Signaler
Partager
Télécharger pour lire hors ligne
Recommandé
Random stuff
This document provides an overview of key topics related to digital markets and network strategy. It discusses the impact of digitization on industries like newspapers and music. It also covers strategic implications of digitization like easy entry, new variety, and new business models. Additionally, it examines characteristics of information like uncertainty, indivisibility, and appropriability. Finally, it poses several questions about strategic considerations for selling information and managing networks.
bsd-group1
Berkeley Software Distribution (BSD), also known as Berkeley Unix, is an operating system derived from Unix developed at the University of California, Berkeley from 1977 to 1995. Key events included the first BSD release in 1977, the addition of virtual memory and networking capabilities, and the release of 4.2BSD and 4.3BSD, which popularized the BSD TCP/IP stack. Variants such as FreeBSD, NetBSD, and OpenBSD continue development of the BSD operating system today.
Presentation1
Berkeley Software Distribution (BSD), also known as Berkeley Unix, is an operating system derived from Unix developed at the University of California, Berkeley from 1977 to 1995. Key events included the first BSD release in 1977, the addition of virtual memory and networking capabilities, and the release of 4.2BSD and 4.3BSD, which popularized the BSD TCP/IP stack. Variants such as FreeBSD, NetBSD, and OpenBSD continue development of the BSD operating system today.
Towards an understanding of diversity in biological and biomedical systems
Towards an understanding of diversity in biological and biomedical systems
Igor zwir
Massive sequencing data analysis workshop
Granada 2011
bsd
Berkeley Software Distribution (BSD), also known as Berkeley Unix, is a Unix operating system derivative developed and distributed by the Computer Systems Research Group at the University of California, Berkeley from 1977 to 1995. Key events included the release of Version 1 in 1977, which added improvements to Pascal and vi. By the late 1980s, BSD code was freely distributed, leading to variants like FreeBSD, NetBSD, and OpenBSD that are still actively developed today. Many modern operating systems are based in part on BSD, including Apple's macOS, Juniper's JunOS, and components of Linux distributions.
Administracion de sistemas
Este documento presenta el plan de estudios para el tercer año del bachillerato técnico en informática con especialización en administración de sistemas. Incluye unidades didácticas en inglés con temas como el uso y abuso de drogas, carreras profesionales, comunicación, salud e información. También cubre temas científicos como galaxias, la vida en otros planetas y el estado del planeta Tierra.
Recommandé
Random stuff
This document provides an overview of key topics related to digital markets and network strategy. It discusses the impact of digitization on industries like newspapers and music. It also covers strategic implications of digitization like easy entry, new variety, and new business models. Additionally, it examines characteristics of information like uncertainty, indivisibility, and appropriability. Finally, it poses several questions about strategic considerations for selling information and managing networks.
bsd-group1
Berkeley Software Distribution (BSD), also known as Berkeley Unix, is an operating system derived from Unix developed at the University of California, Berkeley from 1977 to 1995. Key events included the first BSD release in 1977, the addition of virtual memory and networking capabilities, and the release of 4.2BSD and 4.3BSD, which popularized the BSD TCP/IP stack. Variants such as FreeBSD, NetBSD, and OpenBSD continue development of the BSD operating system today.
Presentation1
Berkeley Software Distribution (BSD), also known as Berkeley Unix, is an operating system derived from Unix developed at the University of California, Berkeley from 1977 to 1995. Key events included the first BSD release in 1977, the addition of virtual memory and networking capabilities, and the release of 4.2BSD and 4.3BSD, which popularized the BSD TCP/IP stack. Variants such as FreeBSD, NetBSD, and OpenBSD continue development of the BSD operating system today.
Towards an understanding of diversity in biological and biomedical systems
Towards an understanding of diversity in biological and biomedical systems
Igor zwir
Massive sequencing data analysis workshop
Granada 2011
bsd
Berkeley Software Distribution (BSD), also known as Berkeley Unix, is a Unix operating system derivative developed and distributed by the Computer Systems Research Group at the University of California, Berkeley from 1977 to 1995. Key events included the release of Version 1 in 1977, which added improvements to Pascal and vi. By the late 1980s, BSD code was freely distributed, leading to variants like FreeBSD, NetBSD, and OpenBSD that are still actively developed today. Many modern operating systems are based in part on BSD, including Apple's macOS, Juniper's JunOS, and components of Linux distributions.
Administracion de sistemas
Este documento presenta el plan de estudios para el tercer año del bachillerato técnico en informática con especialización en administración de sistemas. Incluye unidades didácticas en inglés con temas como el uso y abuso de drogas, carreras profesionales, comunicación, salud e información. También cubre temas científicos como galaxias, la vida en otros planetas y el estado del planeta Tierra.
Edible landscaping
This document discusses different approaches to edible landscaping including biointensive designs, using edible ornamental plants, creating edible structures like arbors, integrating edible water gardens and micro-livestock, mimicking forest gardens, and using mycoscaping. It provides an example of a New England property that produces 10-25% of food for four adults on 1/10 acre using many of these patterns along with preventing pests through resistant crops and habitat plantings without spraying and maintaining soil fertility through groundcovers and nitrogen fixers. Resources for more information are also included.
NGS Data Preprocessing
This document discusses next generation sequencing (NGS) data preprocessing and quality control. It provides a brief history of DNA sequencing technologies and compares current NGS platforms. The importance of quality control and preprocessing NGS data is explained. Key metrics for assessing NGS data quality are described, including per base quality scores, GC content distributions, and k-mer content. Tools for preprocessing (Fastx-toolkit) and quality control (FastQC) are introduced.
Discovery and annotation of variants by exome analysis using NGS
Discovery and annotation of variants by exome analysis using NGS -
Javier Santoyo -
Massive sequencing data analysis workshop -
Granada 2011
Differential expression in RNA-Seq
This document discusses differential expression analysis in RNA-Seq. It begins with an introduction that defines key concepts like expression levels, sequencing depth, and differential expression. It then covers normalization methods to account for biases in RNA-Seq data. The main method discussed is NOISeq, a non-parametric approach that does not require replicates. NOISeq compares signal distributions between conditions to noise distributions within conditions to identify differentially expressed genes. The document concludes with exercises to run NOISeq on sample data.
Computational infrastructure for NGS data analysis
Computational infrastructure for NGS data analysis -
Pablo Escobar -
Massive sequencing data analysis workshop -
Granada 2011
Introduction to NGS
NGS has enabled high-throughput genome sequencing and analysis, changing genomic research. Technologies like Roche 454, Solexa/Illumina, and SOLiD allow massively parallel sequencing of genomes. NGS has applications in de novo genome sequencing, resequencing, RNA-seq, ChIP-seq, methylation analysis, and more. It provides advantages over microarrays like detecting novel transcripts, splicing variants, and sequence variations. NGS data requires processing including quality control, mapping, and variant identification to realize its full potential to revolutionize genomic research and medicine.
Contenu connexe
En vedette
Edible landscaping
This document discusses different approaches to edible landscaping including biointensive designs, using edible ornamental plants, creating edible structures like arbors, integrating edible water gardens and micro-livestock, mimicking forest gardens, and using mycoscaping. It provides an example of a New England property that produces 10-25% of food for four adults on 1/10 acre using many of these patterns along with preventing pests through resistant crops and habitat plantings without spraying and maintaining soil fertility through groundcovers and nitrogen fixers. Resources for more information are also included.
NGS Data Preprocessing
This document discusses next generation sequencing (NGS) data preprocessing and quality control. It provides a brief history of DNA sequencing technologies and compares current NGS platforms. The importance of quality control and preprocessing NGS data is explained. Key metrics for assessing NGS data quality are described, including per base quality scores, GC content distributions, and k-mer content. Tools for preprocessing (Fastx-toolkit) and quality control (FastQC) are introduced.
Discovery and annotation of variants by exome analysis using NGS
Discovery and annotation of variants by exome analysis using NGS -
Javier Santoyo -
Massive sequencing data analysis workshop -
Granada 2011
Differential expression in RNA-Seq
This document discusses differential expression analysis in RNA-Seq. It begins with an introduction that defines key concepts like expression levels, sequencing depth, and differential expression. It then covers normalization methods to account for biases in RNA-Seq data. The main method discussed is NOISeq, a non-parametric approach that does not require replicates. NOISeq compares signal distributions between conditions to noise distributions within conditions to identify differentially expressed genes. The document concludes with exercises to run NOISeq on sample data.
Computational infrastructure for NGS data analysis
Computational infrastructure for NGS data analysis -
Pablo Escobar -
Massive sequencing data analysis workshop -
Granada 2011
Introduction to NGS
NGS has enabled high-throughput genome sequencing and analysis, changing genomic research. Technologies like Roche 454, Solexa/Illumina, and SOLiD allow massively parallel sequencing of genomes. NGS has applications in de novo genome sequencing, resequencing, RNA-seq, ChIP-seq, methylation analysis, and more. It provides advantages over microarrays like detecting novel transcripts, splicing variants, and sequence variations. NGS data requires processing including quality control, mapping, and variant identification to realize its full potential to revolutionize genomic research and medicine.
En vedette (6)
Discovery and annotation of variants by exome analysis using NGS
Discovery and annotation of variants by exome analysis using NGS
Computational infrastructure for NGS data analysis
Computational infrastructure for NGS data analysis