De Bruijn graphs are directed graphs used to represent overlaps between sequences of symbols. They are constructed by splitting a DNA sequence into k-mers (subsequences of length k), creating nodes for each k-mer, and connecting nodes with edges where the k-mers overlap by k-1 nucleotides. De Bruijn graphs are commonly used for genome assembly from next-generation sequencing data by reconstructing the original sequence from the k-mers. They allow mapping short reads onto a reference genome despite the reads being shorter than the repeats within the genome.
This presentation gives an easy introduction to genome assemblies from next-generation sequencing data and is part of a bioinformatics workshop. The accompanying websites are available at http://sschmeier.com/bioinf-workshop/#!genome-assembly/
BITS - Comparative genomics on the genome levelBITS
This is the third presentation of the BITS training on 'Comparative genomics'.
It reviews the basic concepts of sequence homology on the gene
Thanks to Klaas Vandepoele of the PSB department.
De novo genome assembly - T.Seemann - IMB winter school 2016 - brisbane, au ...Torsten Seemann
This document discusses de novo genome assembly, which is the process of reconstructing long genomic sequences from many short sequencing reads without the aid of a reference genome. It is challenging due to factors like short read lengths, repetitive sequences that complicate the assembly graph, and sequencing errors. The goals of assembly are to produce contiguous sequences with high completeness and correctness by resolving overlaps between reads into consensus sequences. Metrics like N50, core gene content, and read remapping are used to assess assembly quality.
An update version of the genome assembly including the mention of techniques such as HiC and Bionano. Also include the QC. These are the same slides used in the course for the UNL in Argentina.
DNA sequencing determines the precise order of nucleotides in a DNA fragment. There are several methods for DNA sequencing, including the chain termination method developed by Sanger, and the Maxam-Gilbert chemical cleavage method. Next generation sequencing is now used, which allows high-throughput sequencing of entire genomes quickly and accurately using automated methods. DNA sequencing has many applications, such as identifying disease-causing genes and mutations.
This document summarizes three main next generation sequencing technologies: Roche/454FLX pyrosequencing, Illumina/Solexa sequencing by synthesis, and Applied Biosystems SOLiD sequencing by ligation. Pyrosequencing works by detecting pyrophosphate released during DNA polymerization, producing light signals to determine the sequence. Roche/454FLX amplifies DNA fragments on beads in emulsions and sequences in picotiter plates. Illumina attaches DNA fragments to a flow cell for bridge amplification and sequencing by synthesis. Applied Biosystems SOLiD performs sequencing by ligation, determining sequences through sequential ligation of oligos.
De Bruijn graphs are directed graphs used to represent overlaps between sequences of symbols. They are constructed by splitting a DNA sequence into k-mers (subsequences of length k), creating nodes for each k-mer, and connecting nodes with edges where the k-mers overlap by k-1 nucleotides. De Bruijn graphs are commonly used for genome assembly from next-generation sequencing data by reconstructing the original sequence from the k-mers. They allow mapping short reads onto a reference genome despite the reads being shorter than the repeats within the genome.
This presentation gives an easy introduction to genome assemblies from next-generation sequencing data and is part of a bioinformatics workshop. The accompanying websites are available at http://sschmeier.com/bioinf-workshop/#!genome-assembly/
BITS - Comparative genomics on the genome levelBITS
This is the third presentation of the BITS training on 'Comparative genomics'.
It reviews the basic concepts of sequence homology on the gene
Thanks to Klaas Vandepoele of the PSB department.
De novo genome assembly - T.Seemann - IMB winter school 2016 - brisbane, au ...Torsten Seemann
This document discusses de novo genome assembly, which is the process of reconstructing long genomic sequences from many short sequencing reads without the aid of a reference genome. It is challenging due to factors like short read lengths, repetitive sequences that complicate the assembly graph, and sequencing errors. The goals of assembly are to produce contiguous sequences with high completeness and correctness by resolving overlaps between reads into consensus sequences. Metrics like N50, core gene content, and read remapping are used to assess assembly quality.
An update version of the genome assembly including the mention of techniques such as HiC and Bionano. Also include the QC. These are the same slides used in the course for the UNL in Argentina.
DNA sequencing determines the precise order of nucleotides in a DNA fragment. There are several methods for DNA sequencing, including the chain termination method developed by Sanger, and the Maxam-Gilbert chemical cleavage method. Next generation sequencing is now used, which allows high-throughput sequencing of entire genomes quickly and accurately using automated methods. DNA sequencing has many applications, such as identifying disease-causing genes and mutations.
This document summarizes three main next generation sequencing technologies: Roche/454FLX pyrosequencing, Illumina/Solexa sequencing by synthesis, and Applied Biosystems SOLiD sequencing by ligation. Pyrosequencing works by detecting pyrophosphate released during DNA polymerization, producing light signals to determine the sequence. Roche/454FLX amplifies DNA fragments on beads in emulsions and sequences in picotiter plates. Illumina attaches DNA fragments to a flow cell for bridge amplification and sequencing by synthesis. Applied Biosystems SOLiD performs sequencing by ligation, determining sequences through sequential ligation of oligos.
Genomic DNA libraries contain representative copies of all DNA fragments in an organism's genome, including both expressed and non-expressed sequences. They are constructed by isolating genomic DNA, fragmenting it, and cloning the fragments into suitable vectors like lambda phage or BACs. cDNA libraries contain only expressed sequences, as they are constructed by isolating mRNA from tissues, reverse transcribing it to cDNA, and cloning the cDNA fragments. Both library types are useful for gene discovery, sequencing, mapping genomes, and studying regulatory sequences.
The document discusses the history and evolution of DNA sequencing techniques. It describes first generation Sanger sequencing and how next generation sequencing (NGS) allows for massively parallel sequencing of entire human genomes in a single day. The principles of NGS involve fragmenting DNA, ligating adaptors, sequencing in parallel, and reassembling the results. Common NGS methods include sequencing by synthesis, pyrosequencing, and ion semiconductor sequencing. Applications of NGS include rapidly sequencing whole genomes, detecting rare mutations, studying gene expression, and analyzing the human microbiome.
This document provides an introduction to next-generation sequencing (NGS) technology. It discusses the evolution of genomic science from Sanger sequencing to NGS. The basics of NGS chemistry including library preparation, cluster generation, sequencing, and data analysis are described. Advances in NGS such as paired-end sequencing, tunable coverage, library preparation improvements, and multiplexing are also summarized. Finally, common NGS methods like whole genome sequencing, RNA sequencing, and targeted sequencing are briefly introduced.
The document summarizes Ion Torrent sequencing technology. It detects hydrogen ions released during DNA polymerization rather than using optics. The sequencing occurs on semiconductor chips patterned through photolithography into wells, each sequencing a different template. As nucleotides are incorporated, hydrogen ions change the pH detected by ion sensors below each well. This allows massively parallel sequencing that is faster, cheaper and simpler than previous technologies.
Guest lecture on comparative genomics for University of Dundee BS32010, delivered 21/3/2016
Workshop/other materials available at DOI:10.5281/zenodo.49447
Genomics is the study of genomes through sequencing and analysis. Key points:
- Genomics involves mapping and sequencing genomes to understand genes and how they function. It uses techniques from genetics and molecular biology.
- The human genome contains 23 chromosome pairs and around 24,000 genes. Genomics aims to sequence whole genomes and analyze gene function.
- Early developments included identifying DNA's structure in 1953 and sequencing the first genome in the 1970s. The Human Genome Project aimed to map the entire human genome between 1990-2003.
- Genomics has applications in medicine like gene therapy for genetic diseases and in understanding health, disease, and drug responses through analysis of genetic variations.
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.
RNA-seq: A High-resolution View of the TranscriptomeSean Davis
The molecular microscopes that we use to examine human biology have advanced significantly with the advent of next generation sequencing. RNA-seq is one application of this technology that leads to a very high-resolution view of the transcriptome. With these new technologies come increased data analysis and data handling burdens as well as the promise of new discovery. These slides present a high-level overview of the RNA-seq technology with a focus on the analysis approaches, quality control challenges, and experimental design.
The document discusses RNA-Seq data analysis. Some key points:
- RNA-Seq involves sequencing steady-state RNA in a sample without prior knowledge of the organism. It can uncover novel transcripts and isoforms.
- Making sense of the large and complex RNA-Seq data depends on the scientific question, such as finding transcribed SNPs for allele-specific expression or novel transcripts in cancer samples.
- Common applications of RNA-Seq include abundance estimation, alternative splicing detection, RNA editing discovery, and finding novel transcripts and isoforms.
- Analysis steps include mapping reads to a reference genome/transcriptome, generating mapping statistics and quality metrics, differential expression analysis, clustering, and pathway analysis using tools like
This document discusses pathway and network analysis. It defines systems biology and biological networks. Some benefits of studying pathways and networks are that it improves statistical power, allows identification of potential causal mechanisms, and facilitates integration of multiple data types. Types of analysis include gene set enrichment and de novo network construction. Visualization is important for representing relationships between molecules and finding subnetworks. Software like Cytoscape can be used to import networks, map gene expression data to node colors/borders, filter networks, and export publication-quality images. A tutorial demonstrates combining expression and network data in Cytoscape to tell biological stories.
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 document provides information on various genetic mapping techniques. It discusses locus, genome, linked genes, genetic distance, and recombination frequency. It then describes genetic mapping and the different types of maps - genetic/linkage maps and physical maps. Genetic maps are based on recombination frequencies while physical maps use techniques like in situ hybridization. Restriction mapping and DNA footprinting are also summarized as methods to determine the order and location of genes and restriction sites on chromosomes. Transposable elements in eukaryotes are classified into Class I and II based on their mechanism of transposition via RNA intermediates or direct DNA-to-DNA movement.
This document discusses DNA sequencing and sequence assembly. It defines sequencing as determining the order of nucleotides in DNA, and sequence assembly as aligning and merging DNA fragments to reconstruct the original sequence. It describes the shotgun sequencing method using Sanger sequencing that randomly fragments DNA, sequences the fragments, and assembles the sequence by finding overlaps between fragments. It provides an example of fragmenting and assembling a DNA sequence. It discusses using long reads for sequencing, which have higher error rates but allow assembly into longer contigs compared to short read sequencing.
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.
Course: Bioinformatics for Biomedical Research (2014).
Session: 1.3- Genome Browsing, Genomic Data Mining and Genome Data Visualization with Ensembl, Biomart and IGV.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
In this lecture tried to introduce some basic methods of DNA sequencing like pyrosequencing, sequencing by ligation, sequencing by synthesis and Ion Semiconductor Sequencing
and describe them. Also introduced some new sequencing method (third generation sequencing) like SMRT (Single Molecule Real-Time Sequencing) and GridION.
This document provides an overview of downstream analyses that can be performed after variant identification and filtering in a typical variant calling pipeline. It discusses visualization of variant data in each gene to identify potential causative variants. It also mentions association studies as another type of downstream analysis where variants are tested for association with disease phenotypes. The goal of downstream analyses is to help prioritize variants for further investigation.
This document provides an overview of clustering techniques for probabilistic language processing. It discusses how clustering can be used to group similar examples or documents together based on a distance or similarity metric. Several clustering algorithms are described, including k-means clustering which assigns documents to clusters based on proximity to centroid points, and conceptual clustering which aims to represent clusters with general rules rather than just enumerating members. The document also discusses challenges like the curse of dimensionality for high-dimensional data and evaluating the optimal number of clusters k.
The document discusses the history and development of artificial neural networks and deep learning. It describes early neural network models like perceptrons from the 1950s and their use of weighted sums and activation functions. It then explains how additional developments led to modern deep learning architectures like convolutional neural networks and recurrent neural networks, which use techniques such as hidden layers, backpropagation, and word embeddings to learn from large datasets.
Genomic DNA libraries contain representative copies of all DNA fragments in an organism's genome, including both expressed and non-expressed sequences. They are constructed by isolating genomic DNA, fragmenting it, and cloning the fragments into suitable vectors like lambda phage or BACs. cDNA libraries contain only expressed sequences, as they are constructed by isolating mRNA from tissues, reverse transcribing it to cDNA, and cloning the cDNA fragments. Both library types are useful for gene discovery, sequencing, mapping genomes, and studying regulatory sequences.
The document discusses the history and evolution of DNA sequencing techniques. It describes first generation Sanger sequencing and how next generation sequencing (NGS) allows for massively parallel sequencing of entire human genomes in a single day. The principles of NGS involve fragmenting DNA, ligating adaptors, sequencing in parallel, and reassembling the results. Common NGS methods include sequencing by synthesis, pyrosequencing, and ion semiconductor sequencing. Applications of NGS include rapidly sequencing whole genomes, detecting rare mutations, studying gene expression, and analyzing the human microbiome.
This document provides an introduction to next-generation sequencing (NGS) technology. It discusses the evolution of genomic science from Sanger sequencing to NGS. The basics of NGS chemistry including library preparation, cluster generation, sequencing, and data analysis are described. Advances in NGS such as paired-end sequencing, tunable coverage, library preparation improvements, and multiplexing are also summarized. Finally, common NGS methods like whole genome sequencing, RNA sequencing, and targeted sequencing are briefly introduced.
The document summarizes Ion Torrent sequencing technology. It detects hydrogen ions released during DNA polymerization rather than using optics. The sequencing occurs on semiconductor chips patterned through photolithography into wells, each sequencing a different template. As nucleotides are incorporated, hydrogen ions change the pH detected by ion sensors below each well. This allows massively parallel sequencing that is faster, cheaper and simpler than previous technologies.
Guest lecture on comparative genomics for University of Dundee BS32010, delivered 21/3/2016
Workshop/other materials available at DOI:10.5281/zenodo.49447
Genomics is the study of genomes through sequencing and analysis. Key points:
- Genomics involves mapping and sequencing genomes to understand genes and how they function. It uses techniques from genetics and molecular biology.
- The human genome contains 23 chromosome pairs and around 24,000 genes. Genomics aims to sequence whole genomes and analyze gene function.
- Early developments included identifying DNA's structure in 1953 and sequencing the first genome in the 1970s. The Human Genome Project aimed to map the entire human genome between 1990-2003.
- Genomics has applications in medicine like gene therapy for genetic diseases and in understanding health, disease, and drug responses through analysis of genetic variations.
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.
RNA-seq: A High-resolution View of the TranscriptomeSean Davis
The molecular microscopes that we use to examine human biology have advanced significantly with the advent of next generation sequencing. RNA-seq is one application of this technology that leads to a very high-resolution view of the transcriptome. With these new technologies come increased data analysis and data handling burdens as well as the promise of new discovery. These slides present a high-level overview of the RNA-seq technology with a focus on the analysis approaches, quality control challenges, and experimental design.
The document discusses RNA-Seq data analysis. Some key points:
- RNA-Seq involves sequencing steady-state RNA in a sample without prior knowledge of the organism. It can uncover novel transcripts and isoforms.
- Making sense of the large and complex RNA-Seq data depends on the scientific question, such as finding transcribed SNPs for allele-specific expression or novel transcripts in cancer samples.
- Common applications of RNA-Seq include abundance estimation, alternative splicing detection, RNA editing discovery, and finding novel transcripts and isoforms.
- Analysis steps include mapping reads to a reference genome/transcriptome, generating mapping statistics and quality metrics, differential expression analysis, clustering, and pathway analysis using tools like
This document discusses pathway and network analysis. It defines systems biology and biological networks. Some benefits of studying pathways and networks are that it improves statistical power, allows identification of potential causal mechanisms, and facilitates integration of multiple data types. Types of analysis include gene set enrichment and de novo network construction. Visualization is important for representing relationships between molecules and finding subnetworks. Software like Cytoscape can be used to import networks, map gene expression data to node colors/borders, filter networks, and export publication-quality images. A tutorial demonstrates combining expression and network data in Cytoscape to tell biological stories.
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 document provides information on various genetic mapping techniques. It discusses locus, genome, linked genes, genetic distance, and recombination frequency. It then describes genetic mapping and the different types of maps - genetic/linkage maps and physical maps. Genetic maps are based on recombination frequencies while physical maps use techniques like in situ hybridization. Restriction mapping and DNA footprinting are also summarized as methods to determine the order and location of genes and restriction sites on chromosomes. Transposable elements in eukaryotes are classified into Class I and II based on their mechanism of transposition via RNA intermediates or direct DNA-to-DNA movement.
This document discusses DNA sequencing and sequence assembly. It defines sequencing as determining the order of nucleotides in DNA, and sequence assembly as aligning and merging DNA fragments to reconstruct the original sequence. It describes the shotgun sequencing method using Sanger sequencing that randomly fragments DNA, sequences the fragments, and assembles the sequence by finding overlaps between fragments. It provides an example of fragmenting and assembling a DNA sequence. It discusses using long reads for sequencing, which have higher error rates but allow assembly into longer contigs compared to short read sequencing.
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.
Course: Bioinformatics for Biomedical Research (2014).
Session: 1.3- Genome Browsing, Genomic Data Mining and Genome Data Visualization with Ensembl, Biomart and IGV.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
In this lecture tried to introduce some basic methods of DNA sequencing like pyrosequencing, sequencing by ligation, sequencing by synthesis and Ion Semiconductor Sequencing
and describe them. Also introduced some new sequencing method (third generation sequencing) like SMRT (Single Molecule Real-Time Sequencing) and GridION.
This document provides an overview of downstream analyses that can be performed after variant identification and filtering in a typical variant calling pipeline. It discusses visualization of variant data in each gene to identify potential causative variants. It also mentions association studies as another type of downstream analysis where variants are tested for association with disease phenotypes. The goal of downstream analyses is to help prioritize variants for further investigation.
This document provides an overview of clustering techniques for probabilistic language processing. It discusses how clustering can be used to group similar examples or documents together based on a distance or similarity metric. Several clustering algorithms are described, including k-means clustering which assigns documents to clusters based on proximity to centroid points, and conceptual clustering which aims to represent clusters with general rules rather than just enumerating members. The document also discusses challenges like the curse of dimensionality for high-dimensional data and evaluating the optimal number of clusters k.
The document discusses the history and development of artificial neural networks and deep learning. It describes early neural network models like perceptrons from the 1950s and their use of weighted sums and activation functions. It then explains how additional developments led to modern deep learning architectures like convolutional neural networks and recurrent neural networks, which use techniques such as hidden layers, backpropagation, and word embeddings to learn from large datasets.
The Tower of Hanoi is a mathematical game. It consists of three rods, and a number of disks of different sizes which can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top, thus making a conical shape.
The objective of the puzzle is to move the entire stack to another rod, obeying the following rules:
• Only one disk may be moved at a time.
• Each move consists of taking the upper disk from one of the rods and sliding it onto another rod, on top of the other disks that may already be present on that rod.
• No disk may be placed on top of a smaller
The document provides an overview of neural networks including:
- Their history from early models in the 1940s to the breakthrough of backpropagation in the 1980s.
- What a neural network is and how it works at the level of individual neurons and when connected together.
- Common applications of neural networks like prediction, classification, and clustering.
- Key considerations in choosing an appropriate neural network architecture and training data for a given problem.
Clustering algorithms group data points together such that there is high similarity between points within a cluster and low similarity between points in different clusters. K-means clustering is a partitional clustering algorithm that partitions data into K mutually exclusive clusters by minimizing the within-cluster sum of squares. It works by iteratively assigning data points to the closest cluster centroid and recalculating centroids based on newly assigned points until cluster assignments stabilize. K-means requires specifying the number of clusters K in advance and is sensitive to initialization but is simple, efficient and intuitive for optimizing intra-cluster similarity.
Mathematics online: some common algorithmsMark Moriarty
Brief overview of some basic algorithms used online and across data-mining, and a word on where to learn them. Prepared specially for UCC Boole Prize 2012.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
3. HISTORY
- The solution came in 1735, when the great mathematician
Leonhard Euler.
- Dutch mathematician Nicolaas de Bruijn(GIVES CYCLIC
CONCEPT WITH LENTH “K”)
DOI: 10.1038/nbt.2023 · Source: PubMed
4. WHY TO LERN ..?
• NGS sequences use various software packages, such as
Velvet, ABySS, Trinity, Oases, etc
• To Reconstruct genomes from NGS libraries
•
•Creates unique pattern
5. HOW TO APPLY…?
1. Select the sequence(read)
2. k-mers are simply length k (read length) subsequences
3. to choose a k-mer size(3)
4. split the original sequence into its k-mer components
4. Directionality: whose last k-1 nucleotides are
overlapping, to the k-mer, whose first k-1 nucleotides
are overlapping.
6. HOW TO APPLY…?
5. Creating nodes
6. Highlight similar nodes closer
7. Glue the similar nodes
8. HOW TO APPLY…?
9. Use whole sequence of first edge
10. The Only Use the last alphabets from the
intermediate edges.
10. Build the genome
9. • Converting NGS sequence library to genome
assembly
• Eulerian walk (from de bruijn to reads)
• RNAseq.
Where to use …?
10. Drawbacks
• De Bruijn graphs do not preserve positional
information.
• Longer the read size, more one has to lose.
• Removing of Tips & Bubbles
• k-mer size should be depends upon the read
length
12. MASSIVE DE BRUJIN GRAPH
ERRORS:-
1) Sequencing
error:-
-creates tips and
unnecessorily
small nucliotides
sequences
TIP
13. • 2) POLYPLOIDY
- this is not the error
This is the real
Scenario
3)REPEATS:-
Eg.- ATATATATATATA
MASSIVE DE BRUJIN GRAPH
Bubble
14. REFERENCES
1) How to apply de Bruijn graphs to genome
assembly :-
DOI: 10.1038/nbt.2023 · Source: PubMed
2) Overlap graphs and de Bruijn graphs:
https://doi.org/10.1007/s40484-019-0181-x
3). HOMOLOG.US :-
https://homolog.us/Tutorials/book4/p1.1.ht
ml
4) COURSERA :- https://www.coursera.org/