, oral medications, or addressing triggers to manage symptoms like pain and soreness.
The Right Way to Become Successful in Burning Fattrg911
This document provides tips for successful weight loss through small, sustainable changes in diet and lifestyle habits over time. It recommends eating breakfast to stabilize blood sugar, eating more fruits and vegetables while avoiding sugary foods, including healthy carbs and fiber, getting regular exercise, and keeping focused on weight loss goals to stay motivated.
The Evolution of IP Storage and Its Impact on the NetworkEMC
- The document discusses the evolution of IP storage networks and their impact on enterprise networks. It argues that as data growth and server virtualization increase pressure on networks, dedicated IP storage networks can provide better performance, availability and manageability compared to consolidating IP storage onto general enterprise networks.
- It provides examples of customers who saw benefits such as improved scalability, efficiency and issue resolution by separating their IP storage networks and allowing their storage teams to manage the dedicated storage networking infrastructure. Isolating the storage network traffic helped provide more predictable performance for mission-critical workloads.
This document contains questions for students as part of a daily bellringer activity. It asks students to reflect on when they feel most and least energetic during the day and what might explain their energy levels. It also prompts students to provide an example of diminishing returns from their own life and to consider how the concept of diminishing returns applies to various contexts like school, purchasing goods, social activities, and business.
Il nostro lavoro vuole analizzare l'usabilità del servizio offerto da Libero Mail, evidenziando le sue problematiche e tentando di comprendere perchè sia ancora tanto utilizzato da un gran numero di utenti.
This document summarizes a Selenium testing setup using Hudson (now Jenkins) for continuous integration. It consists of:
1) Hudson runs Selenium tests once an hour by checking out tests from source control and running them in parallel across different browsers using ANT.
2) A Selenium Grid Hub directs test requests to suitable Grid Nodes based on OS, browser, and version.
3) Grid Nodes of varying OS's (Windows, Ubuntu) run configured browser instances (Firefox, Chrome, IE) to execute tests in parallel, improving stability and reducing test time.
The three recordings took place in different environments. The first was outside with no echoes as it was an open space. The second was inside a canteen where it was louder due to background noise and voices bouncing off walls. The third was under a staircase where it was less clear due to more walls for sound to bounce off, making it louder.
Business impact restrictions on cross border dataRene Summer
This document discusses the importance of cross-border data flows for businesses and the economic impacts of restrictions on such data flows. It notes that information and communication technologies (ICT) have driven globalization and productivity growth. However, some countries impose restrictions on transferring customer or employee personal data across borders or require local data storage. This can negatively impact company revenues and increase costs of service through reduced efficiency, scalability, and ability to innovate quickly. The document recommends lessening such restrictions to facilitate international digital trade and establishing efficient cross-border data transfer regimes.
White Paper: Life Sciences at RENCI, Big Data IT to Manage, Decipher and Info...EMC
This white paper explains how the Renaissance Computing Institute (RENCI) of the University of North Carolina uses EMC Isilon scale-out NAS storage, Intel processor and system technology, and iRODS-based data management to tackle Big Data processing, Hadoop-based analytics, security and privacy challenges in research and clinical genomics.
Enabling Large Scale Sequencing Studies through Science as a ServiceJustin Johnson
Now
“Now” generation sequencing has drastically changed the traditional costs and infrastructure within the sequencing community. There are several technologies, platforms and algorithms that show promise, but it is not always intuitive where to start. This uncertainty is compounded by the fact that commonly used analysis tools are difficult to build, maintain, and run effectively. Sample acquisition and preparation is quickly becoming a bottleneck as projects move from small sample sizes to hundreds or even thousands of samples. We will present case studies highlighting information, methods, challenges and opportunities in leveraging large scale high throughput sequencing and bioinformatics. Specifically we will highlight a recent genome-wide study of methylation patterns in 1575 individuals with Schizophrenia. We will also discuss several cancer transcriptome and exome sequencing projects as well as a human pathogen transcriptome characterization project consisting of multiple organisms and almost a billion reads.
The Future
The Ion Torrent PGM machine is a very promising, rapid throughput, ultra scalable sequencer that could play an integral part in future human health studies. Applications such as microbial whole genome sequencing, metagenomic characterization of environmental and microbiome sample, and targeted resequencing projects stand to benefit from this technology over time. To date we have completed more than 25 runs on a single PGM and will comment on the setup as well as sequence data and analysis.
1) The document discusses performance analysis of DNA analysis using the Genome Analysis Toolkit (GATK).
2) GATK is a software tool used to analyze sequencing data that enables optimized use of CPU and memory for high-throughput and distributed/parallel processing of DNA data.
3) The document provides details on GATK architecture, how it distributes data into shards for scalable analysis, and how it allows merging of multiple data sources and parallelization of jobs.
Closing the Gap in Time: From Raw Data to Real ScienceJustin Johnson
This document discusses science as a service (SaaS) and next-generation sequencing (NGS) data analysis. It summarizes challenges with exponential growth of NGS data, including data management, storage, analysis and sharing. It introduces Edge Bio's approach of distributing computational problems across cloud and HPC resources to avoid bottlenecks. Edge Bio provides full-service NGS analysis pipelines leveraging both commercial and open-source tools.
This document summarizes a research paper that proposes a new approach called DiffP for more energy efficient ad hoc reprogramming of sensor networks. DiffP aims to mitigate the effects of program layout modifications and maximize similarity between old and new software. It also organizes global variables in a novel way to eliminate the effect of variable shifting. The document provides background on challenges with reprogramming deployed sensor networks due to limited energy, processing and memory resources. It reviews related work on dissemination protocols and reprogramming schemes, noting limitations such as producing large patches from layout changes or variable shifts. DiffP is presented as a potential improvement over existing approaches.
Next generation sequencing (NGS) allows for the massively parallel sequencing of DNA sequences. NGS technologies can sequence entire genomes in a single run and provide information useful for pathogen identification, outbreak investigation, and molecular diagnostics. NGS workflows involve sample preparation, sequencing using platforms such as Illumina or Ion Torrent, and bioinformatics analysis to assemble and interpret the large amounts of sequencing data produced. NGS has many applications including mutation discovery, microbial genome mapping, and metagenomics.
IRJET-Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using a convolutional neural network (CNN) to detect breast cancer from medical images. CNNs are a type of deep learning model that can learn image features without manual feature engineering. The proposed system would take a sample medical image as input, preprocess it, and compare it to images in a database labeled as cancerous or non-cancerous. If cancer is detected, the system would determine the cancer stage and recommend appropriate treatment. The CNN model would be built and trained using libraries like Keras, TensorFlow, and Numpy to classify images and detect breast cancer at early stages for better treatment outcomes.
Abstract— During the past year Xilinx, for the first time ever, set out to quantify the soft error rate of a multi-core microprocessor. This work extends on Xilinx’s 10+ years of heritage in FPGA radiation testing. Built on the 28 nanometer technology node, Xilinx’s ZynqTM family of devices integrate a processor subsystem with programmable logic. The processor subsystem includes two 32 bit ARM CortexTM-A9 CPU’s, two NEONTM floating point units, two SIMD processing units, an L1 and L2 cache, on chip SRAM memory and various peripherals. The programmable logic is directly connected with the processing subsystem via ARM’s AMBATM 4 AXI interface. This programmable logic is based on the 7 Series FPGA fabric, consisting of 6-input LUTs and DFFs along with Block RAM, DSP slices, multi-gigabit transceivers, and other blocks. Tests were performed using a proton beam to analyze the soft error susceptibility of the new device. Proton beam testing was deemed acceptable since previous neutron beam and proton beam testing had shown virtually identical cross-sections for 7 Series programmable logic. The results are promising and yield a solid baseline for a typical embedded application targeting any of the Zynq SoC devices. As a foray into processor testing, this Zynq work has laid a solid foundation for future Xilinx SoC test campaigns.
Austin Lesea, Wojciech Koszek, Glenn Steiner, Gary Swift, and Dagan White Xilinx, Inc.
Paper: SELSE 2014 @ Stanford University (PDF, 456KB), 2014
Slides: (PDF, 933KB), 2014
Book of abstract volume 8 no 9 ijcsis december 2010Oladokun Sulaiman
The International Journal of Computer Science and Information Security (IJCSIS) is a publication venue for novel research in computer science and information security. This issue from December 2010 contains 5 research papers. The first paper proposes a 128-bit chaotic hash function that uses the logistic map and MD5/SHA-1 hashes. The second paper discusses constructing an ontology for representing human emotions in videos to improve video retrieval. The third paper proposes an intelligent memory controller for H.264 encoders to reduce external memory access. The fourth paper investigates the impact of fragmentation on query performance in distributed databases. The fifth paper examines the effect of guard intervals in a proposed MIMO-OFDM system for wireless communication.
This document provides an overview of cloud bioinformatics and the challenges of analyzing large datasets from next-generation sequencing (NGS). It discusses how bioinformatics uses computational methods to study genes, proteins, and genomes. The advent of NGS has led to huge datasets that require high-performance computing. Cloud computing provides access to pooled computing resources in a cost-effective manner and helps address the bioinformatics challenge of assembling and analyzing NGS data. The document also outlines common bioinformatics software and resources available through WestGrid and Galaxy that can be used for sequence assembly, annotation, and other applications.
It is widely agreed that complex diseases are typically caused by joint effects of multiple genetic variations, rather than a single genetic variation. Multi-SNP interactions, also known as epistatic interactions, have the potential to provide information about causes of complex diseases, and build on GWAS studies that look at associations between single SNPs and phenotypes. However, epistatic analysis methods are both computationally expensive, and have limited accessibility for biologists wanting to analyse GWAS datasets due to being command line based. Here we present APPistatic, a prototype desktop version of a pipeline for epistatic analysis of GWAS datasets. his application combines ease-of-use, via a GUI, with accelerated implementation of BOOST and FaST-LMM epistatic analysis methods.
The document discusses representing digital data in DNA for archival storage purposes. It describes how DNA can be used to store digital data by mapping the data to DNA nucleotide sequences. It presents challenges in DNA-based storage such as errors during DNA synthesis and sequencing. The document proposes a new encoding scheme that offers controllable redundancy to improve reliability while maintaining high storage density. It also proposes a method for random access of stored data using polymerase chain reaction to amplify only the desired DNA sequences.
De novo transcriptome assembly of solid sequencing data in cucumis melobioejjournal
As sequencing technologies progress, focus shifts towards solving bioinformatic challenges, of which sequence read assembly is the first task. In the present study, we have carried out a comparison of two assemblers (SeqMan and CLC) for transcriptome assembly, using a new dataset from Cucumis melo. Between two assemblers SeqMan generated an excess of small, redundant contigs where as CLC generated the least redundant assembly. Since different assemblers use different algorithms to build contigs, wefollowed the merging of assemblies by CAP3 and found that the merged assembly is better than individual assemblies and more consistent in the number and size of contigs. Combining the assemblies from different programs gave a more credible final product, and therefore this approach is recommended for quantitative
output.
DE NOVO TRANSCRIPTOME ASSEMBLY OF SOLID SEQUENCING DATA IN CUCUMIS MELObioejjournal
As sequencing technologies progress, focus shifts towards solving bioinformatic challenges, of which
sequence read assembly is the first task. In the present study, we have carried out a comparison of two
assemblers (SeqMan and CLC) for transcriptome assembly, using a new dataset from Cucumis melo.
Between two assemblers SeqMan generated an excess of small, redundant contigs where as CLC generated
the least redundant assembly. Since different assemblers use different algorithms to build contigs, we
followed the merging of assemblies by CAP3 and found that the merged assembly is better than individual
assemblies and more consistent in the number and size of contigs. Combining the assemblies from different
programs gave a more credible final product, and therefore this approach is recommended for quantitative
output
DE NOVO TRANSCRIPTOME ASSEMBLY OF SOLID SEQUENCING DATA IN CUCUMIS MELObioejjournal
As sequencing technologies progress, focus shifts towards solving bioinformatic challenges, of which sequence read assembly is the first task. In the present study, we have carried out a comparison of two assemblers (SeqMan and CLC) for transcriptome assembly, using a new dataset from Cucumis melo. Between two assemblers SeqMan generated an excess of small, redundant contigs where as CLC generated
the least redundant assembly. Since different assemblers use different algorithms to build contigs, we followed the merging of assemblies by CAP3 and found that the merged assembly is better than individual assemblies and more consistent in the number and size of contigs. Combining the assemblies from different programs gave a more credible final product, and therefore this approach is recommended for quantitative
output.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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White Paper: Next-Generation Genome Sequencing Using EMC Isilon Scale-Out NAS: Sizing and Performance Guidelines
1. White Paper
Abstract
This EMC Isilon Sizing and Performance Guideline white paper
reviews the Key Performance Indicators (KPIs) that most
strongly impact the production processes for Next-Generation
Sequencing (NGS) workflows.
July 2013
NEXT-GENERATION GENOME SEQUENCING
USING EMC ISILON SCALE-OUT NAS: SIZING
AND PERFORMANCE GUIDELINES
4. Executive summary
Next-generation sequencing (NGS) workflows are comprised of genome sequencer
instrumentation, high-performance computing (HPC) infrastructure, a network-
attached storage (NAS) platform, and the network infrastructure connecting these
components together.
Raw NGS data is the largest component of an NGS process, making data storage
capacity and scalability important factors in NGS performance. The raw TIFF image
from the sequencer can be up to 70 percent of the total dataset. These files may be
compressed and stored for later use. Most organizations do not save the TIFF images,
but retain either the BCL or FASTQ files as the raw files. Each sequencing run can also
generate analysis data in the range of 50-200 gigabytes (GB). With faster sequencers
and larger read lengths, this can add up to between approximately 1 petabyte (PB)
and 2 PB per year for a facility with three NGS sequencers.
Beyond capacity scalability, I/O performance is also a critical file storage attribute for
overall NGS performance and efficiency. NGS is I/O-bound rather than processor-bound,
and therefore storage I/O performance has a high impact on overall NGS performance
in relation to other NGS workflow parameters.
Internal EMC testing has determined that the key performance indicators (KPIs) that
most affect the performance of NGS applications are:
• Total random access memory (RAM) size on HPC cluster nodes (recommended at
3 GB/core)
• RAM and SSD allocation on the EMC®
Isilon®
storage cluster—place maximum
allowable RAM on the performance layer and minimum recommended on the
archival layer with about 1 percent to 2 percent of the raw storage capacity
as SSD
• Storage configuration parameters: NFS version 4, NFS async-enabled, TCP
MTU (jumbo frames), LACP (2x 1 Gb/s or 4x 1 Gb/s), and tuning the Grid
Engine package
Introduction
Over the past five years, the precision and effectiveness of sequencing technology have
considerably increased the pace of biological research and discovery. The resources
focused on molecular biology, cellular biology, and bioinformatics continue to accelerate
at a significant pace. Projections indicate that before the end of the 21st century, we
could gain a full understanding of the workings of our DNA. Such knowledge could
allow us to improve our collective quality of life through a better understanding of
how a specific genetic variation impacts a drug’s efficacy or toxicity, or by possibly
providing the knowledge to eradicate a range of genetically based disorders.
DNA exome sequencing is an approach to selectively sequence the coding regions of
the genome as an easier yet still effective alternative to whole genome sequencing.
The exome of the human genome is formed by exons. Exons are short, functionally
4Next-generation genome sequencing using EMC Isilon scale-out NAS
5. important coding sequences of DNA within the gene’s mature messenger RNA that
constitute about 1.5 percent of the human genome.1
Many large-scale exome sequencing projects are underway to analyze human
diseases. This technology is often the choice as it is more affordable than whole
genome sequencing (WGS) and therefore allows the analysis of more patients. In
addition, it has an advantage in that resulting data volumes are much smaller and
therefore easier to handle. However, recent studies2
focused on this question found
that both technologies complement each other. As neither the whole genome nor the
large-scale exome sequencing technologies cover all sequencing variants, it is optimal
to conduct both experiments in parallel.
A single human genome—composed of a total of about 3.2 billion base pairs—requires
about 1.2 GB of unassembled storage. Industry analysts predict that the estimated
number of human whole genomes sequenced will explode from 25,000 genomes in
2012, to between 50,000 and 100,000 in 2013, and up to about one million by 2015.
The key enabling technologies for NGS are the many commercial sequencers available
from various companies, including Illumina, Life Technologies, Roche/454 Life Sciences,
and others. These sequencers interface to a computer network, which correlates and
concatenates the billions of overlapping segments of DNA sequence short reads that
have been streamed to or stored on a NAS system.
Accommodating the output rate of the sequencers requires a precisely designed and
balanced system. The peak rate of data (base pairs) produced by an Illumina sequencer,
for example, is already approaching 600 GigaBases per week, equivalent to about 100
whole human genomes. The range of data per year for an Illumina sequencer is from
350 TB to 1 PB.
The components of the NGS workflow are comprised of:
• Genome sequencer instruments
• HPC infrastructure
• NAS platform
• Network infrastructure that stitches these components together
These four components make up the hierarchy of the NGS gene-sequencing architecture.
Each component depends on the other and must have the ability to adapt and scale
to meet current and future sequencing needs. If one component creates a bottleneck,
then the performance of the entire NGS system suffers. The focus of this document is
optimum performance as well as sizing guidelines for the core components of NGS:
the HPC infrastructure and network-attached storage.
1
See Gilbert W (February 1978). “Why genes in pieces?”. Nature 271 (5645): 501.
2
See Performance comparison of exome DNA sequencing technologies. Clark MJ, Chen R, Lam HY,
Karczewski KJ, Chen R, Euskirchen G, Butte AJ, Snyder M, Department of Genetics, Stanford University
School of Medicine, Stanford, CA, USA.
5Next-generation genome sequencing using EMC Isilon scale-out NAS
6. NGS workflow—sequencing instruments and file types
The applications at the heart of NGS data creation come from important established
and emerging organizations involved in bringing NGS to market. The list includes
software from Illumina, Life Technologies (Applied Biosystems), Roche/454, Ion
Torrent, Pacific Biosciences, and a myriad of open source offerings such as Galaxy.
Running these applications in a research and analysis environment places complex
and special requirements on the IT systems, and in particular, the storage
infrastructure. This document will focus on the Illumina technologies—specifically
CASAVA for the sequence analysis software. Other genome assembly and analysis
platforms like Galaxy would be summarized in subsequent documents.
An NGS environment typically consists of scientific, lab, and analysis users:
• The scientific user initiates the method of genome sequencing and
instrumentation. This may also be the analysis user.
• The lab user runs the experiment (chemistry workflow) using a multiplexed
sampling scheme (or lanes) supported by the NGS instrument.
• The analysis user works on the results from the genome sequencing study with
bioinformatics tools and algorithms.
Most commercial NGS data centers also have a trained storage administrator on their
staff. With the growing use of NGS technologies, a new user has emerged for these
storage systems. The scientist or researcher running the experiments frequently handles
the data directly. Data management has to be intuitive to allow this new user to run
experiments and administer the data with minimal difficulty. In addition, the storage
administrator needs access to the more advanced management features to set
sophisticated management policies. These help with optimization of performance
and use of the storage system. It is important that the storage system deployed
provide management capabilities tuned to both types of users.
A graphical representation of the typical NGS data flow is shown below in Figure 1:
Figure 1. NGS architecture, data flow, and file types
6Next-generation genome sequencing using EMC Isilon scale-out NAS
7. The results stage of the NGS workflow as shown in Figure 1 consists of a number of
successive steps, each involving file conversions and each resulting in approximately
5x smaller file sizes. These steps include conversion of the raw image file into base-
call data, then of base call data into FASTQ text-based file format for storing both
biological sequence and its corresponding quality scores—for example, using LQUAL
or QUAL formats. This is followed by conversion into BAM (Binary Alignment Map) file
data followed by conversion into Variant Call Format (VCF) file data, which is converted
next into results data in SRA format. This tertiary file data is typically kept forever,
needs to be kept safe and available, and accumulates over time.
Today’s instruments produce higher level information and may avoid some of the
intermediate steps, thus reducing output data compared to previous NGS systems.
Therefore, data flows generated by the latest NGS instruments have typically decreased
in size per run. This decrease has been offset by a larger number of experiments,
secondary data, and increased consumption by users working downstream on many
different efforts and workflows. The size and characteristics of data produced from
these efforts place unpredictable demands on capacity as well as on throughput of the
storage systems. NGS storage environments need to be able to adapt to demands for
more capacity from post-processing work done by researchers downstream from the
first data capture.
NGS workflow—HPC
NGS applications have both common and unique analysis tools. All applications generate
large files that must be managed through multiple rounds of processing. Although many
tools were written specifically for easy implementation on a high-end desktop computer
(e.g., 64-bit dual- or quad-core, 16 GB RAM), routine analysis is typically conducted
on high-performance compute clusters.
Using a high-performance compute cluster, secondary analysis processing can generally
be done at a rate equal to or faster than primary data generation. Due to the open-ended
nature of tertiary analysis, a similar rate estimate cannot be precisely stated.
It is important that the parallelization of the NGS analysis platform be well understood
before planning on optimum server CPU core sizing. Most of the NGS tools are at least
multiprocessor-aware or are highly parallelized by simply dividing the sequence data,
the assembly algorithm, variant calling, or all, and starting separate analysis on these
data subsets. For NGS applications, the current parallelization per process is typically
between 75 percent and 90 percent.
As genomics has very large, semi-structured, file-based data and is modeled on post-
process streaming data access and I/O patterns that can be parallelized, it is ideally
suited for the Hadoop software framework3
which consists of two main components:
a file system and a compute system—the Hadoop Distributed File System (HDFS)
and the MapReduce framework, respectively.
3
See Hadoop in the life sciences: an Isilon Systems white paper. Joshi S.
7Next-generation genome sequencing using EMC Isilon scale-out NAS
8. Figure 2. Amdahl’s Law and parallelization
One of the basic tenets in HPC, Amdahl’s Law4
, postulates that adding more
microprocessor cores to a process does not speed it up linearly. A 64-core HPC
platform is estimated to be the performance threshold for 75 percent parallelization
per NGS process, which delivers a speedup of 4x (see Figure 2). Even more than
100 cores per active NGS process do not speed up the process substantially when
the algorithm(s) are between 75 percent and 90 percent parallelization. During
actual testing of the NGS processes in the range of 75-90 percent parallelization,
the speedup from 12 cores to 72 cores was found to be only about 1.25x.
Horizontal platforms like Hadoop that combine compute and data in a parallel
context would benefit genome assembly considerably.
4
See “Validity of the single processor approach to achieving large-scale computing capabilities,”
Amdahl G, AFIPS Conference Proceedings (30): 483–485, 1967
8Next-generation genome sequencing using EMC Isilon scale-out NAS
9. Figure 3. Performance curves for NGS using Illumina CASAVA
As shown in Figure 3 above, the NGS process is storage I/O and memory-bound. The
performance curves show a direct relationship between NGS performance and saturation
of read/write I/O and memory functionality. In contrast, there is an inverse relationship
between the CPU core utilization and storage I/O and memory functionality. This number
may be due to mutual dependencies or portions of the process that can only be
performed sequentially; NGS algorithms requiring movement of large amounts of data
in and out of the CPU; startup overhead including base calling and other large numbers
of small file writes; and degree of serialization involved in communication.
In view of the above discussion, it is recommended that the HPC server hardware
platform be configured with:
• Best I/O chipset, for example, using the latest generation Intel I/O controllers
• Highest DRAM speed (with a minimum of 3 GB per core of RAM)
• Multicore CPU set with > 2 GHz processors
• Simplified BIOS and driver upgrades with a single management console for all
driver upgrades
• Linux driver compatibility (over 90 percent of all HPC systems are Linux-based)
• Disk drives between 200 GB and 600 GB with RAID 10
• Cluster management tools such as Ganglia
Increasing the network bandwidth up to 4 Gbps would alleviate the read I/O and
memory saturation.
9Next-generation genome sequencing using EMC Isilon scale-out NAS
10. NGS workflow—Isilon scale-out NAS
Figure 4. Data flow using Illumina NGS process
NGS production processes generate potentially millions of files with terabytes of
aggregate storage impacting the capacity and manageability limits of existing file
server structures.
Figure 4 shows the data flow including a file number and capacity summary of an actual
NGS process using an Illumina sequencer and Isilon scale-out NAS storage. As can be
seen, the process generates over 500,000 files having aggregate size of greater than 5
TB over the course of the 48-hour run.
Raw NGS data is the largest component of an NGS process. The raw TIFF image can
be up to 70 percent of the total dataset. These files may be compressed and stored
for later use. Most organizations do not save the TIFF images, but retain either the
BCL or FASTQ files. If sequencing as a service is used, the input to the process is a
BAM file. Each sequencing run can also generate intermediate and final analysis data
10Next-generation genome sequencing using EMC Isilon scale-out NAS
11. in the range of 50-200 GB. With faster sequencers and larger read lengths, this can
add up to between 1 PB and 2 PB per year for a facility with three NGS sequencers.
Genomics is a data reduction process from the raw instrument information (images
or voltages) to the variants. This reduction process follows the “Rule of One-Fifth”
as shown in the sizing table below:
Table 1. Data reduction for the NGS process; human whole genome; all file
sizes are approximate
File
format
Size, GB
Illumina
Size, GB
Ion Torrent
Comments
TIFF,
WELLS
2500 750 TIFF range: 2.5 to 4 TB,
Ion Torrent is WELLS
voltage format
BCL/SFF 500 500 Ion Torrent uses SFF
BAM 100 100 2x compression
(~200 GB normal)
VCF 20 20 Variant calls
SRA,
EMR
4 4 EMR (Electronic Medical
Record) includes radiology
and pathology images
Raw instrument data typically consists of large image files (2–5 TB per run is the
norm), usually in TIFF format or an electropherogram file format native to a sequencer
(for example, the SEQ format native to the Illumina sequencer). These files are only
kept long enough (7–10 days) to verify that the experiment worked. The image file
for the experiment is usually the largest file size in NGS.
Intermediate or secondary data consists of raw data processed into information
of increasing value, stored for medium- to long-term storage (one year or more),
requires high-bandwidth access for fast analysis, and is expensive to re-create, so
storage needs to be highly available. These include files in BCL format for base calling
and conversion with an aggregate ratio of approximately one-fifth compared to raw
instrument data.
Beyond capacity scalability, I/O performance is also a critical file storage attribute for
overall NGS performance and efficiency. As discussed earlier, NGS is I/O-bound, rather
than processor-bound, and thus storage I/O performance has a high impact on overall
NGS performance in relation to other NGS workflow parameters. As a result, NGS
environments require a file storage infrastructure that is purpose-built to address the
11Next-generation genome sequencing using EMC Isilon scale-out NAS
12. capacity and performance scalability, efficiency, availability, and manageability
challenges of NGS environments.
EMC Isilon scale-out NAS overview
NGS is an unstructured file-based process, not a block-based storage process. EMC Isilon
scale-out NAS manages unstructured file data through a single namespace through its
storage appliance nodes arranged in clusters, which support massive scalability.
A short description of the EMC Isilon storage solution and the EMC Isilon OneFS®
file operating system with each of its features summarized below confirms its
suitability for next-generation genomic sequencing:
Simple
OneFS combines the three layers of traditional storage architectures—the file system,
volume manager, and RAID/data protection—into one unified software layer, creating
a single intelligent distributed file system that runs on an Isilon storage cluster.
Figure 5: OneFS eliminates the need for complex file management
This scale-out hardware provides the appliance on which the OneFS distributed file
system resides. A single EMC Isilon cluster consists of multiple storage nodes, which
are rack-mountable enterprise appliances containing memory, CPU, networking, NVRAM,
storage media, and the InfiniBand back-end network that connects the nodes together.
Hardware components are best-of-breed and benefit from ever-improving cost and
efficiency curves. OneFS allows nodes to be added or removed from the cluster at
will and at any time, abstracting the data and applications away from the hardware.
Adding nodes—instead of adding volumes and LUNs via physical disks—becomes an
extremely simple task at the petabyte (PB) scale, which is common in NGS.
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13. Scalable
Figure 6: Linear scalability with OneFS
EMC Isilon provides a high-performance, fully symmetric cluster-based distributed
storage platform. It has linear scalability with increasing capacity—from 18 TB to
20 PB in a single file system—as compared to traditional storage. The concept of
node-based capacity growth with linear scaling is critical to NGS, where scale needs
to be painless, since the process can generate upwards of 8 TB per week per instrument.
The researchers and clinicians need to focus on managing scientific data and patients,
not managing storage.
Predictable
Along with raw scaling of capacity, balancing of the content across the new nodes needs
to be predictable for an NGS workflow, due to its sustained throughput requirement.
Since the instrument end keeps changing with newer technologies faster than the HPC
or storage, this balancing and scale become invaluable. Dynamic content balancing
is performed as nodes are added or data capacity changes. There is no added
management time for the administrator, or increased complexity within the storage
system. The storage reporting application, InsightIQ™, can be used to plan the growth
of a system from storage statistics both for infrastructure and for budgeting.
Efficient
Operational Expenditure (OPEX) hinges upon efficiency, specifically in NGS, since the
total storage can run into petabytes. A recent survey conducted by Scripps Institute
concluded that more than 35 percent of institutions today are at petabyte scale in
NGS with a 10 percent year-over-year growth.
Isilon scale-out NAS offers an 80 percent efficiency ratio and “smart pooling” of the
data across multiple tiers, making dynamic, rule-based data transfer between storage
pools an integral piece of the NGS process. This efficiency is at the application level
and tiered by the performance types:
• S-Series node for high performance (I/O per second)
• X-Series node for high throughput
• NL-Series node for archive
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14. Figure 7: Storage tiering based on node type
The tiers in the storage cluster as shown in Figure 7 above are identified as “pools”
and managed by the EMC Isilon SmartPools®
application. A pool is a group of similar
nodes, which is defined by the user and is based on the functionality or workflow. A
pool is governed by policies that can be changed based on needs; default policies are
built in. Policies may be defined by any standard file metadata: file type, size, name,
location, owner, age, last accessed, etc. Data can be migrated from pool to pool. The
timing for this data movement is configurable: default is 1x per day at 10:00 p.m.
Available
Data availability and redundancy are the core requirements of the scientific and clinical
staff in NGS. As NGS moves into the clinical realm, availability becomes even more
important. Flexible data protection occurs during power loss, node or disk failures,
loss of quorum, and storage rebuild. OneFS avoids the use of hot spare drives, and
simply borrows from the available free space in the system in order to recover from
failures; this technique is called virtual hot spare.
Since all data, metadata, and parity information is distributed across the nodes of
the cluster, the Isilon cluster does not require a dedicated parity node or drive, or a
dedicated device or set of devices to manage metadata. This helps to ensure that no
one node can become a single point of failure and makes the cluster “self-healing.”
Enterprise-ready
The NGS data system does not exist as an island; it usually coexists with other storage
and IT systems. The standard protocols that OneFS supports build the standards-based
protocol bridges to other information systems from NGS. Specifically, connectivity to
the Isilon scale-out NAS cluster is via standard protocols: CIFS, SMB, NFS, FTP/HTTP,
Object, and HDFS. The complete data lifecycle is accessible to the centralized IT group.
Snapshots, replication, and quotas are supported via a simple Web-based UI.
Data is given infinite longevity and future-proofs the enterprise from evolving hardware
generations—eliminating the cost and pain of data migrations and hardware refreshes.
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15. Standardized authentication and access control are available at scale: Active Directory
(AD), LDAP, NIS, and local users. Simultaneous or rolling upgrades to OneFS are
possible, with little or no impact to the production environment.
Figure 8: Standard protocols are critical to enterprises
The software to manage OneFS is automated to eliminate complexity, as shown in
Figure 9 below:
Figure 9: OneFS software management suite
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16. All of the applications shown above are available as software licenses and are Web-based
through the main administrative user interface. A comprehensive command-line–based
administration interface is also available.
Table 2. Functional overview of the OneFS software suite
OneFS software management suite
Making data management easier for NGS
OneFS infrastructure software solutions meet critical data protection, access,
management, and availability needs.
Application Category What it does
SmartPools®
Resource
management
Implements a highly efficient, automated
tiered storage strategy to optimize
storage performance and costs
SmartConnect™ Data access Enables load balancing and dynamic NFS
failover and failback of client connections
across storage nodes to optimize use of
cluster resources
SnapshotIQ™ Data protection Protects data efficiently and reliably
with secure, near-instantaneous
snapshots while incurring little to
no performance overhead
InsightIQ™ Performance
management
Maximizes performance of your
Isilon scale-out storage system with
innovative performance-monitoring
and reporting tools
SmartQuotas™ Data
management
Assigns and manages quotas that
partition storage into easily managed
segments at the cluster, directory,
sub-directory, user, and group levels
SyncIQ®
Data replication Replicates and distributes large, mission-
critical data sets to multiple shared
storage systems in multiple sites for
reliable disaster recovery capability
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18. Total RAM size: NGS analysis requires large file processing, including functions
related to string processing, clustering of large files, and statistical quality measures,
and thus easily becomes memory-bound. As a result, a large DDR3-based RAM pool
is optimal.
Network infrastructure parameters
TCP MTU: The default maximum transmission unit (MTU) (or frame size) of current
Ethernet systems is 1500 B. However, higher bandwidth network infrastructures can
handle a much higher MTU of 9000 B (called “jumbo frames”) for efficient data transfer.
Please note that the jumbo frame setting needs to be completed both on the HPC
server node(s) and the switch(es).
Ethernet bonding (LACP): Ethernet bonding using the Link Aggregation Control Protocol
(LACP) is a method used to alleviate bandwidth limitations and port-cable-port failure
issues. By combining several Ethernet interfaces to a virtual “bond” interface, the
network bandwidth can be increased since LACP splits the communications and sends
frames among all the Ethernet links. Bonding 2x 1 GbE interfaces provides the
required bandwidth between HPC server nodes and NAS file storage.
Isilon storage configuration parameters
NFS master OS: By default, EMC Isilon OneFS operating system is the NFS server. It
is recommended that this default be maintained since SmartConnect and other OneFS
features may be affected if the HPC master node OS is chosen as the NFS server.
NFSv4: NFSv4 provides improved performance, security, and robustness vis-à-vis
NFSv3. These include support of multiple operations per RPC operation (vs. a single
operation per RPC in NFSv3), use of Kerberos and access control lists (ACLs) for
security (vs. UNIX file permissions in NFSv3), use of TCP transport (vs. UDP in
NFSv3), and integrated file locking (vs. use of the adjunct Network Lock Manager
protocol for NFSv3). As a result, it is recommended that sites utilize NFSv4 for NGS
environments. Please note that initial setting-up of NFSv4 can be cumbersome.
NFS async: The NFS async (asynchronous) mode allows the server to reply to client
requests as soon as it has processed the request and handed it off to the local file
system, without waiting for the data to be written to stable storage. However, write
performance is better when synchronous mode is used (also called “noasync”),
especially for smaller file sizes. This is the recommended mode, especially since
NFSv4 uses TCP connectivity.
NFS number of threads: This is the number of NFS server daemon threads that are
started when the system boots. The OneFS NFS server usually has 16 threads as its
default setting; this value can be changed via the Command Line Interface (CLI):
isi_sysctl_cluster sysctl vfs.nfsrv.rpc.[minthreads,maxthreads]
Increasing the number of NFS daemon threads improves response minimally; the
maximum number of NFS threads needs to be limited to 64.
NFS ACL: The NFS ACL (access control list) for NFSv4 is a list of permissions associated
with a set of files or directories that contain one or more access control entries (ACEs).
There are four types of ACEs: Allow, Deny, Audit, and Alarm; with three kinds of
flags: group, inheritance, and administrative. There are 13 file permissions and
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19. 14 directory permissions. OneFS manages NFS ACLs, which need to be mapped to
the NFS client using the idmapd configuration.
NFS locks: The mounting and locking processes have been enhanced in NFSv4, which
supports mandatory as well as advisory locking. Caching and open delegation provide
performance improvements in most situations. More information about state is stored
on the servers in the HPC tier, enabling recovery of the files when they are in use.6,7
Maximum number of directories at a level and files within a directory: While
Isilon OneFS supports an upper bound of 100,000 files in a directory, as well as number
of directories at a level, in order to ensure highest performance while traversing a
directory tree, the maximum number of directories at a level and the maximum number
of files within a directory needs to be below 10,000.
Number of small (<8 KB) files: Random-write operations on small files have low
response times and can degrade overall application performance. In order to optimize
performance, it is recommended that Base Call files that are typically <8 KB be
aggregated into 128 KB or larger ZIP archive files.
SGE number of nodes: The Sun Grid Engine (SGE) package is a popular distributed
resource manager (DRM) and scheduler package for controlling access to and control
of cluster resources. It is recommended that at least a minimum of three SGE nodes be
used for NGS for performance and backup reasons. While a commercial version of
SGE is available from Oracle, SGE is also available as open source. Other popular
open source DRM packages are Torque/Maui and Lava.
Execution daemons: The SGE PAR_EXECD_INST_COUNT variable contained within
the SGE configuration file defines the number of parallel execd (execution daemons)
for the NGS HPC cluster.
DNS location: If the HPC NGS system is run within a private network, it is
recommended that Linux BIND be installed on the HPC master node with DNS
forwarding to the organization’s DNS server.
Summary
Internal EMC testing determined that the KPIs that affect the performance the most are:
• RAM on HPC cluster server nodes (recommended at 3 GB/core)
• RAM and SSD on the Isilon storage cluster—maximum allowable RAM on the
performance layer and minimum recommended on the archival layer with about
1 percent to 2 percent of the raw storage capacity as SSD
• Storage configuration parameters: NFSv 4, NFS async enabled, TCP MTU (jumbo
frames), LACP, and the Grid Engine package
6
See info on Isilon SmartLock: http://www.emc.com/collateral/software/white-papers/h8325-wp-isd-
smartlock.pdf
7
See info on Isilon high-performance computing: http://www.isilon.com/high-performance-computing
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20. Conclusion
NGS production processes generate potentially millions of files with terabytes
of aggregate storage impacting the capacity and manageability limits of existing
file server structures. Raw instrument data typically consists of large image files
(2–5 TB per run is the norm), usually in TIFF format. The image file for the
experiment is usually the largest file size in NGS.
Genomics is a data reduction process from the raw instrument information (images
or voltages) to the variants, which follows the “Rule of One-Fifth.” Intermediate or
secondary data consists of raw data files, including files in BCL format for base calling
and conversion, and have an aggregate ratio of approximately one-fifth compared to
raw instrument data.
Internal EMC testing has determined that the KPIs that affect the performance of
NGS applications the most are: total RAM size on HPC cluster nodes (recommended
at 3 Gb/core), RAM and SSD on the Isilon storage cluster (typically 1 percent of RAM
storage), and storage configuration parameters with NFSv4, NFS async enabled, TCP
MTU (jumbo frames), LACP (2x 1 Gb/s or 4x 1 Gb/s), and a Grid Engine package.
NGS environments require a file storage infrastructure that is purpose-built to address
the capacity and performance scalability, efficiency, availability, and manageability
challenges of NGS applications. Cumulative network bandwidth between HPC and
NAS increases with the total number of Isilon nodes on the storage cluster.
Isilon scale-out NAS presents a range of benefits optimal for NGS. The Isilon approach
of enabling storage I/O and capacity growth through addition of cluster nodes is optimal,
since NGS requires storage performance and capacity scalability to be implemented as
seamlessly as possible. In addition, dynamic content balancing performed within Isilon
scale-out NAS as nodes are added or data capacity changes is ideal for an NGS
workflow due to its sustained throughput requirement.
Isilon scale-out NAS also offers an 80 percent efficiency ratio and “smart pooling” of
the data across multiple performance tiers, making dynamic, rule-based data transfer
between storage pools an integral piece of the NGS process. Flexible, multidimensional
data protection, which occurs within Isilon scale-out NAS during power loss, node or disk
failures, loss of quorum, and storage rebuild, enables non-stop data availability for NGS.
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