An introduction to second generation sequencing will be given with focus on the basic production informatics: The approach of raw data conversion and quality control will be discussed.
The document discusses Sanger sequencing, a method of DNA sequencing. It provides a brief history of DNA sequencing, noting that Sanger developed an enzymatic DNA sequencing technique in 1977. The document then describes the key steps of Sanger sequencing, including separating the DNA strands, copying one strand with chemically altered bases that cause termination, and analyzing the fragments to reveal the DNA sequence. It also compares Sanger sequencing to the Maxam-Gilbert chemical degradation method.
Whole genome shotgun sequencing involves randomly breaking genomic DNA into small fragments, sequencing the fragments, and then reassembling the sequences using overlapping regions. The document outlines the history and procedure of shotgun sequencing. Genomic DNA is first fragmented, end-repaired, and size-selected into small, medium, and large fragments. Libraries are created for each size fragment and sequenced. A base caller filters poor calls and an assembler finds overlaps to generate continuous nucleotide sequences or contigs of the whole genome.
Nanopore DNA sequencing is a fourth generation sequencing technique that involves passing single strands of DNA through a nanopore and detecting changes in electrical current caused by each nucleotide base. There are two main types of nanopores - biological nanopores which are protein channels inserted into membranes, and solid-state nanopores fabricated in thin materials like silicon nitride or graphene. Some examples of biological nanopores used for sequencing are the alpha-hemolysin pore and the MspA pore. Nanopore sequencing has advantages over other techniques in being label-free, capable of very long reads, and requiring low sample amounts. However, challenges remain in slowing DNA translocation for higher resolution and reducing noise in the electrical signals.
This document discusses the history and various methods of DNA sequencing. It begins with a brief overview of DNA sequencing and its uses. It then outlines some of the major developments in DNA sequencing techniques, including the earliest RNA sequencing in 1972, Sanger sequencing in 1977, and the first complete genome of Haemophilus influenzae in 1995. The document proceeds to provide more detailed explanations of several DNA sequencing methods, such as Sanger sequencing, pyrosequencing, shotgun sequencing, Illumina sequencing, and SOLiD sequencing.
Microarrays allow researchers to examine gene expression patterns across thousands of genes simultaneously. A microarray contains probes for known genes that are used to detect complementary mRNA in a biological sample. Microarrays can be used to study gene expression differences between normal and diseased tissues, classify tumor subtypes, and diagnose cancers. They also show promise for personalized cancer treatment by predicting patient prognosis and response to therapy.
Whole genome sequencing is the process of determining the complete DNA sequence of an organism's genome. It involves sequencing all chromosomal and organellar DNA. Key methods include shotgun sequencing, which randomly fragments DNA for sequencing, and single molecule real time sequencing, which observes individual DNA polymerases incorporating nucleotides in real time using fluorescent tags. Whole genome sequencing has provided insights into evolutionary biology and may help predict disease susceptibility, though technical challenges remain such as fully sequencing repetitive regions.
This document provides an overview and introduction to RNA-seq analysis using Next Generation Sequencing. It discusses the RNA-seq workflow including mapping reads with TopHat2, transcript assembly with Cufflinks, and differential expression analysis. Key points covered include the advantages of RNA-seq over microarrays, the exponential drop in sequencing costs, mapping strategies for junction reads including TopHat, and running TopHat from the command line.
Deciphering DNA sequences is essential for virtually all branches of biological research. With the
advent of capillary electrophoresis (CE)-based Sanger sequencing, scientists gained the ability to
elucidate genetic information from any given biological system. This technology has become widely
adopted in laboratories around the world, yet has always been hampered by inherent limitations in
throughput, scalability, speed, and resolution that often preclude scientists from obtaining the essential
information they need for their course of study. To overcome these barriers, an entirely new technology
was required—Next-Generation Sequencing (NGS), a fundamentally different approach to sequencing
that triggered numerous ground-breaking discoveries and ignited a revolution in genomic science.
The document discusses Sanger sequencing, a method of DNA sequencing. It provides a brief history of DNA sequencing, noting that Sanger developed an enzymatic DNA sequencing technique in 1977. The document then describes the key steps of Sanger sequencing, including separating the DNA strands, copying one strand with chemically altered bases that cause termination, and analyzing the fragments to reveal the DNA sequence. It also compares Sanger sequencing to the Maxam-Gilbert chemical degradation method.
Whole genome shotgun sequencing involves randomly breaking genomic DNA into small fragments, sequencing the fragments, and then reassembling the sequences using overlapping regions. The document outlines the history and procedure of shotgun sequencing. Genomic DNA is first fragmented, end-repaired, and size-selected into small, medium, and large fragments. Libraries are created for each size fragment and sequenced. A base caller filters poor calls and an assembler finds overlaps to generate continuous nucleotide sequences or contigs of the whole genome.
Nanopore DNA sequencing is a fourth generation sequencing technique that involves passing single strands of DNA through a nanopore and detecting changes in electrical current caused by each nucleotide base. There are two main types of nanopores - biological nanopores which are protein channels inserted into membranes, and solid-state nanopores fabricated in thin materials like silicon nitride or graphene. Some examples of biological nanopores used for sequencing are the alpha-hemolysin pore and the MspA pore. Nanopore sequencing has advantages over other techniques in being label-free, capable of very long reads, and requiring low sample amounts. However, challenges remain in slowing DNA translocation for higher resolution and reducing noise in the electrical signals.
This document discusses the history and various methods of DNA sequencing. It begins with a brief overview of DNA sequencing and its uses. It then outlines some of the major developments in DNA sequencing techniques, including the earliest RNA sequencing in 1972, Sanger sequencing in 1977, and the first complete genome of Haemophilus influenzae in 1995. The document proceeds to provide more detailed explanations of several DNA sequencing methods, such as Sanger sequencing, pyrosequencing, shotgun sequencing, Illumina sequencing, and SOLiD sequencing.
Microarrays allow researchers to examine gene expression patterns across thousands of genes simultaneously. A microarray contains probes for known genes that are used to detect complementary mRNA in a biological sample. Microarrays can be used to study gene expression differences between normal and diseased tissues, classify tumor subtypes, and diagnose cancers. They also show promise for personalized cancer treatment by predicting patient prognosis and response to therapy.
Whole genome sequencing is the process of determining the complete DNA sequence of an organism's genome. It involves sequencing all chromosomal and organellar DNA. Key methods include shotgun sequencing, which randomly fragments DNA for sequencing, and single molecule real time sequencing, which observes individual DNA polymerases incorporating nucleotides in real time using fluorescent tags. Whole genome sequencing has provided insights into evolutionary biology and may help predict disease susceptibility, though technical challenges remain such as fully sequencing repetitive regions.
This document provides an overview and introduction to RNA-seq analysis using Next Generation Sequencing. It discusses the RNA-seq workflow including mapping reads with TopHat2, transcript assembly with Cufflinks, and differential expression analysis. Key points covered include the advantages of RNA-seq over microarrays, the exponential drop in sequencing costs, mapping strategies for junction reads including TopHat, and running TopHat from the command line.
Deciphering DNA sequences is essential for virtually all branches of biological research. With the
advent of capillary electrophoresis (CE)-based Sanger sequencing, scientists gained the ability to
elucidate genetic information from any given biological system. This technology has become widely
adopted in laboratories around the world, yet has always been hampered by inherent limitations in
throughput, scalability, speed, and resolution that often preclude scientists from obtaining the essential
information they need for their course of study. To overcome these barriers, an entirely new technology
was required—Next-Generation Sequencing (NGS), a fundamentally different approach to sequencing
that triggered numerous ground-breaking discoveries and ignited a revolution in genomic science.
Pyrosequencing is a sequencing method that detects light signals from enzymatic reactions triggered by nucleotide additions during DNA synthesis. It was developed in 1996 and allows high-throughput sequencing. There are solid and liquid phase variants, with the latter using an additional enzyme to eliminate washing steps. The process involves preparing DNA fragments, attaching to beads, amplification by PCR, and sequencing by flowing nucleotides over wells containing DNA-coated beads and enzymes, detecting light signals with each nucleotide incorporation.
Nanopore sequencing is a fourth generation DNA sequencing technique that involves monitoring changes in electric current as DNA molecules pass through nanopores. There are two main types of nanopores: biological nanopores made of protein complexes like alpha-hemolysin, and solid state nanopores made in thin silicon nitride membranes. Nanopore sequencing has advantages of being label-free, producing long reads at high throughput with low material requirements, but challenges include slowing DNA translocation and reducing noise. Potential applications are in single molecule sensing for analysis of biomolecules.
Genome sequencing is the process of determining the order of nucleotide bases - A, C, G, and T - that make up an organism's DNA. Shotgun sequencing involves randomly breaking the genome into small fragments, sequencing those pieces, and reassembling the sequence by identifying overlapping regions. It was originally used by Sanger to sequence small genomes like viruses and bacteria. There are two main methods - hierarchical shotgun sequencing for larger genomes containing repeats, and whole genome shotgun sequencing for smaller genomes.
Next generation sequencing (NGS) refers to modern DNA sequencing technologies that allow for high-speed, low-cost sequencing of entire genomes. NGS works by massively parallel sequencing of millions of DNA fragments. The Illumina sequencing by synthesis method is the most commonly used NGS approach. It involves library preparation, cluster generation on a flow cell, sequencing via reversible dye-terminator chemistry, and computational analysis of sequenced reads. Key advantages of NGS include its scalability, unlimited dynamic range, tunable coverage levels, and ability to multiplex many samples simultaneously in a single run.
Whole genome sequencing is a technique to sequence the entire genome of an organism. It involves breaking the genome into small fragments, copying the fragments, sequencing the fragments, and reassembling the sequence data into the full genome. Key steps include isolating DNA, fragmenting it, ligating fragments into plasmids, amplifying the plasmids, sequencing the fragments using Sanger sequencing, and assembling the sequence reads into the complete genome. Whole genome sequencing allows researchers to discover coding and non-coding regions, predict disease susceptibility, and perform evolutionary studies by comparing species.
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
The document discusses genome sequencing and related topics. It begins by defining what a genome is - the complete set of DNA in an organism. It then discusses the different types of genomes, such as prokaryotic and eukaryotic, including nuclear, mitochondrial, and chloroplast genomes. The document also defines genomics as the comprehensive study of whole genomes and all gene interactions, distinguishing it from traditional genetics which focuses on single genes. It outlines some key milestones in genomic sequencing and the technical foundations that enabled sequencing whole genomes. Finally, it describes the main approaches used for genome sequencing projects, including hierarchical shotgun sequencing and whole genome shotgun sequencing.
The document describes the steps of Illumina sequencing. Genomic DNA is first fragmented and adapters are ligated to create single-stranded DNA fragments. These fragments are attached to a flow cell and undergo bridge amplification to create clusters of identical DNA fragments. Sequencing occurs through cycles of reversible terminator-based sequencing using fluorescently labeled dNTPs, imaging of the fluorescence, and cleavage of the label and terminator to allow the next cycle. After multiple cycles, the sequenced reads are aligned to the reference genome to determine the original sequence.
whole genome analysis
history
needs
steps involved
human genome data
NGS
pyrosequencing
illumina
SOLiD
Ion torrent
PacBio
applications
problems
benefits
Ion Torrent (Proton/PGM) and SOLiD sequencing are two types of next-generation sequencing technologies. Ion Torrent uses semiconductor sequencing to detect hydrogen ions released during DNA synthesis, while SOLiD uses ligation of octamer probes and fluorescent dyes to determine sequences in color space. Both have advantages such as fast run times and high throughput but also limitations including errors in homopolymers for Ion Torrent and issues with palindromic sequences for SOLiD.
Pyrosequencing is a sequencing by synthesis technique that uses a luciferase enzyme system to monitor DNA synthesis. It works by adding DNA polymerase and a single nucleotide to the DNA fragments, generating pyrophosphate that is converted to light. The light is detected and identifies the nucleotide incorporated. Pyrosequencing has applications in cDNA analysis, mutation detection, re-sequencing of disease genes, and identifying single nucleotide polymorphisms and typing bacteria and viruses.
This document discusses DNA microarrays, including:
1. DNA microarrays contain many DNA probes attached to a solid surface that allow measurement of gene expression levels or genotyping of many regions simultaneously through hybridization.
2. The core principle is hybridization - complementary nucleic acid sequences pair through hydrogen bonds, and fluorescent labeling allows detection of binding to quantify expression.
3. DNA microarrays have many applications including gene expression profiling, disease diagnosis, drug discovery, and toxicology research.
Pyrosequencing is a sequencing method that detects DNA polymerase activity by measuring the release of pyrophosphate using a cascade of enzymatic reactions that generate visible light. It utilizes emulsion PCR to amplify DNA fragments on beads in microreactors. The beads are then loaded into wells and sequenced by sequentially adding nucleotides and detecting light produced upon incorporation using a CCD camera. Key advantages are its accuracy, high throughput of up to 48,000 probes per day, and ease of automation. However, it requires specialized equipment and software.
DNA microarrays allow analysis of gene expression across thousands of genes simultaneously. They consist of DNA probes attached to a solid surface in an organized grid pattern, with each spot representing a single gene. Samples are labeled with fluorescent dyes and hybridized to the chip. Complementary sequences pair via hydrogen bonds, while non-specific sequences are washed away. The signal intensity at each spot indicates the amount of target sequence present and thus gene expression levels. DNA microarrays have applications in clinical diagnosis, drug discovery, and other fields by profiling gene expression patterns.
This document summarizes a class seminar presentation on next generation sequencing technologies. It begins with an overview of DNA sequencing and its importance. It then reviews the history of sequencing technologies, from the discovery of DNA to the development of Sanger sequencing and next generation sequencing platforms. The document focuses on describing the Illumina/Solexa, 454, and SOLiD next generation sequencing methods. It explains the key steps in library preparation, cluster amplification, and sequencing by synthesis or ligation for these platforms. The advantages of next generation sequencing technologies over Sanger sequencing are also highlighted.
This document discusses different types of polymerase chain reaction (PCR) techniques. It begins by providing background on PCR and its development. It then describes several types of PCR including multiplex PCR, which allows for simultaneous detection of multiple pathogens; nested PCR, which increases specificity; reverse transcription PCR (RT-PCR) and quantitative real-time PCR (qRT-PCR), which are used to detect RNA; quantitative PCR, which measures specific target DNA/RNA amounts; and other variants like hot-start PCR, touchdown PCR, and methylation-specific PCR. Each type is briefly explained along with its uses and applications in medical research.
The document discusses Ion Torrent semiconductor sequencing. It begins by providing background on first and next generation sequencing. It then describes Ion Torrent sequencing, noting that it detects pH changes from nucleotide incorporation rather than using modified nucleotides or optics. The principle, procedure involving fragmentation, ligation, amplification and pH detection on a CMOS chip, applications in genetics and medicine, advantages of speed and lower cost, and challenges including high cost per nucleotide and analysis complexity are summarized.
This document provides an introduction to next generation sequencing (NGS) technologies. It begins with an outline of topics to be covered, including the evolution of NGS technologies, their descriptions and comparisons, bioinformatics challenges of NGS data analysis, and some aspects of NGS data analysis workflows and tools. The document then delves into explanations of specific NGS platforms, their performance characteristics, and the sequencing processes. It discusses the large computational infrastructure and data management needs of NGS, as well as quality control, preprocessing of NGS data, and popular analysis tools and workflows.
New Generation Sequencing Technologies: an overviewPaolo Dametto
The document provides a history of DNA sequencing technologies. It begins with the discovery of DNA's structure in 1953 and the development of recombinant DNA technology in the 1970s. First generation Sanger sequencing produced short reads over 1,000 years to sequence the human genome. Next generation sequencing (NGS) platforms since 2005 have dramatically reduced costs while increasing throughput. NGS methods like Roche/454 pyrosequencing, Illumina/Solexa sequencing by synthesis, SOLiD ligation sequencing, and single-molecule real-time sequencing by Pacific Biosciences now enable large-scale genome and transcriptome analysis.
Pyrosequencing is a sequencing method that detects light signals from enzymatic reactions triggered by nucleotide additions during DNA synthesis. It was developed in 1996 and allows high-throughput sequencing. There are solid and liquid phase variants, with the latter using an additional enzyme to eliminate washing steps. The process involves preparing DNA fragments, attaching to beads, amplification by PCR, and sequencing by flowing nucleotides over wells containing DNA-coated beads and enzymes, detecting light signals with each nucleotide incorporation.
Nanopore sequencing is a fourth generation DNA sequencing technique that involves monitoring changes in electric current as DNA molecules pass through nanopores. There are two main types of nanopores: biological nanopores made of protein complexes like alpha-hemolysin, and solid state nanopores made in thin silicon nitride membranes. Nanopore sequencing has advantages of being label-free, producing long reads at high throughput with low material requirements, but challenges include slowing DNA translocation and reducing noise. Potential applications are in single molecule sensing for analysis of biomolecules.
Genome sequencing is the process of determining the order of nucleotide bases - A, C, G, and T - that make up an organism's DNA. Shotgun sequencing involves randomly breaking the genome into small fragments, sequencing those pieces, and reassembling the sequence by identifying overlapping regions. It was originally used by Sanger to sequence small genomes like viruses and bacteria. There are two main methods - hierarchical shotgun sequencing for larger genomes containing repeats, and whole genome shotgun sequencing for smaller genomes.
Next generation sequencing (NGS) refers to modern DNA sequencing technologies that allow for high-speed, low-cost sequencing of entire genomes. NGS works by massively parallel sequencing of millions of DNA fragments. The Illumina sequencing by synthesis method is the most commonly used NGS approach. It involves library preparation, cluster generation on a flow cell, sequencing via reversible dye-terminator chemistry, and computational analysis of sequenced reads. Key advantages of NGS include its scalability, unlimited dynamic range, tunable coverage levels, and ability to multiplex many samples simultaneously in a single run.
Whole genome sequencing is a technique to sequence the entire genome of an organism. It involves breaking the genome into small fragments, copying the fragments, sequencing the fragments, and reassembling the sequence data into the full genome. Key steps include isolating DNA, fragmenting it, ligating fragments into plasmids, amplifying the plasmids, sequencing the fragments using Sanger sequencing, and assembling the sequence reads into the complete genome. Whole genome sequencing allows researchers to discover coding and non-coding regions, predict disease susceptibility, and perform evolutionary studies by comparing species.
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
The document discusses genome sequencing and related topics. It begins by defining what a genome is - the complete set of DNA in an organism. It then discusses the different types of genomes, such as prokaryotic and eukaryotic, including nuclear, mitochondrial, and chloroplast genomes. The document also defines genomics as the comprehensive study of whole genomes and all gene interactions, distinguishing it from traditional genetics which focuses on single genes. It outlines some key milestones in genomic sequencing and the technical foundations that enabled sequencing whole genomes. Finally, it describes the main approaches used for genome sequencing projects, including hierarchical shotgun sequencing and whole genome shotgun sequencing.
The document describes the steps of Illumina sequencing. Genomic DNA is first fragmented and adapters are ligated to create single-stranded DNA fragments. These fragments are attached to a flow cell and undergo bridge amplification to create clusters of identical DNA fragments. Sequencing occurs through cycles of reversible terminator-based sequencing using fluorescently labeled dNTPs, imaging of the fluorescence, and cleavage of the label and terminator to allow the next cycle. After multiple cycles, the sequenced reads are aligned to the reference genome to determine the original sequence.
whole genome analysis
history
needs
steps involved
human genome data
NGS
pyrosequencing
illumina
SOLiD
Ion torrent
PacBio
applications
problems
benefits
Ion Torrent (Proton/PGM) and SOLiD sequencing are two types of next-generation sequencing technologies. Ion Torrent uses semiconductor sequencing to detect hydrogen ions released during DNA synthesis, while SOLiD uses ligation of octamer probes and fluorescent dyes to determine sequences in color space. Both have advantages such as fast run times and high throughput but also limitations including errors in homopolymers for Ion Torrent and issues with palindromic sequences for SOLiD.
Pyrosequencing is a sequencing by synthesis technique that uses a luciferase enzyme system to monitor DNA synthesis. It works by adding DNA polymerase and a single nucleotide to the DNA fragments, generating pyrophosphate that is converted to light. The light is detected and identifies the nucleotide incorporated. Pyrosequencing has applications in cDNA analysis, mutation detection, re-sequencing of disease genes, and identifying single nucleotide polymorphisms and typing bacteria and viruses.
This document discusses DNA microarrays, including:
1. DNA microarrays contain many DNA probes attached to a solid surface that allow measurement of gene expression levels or genotyping of many regions simultaneously through hybridization.
2. The core principle is hybridization - complementary nucleic acid sequences pair through hydrogen bonds, and fluorescent labeling allows detection of binding to quantify expression.
3. DNA microarrays have many applications including gene expression profiling, disease diagnosis, drug discovery, and toxicology research.
Pyrosequencing is a sequencing method that detects DNA polymerase activity by measuring the release of pyrophosphate using a cascade of enzymatic reactions that generate visible light. It utilizes emulsion PCR to amplify DNA fragments on beads in microreactors. The beads are then loaded into wells and sequenced by sequentially adding nucleotides and detecting light produced upon incorporation using a CCD camera. Key advantages are its accuracy, high throughput of up to 48,000 probes per day, and ease of automation. However, it requires specialized equipment and software.
DNA microarrays allow analysis of gene expression across thousands of genes simultaneously. They consist of DNA probes attached to a solid surface in an organized grid pattern, with each spot representing a single gene. Samples are labeled with fluorescent dyes and hybridized to the chip. Complementary sequences pair via hydrogen bonds, while non-specific sequences are washed away. The signal intensity at each spot indicates the amount of target sequence present and thus gene expression levels. DNA microarrays have applications in clinical diagnosis, drug discovery, and other fields by profiling gene expression patterns.
This document summarizes a class seminar presentation on next generation sequencing technologies. It begins with an overview of DNA sequencing and its importance. It then reviews the history of sequencing technologies, from the discovery of DNA to the development of Sanger sequencing and next generation sequencing platforms. The document focuses on describing the Illumina/Solexa, 454, and SOLiD next generation sequencing methods. It explains the key steps in library preparation, cluster amplification, and sequencing by synthesis or ligation for these platforms. The advantages of next generation sequencing technologies over Sanger sequencing are also highlighted.
This document discusses different types of polymerase chain reaction (PCR) techniques. It begins by providing background on PCR and its development. It then describes several types of PCR including multiplex PCR, which allows for simultaneous detection of multiple pathogens; nested PCR, which increases specificity; reverse transcription PCR (RT-PCR) and quantitative real-time PCR (qRT-PCR), which are used to detect RNA; quantitative PCR, which measures specific target DNA/RNA amounts; and other variants like hot-start PCR, touchdown PCR, and methylation-specific PCR. Each type is briefly explained along with its uses and applications in medical research.
The document discusses Ion Torrent semiconductor sequencing. It begins by providing background on first and next generation sequencing. It then describes Ion Torrent sequencing, noting that it detects pH changes from nucleotide incorporation rather than using modified nucleotides or optics. The principle, procedure involving fragmentation, ligation, amplification and pH detection on a CMOS chip, applications in genetics and medicine, advantages of speed and lower cost, and challenges including high cost per nucleotide and analysis complexity are summarized.
This document provides an introduction to next generation sequencing (NGS) technologies. It begins with an outline of topics to be covered, including the evolution of NGS technologies, their descriptions and comparisons, bioinformatics challenges of NGS data analysis, and some aspects of NGS data analysis workflows and tools. The document then delves into explanations of specific NGS platforms, their performance characteristics, and the sequencing processes. It discusses the large computational infrastructure and data management needs of NGS, as well as quality control, preprocessing of NGS data, and popular analysis tools and workflows.
New Generation Sequencing Technologies: an overviewPaolo Dametto
The document provides a history of DNA sequencing technologies. It begins with the discovery of DNA's structure in 1953 and the development of recombinant DNA technology in the 1970s. First generation Sanger sequencing produced short reads over 1,000 years to sequence the human genome. Next generation sequencing (NGS) platforms since 2005 have dramatically reduced costs while increasing throughput. NGS methods like Roche/454 pyrosequencing, Illumina/Solexa sequencing by synthesis, SOLiD ligation sequencing, and single-molecule real-time sequencing by Pacific Biosciences now enable large-scale genome and transcriptome analysis.
Next-generation sequencing techniques such as Illumina and 454 pyrosequencing were discussed for applications including microbial genome sequencing and metagenomic profiling of microbial communities from targeted gene markers or shotgun sequencing. Key steps include library preparation, sequencing, and downstream bioinformatics analysis of sequencing data for tasks like genome assembly, gene annotation, and taxonomic classification of microbial taxa.
This document summarizes a presentation on RNA-Seq and differential expression analysis. It discusses the history of sequencing technologies, how RNA-Seq works, and key steps in the analysis process like quality control, alignment, and differential expression. The goal is to leverage large datasets from declining sequencing costs to gain new insights into cancer through systems biology approaches.
Variant (SNPs/Indels) calling in DNA sequences, Part 2Denis C. Bauer
Abstract: This session will focus on the steps involved in identifying genomic variants after an initial mapping was achieved: improvement the mapping, SNP and indel calling and variant filtering/recalibration will be introduced.
Variant (SNPs/Indels) calling in DNA sequences, Part 1 Denis C. Bauer
This document discusses various topics related to mapping short sequencing reads to a reference genome, including:
- File formats like FASTQ that store sequencing reads and BAM/SAM formats for aligned reads.
- Alignment algorithms like hash table-based (MAQ, BWA) and suffix tree-based (BWA, Bowtie) mappers.
- Visualizing alignments using the Integrative Genomics Viewer (IGV).
- Performing quality control on BAM files by checking the percentage of mapped reads and coverage uniformity.
- The next session will focus on identifying genomic variants from mapped reads through SNP/indel calling and filtering.
The document discusses challenges in identifying causal variants for complex diseases from sequencing data. It notes that while ideal situations may involve finding a variant common in all affected individuals and absent in unaffected, reality involves sifting through around 3.5 million SNPs. Methods like genome-wide association studies and focusing on exonic variants can help prioritize, but functional variants may also reside outside of protein coding regions. Considering combinations of variants through statistical genetics approaches may be needed to explain disease heritability. Quality control, annotation, and filtering are important but finding causal variants remains difficult.
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
Bridge amplification is a process used in Illumina sequencing that involves preparing genomic DNA samples by fragmenting and ligating them to adapters before attaching them to a flow cell surface. Primers on the surface initiate bridge amplification where unlabeled nucleotides and polymerase enzymes are added to synthesize new strands that become double stranded. The original strands are then washed away and the process repeats to amplify multiple copies of each fragment in parallel.
The document describes a workshop on molecular methods in water engineering, including amplicon sequencing and omics approaches. The agenda includes talks on amplicon sequencing principles and limitations, the importance of curated 16S databases, DNA extraction and primer selection, metagenomics and metatranscriptomics principles and challenges, and data informatics and management. The workshop aims to discuss the potential and limitations of novel molecular techniques for analyzing water systems.
This document provides an overview of community profiling using QIIME. It discusses how next-generation sequencing is generating massive amounts of microbial sequence data. QIIME is introduced as a widely used open-source bioinformatics pipeline for analyzing microbiome census data from high-throughput sequencing experiments. The document outlines the typical QIIME workflow, which involves preprocessing raw sequencing data, picking operational taxonomic units (OTUs), assigning taxonomy, computing diversity metrics, and building phylogenetic trees.
This document contains a histology portfolio with slides and descriptions of various tissues and cells. It includes sections on the cell, mitosis, epithelial tissue, connective tissue, blood smears, cartilage and bone, muscle tissue, and nervous tissue. For each section, there are photomicrographs at different magnifications of the featured tissues and descriptions of the key structures visible in the images.
Bioinformatics is an interdisciplinary field that merges biology, computer science, and information technology. It is applied in areas like genomics, proteomics, and systems biology. While some basic analysis can be done through user-friendly tools, truly customized work requires programming skills and an understanding of underlying algorithms. Bioinformatics is not just a service field but rather involves scientific experimentation throughout the entire analysis process from experimental design to evaluation. It is a dedicated field of research in its own right, not a quick or interchangeable task.
This document discusses genome size variation in organisms. It begins by defining the genome and describing genome organization in prokaryotes, viruses and eukaryotes. In eukaryotes, DNA is organized into chromosomes within the nucleus. The document then describes models of chromatin fiber structure and components of the nucleosome. It explains that genome size refers to the total DNA content and can be measured in picograms or megabases. Genome size varies significantly between plant species from 130 Mbp in Arabidopsis to 2.5 Gbp in maize. Factors influencing genome size include cell size, developmental rate, transposable elements and chromosomal mutations.
Evolution of DNA Sequencing - talk by Jonathan Eisen for the Bodega Workshop ...Jonathan Eisen
This document contains slides for a talk on the evolution of DNA sequencing technologies. It reviews early manual sequencing methods developed by Sanger and others. It then summarizes the development of next-generation sequencing platforms including Roche 454 pyrosequencing, Illumina sequencing by synthesis, and others. The slides describe the key steps in library preparation, cluster generation, sequencing chemistry, and data analysis for various platforms. It provides a historical timeline of major advances that have enabled massive parallel sequencing of DNA.
Data Management for Quantitative Biology - Data sources (Next generation tech...QBiC_Tue
Introduction to next generation sequencing (NGS); NGS data; data management of NGS data; third generation sequencing; NGS pipelines; NGS experimental design
Part 1 of RNA-seq for DE analysis: Defining the goalJoachim Jacob
First part of the training session 'RNA-seq for Differential expression' analysis. We explain how we can detect differential expression based on RNA-seq data. Interested in following this session? Please contact http://www.jakonix.be/contact.html
The Feulgen stain is a histological technique discovered in 1924 that uses acid hydrolysis and Schiff's reagent to specifically identify chromosomal material and DNA. It involves hydrolyzing tissue samples in hydrochloric acid to cleave nitrogen bases from DNA and form aldehyde groups, then staining the samples with Schiff's reagent to form a purple compound where aldehydes are present, selectively identifying DNA. The staining intensity is proportional to the DNA concentration and it allows DNA to be visualized microscopically.
Uses of Artificial Intelligence in BioinformaticsPragya Pai
This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
A Workshop at the Stowers Institute for Medical Research.
The document provides an overview of plant genome sequence assembly, including:
1) A brief history of sequencing technologies and their improvements over time, from Sanger sequencing to newer technologies producing longer reads.
2) Key steps in a sequencing project including read processing, filtering, and corrections before assembly into contigs and scaffolds using appropriate software.
3) Factors to consider for experimental design and assembly optimization such as sequencing depth, library types, and software choices depending on the genome and data characteristics.
DNA sequencing: rapid improvements and their implicationsJeffrey Funk
these slides analyze the rapid improvements in DNA sequencers and the implications for these rapid improvements for drug discovery, new crops, materials creation, and new bio-fuels. Many of the rapid improvements are from "reductions in scale." As with integrated circuits, reducing the size of features on DNA sequencers has enabled many orders of magnitude improvements in them. Unlike integrated circuits, the improvements are also due to changes in technology. For example, changes from pyrosequencing to semiconductor and nanopore sequencing have also been needed to achieve the reductions in scale. Second, pyrosequencing also benefited from improvements in lasers and camera chips.
Processing Amplicon Sequence Data for the Analysis of Microbial CommunitiesMartin Hartmann
This document provides an overview of next-generation sequencing (NGS) technologies and their usefulness for analyzing microorganisms associated with plants. It discusses how NGS methods allow addressing previously impossible questions about the composition, function, and interactions of microbial communities in environments like the rhizosphere and phyllosphere. While powerful, NGS platforms have limitations that can introduce errors or biases, but methods exist to overcome these issues. The review highlights applications of NGS in metagenomic studies of plant-associated microbiomes and how these new techniques are transforming the field.
This document provides an overview of different DNA sequencing technologies, including:
- Sanger sequencing, the first generation method using chain termination.
- Next generation sequencing methods like Illumina that use sequencing by synthesis and massively parallel approaches.
- Third generation long-read sequencing methods like PacBio and Oxford Nanopore that sequence single native DNA molecules and can detect modifications but have lower throughput.
It describes the key innovations, working mechanisms, and tradeoffs of read length, output, and accuracy between Sanger, next generation, and long-read third generation sequencing technologies. It also highlights the portability of Oxford Nanopore sequencing with the MinION device.
This document discusses computational methods and challenges for genome assembly using next-generation sequencing data. It describes the four main stages of genome assembly as preprocessing filtering, graph construction, graph simplification, and postprocessing filtering. Each stage processes the data from the previous stage to build the assembly graph and reduce complexity, though some assemblers delay filtering steps.
The Transformation of Systems Biology Into A Large Data ScienceRobert Grossman
Systems biology is becoming a data-intensive science due to the exponential growth of genomic and biological data. Large projects now produce petabytes of data that require new computational infrastructure to store, manage, and analyze. Cloud computing provides elastic resources that can scale to support the increasing data needs of systems biology. Case studies show how clouds are used for large-scale data integration and analysis, running combinatorial analysis over genomic marks, and enabling reanalysis of biological data through elastic virtual machines. The Open Cloud Consortium is working to provide open cloud resources for biological and biomedical research through testbeds and proposed bioclouds.
Comparison between RNASeq and Microarray for Gene Expression AnalysisYaoyu Wang
Transcriptome profiling using RNA-Seq or microarrays allows determination of differential gene expression between samples like normal vs. tumor. While RNA-Seq and microarrays are generally concordant, RNA-Seq provides more information like alternative splicing and novel transcripts but requires more computational resources. While the cost per sample of RNA-Seq is decreasing, storage and analysis of the large datasets requires specialized infrastructure.
This document summarizes a presentation given by Luke Hickey of Pacific Biosciences on human genome sequencing using PacBio systems. It discusses PacBio sequencing technology developments, sequencing and assembly of the NA12878 genome, and the role of the NIST Genome in a Bottle (GIAB) reference materials. Specifically, it notes that PacBio sequenced the GIAB Ashkenazim trio genomes to high coverage and made the data publicly available. The sequencing and assembly of these genomes helps validate and improve PacBio sequencing technologies and supports the development and release of the trio as new NIST reference materials.
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.
Introduction to Next-Generation Sequencing (NGS) TechnologyQIAGEN
The continuous evolution of NGS technology has led to an enormous diversification in NGS applications and dramatically decreased the costs to sequence a complete human genome.
In this presentation, we will discuss the following major topics:
• Basic overview of NGS sequencing technologies
• Next-generation sequencing workflow
• Spectrum of NGS applications
• QIAGEN universal NGS solutions
1) Wnt3a protein rapidly increases the frequency of miniature excitatory synaptic currents in hippocampal neurons through a mechanism involving calcium influx and post-translational modifications enhancing vesicle exocytosis.
2) While previous studies suggested Wnt signaling modulates neurotransmission, this is the first to demonstrate a direct effect of a purified Wnt ligand, Wnt3a, on synaptic transmission.
3) The results identify Wnt3a and its receptor LRP6 as key molecules in neurotransmission modulation and suggest crosstalk between canonical and Wnt/calcium signaling in central neurons.
Examining gene expression and methylation with next gen sequencingStephen Turner
Slides on RNA-seq and methylation studies using next-gen sequencing given at the University of Miami Hussman Institute for Human Genomics "Genetic Analysis of Complex Human Diseases" course in 2012 (http://hihg.med.miami.edu/educational-programs/analysis-of-complex-human-diseases/genetic-analysis-of-complex-human-diseases/)
This document summarizes the work of Hans Jansen and Christiaan Henkel with long read nanopore sequencing. They have sequenced several genomes including carp, eel, king cobra, and Agrobacterium using MinION. Their longest reads were 120 kbp and 93.5 kbp. They also established the MinION Access Program to improve genomes by resolving repeats. As part of this, they formed the MinION Analysis and Reference Consortium to standardize protocols and understand variability between labs. Their work with the E. coli genome demonstrated sources of variation in read counts, lengths, and alignments between labs.
This document provides an overview of the course BIONF/BENG 203: Functional Genomics. It discusses the grading breakdown, course outline, sources of functional genomic data including expression data from microarrays and RNA-Seq, proteomic data from mass spectrometry, protein-protein interaction data, and systematic phenotyping data. High-throughput methods for measuring these various types of omics data are also summarized.
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Progress report 2016: GMI proficiency testing: Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management -23-25 May 2016, Rome, Italy.
The document discusses various DNA and RNA sequencing methods and technologies. It begins with an overview of sequencing-based markers like DNA sequencing, RNA sequencing, SNPs, epigenetic markers, and omics. The document then provides more details on the history and development of sequencing technologies, including early methods like Sanger and Maxam-Gilbert sequencing. It discusses next generation sequencing platforms like MPSS, 454 pyrosequencing, Illumina, Ion Torrent, ABI-SOLiD, and their approaches. The document concludes with an overview of third generation long-read sequencing technologies like SMRT and nanopore sequencing.
Cloud-native machine learning - Transforming bioinformatics research Denis C. Bauer
Cloud computing and artificial intelligence transforms bioinformatics research
Denis Bauer, Transformational Bioinformatics Team
Genomic data is outpacing traditional Big Data disciplines, producing more information than Astronomy, twitter, and YouTube combined. As such, Genomic research has leapfrogged to the forefront of Big Data and Cloud solutions. We developed software platforms using the latest in cloud architecture, artificial intelligence and machine learning to support every aspect genome medicine; from disease gene detection through to validation and personalized medicine.
This talk outlines how we find disease genes for complex genetic diseases, such as ALS, using VariantSpark, which is a custom machine learning implementation capable of dealing with Whole Genome Sequencing data of 80 million common and rare variants. To support disease gene validation, we created GT-Scan, which is an innovative web application, which we think of it as the “search engine for the genome”. It enables researchers to identify the optimal editing spot to create animal models efficiently. The talk concludes by demonstrating how cloud-based software distribution channels (digital Marketplaces) can be harnessed to share bioinformatics tools internationally and make research more reproducible.
Translating genomics into clinical practice - 2018 AWS summit keynoteDenis C. Bauer
CSIRO's part of the co-presented Keynote at the AWS Public Sector Summit in Canberra on genomics health care. Three key messages: 1) We need a shift from treatment towards prevention 2) Once you go serverless you never go back 3) DevOps 2.0: Hypothesis-driven architecture evolution
Going Server-less for Web-Services that need to Crunch Large Volumes of DataDenis C. Bauer
AgileIndia Breakout session on serverless applications. This talk covers how AWS serverless infrastructure can be used for a wide range of applications, such as compute intensive tasks (GT-Scan), tasks requiring continuous learning (CryptoBreeder), data intensive tasks (PhenGen Database).
How novel compute technology transforms life science researchDenis C. Bauer
AgileIndia 2018 Keynote. This talk covers how ‘Datafication’ will make data ‘wider’ (more features describing a data point), which represents a paradigm shift for Machine Learning applications. It also covers serverless architecture, which can cater for even compute-intensive tasks. It concludes by stating that business and life-science research are not that different: so let’s build a community together!
How novel compute technology transforms life science researchDenis C. Bauer
Unprecedented data volumes and pressure on turnaround time driven by commercial applications require bioinformatics solutions to evolve to meed these new demands. New compute paradigms and cloud-based IT solutions enable this transition. Here I present two solution capable of meeting these demands for genomic variant analysis, VariantSpark, as well as genome engineering applications, GT-Scan2.
VariantSpark classifies 3000 individuals with 80 Million genomic variants each in under 30 minutes. This Hadoop/Spark solution for machine learning application on genomic data is hence capable to scale up to population size cohorts.
GT-Scan2, identifies CRISPR target sites by minimizing off-target effects and maximizing on-target efficiency. This optimization is powered by AWS Lambda functions, which offer an “always-on” web service that can instantaneously recruit enough compute resources keep runtime stable even for queries with several thousand of potential target sites.
VariantSpark: applying Spark-based machine learning methods to genomic inform...Denis C. Bauer
Genomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. Here we introduce VariantSpark, which utilizes Hadoop/Spark along with its machine learning library, MLlib, providing the means of parallelisation for population-scale bioinformatics tasks. VariantSpark is the interface to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results.
To demonstrate the capabilities of VariantSpark, we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VariantSpark is 80% faster than the Spark-based genome clustering approach, ADAM, the comparable implementation using Hadoop/Mahout, as well as Admixture, a commonly used tool for determining individual ancestries. It is over 90% faster than traditional implementations using R and Python. These benefits of speed, resource consumption and scalability enables VariantSpark to open up the usage of advanced, efficient machine learning algorithms to genomic data.
The package is written in Scala and available at https://github.com/BauerLab/VariantSpark.
Population-scale high-throughput sequencing data analysisDenis C. Bauer
This document provides an overview of a presentation on population-scale high-throughput sequencing data analysis. It discusses:
1) The background and goals of the CSIRO/Omics Project which aims to investigate colorectal cancer susceptibility using sequencing data from 500 individuals.
2) Methods for processing large-scale NGS data on high-performance computing clusters and cloud infrastructure using the NGSANE framework, which allows processing modules to be run in parallel.
3) Preliminary research outcomes identifying cancer-associated and microbiome changes from analysis of colorectal cancer and control samples.
The primary goal of my trip to Seattle was to establish a collaboration with a world-leading group on data integration. But by having chosen Seattle, a hub for technology companies, I also learned about synergies between business and research: Ilya Shmulevich from the Institute for Systems Biology makes use of Amazon's ''Random Forest" implementation and Google's 600.000 CPU cluster for cancer genomic association discovery. I also met with experts from University of Washington and Microsoft research to learn about technological advancements to tackle BigData and commoditizing parallelization. Finally, I observed a government funded research agency invest in solutions geared towards their enterprise structure rather than adopt solutions designed for research institutes without active computational community. In conclusion: CSIRO has unique properties and skill-sets that many collaborators would be interested in benefiting from, in return such collaborations would propel CSIRO instantly to the forefront of technology, which in particular for the analysis of big, unstructured datasets could be very rewarding.
Allelic Imbalance for Pre-capture Whole Exome SequencingDenis C. Bauer
Exome sequencing has emerged as an economical way of focusing DNA sequencing efforts on the most functionally understood regions of the genome. Pre-capture pooling, where one bait library is used to pull down the exonic regions of several pooled samples simultaneously is a further financial improvement.
However, rare alleles in the pool might not be able to attract baits at the same rate as reference conform sequences can, and may hence be underrepresented. We investigated this potential issue by sequencing a hapmap family (4 individuals) using the pre-capture protocol from Illumina and Nimblegen. We did not observe clear evidence that heterozygote variants are missed but noted a trend for indels to be imbalanced.
Our findings do not provide clear evidence to rule out allelic imbalance or bias having an impact on research findings, this may be especially critical for low cellular cancer tissue where rare alleles are more ubiquitous.
The first steps of analysing sequencing data (2GS,NGS) has entered a transitional period where on one hand most analysis steps can be automated and standardized (pipeline), while on the other constantly evolving protocols and software updates makes maintaining these analysis pipelines labour intensive.
I propose a centralized system within CSIRO that is flexible to cater for different analyses while also being generic to efficiently disseminate labour intensive maintenance and extension amongst the user community.
Qbi Centre for Brain genomics (Informatics side)Denis C. Bauer
An overview of QBI’s production informatics framework with an emphasis on what service will be provided and how the resulting data is made available: from interactive quality control to integration with external data on the genome browser.
This session will follow up from transcript quantification of RNAseq data and discusses statistical means of identifying differentially regulated transcripts, and isoforms and contrasts these against microarray analysis approaches.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
Critical Run files can be missing/corrupt after the Run folder was transferred from the HiSeq storage to the cluster storage. This presentation discusses the issue and suggests four workarounds.
Deciphering the regulatory code in the genomeDenis C. Bauer
There are messages hidden within our genome, regulating when and how long a gene is switched on. The presentation describes a method, STREAM, targeted at deciphering this regulatory code.
This was our presentation for our imaginary product for the commercialization workshop. Note, all "research results" and illustrations are totally made up and and therefore not necessarily reflecting reality (== biological processes). This presentation was created as part of the learning experience of how to pitch biological research to venture capitalists.
The presentation was given at the CIBCB, 2005, in San Diego about our approach to predict recombination sites in protein sequence. Recombination is the method of choice for designing new proteins with desired new or enhanced properties.
The publication is :
Bauer, D.C., Bodén, M., Thier, R. and Gillam, E. M. “STAR: Predicting recombination sites from amino acid sequence.” BMC Bioinformatics, 2006 Oct 8; 7:437. PMID: 17026775
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
2. Production Informatics and Bioinformatics June 23, 2011 Produce raw sequence reads Basic Production Informatics Map to genome and generate raw genomic features (e.g. SNPs) Advanced Production Inform. Analyze the data; Uncover the biological meaning Bioinformatics Research Per one-flowcell project
3.
4. What steps are involved in sequencing ? June 23, 2011 sequencing by synthesis (SBS) technology Fragmentation Library generation Amplification Sequencing Analysis Illumina Marketing: “3h 10 minutes wet-lab 30 minutes dry lab”
7. Output: 1.5 Terabyte of data June 23, 2011 Inspired by anzska information booklet
8. Sequencer Output Conversion: Production Informatics 1.5 TB data : 6 billion clusters with 100 bp reads = 600 billion data points June 23, 2011 HiSeq CASAVA … × read length For HiSeq: images are converted to flat files (*.bcl or *.cif) visualpharm.com Maysoft
9. Multiplexing 6 billion reads: 750 million reads per lane Currently 12-plex (soon 96-plex): One run June 23, 2011 Oliver Twardowski
14. Fastq: Quality control Base-pair quality score Adapter contamination Uneven Amplification June 23, 2011
15. Three things to remember Don’t be fooled by marketing Fastqfiles are not directly usable Basic-run QC can be made from fastq file June 23, 2011 “All modern genomics projects are now bottlenecked at the stage of data analysis rather than data production” Ewan Birney European Bioinformatics Institute Wellcome Trust David S. Roos Bioinformatics--Trying to Swim in a Sea of Data;Science 16 February 2001: Vol. 291 no. 5507 pp. 1260-1261 DOI: 10.1126/science.291.5507.1260
16. Next Week: June 23, 2011 Abstract: This session will focus on identifying SNPs from whole genome, exome capture or targeted resequencing data. The approaches of mapping, local realigment, recalibration, SNP calling, and SNP recalibration will be introduced and quality metrics discussed.
19. Helicos true Single Molecule Sequencing(tSMS)™ technology Sequencing by synthesis but much more sensitive so no amplification June 23, 2011
20. Life Technology - Ion Torrent Hydrogen Ion is released by the incorporation of a nucleotide, which is measured by a semiconductor Depending on which nucleotide wash cycle the signal coincides June 23, 2011
21. PacBio Immobilized polymerase at the bottom of a well Fluorescent nucleotides float around and if they are incorporated they are held still for tens of milliseconds, which is the signal that is recorded No upper limit on the length June 23, 2011 http://www.pacificbiosciences.com/smrt-biology/smrt-technology?page=4
22. Nanopore Molecule is sucked through a poor and the change in the membrane charge due to the different nucleotides is recorded. June 23, 2011 http://www.nanoporetech.com/sections/index/82
PCR where a labeled nucleotide is incorporated at random that terminates the PCR reaction. These fragments of different length are then separated on a gel and the sequence can be manually read from the labeled end nucleotides.
Some of you have done some library prep already so you have a feel for how realistic 3h10 min are for this. This seminar goes through the analysis steps that are required to answer the question the data was generated for. So by the end of this seminar series you’ll have also a feel for how realistic 30 minutes is for the data analysis.
PCR where a labeled nucleotide is incorporated at random that terminates the PCR reaction. These fragments of different length are then separated on a gel and the sequence can be manually read from the labeled end nucleotides.