ChIP-sequencing is a method to identify genomic regions bound by specific proteins or modifications. It involves cross-linking proteins to DNA, immunoprecipitating the protein-DNA complexes, sequencing the retrieved DNA fragments to determine the genomic binding sites. The key steps are sample preparation involving cross-linking, fragmentation and enrichment, followed by high-throughput sequencing and computational analysis including mapping, peak calling, annotation and visualization of results.
Pipeline development for analysis of solid mutants of Solanum tuberosum Gr. ...AlexisQuintero28
This document outlines the development of a bioinformatics analysis pipeline for studying gene expression and single nucleotide polymorphisms (SNPs) in solid mutants of Solanum tuberosum Gr. Phureja obtained through cobalt-60 irradiation. The pipeline includes steps for raw data cleaning, read mapping, SNP surveying, and differential expression analysis. Key considerations for pipeline design include breaking the workflow into discrete steps and establishing rules for processing inputs and outputs. The pipeline will be implemented using a workflow management system to ensure reproducibility and scalability. The overall goals are to identify major expression pathways involved in dormancy and study genes of interest in the research group.
This document discusses recent trends in bioinformatics, including the analysis of cDNA microarray data, protein tertiary structure prediction using Ramachandran plots, and the Protein Data Bank (PDB) which contains experimentally determined protein structures. It also discusses protein structure prediction techniques like CASP and TMW which aim to predict protein structures theoretically based on sequence. Predictions start from an initial conformation and use internal coordinates and planar geometry to model the structure as a tree. Further proteomics research can study protein function once a structure is obtained.
I. The document outlines a proteogenomics course at EMBL-EBI, discussing integrating proteomics and genomics data.
II. It discusses what proteogenomics is, using multi-omics approaches to correlate genomic and proteomic sequence events like mutations and modifications.
III. The talk will cover integrating proteomics data into Ensembl and UCSC trackhubs, as well as tools for proteogenomics analysis.
A novel optimized deep learning method for protein-protein prediction in bioi...IJECEIAES
Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence- dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
OVium Bio-Information Solutions use forefront algorithms to analyze key data resources such NCBI, EBLM and PDB to develop cell signal pathways.
OVium employs cloud and MPP computing solutions with homology and signal network mapping to develop chemical and protein pathways for discovery research.
The document summarizes a webinar about using the GNC Pro software to analyze gene expression results from PCR array experiments. The webinar demonstrates how to input gene lists into GNC Pro, walk around the gene interaction network to find new candidate genes, verify gene interactions, check tissue-specific expression, and export results. It also discusses upcoming features like canonical pathways and color-coded fold changes. The webinar uses a case study analyzing gene expression changes in human PBMCs stimulated with phorbol ester and ionomycin to demonstrate the software.
Mining frequent pattern is a NP-hard problem and has become a hot topic in recent researches. Moreover,
protein dataset contains distinct Pattern that can be used in many areas such as drug discovery, disease
prediction, etc. In early decades, pattern discovery and protein fold recognition was determined by
biophysics and biochemistry approach; and X-ray and NMR have been used for protein structure
prediction which are very expensive and time consuming while, a mathematical approach can reduce the
cost of such laboratory experiments. Many computer based tests have been applied for the protein fold
detection such as graph based algorithms and data mining viewpoints like classification or clustering, and
all have their advantages and drawbacks. Pattern matching in protein sequential dataset for fold
recognition plays a meaningful role in the field of bioinformatics since it evolved prediction of unknown
protein function. There are lots of pattern recognition algorithms but in this work we used PrefixSpan. The
reason of selecting this algorithm will be discussed below in section 2. For evaluating the result of
experiments we used SCOPE dataset which is a classified protein dataset and ASTRAL, a discriminative
sequential dataset of SCOPE.
The document describes semantic provenance modeling for scientific data and experiments. It discusses developing an upper-level provenance ontology called Provenir to serve as a foundation for domain-specific provenance ontologies. It also covers tracking provenance information for scientific workflows and experiments in a modular, multi-ontology approach.
Pipeline development for analysis of solid mutants of Solanum tuberosum Gr. ...AlexisQuintero28
This document outlines the development of a bioinformatics analysis pipeline for studying gene expression and single nucleotide polymorphisms (SNPs) in solid mutants of Solanum tuberosum Gr. Phureja obtained through cobalt-60 irradiation. The pipeline includes steps for raw data cleaning, read mapping, SNP surveying, and differential expression analysis. Key considerations for pipeline design include breaking the workflow into discrete steps and establishing rules for processing inputs and outputs. The pipeline will be implemented using a workflow management system to ensure reproducibility and scalability. The overall goals are to identify major expression pathways involved in dormancy and study genes of interest in the research group.
This document discusses recent trends in bioinformatics, including the analysis of cDNA microarray data, protein tertiary structure prediction using Ramachandran plots, and the Protein Data Bank (PDB) which contains experimentally determined protein structures. It also discusses protein structure prediction techniques like CASP and TMW which aim to predict protein structures theoretically based on sequence. Predictions start from an initial conformation and use internal coordinates and planar geometry to model the structure as a tree. Further proteomics research can study protein function once a structure is obtained.
I. The document outlines a proteogenomics course at EMBL-EBI, discussing integrating proteomics and genomics data.
II. It discusses what proteogenomics is, using multi-omics approaches to correlate genomic and proteomic sequence events like mutations and modifications.
III. The talk will cover integrating proteomics data into Ensembl and UCSC trackhubs, as well as tools for proteogenomics analysis.
A novel optimized deep learning method for protein-protein prediction in bioi...IJECEIAES
Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence- dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
OVium Bio-Information Solutions use forefront algorithms to analyze key data resources such NCBI, EBLM and PDB to develop cell signal pathways.
OVium employs cloud and MPP computing solutions with homology and signal network mapping to develop chemical and protein pathways for discovery research.
The document summarizes a webinar about using the GNC Pro software to analyze gene expression results from PCR array experiments. The webinar demonstrates how to input gene lists into GNC Pro, walk around the gene interaction network to find new candidate genes, verify gene interactions, check tissue-specific expression, and export results. It also discusses upcoming features like canonical pathways and color-coded fold changes. The webinar uses a case study analyzing gene expression changes in human PBMCs stimulated with phorbol ester and ionomycin to demonstrate the software.
Mining frequent pattern is a NP-hard problem and has become a hot topic in recent researches. Moreover,
protein dataset contains distinct Pattern that can be used in many areas such as drug discovery, disease
prediction, etc. In early decades, pattern discovery and protein fold recognition was determined by
biophysics and biochemistry approach; and X-ray and NMR have been used for protein structure
prediction which are very expensive and time consuming while, a mathematical approach can reduce the
cost of such laboratory experiments. Many computer based tests have been applied for the protein fold
detection such as graph based algorithms and data mining viewpoints like classification or clustering, and
all have their advantages and drawbacks. Pattern matching in protein sequential dataset for fold
recognition plays a meaningful role in the field of bioinformatics since it evolved prediction of unknown
protein function. There are lots of pattern recognition algorithms but in this work we used PrefixSpan. The
reason of selecting this algorithm will be discussed below in section 2. For evaluating the result of
experiments we used SCOPE dataset which is a classified protein dataset and ASTRAL, a discriminative
sequential dataset of SCOPE.
The document describes semantic provenance modeling for scientific data and experiments. It discusses developing an upper-level provenance ontology called Provenir to serve as a foundation for domain-specific provenance ontologies. It also covers tracking provenance information for scientific workflows and experiments in a modular, multi-ontology approach.
BioAssay Express: Creating and exploiting assay metadataPhilip Cheung
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
The document summarizes an IMGS 2011 bioinformatics workshop. It discusses next-generation sequencing technologies including Roche 454, Illumina/Solexa, and AB SOLiD. It also covers topics like sequence alignments, file formats, tools for analysis including BWA and TopHat, and visualization. The document provides links to video tutorials and resources on sequencing technologies, alignments, and analyzing RNA-seq data.
FAIR as a Working Principle for Cancer Genomic DataIan Fore
This document discusses making cancer genomic data FAIR (Findable, Accessible, Interoperable, and Reusable) as a working principle. It summarizes a talk given by Ian Fore of the National Cancer Institute on using FAIR data principles for cancer genomic data. The document also briefly describes several other talks from a conference track on FAIR data.
This document summarizes a presentation about the EnVisioning Pathways project. It discusses:
1) The EnCORE integration platform developed by the ENFIN Network of Excellence to enable mining data across different biological domains, sources, formats and types through a standardized XML format and web services.
2) Examples of EnCORE services that retrieve interaction data from databases like IntAct and Pride, map between identifiers, and represent results in biological pathways in databases like Reactome.
3) Efforts to adapt EnCORE to utilize standards and create a federated system to integrate information from different biological domains. This includes building predefined and user-selected workflows between EnCORE services.
This document presents a probabilistic model to increase the reliability of data analysis from multiplex sequencing performed on the SOLiD sequencing platform. The model characterizes multiplex runs and filters low-quality data by considering the intrinsic characteristics of each sequencing. It aims to identify faults in the sequencing process, guide filtering without discarding useful sequences, and assign confidence levels to the generated data. The model analyzes the SOLiD barcoding system used to tag multiple samples, as this reflects the overall sequencing quality and has lower processing costs than analyzing sequences of interest.
Next-generation DNA sequencing technologies have significantly impacted genetics research. Three major platforms - Roche/454, Illumina Genome Analyzer, and Applied Biosystems SOLiD - utilize massively parallel sequencing to generate large amounts of sequence data. Roche/454 uses emulsion PCR to amplify DNA fragments on beads and pyrosequencing to determine sequences. Illumina performs bridge amplification on a flow cell to generate DNA clusters then sequences by synthesis. Applied Biosystems SOLiD uses ligation-based sequencing. These new methods have enabled genome-wide studies and applications such as ancient DNA sequencing and metagenomics that were previously difficult or impossible.
Homology Modelling through modeller and its analysis using Ramachandran Plot
Modeller practical. Full tutorial created by Zarlish Attique
https://salilab.org/modeller/
Lessons in Modeling from 3-D Structural & Data Science PerspectivesPhilip Bourne
The document discusses how macromolecular structure and data science can inform modeling. It notes that 3D structure provides insights but knowledge is distributed across many papers, while data science allows a new appreciation of data and new methodologies. Combined, they enable multi-scale modeling, drug refinement and repurposing. Specific examples are given on signaling pathways, protein-ligand docking to predict drug efficacy, and integrating data to represent biological networks. The future involves overcoming cultural roadblocks to fully leverage multi-scale integration across levels from DNA to populations.
This document describes two challenges presented as part of the DREAM initiative to evaluate methods for parameter estimation and network topology inference from experimental data. In the first challenge, participants were given the topology of a 9-gene network and asked to estimate 45 kinetic parameters. In the second challenge, participants were given an incomplete 11-gene network and asked to identify 3 missing links and associated parameters. Participants could purchase simulated experimental data using a credit system, allowing iterative experimental design. While parameter estimation was accomplished well using fluorescence data, topology inference was more difficult. Aggregating submissions produced better solutions than individual methods.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: Pfizer’s Recent Use of tr...David Peyruc
The document summarizes Pfizer's use of the tranSMART platform for various genomics and clinical data analyses including genome-wide association studies (GWAS), supporting exploratory data types like metabolomics and FACS data, and large collaborative efforts like the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Parkinson's Progression Markers Initiative (PPMI) datasets. It also discusses analytical integration with Genedata Expressionist and plans for future enhancements to tranSMART like improved GWAS support and additional genotype data. Contributors to these efforts are acknowledged.
58.Comparative modelling of cellulase from Aspergillus terreusAnnadurai B
The document discusses homology modeling of the cellulase enzyme in Aspergillus terreus. It begins with an abstract that describes cellulase as a widely used hydrolytic enzyme involved in converting biomass to simpler sugars. It then provides details on homology modeling and the steps involved, which include template recognition, alignment, backbone and loop modeling, and model validation. The document discusses modeling of the cellulase protein from Aspergillus terreus using templates from the PDB and visualization software. It evaluates the modeled cellulase structure using validation servers to check accuracy.
BAR.utoronto.ca is a bio-analytic resource that allows exploration of large biological datasets for hypothesis generation. It contains over 128,000 SNPs, 150 million gene expression measurements, subcellular localizations for 9,300 proteins and predicted localizations for the Arabidopsis proteome, over 70,000 predicted and 36,000 documented protein-protein interactions, and over 67,000 predicted and 700 experimentally determined protein structures. The site provides easy-to-use tools for exploring these datasets to facilitate research.
This document provides an overview of next generation sequencing technologies and applications. It summarizes an upcoming webinar series on next generation sequencing and its role in cancer biology. The first webinar will provide an introduction to next generation sequencing technologies and applications and be presented by Quan Peng on April 4, 2013. The following two webinars will focus on next generation sequencing for cancer research and data analysis and be presented on April 11 and 18, 2013 respectively.
The document summarizes the Genome in a Bottle (GIAB) project, which aims to develop reference materials and benchmarks for evaluating human genome sequencing. GIAB has characterized 7 human genomes to high accuracy using multiple sequencing technologies and bioinformatics analyses. The characterized genomes and variant calls are made publicly available to benchmark sequencing performance. Recently, GIAB has incorporated linked and long read sequencing to expand reference benchmarks to more difficult genomic regions and develop benchmarks for structural variants.
Exploiting technical replicate variance in omics data analysis (RepExplore)Enrico Glaab
High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.
We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics.
Availability and implementation: Freely available at http://www.repexplore.tk
Journal publication: http://bioinformatics.oxfordjournals.org/content/31/13/2235.long (Glaab, E., & Schneider, R. (2015). RepExplore: Addressing technical replicate variance in proteomics and metabolomics data analysis. Bioinformatics, 31 (13): 2235-2237)
Making effective use of graphics processing units (GPUs) in computationsOregon State University
Graphics processing units (GPUs) are specialized computer processors used in computers and video game systems to accelerate the creation and display of images. Due to their inherent parallel structure, they also have great potential to speed up computations in many scientific and engineering applications. GPUs are attractive for their ability to perform a large number of computations in parallel at an attractive price. Many of the world¹s largest supercomputers use GPUs to achieve their high performance, and personal computers and laptops use them for graphics displays and image processing. This seminar will explore the use of GPUs in general, describe examples of the use of GPUs in computations, and introduce some best practices for GPU computing.
The document summarizes research presented at the CNCP 2010 conference. It describes work in several areas of proteomics research including fragmentation analysis, labeling strategies, de novo sequencing, identification, label-free quantification, database construction, data quality control, data processing platforms, glycoproteomics, and proteogenomics. Specific projects are mentioned that improved peptide identification, developed in vivo termini amino acid labeling, performed de novo sequencing of peptides from unknown genomes, optimized peptide mass fingerprinting for protein mixtures, detected post-translational modifications, and more.
The document discusses the ISA infrastructure, which provides a standardized format (ISA-TAB) for experimental metadata and data exchange. It can be used across various domains like toxicology, systems biology, and nanotechnology. The Risa R package integrates experimental metadata with analysis and allows updating metadata. Nature Scientific Data is a new publication for describing valuable datasets. The ISA framework has been adopted by over 30 public and private resources and is growing in use for facilitating reuse of investigations in various life science domains. Toxicity examples include EU projects on predictive toxicology and a rat study of drug candidates. Questions can be directed to the ISA tools group.
The document describes Phase II of the ABRF Next Generation Sequencing Study which aims to establish reference data sets for evaluating DNA sequencing performance across multiple platforms and laboratories. Phase II will sequence various human and bacterial genomic samples to assess accuracy, coverage, and limits of detection using different platforms and library preparation methods. A collaboration with NIST Genome in a Bottle will provide standardized samples to the participating laboratories. The study aims to provide a resource for ongoing method development and evaluation of sequencing performance.
Exploiting bigger data and collaborative tools for predictive drug discovery Sean Ekins
This document summarizes Sean Ekins' work exploiting big data and collaborative tools for predictive drug discovery. Some key points:
- CDD has screened over 250,000 molecules through Bayesian models to identify hits for tuberculosis. Around 750 molecules were tested in vitro, identifying 198 active molecules.
- Machine learning models have been over 20% accurate in prospective tests at identifying active molecules. Models have shown 3-10 fold enrichment in retrospective tests.
- There is a lack of data on compounds tested in vivo for tuberculosis. Only a small fraction of compounds tested in vitro are also tested in vivo. Building a mouse tuberculosis database could help prioritize further testing.
- Open source implementations of fingerprints and machine learning methods
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
BioAssay Express: Creating and exploiting assay metadataPhilip Cheung
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
The document summarizes an IMGS 2011 bioinformatics workshop. It discusses next-generation sequencing technologies including Roche 454, Illumina/Solexa, and AB SOLiD. It also covers topics like sequence alignments, file formats, tools for analysis including BWA and TopHat, and visualization. The document provides links to video tutorials and resources on sequencing technologies, alignments, and analyzing RNA-seq data.
FAIR as a Working Principle for Cancer Genomic DataIan Fore
This document discusses making cancer genomic data FAIR (Findable, Accessible, Interoperable, and Reusable) as a working principle. It summarizes a talk given by Ian Fore of the National Cancer Institute on using FAIR data principles for cancer genomic data. The document also briefly describes several other talks from a conference track on FAIR data.
This document summarizes a presentation about the EnVisioning Pathways project. It discusses:
1) The EnCORE integration platform developed by the ENFIN Network of Excellence to enable mining data across different biological domains, sources, formats and types through a standardized XML format and web services.
2) Examples of EnCORE services that retrieve interaction data from databases like IntAct and Pride, map between identifiers, and represent results in biological pathways in databases like Reactome.
3) Efforts to adapt EnCORE to utilize standards and create a federated system to integrate information from different biological domains. This includes building predefined and user-selected workflows between EnCORE services.
This document presents a probabilistic model to increase the reliability of data analysis from multiplex sequencing performed on the SOLiD sequencing platform. The model characterizes multiplex runs and filters low-quality data by considering the intrinsic characteristics of each sequencing. It aims to identify faults in the sequencing process, guide filtering without discarding useful sequences, and assign confidence levels to the generated data. The model analyzes the SOLiD barcoding system used to tag multiple samples, as this reflects the overall sequencing quality and has lower processing costs than analyzing sequences of interest.
Next-generation DNA sequencing technologies have significantly impacted genetics research. Three major platforms - Roche/454, Illumina Genome Analyzer, and Applied Biosystems SOLiD - utilize massively parallel sequencing to generate large amounts of sequence data. Roche/454 uses emulsion PCR to amplify DNA fragments on beads and pyrosequencing to determine sequences. Illumina performs bridge amplification on a flow cell to generate DNA clusters then sequences by synthesis. Applied Biosystems SOLiD uses ligation-based sequencing. These new methods have enabled genome-wide studies and applications such as ancient DNA sequencing and metagenomics that were previously difficult or impossible.
Homology Modelling through modeller and its analysis using Ramachandran Plot
Modeller practical. Full tutorial created by Zarlish Attique
https://salilab.org/modeller/
Lessons in Modeling from 3-D Structural & Data Science PerspectivesPhilip Bourne
The document discusses how macromolecular structure and data science can inform modeling. It notes that 3D structure provides insights but knowledge is distributed across many papers, while data science allows a new appreciation of data and new methodologies. Combined, they enable multi-scale modeling, drug refinement and repurposing. Specific examples are given on signaling pathways, protein-ligand docking to predict drug efficacy, and integrating data to represent biological networks. The future involves overcoming cultural roadblocks to fully leverage multi-scale integration across levels from DNA to populations.
This document describes two challenges presented as part of the DREAM initiative to evaluate methods for parameter estimation and network topology inference from experimental data. In the first challenge, participants were given the topology of a 9-gene network and asked to estimate 45 kinetic parameters. In the second challenge, participants were given an incomplete 11-gene network and asked to identify 3 missing links and associated parameters. Participants could purchase simulated experimental data using a credit system, allowing iterative experimental design. While parameter estimation was accomplished well using fluorescence data, topology inference was more difficult. Aggregating submissions produced better solutions than individual methods.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: Pfizer’s Recent Use of tr...David Peyruc
The document summarizes Pfizer's use of the tranSMART platform for various genomics and clinical data analyses including genome-wide association studies (GWAS), supporting exploratory data types like metabolomics and FACS data, and large collaborative efforts like the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Parkinson's Progression Markers Initiative (PPMI) datasets. It also discusses analytical integration with Genedata Expressionist and plans for future enhancements to tranSMART like improved GWAS support and additional genotype data. Contributors to these efforts are acknowledged.
58.Comparative modelling of cellulase from Aspergillus terreusAnnadurai B
The document discusses homology modeling of the cellulase enzyme in Aspergillus terreus. It begins with an abstract that describes cellulase as a widely used hydrolytic enzyme involved in converting biomass to simpler sugars. It then provides details on homology modeling and the steps involved, which include template recognition, alignment, backbone and loop modeling, and model validation. The document discusses modeling of the cellulase protein from Aspergillus terreus using templates from the PDB and visualization software. It evaluates the modeled cellulase structure using validation servers to check accuracy.
BAR.utoronto.ca is a bio-analytic resource that allows exploration of large biological datasets for hypothesis generation. It contains over 128,000 SNPs, 150 million gene expression measurements, subcellular localizations for 9,300 proteins and predicted localizations for the Arabidopsis proteome, over 70,000 predicted and 36,000 documented protein-protein interactions, and over 67,000 predicted and 700 experimentally determined protein structures. The site provides easy-to-use tools for exploring these datasets to facilitate research.
This document provides an overview of next generation sequencing technologies and applications. It summarizes an upcoming webinar series on next generation sequencing and its role in cancer biology. The first webinar will provide an introduction to next generation sequencing technologies and applications and be presented by Quan Peng on April 4, 2013. The following two webinars will focus on next generation sequencing for cancer research and data analysis and be presented on April 11 and 18, 2013 respectively.
The document summarizes the Genome in a Bottle (GIAB) project, which aims to develop reference materials and benchmarks for evaluating human genome sequencing. GIAB has characterized 7 human genomes to high accuracy using multiple sequencing technologies and bioinformatics analyses. The characterized genomes and variant calls are made publicly available to benchmark sequencing performance. Recently, GIAB has incorporated linked and long read sequencing to expand reference benchmarks to more difficult genomic regions and develop benchmarks for structural variants.
Exploiting technical replicate variance in omics data analysis (RepExplore)Enrico Glaab
High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.
We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics.
Availability and implementation: Freely available at http://www.repexplore.tk
Journal publication: http://bioinformatics.oxfordjournals.org/content/31/13/2235.long (Glaab, E., & Schneider, R. (2015). RepExplore: Addressing technical replicate variance in proteomics and metabolomics data analysis. Bioinformatics, 31 (13): 2235-2237)
Making effective use of graphics processing units (GPUs) in computationsOregon State University
Graphics processing units (GPUs) are specialized computer processors used in computers and video game systems to accelerate the creation and display of images. Due to their inherent parallel structure, they also have great potential to speed up computations in many scientific and engineering applications. GPUs are attractive for their ability to perform a large number of computations in parallel at an attractive price. Many of the world¹s largest supercomputers use GPUs to achieve their high performance, and personal computers and laptops use them for graphics displays and image processing. This seminar will explore the use of GPUs in general, describe examples of the use of GPUs in computations, and introduce some best practices for GPU computing.
The document summarizes research presented at the CNCP 2010 conference. It describes work in several areas of proteomics research including fragmentation analysis, labeling strategies, de novo sequencing, identification, label-free quantification, database construction, data quality control, data processing platforms, glycoproteomics, and proteogenomics. Specific projects are mentioned that improved peptide identification, developed in vivo termini amino acid labeling, performed de novo sequencing of peptides from unknown genomes, optimized peptide mass fingerprinting for protein mixtures, detected post-translational modifications, and more.
The document discusses the ISA infrastructure, which provides a standardized format (ISA-TAB) for experimental metadata and data exchange. It can be used across various domains like toxicology, systems biology, and nanotechnology. The Risa R package integrates experimental metadata with analysis and allows updating metadata. Nature Scientific Data is a new publication for describing valuable datasets. The ISA framework has been adopted by over 30 public and private resources and is growing in use for facilitating reuse of investigations in various life science domains. Toxicity examples include EU projects on predictive toxicology and a rat study of drug candidates. Questions can be directed to the ISA tools group.
The document describes Phase II of the ABRF Next Generation Sequencing Study which aims to establish reference data sets for evaluating DNA sequencing performance across multiple platforms and laboratories. Phase II will sequence various human and bacterial genomic samples to assess accuracy, coverage, and limits of detection using different platforms and library preparation methods. A collaboration with NIST Genome in a Bottle will provide standardized samples to the participating laboratories. The study aims to provide a resource for ongoing method development and evaluation of sequencing performance.
Exploiting bigger data and collaborative tools for predictive drug discovery Sean Ekins
This document summarizes Sean Ekins' work exploiting big data and collaborative tools for predictive drug discovery. Some key points:
- CDD has screened over 250,000 molecules through Bayesian models to identify hits for tuberculosis. Around 750 molecules were tested in vitro, identifying 198 active molecules.
- Machine learning models have been over 20% accurate in prospective tests at identifying active molecules. Models have shown 3-10 fold enrichment in retrospective tests.
- There is a lack of data on compounds tested in vivo for tuberculosis. Only a small fraction of compounds tested in vitro are also tested in vivo. Building a mouse tuberculosis database could help prioritize further testing.
- Open source implementations of fingerprints and machine learning methods
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
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.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
2. Introduction
6/3/2023 5:43 PM 2
Introduction Sample Preparation Data Analysis
Considerations References
Figure 1: DNA Organization.
Adapted from Henrik's Lab. " ChIP seq - Chromatin Immunoprecipitation sequencing”, YouTube, 12 May
2021.
A method used to identify genomic regions bound by specific proteins or
protein modifications, providing insights into gene regulation and
chromatin structure.
3. Sample Preparation
6/3/2023 5:43 PM 3
Chemical
treatment
(Formaldehyde)
TF, Modified
Histone, RNA pol
Introduction Sample Preparation Considerations Data Analysis References
Figure 2: Sample preparation for ChIP-Seq
Adapted from Henrik's Lab. " ChIP seq - Chromatin Immunoprecipitation sequencing”, YouTube, 12 May 2021.
.
4. 6/3/2023 5:43 PM 4
Sample Preparation
100-300 bp
Cell Disruption and
DNA fragmentation
Introduction Sample Preparation Considerations Data Analysis References
Figure 2 (contd..): Sample preparation for ChIP-Seq
Adapted from Henrik's Lab. " ChIP seq - Chromatin Immunoprecipitation sequencing”, YouTube, 12 May 2021.
.
5. Target Enrichment
6/3/2023 5:43 PM 5
Immunoprecipitation
Introduction Sample Preparation Considerations Dara Analysis References
Figure 2 (contd..): Sample preparation for ChIP-Seq
Adapted from Henrik's Lab. " ChIP seq - Chromatin Immunoprecipitation sequencing”, YouTube, 12 May 2021.
6. Sequencing
6/3/2023 5:43 PM 6
Cross-linked reversal and
Library preparation
Sequencing of target DNA
fragment
NovaSeq
6000 System
Introduction Sample Preparation Data Analysis Considerations References
Figure 2: Sample preparation for ChIP-Seq
Adapted from Henrik's Lab. " ChIP seq - Chromatin Immunoprecipitation sequencing”, YouTube, 12 May 2021.
7. Experimental Design Considerations
1. Antibody selection
2. Chromatin fragmentation
3. Cross-linking conditions
4. Sufficient amount of starting material - 2 x 106 cells per
immunoprecipitation.
5. Control libraries
6. Reducing artifacts - normalization
7. Biological replicates ≥ 3.
6/3/2023 5:43 PM 7
Introduction Sample Preparation Data Analysis
Considerations References
8. Sequencing Considerations
Parameters Values
Read Length 50-150 bp
Sequencing Mode SE, PE
Sequencing Depth 20-40 M total read depth (for TF)
≥ 40 M for Histone marks
6/3/2023 5:43 PM 8
Table1: Sequencing considerations for ChIP-Seq
Introduction Sample Preparation Data Analysis
Considerations References
9. Sequencing Considerations
6/3/2023 5:43 PM 9
Figure 3: No. of peaks called vs. sequencing depth
Adapted from Landt et al., "ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia,"
Genome Research, 22(9), 1813-1831, 2012.
Introduction Sample Preparation Data Analysis
Considerations References
10. Data Analysis
6/3/2023 5:43 PM 10
Introduction Sample Preparation Data Analysis
Considerations References
Figure 4: ChIP-Seq data analysis pipeline
Quality
control
Read
mapping
Peak calling
Data
visualization
Functional
analysis
Motif analysis
Differential
analysis
Integration
with other
data types
Reproducibility
11. 1. Preprocessing
• Quality Control (QC)
• Read trimming and filtering
• PCR duplicate removal
Important Quality Matrices
a) Per base sequence quality
b) GC content
c) Over represented sequences
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FastQC, MultiQC
Introduction Sample Preparation Data Analysis
Considerations References
12. 2. Alignment
6/3/2023 5:43 PM 12
Preprocessed reads are
mapped to the reference
genome using tools like BWA
or SAMtools
Input = FASTQ
Output = SAM,
BAM
BWA, Bowtie,
STAR,
NovoAlign
Figure 5: Alignment results from BWA
Adapted from Zymo Research, https://github.com/Zymo-Research/service-pipeline-documentation, Accessed May
6, 2023.
Introduction Sample Preparation Data Analysis
Considerations References
13. 3. Peak Calling
6/3/2023 5:43 PM 13
Identification of enriched
loci in the genome.
Output = BED
Format
MACS,
SICER, Bayes
Peak
Figure 6: Peaks calling summary statistics using MACS2
Adapted from Zymo Research, https://github.com/Zymo-Research/service-pipeline-documentation, Accessed May
6, 2023.
Introduction Sample Preparation Data Analysis
Considerations References
14. 4. Visualization
6/3/2023 5:43 PM 14
Figure 7: Peaks visualization by DROMPAplus
Adapted from Nokato et al., "Methods for ChIP-seq analysis: A practical workflow and advanced applications," Journal of
Biochemistry, 159(4), 335-345, 2016, doi: 10.1093/jb/mvv124.
Introduction Sample Preparation Data Analysis
Considerations References
15. 4. Visualization
6/3/2023 5:43 PM 15
Figure 8: Peaks visualization by DROMPAplus
Adapted from Nokato et al., "Methods for ChIP-seq analysis: A practical workflow and advanced applications," Journal of
Biochemistry, 159(4), 335-345, 2016, doi: 10.1093/jb/mvv124.
Introduction Sample Preparation Data Analysis
Considerations References
16. 4. Visualization
6/3/2023 5:43 PM 16
Peaks can be viewed
directly in genome
browser e.g. UCSC
Genome Browser
ChIPseeker,
IGV
Figure 9: Peaks visualization by UCSC Genome Browser
Adapted from Zymo Research, https://github.com/Zymo-Research/service-pipeline-documentation, Accessed May 6, 2023.
Introduction Sample Preparation Data Analysis
Considerations References
17. 5. Peak Annotation
6/3/2023 5:43 PM 17
ReMap, MGA,
RSAT,
rGADEM
Figure 10: Peaks annotation by HOMER
Adapted from Zymo Research, https://github.com/Zymo-Research/service-pipeline-documentation, Accessed May 6, 2023.
Introduction Sample Preparation Data Analysis
Considerations References
18. 5. Peak Annotation
6/3/2023 5:43 PM 18
Figure 11: Peaks annotation by HOMER
Adapted from Zymo Research, https://github.com/Zymo-Research/service-pipeline-documentation, Accessed May 6, 2023.
Introduction Sample Preparation Data Analysis
Considerations References
19. References
1. Nakato, R., Shirahige, K., & Takahata, S. (2021). Methods for ChIP-seq
analysis: A practical workflow and advanced applications. Genes to Cells,
26(6), 371-382. doi: 10.1111/gtc.12863.
2. Landt, S.G., Marinov, G.K., Kundaje, A. et al. (2012). ChIP-seq guidelines
and practices of the ENCODE and modENCODE consortia. Genome Res.
22(9), 1813-1831. doi: 10.1101/gr.136184.111.
3. Zymo Research. (n.d.). Service Pipeline Documentation. GitHub.
https://github.com/Zymo-Research/service-pipeline-documentation
6/3/2023 5:43 PM 19
Introduction Sample Preparation Data Analysis
Considerations References
Editor's Notes
Cross-linking between proteins and DNA in ChIP-seq samples is typically reversed by using heat and/or a chemical agent to break the cross-linking bonds and release the protein-DNA complexes.
The library preparation step in ChIP-seq (chromatin immunoprecipitation sequencing) involves converting the fragmented DNA (or chromatin) obtained from the ChIP-seq sample into a sequencing library, which can be used for high-throughput sequencing.
The library preparation step typically includes the following key steps:
End repair: The fragmented DNA ends are repaired to generate blunt ends, suitable for ligation to sequencing adapters.
Adaptor ligation: DNA sequencing adapters are ligated to the repaired DNA fragments. These adapters contain sequences that are required for the subsequent steps of the sequencing process.
Size selection: The adapter-ligated DNA fragments are size-selected to remove any unligated adapters or fragments that are too small or too large for sequencing.
PCR amplification: The size-selected DNA fragments are amplified by PCR (polymerase chain reaction) to generate sufficient material for sequencing. PCR primers specific to the adapter sequences are used to selectively amplify only the adapter-ligated fragments.
Quality control: The resulting library is evaluated for quality and quantity using various methods, such as gel electrophoresis, qPCR (quantitative PCR), or fluorometry.
The type of library for ChIP-seq (chromatin immunoprecipitation sequencing) can be either single-end or paired-end, depending on the sequencing platform and experimental design.
In single-end sequencing, only one end of the DNA fragment is sequenced, while in paired-end sequencing, both ends of the DNA fragment are sequenced. Paired-end sequencing generates more information per fragment and allows for more accurate mapping of reads to the reference genome.
Most commonly, ChIP-seq libraries are prepared as paired-end libraries, as this allows for more accurate identification of the precise binding location of the protein of interest. However, single-end sequencing may be used in some cases where cost or experimental constraints prohibit the use of paired-end sequencing.
1. An ideal antibody for ChIP-seq should have high specificity, sensitivity, and affinity for the protein of interest. It should be able to recognize the native conformation of the protein and not cross-react with other proteins in the sample. Additionally, the antibody should be able to capture the protein-DNA complexes in a highly efficient and reproducible manner.
2. If the chromatin is over-fragmented, then the DNA fragments may become too short, leading to decreased specificity and accuracy of the ChIP-seq assay. On the other hand, if the chromatin is under-fragmented, then the DNA fragments may become too large, leading to lower resolution of the assay and decreased ability to identify binding sites.
3. Crosslinking is a critical step in ChIP-seq (chromatin immunoprecipitation sequencing) as it plays a crucial role in preserving the protein-DNA interactions within the chromatin and ensuring accurate and reliable results. The conditions used for crosslinking, such as the concentration of formaldehyde, duration of crosslinking, and temperature, are all critical factors that can significantly affect the quality and specificity of the ChIP-seq data.
4. Ensure that you have a sufficient amount of starting material because the ChIP will only enrich for a small proportion. For a standard protocol, you want approximately 2 x 106 cells per immunoprecipitation. If it is difficult to obtain that many samples from your experiment, consider using low input methods. Ultimately, higher amounts of starting material yield more consistent and reproducible protein-DNA enrichments.
5. A ChIP-Seq peak should be compared with the same region of the genome in a matched control sample because only a fraction of the DNA in our ChIP sample corresponds to actual signal amidst background noise. Control libraries are an essential component of ChIP-seq (chromatin immunoprecipitation sequencing) experiments. In a ChIP-seq experiment, the goal is to identify the genomic regions bound by a specific protein of interest. However, this cannot be accomplished without taking into account the background noise and non-specific binding events that can occur during the experiment.
Control libraries provide a baseline for comparison with the experimental libraries, allowing the identification of regions that are specifically enriched for the protein of interest versus regions that are non-specifically bound or enriched due to experimental noise. The most commonly used control library is a "mock IP" or "IgG" control, which involves performing the entire ChIP-seq protocol using an antibody that does not recognize any of the proteins of interest in the sample.
6. There are a number of artifacts that tend to generate pileups of reads that could be interpreted as a false positive peaks. These include:
Open chromatin regions that are fragmented more easily than closed regions due to the accessibility of the DNA
The presence of repetitive sequences
An uneven distribution of sequence reads across the genome due to DNA composition
‘hyper-ChIPable’ regions: loci that are commonly enriched in ChIP datasets. Certain genomic regions are more susceptible to immunoprecipitation, therefore show increased ChIP signals for unrelated DNA-binding and chromatin-binding proteins.
Single-end reads are sufficient in most cases. Paired-end is good (and necessary) for allele-specific chromatin events, and investigations of transposable elements. Sequence the input controls to equal or higher depth than your ChIP samples.
A minimum of 40M total read depth; more is better for detecting some histone marks
During the PCR amplification step of library preparation, some DNA fragments may be over-amplified, resulting in multiple identical copies of the same fragment. These PCR duplicates can bias the estimation of the true fragment frequency and affect the accuracy of peak calling and differential binding analysis.
Overrepresented sequences are sequences that are found in high abundance in a ChIP-seq dataset. These sequences can arise from a variety of sources, such as sequencing adapters, PCR duplicates, or genomic regions with high GC content.
Overrepresented sequences are an important quality metric in ChIP-seq preprocessing because they can indicate potential issues with the sequencing library, such as poor sequencing quality, contamination, or bias. High levels of overrepresented sequences can lead to reduced sequencing depth, false positive peaks, and decreased sensitivity and specificity of peak calling algorithms.
Identifying and removing overrepresented sequences is an important step in ChIP-seq preprocessing to ensure the accuracy and reliability of downstream analysis. This can be done using bioinformatics tools that detect and filter out sequences that exceed a certain threshold of frequency or similarity to known contaminants or artifacts.
1. The alignment step in ChIP-seq (chromatin immunoprecipitation sequencing) is the process of mapping the sequencing reads generated from the ChIP and control libraries to a reference genome or transcriptome. The goal of the alignment step is to assign each read to its original genomic location with high accuracy and specificity, so that the genomic regions with significant binding enrichment can be identified and analyzed.
The alignment step typically involves several sub-steps, including read quality control, adapter trimming, sequence alignment, and read sorting and indexing. Different software tools and algorithms can be used for these sub-steps, depending on the type and quality of the sequencing data, the genome or transcriptome of interest, and the specific research questions.
Peak calling is a key step in the analysis of ChIP-seq (chromatin immunoprecipitation sequencing) data, which aims to identify genomic regions with significant enrichment of ChIP-seq signal over the control or background signal. These enriched regions, also called peaks, represent putative binding sites of the protein or factor of interest on the chromatin. Some common peak calling algorithms include MACS (Model-based Analysis of ChIP-Seq), SICER (Spatial Clustering for Identification of ChIP-Enriched Regions), and MAnorm (Model-based Analysis of Nucleosome Organization and Relationship to Transcription). These algorithms may also incorporate downstream analysis steps such as peak annotation, motif analysis, and gene ontology enrichment analysis.
BED (Browser Extensible Data) format is a commonly used file format for representing genomic intervals, such as the genomic coordinates of ChIP-seq peaks, gene exons, or genomic variants. The BED file format is tab-delimited, and each line in the file represents a single genomic interval.
A BED file typically contains at least three columns, representing the chromosome name, start position, and end position of the interval. Optionally, additional columns can be included to represent the name of the interval, the strand orientation, and additional metadata such as score, p-value, or functional annotations.
The basic BED format has the following three mandatory columns:
Chromosome: The name of the chromosome or contig where the interval is located.
Start: The starting position of the interval on the chromosome, using 0-based coordinates.
End: The ending position of the interval on the chromosome, using 1-based coordinates.
FRiP (Fraction of Reads in Peaks) is a commonly used quality metric for ChIP-seq data analysis. It measures the fraction of aligned reads that fall within peaks, which are genomic regions with a high density of ChIP-seq signal.
The FRiP score is calculated by dividing the number of reads that fall within called peaks by the total number of aligned reads. A high FRiP score indicates that a large proportion of aligned reads are in peaks, suggesting high enrichment of the target protein or histone modification.
FRiP scores are often used to compare the quality of different ChIP-seq experiments, and a typical cutoff for a high-quality ChIP-seq experiment is a FRiP score of at least 20%. However, the appropriate cutoff may depend on the specific biological question and the type of sample being analyzed.
In ChIP-seq data analysis, two types of peaks are commonly observed: sharp peaks and broad peaks.
Sharp peaks are typically narrow and well-defined, indicating the precise location of a protein-DNA interaction, such as a transcription factor binding site or a histone modification. Sharp peaks are characterized by a high peak summit and a steep drop-off on either side of the peak summit.
Broad peaks, on the other hand, are wider and more diffuse than sharp peaks, indicating a more extended region of protein-DNA interaction, such as a histone modification that spans a large genomic region. Broad peaks are characterized by a lower peak summit and a more gradual drop-off on either side of the peak summit.
The distinction between sharp and broad peaks is important because different peak calling algorithms may be better suited to identify one type of peak versus the other, and different downstream analyses may be required depending on the type of peak. For example, motif discovery algorithms may be more effective at identifying transcription factor binding motifs within sharp peaks, while functional annotation tools may be better suited to identifying biological pathways associated with broad peaks.
identifies the genomic region and feature a peak overlaps with, such as exon, intron, promoter of a specific gene, or intergenic, etc. It also identifies the nearest TSS to the peak, including the distance and gene
The peak score in the peak annotation file generated by HOMER is a score assigned to each peak based on the strength of the signal in that region. HOMER uses a statistical model to calculate the peak score, which takes into account the distribution of signal intensity across the genome and the size of the peak.
The peak score is a useful metric for ranking peaks by their strength and for comparing the strength of peaks across different samples. In HOMER, peaks with higher scores are considered to have stronger signals and are more likely to be biologically meaningful.
TSS= Transcription start site