Fast qPCR assay optimization and validation techniques for HTS discusses steps for designing a successful qPCR assay, including:
1) Designing primers after running a BLAST search to check for homologous sequences and secondary structures in the target cDNA that could interfere with amplification.
2) Testing primer pairs through a temperature gradient and dilution series to validate the assay's dynamic range before running it hundreds or thousands of times.
3) Choosing an amplicon size between 75-200bp ideally, though larger sizes are possible with new fast reagents. Following these steps at the beginning leads to more effective analysis later.
This document discusses multiple sequence alignment. It begins by explaining that pairwise sequence alignment is not reliable for more distantly related sequences, as there may be many possible alignments with the same score. Multiple sequence alignment allows discovering conserved motifs across a protein family. The document then discusses different scoring systems for multiple sequence alignments, including sum-of-pairs and entropy-based scores. It also describes the dynamic programming solution and progressive alignment approaches like CLUSTALW and T-COFFEE. The document concludes by mentioning faster methods like MUSCLE that use hashing to find short matches and build an initial sequence similarity tree.
The document provides an overview of Chip-seq data analysis. It discusses the Chip-seq technology, visualization of genomic data, command line analysis including quality checking, alignment, peak calling, annotation, and motif finding. It also discusses downstream analysis such as comparing samples, analyzing region occupancy, and web resources for Chip-seq analysis.
The document discusses using the PRINSEQ tool to filter raw sequencing data. PRINSEQ is used to trim sequences, filter low quality reads, and generate quality statistics. It analyzes the original raw fastq file along with the "good" and "bad" fastq files generated after filtering. The raw file contained over 17 million reads, the good file had over 16 million reads, and the bad file contained around 0.4 million reads filtered out as low quality.
This document discusses methods for predicting and analyzing the secondary structure of RNA molecules. It begins by covering RNA folding basics like canonical base pairs. It then describes two main approaches to secondary structure prediction: dynamic programming which aims to maximize base pairing, and energy minimization which considers thermodynamic stability. Dynamic programming uses a recurrence relation and bifurcation in a dynamic programming algorithm. Energy minimization computes the single most stable structure but may not be biologically accurate. Covariance models incorporate similarity-based methods and use hidden Markov models to represent consensus structures allowing for flexible sequence alignments based on observed co-varying mutations.
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.
This document provides an overview of next generation sequencing (NGS) analysis. It discusses various NGS platforms such as Illumina, Roche 454, PacBio, and Ion Torrent. It also covers common file formats for sequencing data like FASTQ, quality control measures to assess data quality, and applications of NGS such as RNA-seq and ChIP-seq. The document aims to introduce researchers to basic concepts in NGS analysis and highlights available resources for storing and analyzing large sequencing datasets.
Multi version concurrency control techniques
This approach maintains a number of versions of a data item and allocates the right version to a read operation of a transaction. Thus unlike other mechanisms a read operation in this mechanism is never rejected.
This document discusses multiple sequence alignment. It begins by explaining that pairwise sequence alignment is not reliable for more distantly related sequences, as there may be many possible alignments with the same score. Multiple sequence alignment allows discovering conserved motifs across a protein family. The document then discusses different scoring systems for multiple sequence alignments, including sum-of-pairs and entropy-based scores. It also describes the dynamic programming solution and progressive alignment approaches like CLUSTALW and T-COFFEE. The document concludes by mentioning faster methods like MUSCLE that use hashing to find short matches and build an initial sequence similarity tree.
The document provides an overview of Chip-seq data analysis. It discusses the Chip-seq technology, visualization of genomic data, command line analysis including quality checking, alignment, peak calling, annotation, and motif finding. It also discusses downstream analysis such as comparing samples, analyzing region occupancy, and web resources for Chip-seq analysis.
The document discusses using the PRINSEQ tool to filter raw sequencing data. PRINSEQ is used to trim sequences, filter low quality reads, and generate quality statistics. It analyzes the original raw fastq file along with the "good" and "bad" fastq files generated after filtering. The raw file contained over 17 million reads, the good file had over 16 million reads, and the bad file contained around 0.4 million reads filtered out as low quality.
This document discusses methods for predicting and analyzing the secondary structure of RNA molecules. It begins by covering RNA folding basics like canonical base pairs. It then describes two main approaches to secondary structure prediction: dynamic programming which aims to maximize base pairing, and energy minimization which considers thermodynamic stability. Dynamic programming uses a recurrence relation and bifurcation in a dynamic programming algorithm. Energy minimization computes the single most stable structure but may not be biologically accurate. Covariance models incorporate similarity-based methods and use hidden Markov models to represent consensus structures allowing for flexible sequence alignments based on observed co-varying mutations.
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.
This document provides an overview of next generation sequencing (NGS) analysis. It discusses various NGS platforms such as Illumina, Roche 454, PacBio, and Ion Torrent. It also covers common file formats for sequencing data like FASTQ, quality control measures to assess data quality, and applications of NGS such as RNA-seq and ChIP-seq. The document aims to introduce researchers to basic concepts in NGS analysis and highlights available resources for storing and analyzing large sequencing datasets.
Multi version concurrency control techniques
This approach maintains a number of versions of a data item and allocates the right version to a read operation of a transaction. Thus unlike other mechanisms a read operation in this mechanism is never rejected.
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.
Phylogenetic trees graphically represent the evolutionary relationships between biological entities like species and sequences. They show the branching order and amount of evolutionary change between nodes. There are different types of phylogenetic trees - cladograms are not scaled while phylograms are scaled to represent time between generations. Rooted trees show evolution stemming from a single ancestor while unrooted trees show relationships without a common ancestor. Phylogenetic trees are made up of branches representing connections and nodes representing speciation events or the production of new species from a common ancestor. They help study evolutionary histories and how species spread geographically.
The Gene Ontology (GO) provides a controlled vocabulary for describing gene and gene product attributes across species. It consists of three ontologies covering biological processes, molecular functions, and cellular components. GO terms are organized into a directed acyclic graph structure and can have relationships like "is_a" and "part_of". Genes are annotated with GO terms to capture functional information, which is shared across species to facilitate research. While useful, the GO has some limitations like unclear reasoning principles and lack of validation procedures.
The document discusses functional annotation of differentially expressed genes using various bioinformatics tools. It describes using g:Profiler and DAVID to identify gene ontology terms enriched in differentially expressed genes. Specific steps are outlined, including uploading gene lists to the tools, selecting appropriate organisms, downloading results tables containing significantly enriched terms and associated genes. Functional networks can also be generated using the ClueGO app in Cytoscape. The purpose is to understand the biological processes, molecular functions and cellular components that may be perturbed based on changes in gene expression.
This document contains information about Benben Miao's omics research interests and experience. It lists their contact information and links to their Github and website at the top. The rest of the document outlines different omics technologies that Benben works with, including genomics, transcriptomics, microbiome analysis, proteomics, metabolomics, and epigenomics. For each type of omics, it provides examples of relevant experimental techniques and analysis workflows/software used. It also includes links to relevant databases and analysis tools at the bottom. In summary, this document profiles Benben Miao's background in multi-omics data analysis and the technologies and approaches they apply in their research.
Systems biology is the computational and mathematical modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.
Sequence alignment involves arranging two or more biological sequences, like DNA, RNA or proteins, in a way that optimally matches their elements. It helps infer functional, structural or evolutionary relationships between sequences. There are two main types of sequence alignment - global alignment, which aligns entire sequences end-to-end, and local alignment, which finds short, locally matching regions. Popular algorithms for sequence alignment include BLAST, FASTA and CLUSTAL, with BLAST being the most widely used due to its speed.
Event: Plant and Animal Genomes conference 2012
Speaker: Rachael Huntley
The Gene Ontology (GO) is a well-established, structured vocabulary used in the functional annotation of gene products. GO terms are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently, GO consists of more than 35,000 terms describing the molecular function, biological process and subcellular location of a gene product in a generic cell. The UniProt-Gene Ontology Annotation (UniProt-GOA) database1 provides high-quality manual and electronic GO annotations to proteins within UniProt. By annotating well-studied proteins with GO terms and transferring this knowledge to less well-studied and novel proteins that are highly similar, we offer a valuable contribution to the understanding of all proteomes. UniProt-GOA provides annotated entries for over 387,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. Annotation files for various proteomes are released each month, including human, mouse, rat, zebrafish, cow, chicken, dog, pig, Arabidopsis and Dictyostelium, as well as a file for the multiple species within UniProt. The UniProt-GOA dataset can be queried through our user-friendly QuickGO browser2 or downloaded in a parsable format via the EBI3 and GO Consortium FTP4 sites. The UniProt-GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results. The annotations produced by UniProt-GOA are additionally cross-referenced in databases such as Ensembl and NCBI Entrez Gene.
1 http://www.ebi.ac.uk/GOA
2 http://www.ebi.ac.uk/QuickGO
3 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa
4 ftp://ftp.geneontology.org/pub/go/gene-associations
PCA is a technique used to simplify complex datasets by transforming correlated variables into a set of uncorrelated variables called principal components. It identifies patterns in high-dimensional data and expresses the data in a way that highlights similarities and differences. PCA is useful for analyzing data and reducing dimensionality without much loss of information. It works by rotating the existing axes to capture major variability in the data while ignoring smaller variations.
Systems biology: Bioinformatics on complete biological systemsLars Juhl Jensen
Systems biology uses mathematical modeling and computational analysis to study complete biological systems and molecular networks. It requires integrating diverse experimental data, literature knowledge and computational predictions. Several web resources can be used together to analyze gene and protein interaction networks, find related diseases and compounds, while accounting for tissue specificity and other contextual factors. These resources include STRING, STITCH, COMPARTMENTS and DISEASES.
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.
This document summarizes a webinar presentation about adaptive sample size re-estimation for confirmatory time-to-event trials. The presentation discusses a motivating lung cancer trial example and introduces a promising zone design where the sample size is increased only if interim results fall within a promising zone. It demonstrates the design, simulation, and interim monitoring capabilities of East®SurvAdapt software. Key aspects of the adaptive design methodology are discussed, including conditional power calculations, maintaining type 1 error control, and balancing sample size increases with trial duration.
The document discusses algorithms for optimal sequence alignment using dynamic programming. It describes the Needleman-Wunsch algorithm for global alignment and the Smith-Waterman algorithm for local alignment. Both algorithms use dynamic programming to find the highest scoring alignment between two sequences by dividing the problem into independent subproblems. The Needleman-Wunsch algorithm allows gaps across the entire length of the sequences, while Smith-Waterman only considers local regions with high similarity.
Plant Introductions & Evolution: Hybrid Speciation and Gene TransferUniversity of Adelaide
Professor Richard Abbott presents a seminar entitled "Gene transfer and plant evolution: What we have learnt from Senecio." Richard has been at St Andrews University since October 1971 and currently holds a Chair in Plant Evolution. He is also an Editor of New Phytologist, and Associate Editor of Molecular Ecology, and Plant Ecology & Diversity. Richard’s main research focus is on the evolutionary consequences of hybridization in plants using the genus Senecio (Asteraceae) as a system for study.
This document discusses Biopython, a Python package for biological data analysis. It provides concise summaries of key Biopython concepts:
1) Biopython is an object-oriented Python package that consists of modules for common biological data operations like working with sequences.
2) Key Biopython classes include Alphabet for sequence alphabets, Seq for representing sequences, SeqRecord for sequences with metadata, and SeqIO for reading/writing sequences to files.
3) Classes specify attributes (data) and methods (functions) that objects can have. For example, Seq objects have attributes like sequence and alphabet, and methods like translate() and complement().
This document outlines a presentation on biological networks and the software Cytoscape. It begins with an introduction to biological networks and their taxonomy, as well as analytical approaches and visualization techniques. It then provides an overview of Cytoscape, covering core concepts like networks and tables, visual properties, and apps. The document demonstrates how to load networks and data, use visual style managers, and save and export networks. It concludes with tips and tricks for using Cytoscape and a link to a hands-on tutorial.
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The following slides were prepared by POORNIMA M.S student of II M.Sc., Life Science Bangalore University, Bangalore
This document discusses RNA secondary structure prediction. It begins by defining RNA and its primary and secondary structures. The problem of predicting secondary structure given a primary sequence is introduced. Approaches include physical/chemical experiments and computational prediction using a single sequence. The Nussinov and Zuker algorithms are described. Nussinov finds the structure with maximum base pairs using dynamic programming. Zuker finds the minimum free energy structure also using dynamic programming. Addressing pseudoknots and other interactions is discussed as future work.
The document describes a workshop on molecular methods in water engineering, including amplicon sequencing and omics approaches. The agenda includes talks on amplicon sequencing principles and limitations, the importance of curated 16S databases, DNA extraction and primer selection, metagenomics and metatranscriptomics principles and challenges, and data informatics and management. The workshop aims to discuss the potential and limitations of novel molecular techniques for analyzing water systems.
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.
Phylogenetic trees graphically represent the evolutionary relationships between biological entities like species and sequences. They show the branching order and amount of evolutionary change between nodes. There are different types of phylogenetic trees - cladograms are not scaled while phylograms are scaled to represent time between generations. Rooted trees show evolution stemming from a single ancestor while unrooted trees show relationships without a common ancestor. Phylogenetic trees are made up of branches representing connections and nodes representing speciation events or the production of new species from a common ancestor. They help study evolutionary histories and how species spread geographically.
The Gene Ontology (GO) provides a controlled vocabulary for describing gene and gene product attributes across species. It consists of three ontologies covering biological processes, molecular functions, and cellular components. GO terms are organized into a directed acyclic graph structure and can have relationships like "is_a" and "part_of". Genes are annotated with GO terms to capture functional information, which is shared across species to facilitate research. While useful, the GO has some limitations like unclear reasoning principles and lack of validation procedures.
The document discusses functional annotation of differentially expressed genes using various bioinformatics tools. It describes using g:Profiler and DAVID to identify gene ontology terms enriched in differentially expressed genes. Specific steps are outlined, including uploading gene lists to the tools, selecting appropriate organisms, downloading results tables containing significantly enriched terms and associated genes. Functional networks can also be generated using the ClueGO app in Cytoscape. The purpose is to understand the biological processes, molecular functions and cellular components that may be perturbed based on changes in gene expression.
This document contains information about Benben Miao's omics research interests and experience. It lists their contact information and links to their Github and website at the top. The rest of the document outlines different omics technologies that Benben works with, including genomics, transcriptomics, microbiome analysis, proteomics, metabolomics, and epigenomics. For each type of omics, it provides examples of relevant experimental techniques and analysis workflows/software used. It also includes links to relevant databases and analysis tools at the bottom. In summary, this document profiles Benben Miao's background in multi-omics data analysis and the technologies and approaches they apply in their research.
Systems biology is the computational and mathematical modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.
Sequence alignment involves arranging two or more biological sequences, like DNA, RNA or proteins, in a way that optimally matches their elements. It helps infer functional, structural or evolutionary relationships between sequences. There are two main types of sequence alignment - global alignment, which aligns entire sequences end-to-end, and local alignment, which finds short, locally matching regions. Popular algorithms for sequence alignment include BLAST, FASTA and CLUSTAL, with BLAST being the most widely used due to its speed.
Event: Plant and Animal Genomes conference 2012
Speaker: Rachael Huntley
The Gene Ontology (GO) is a well-established, structured vocabulary used in the functional annotation of gene products. GO terms are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently, GO consists of more than 35,000 terms describing the molecular function, biological process and subcellular location of a gene product in a generic cell. The UniProt-Gene Ontology Annotation (UniProt-GOA) database1 provides high-quality manual and electronic GO annotations to proteins within UniProt. By annotating well-studied proteins with GO terms and transferring this knowledge to less well-studied and novel proteins that are highly similar, we offer a valuable contribution to the understanding of all proteomes. UniProt-GOA provides annotated entries for over 387,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. Annotation files for various proteomes are released each month, including human, mouse, rat, zebrafish, cow, chicken, dog, pig, Arabidopsis and Dictyostelium, as well as a file for the multiple species within UniProt. The UniProt-GOA dataset can be queried through our user-friendly QuickGO browser2 or downloaded in a parsable format via the EBI3 and GO Consortium FTP4 sites. The UniProt-GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results. The annotations produced by UniProt-GOA are additionally cross-referenced in databases such as Ensembl and NCBI Entrez Gene.
1 http://www.ebi.ac.uk/GOA
2 http://www.ebi.ac.uk/QuickGO
3 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa
4 ftp://ftp.geneontology.org/pub/go/gene-associations
PCA is a technique used to simplify complex datasets by transforming correlated variables into a set of uncorrelated variables called principal components. It identifies patterns in high-dimensional data and expresses the data in a way that highlights similarities and differences. PCA is useful for analyzing data and reducing dimensionality without much loss of information. It works by rotating the existing axes to capture major variability in the data while ignoring smaller variations.
Systems biology: Bioinformatics on complete biological systemsLars Juhl Jensen
Systems biology uses mathematical modeling and computational analysis to study complete biological systems and molecular networks. It requires integrating diverse experimental data, literature knowledge and computational predictions. Several web resources can be used together to analyze gene and protein interaction networks, find related diseases and compounds, while accounting for tissue specificity and other contextual factors. These resources include STRING, STITCH, COMPARTMENTS and DISEASES.
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.
This document summarizes a webinar presentation about adaptive sample size re-estimation for confirmatory time-to-event trials. The presentation discusses a motivating lung cancer trial example and introduces a promising zone design where the sample size is increased only if interim results fall within a promising zone. It demonstrates the design, simulation, and interim monitoring capabilities of East®SurvAdapt software. Key aspects of the adaptive design methodology are discussed, including conditional power calculations, maintaining type 1 error control, and balancing sample size increases with trial duration.
The document discusses algorithms for optimal sequence alignment using dynamic programming. It describes the Needleman-Wunsch algorithm for global alignment and the Smith-Waterman algorithm for local alignment. Both algorithms use dynamic programming to find the highest scoring alignment between two sequences by dividing the problem into independent subproblems. The Needleman-Wunsch algorithm allows gaps across the entire length of the sequences, while Smith-Waterman only considers local regions with high similarity.
Plant Introductions & Evolution: Hybrid Speciation and Gene TransferUniversity of Adelaide
Professor Richard Abbott presents a seminar entitled "Gene transfer and plant evolution: What we have learnt from Senecio." Richard has been at St Andrews University since October 1971 and currently holds a Chair in Plant Evolution. He is also an Editor of New Phytologist, and Associate Editor of Molecular Ecology, and Plant Ecology & Diversity. Richard’s main research focus is on the evolutionary consequences of hybridization in plants using the genus Senecio (Asteraceae) as a system for study.
This document discusses Biopython, a Python package for biological data analysis. It provides concise summaries of key Biopython concepts:
1) Biopython is an object-oriented Python package that consists of modules for common biological data operations like working with sequences.
2) Key Biopython classes include Alphabet for sequence alphabets, Seq for representing sequences, SeqRecord for sequences with metadata, and SeqIO for reading/writing sequences to files.
3) Classes specify attributes (data) and methods (functions) that objects can have. For example, Seq objects have attributes like sequence and alphabet, and methods like translate() and complement().
This document outlines a presentation on biological networks and the software Cytoscape. It begins with an introduction to biological networks and their taxonomy, as well as analytical approaches and visualization techniques. It then provides an overview of Cytoscape, covering core concepts like networks and tables, visual properties, and apps. The document demonstrates how to load networks and data, use visual style managers, and save and export networks. It concludes with tips and tricks for using Cytoscape and a link to a hands-on tutorial.
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The following slides were prepared by POORNIMA M.S student of II M.Sc., Life Science Bangalore University, Bangalore
This document discusses RNA secondary structure prediction. It begins by defining RNA and its primary and secondary structures. The problem of predicting secondary structure given a primary sequence is introduced. Approaches include physical/chemical experiments and computational prediction using a single sequence. The Nussinov and Zuker algorithms are described. Nussinov finds the structure with maximum base pairs using dynamic programming. Zuker finds the minimum free energy structure also using dynamic programming. Addressing pseudoknots and other interactions is discussed as future work.
The document describes a workshop on molecular methods in water engineering, including amplicon sequencing and omics approaches. The agenda includes talks on amplicon sequencing principles and limitations, the importance of curated 16S databases, DNA extraction and primer selection, metagenomics and metatranscriptomics principles and challenges, and data informatics and management. The workshop aims to discuss the potential and limitations of novel molecular techniques for analyzing water systems.
how to analyze the data which is available with the wet lab results and we can analyze more by using bioinformatics tools. here we can learn how to analyze the unknown data.
This document discusses recombinant DNA technology and its applications. It covers key steps like cloning vectors, restriction modification systems, DNA ligases, transformation, and screening cDNA libraries to identify genes. Examples are given of recombinant proteins used as drugs to treat diseases like growth hormone deficiency, diabetes, and hemophilia. The last section briefly discusses genetic modification of animals and plants, and debates around genetically engineered salmon.
This document discusses genetic markers used in plant breeding. It defines genetic markers as any phenotypic difference controlled by genes that can be used to study inheritance or select for traits. The document describes two main types of genetic markers - morphological markers which are visible traits, and molecular markers which are detected at the DNA level. It provides details on various molecular marker techniques including RFLP, RAPD, AFLP, SSRs, and SNPs. It explains how each technique works and its advantages and disadvantages. The key information is that genetic markers are tools that allow studying inheritance of genes and selecting plants for breeding based on associated genetic differences.
Metagenomic projects provide a unique window into the genetic composition of microbial communities. To date, metagenomic analyses have focused primarily on studying the composition of microbial populations and inferring shared metabolic pathways. In this work we analyze how high-quality metagenomic data can be leveraged to infer the composition of transcriptional regulatory networks through a combination of in silico and in vitro methods. Using the SOS response as a case example, we analyze human gut microbiome data to determine the composition of the SOS meta-regulon in a natural context. Our analysis provides proof of concept that the existing knowledgebase on regulatory networks and reference genomes can be effectively leveraged to mine meta-genomic data and reconstruct multi-species regulatory networks. This approach allows us to identify de novo the core elements of the human gut SOS meta-regulon, highlighting the relevance of error-prone polymerases in this stress response, and identifies putative novel SOS protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification. The methodology implemented in this work can be applied to other metagenomic datasets and transcriptional systems, potentially providing the means to compare regulatory networks across metagenomes. The use of metagenomic data to analyze transcriptional regulatory networks provides a realistic snapshot of these systems in their natural context and allows probing at their extended composition in non-culturable organisms, yielding insights into their interconnection and into the overall structure of transcriptional systems in microbiomes.
The document discusses the genome assembly problem which involves reconstructing the full genome sequence from fragmented short reads. It describes how short reads are fragmented and sequenced from the genome. To solve this problem, overlapping short reads must be found which is challenging with millions of reads. The document then explains how de Bruijn graphs can be used to represent overlaps between short reads by converting them to k-mers and building a graph from the k-mers to traverse and reconstruct the full genome sequence.
Site directed mutagenesis of β2-microglobulin PowerPoint PresentationTyler Liang
This document describes site-directed mutagenesis experiments performed on beta-2 microglobulin (β2m) to study its role in amyloidosis. Primers were designed to introduce mutations D39V and I5T into β2m. Polymerase chain reaction and transformation were used to generate mutant plasmids, which were sequenced to confirm the mutations. Future studies are planned to introduce mutations W61F and W96F to further study β2m stability and aggregation.
S.Prasanth Kumar describes the Serial Analysis of Gene Expression (SAGE) technology for quantitative and simultaneous analysis of large numbers of transcripts in cells or tissues. SAGE involves extracting short sequence tags from mRNA transcripts and concatenating them for sequencing. This allows identification and quantification of expressed genes from the sequenced tags by comparing them to genome databases. The procedure isolates 9-10 base pair tags from mRNA, concatenates them, and sequences the ditags to determine which genes are expressed and their relative abundances under different conditions.
The document describes the Sanger method for DNA sequencing, which was developed in 1977. The method uses DNA polymerase and dideoxynucleotides to terminate DNA strand elongation at random positions, generating DNA fragments of different lengths that can be used to determine the DNA sequence. The sequence is read by comparing the fragment lengths generated from reactions with different labeled dideoxynucleotides.
The document describes the process of protein synthesis. mRNA transcribed from a DNA strand interacts with a ribosome in the cytoplasm. The ribosome translates the mRNA into a protein using tRNA molecules and forming peptide bonds between amino acids specified by codons until a stop codon is reached. The resulting amino acid chain folds into its functional protein structure.
The document describes the process of transcription and translation in a cell. It shows how DNA is transcribed into mRNA by RNA polymerase in the nucleus. The mRNA is then exported through the nuclear pore into the cytoplasm. In the cytoplasm, ribosomes translate the mRNA into a protein by linking amino acids specified by the mRNA codons.
Coding & Best Practice in Programming in the NGS eraLex Nederbragt
This document discusses the importance of best coding practices and reproducibility in programming for next-generation sequencing data analysis. It notes the large amount of data and numerous software tools now available pose challenges around obtaining correct and reproducible results. The document recommends following best practices like automated testing, version control, documentation, collaboration, and benchmarking tools to help address these challenges.
This document describes a genomic DNA sequence from the 16S ribosomal RNA gene of Alcaligenes pakistanensis strain Ap AB003 that was isolated from a clinical pus sample in India. The sequence is 1537 base pairs long and contains the partial 16S rRNA gene that can be used to identify the bacterial strain. It was submitted to GenBank and assigned the accession number MZ048017.1.
Perennial Ryegrass (Lolium perenne L.) Improvement Through Cisgenics®sathish_p
The document discusses using cisgenics to improve perennial ryegrass through biotechnology. Cisgenics involves using genes from the same species rather than across species. This avoids issues with transferring genes between unrelated organisms. The document outlines using techniques like gene threshing and SAGE to analyze the ryegrass genome and identify beneficial genes that could be used in cisgenic improvement of ryegrass traits like drought tolerance and flowering time. The goal is to capture untapped genetic potential in ryegrass to increase the productivity of New Zealand's pastoral industries.
This document provides an overview of using Perl for whole-genome analysis and comparison using a graphical display called the W-curve. It discusses the challenges of comparing genes due to variability within and between species. The author adapted a W-curve algorithm to make use of Perl and BioPerl for bulk data analysis. Techniques like triangular comparison using hashes and integer sequences helped enable comparisons of whole genomes.
Advenced molecular techniques in molecular medical genetics laboratoryPeyman Ghoraishizadeh
The document discusses various molecular techniques used in genetic testing and molecular medical genetics laboratories, including:
1. DNA and RNA extraction from samples like blood and tissue.
2. Polymerase chain reaction (PCR) and its uses like detecting genetic mutations. Real-time PCR allows for quantitative analysis.
3. Techniques like ARMS-PCR, RFLP, and MLPA that can detect mutations, deletions, duplications to diagnose genetic disorders.
The techniques discussed have applications in prenatal diagnosis, forensic analysis like DNA fingerprinting, and identifying genetic causes of diseases. Molecular genetics labs aim to precisely analyze genetic information at the DNA and RNA level.
The document provides a 6-step guide to designing sgRNA-coding oligonucleotides for CRISPR experiments. It explains that the oligos are used to clone DNA sequences encoding sgRNA into plasmids. The 6 steps are: 1) determine the genomic target sequence, 2) add the PAM sequence, 3) design the spacer sequence, 4) add overhangs to the spacer for the top oligo, 5) design the bottom oligo as the complement, and 6) check that the oligos anneal correctly. Verification steps are also recommended to check for errors like confusing DNA and RNA sequences.
This document provides instructions for transforming Lactobacillus bacteria using electroporation. Key steps include inoculating a bacterial culture, harvesting cells at stationary phase, washing the cells, subjecting them to thermal shock and electroporation with plasmid DNA, incubating to allow for expression, then plating and selecting for transformed colonies using antibiotic resistance. The overall process is estimated to take 3-4 days.
Similar to Fast qPCR assay optimization and validation techniques for HTS (20)
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
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- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
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Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Muscles of Mastication by Dr. Rabia Inam Gandapore.pptx
Fast qPCR assay optimization and validation techniques for HTS
1. Fast qPCR assay
optimization and validation
techniques for HTS
Francisco Bizouarn
International Field Application Specialist
Gene Expression Division
Bio-Rad Laboratories
2. Generating a good assay is easy
AMPLIFICATION
• Following a few simple steps:
– Design assay
– Run a gradient
– Run a dilution series to validate
assay dynamic range
• A little extra effort in the beginning
will make a tremendous amount of
difference in the analysis when the
assay is run hundreds or
thousands of times.
www.bio-rad.com/genomics/pcrsupport
3. Assay design
AMPLIFICATION
• Often oversimplified by the use of software or by
many companies that offer design services and
softwares.
• Design a critical parameter.
• Following a few simple steps will increase the
chances of designing a successful assay.
• Let’s use an example: target CCL26 in HUVEC cells
www.bio-rad.com/genomics/pcrsupport
5. Sequence Alignment (BLAST)
AMPLIFICATION
• Prior to designing primers, it’s
a good idea to run a
sequence homology analysis.
(BLAST)
• This allows the identification
of sequences that may co-
amplify or interfere with our
intended target.
• The data is freely available,
so why not make use of it.
• http://blast.ncbi.nlm.nih.gov
www.bio-rad.com/genomics/pcrsupport
8. 2nd structure analysis of CCL26
AMPLIFICATION
• DNA is often seen as a linear
polymer.
• In it’s single stranded state
(cDNA) regions that have
complimentary sequences will
tend to hybridize generating
hairpins that may inhibit
primer annealing.
• Avoiding these sequences
when possible will improve
amplification effiecency.
• http://mfold.bioinfo.rpi.edu/cgi-bin/dna-
form1.cgi
www.bio-rad.com/genomics/pcrsupport
11. Amplicon size
AMPLIFICATION
• Classic qPCR rules dictate that amplification products be
between 75 and 200 bp in length.
• These limits are not absolute. It is better to design a larger
amplicon than to risk target specificity and primer annealing
issues
• New “ultra fast” reagents allow much larger amplicons to be
used in qPCR.
www.bio-rad.com/genomics/pcrsupport
12. Design primers
AMPLIFICATION
• Some primer design packages will
take both sequence homology and
secondary structure issues into
account when designing assays.
• Due to the restrictions imposed on
the design software, they can fail.
• Although not recommended,
designing assays by “thumb” can be
performed.
GCGGAATCTT TTCTGAAGGC TACATGGACC
• There are also databases of freely
available primers and probes that
have been previously tested.
www.bio-rad.com/genomics/pcrsupport
14. Using Thermal Gradients
AMPLIFICATION
• Thermal optimization is often the first parameter an individual
using PCR will test to get the optimal reaction conditions.
• Unfortunately many qPCR users often ignore this parameter, as
though antiquated, in favor of more elaborate primer design
software packages.
• Finding the correct annealing temperature at which to run an
assay is critical.
www.bio-rad.com/genomics/pcrsupport
15. Assay optimization
AMPLIFICATION
For 1 Rev 1
5’ 3’
For 2 Rev 2
For 1 For 2
Rev 1 Rev 2
10o above
design
{
5o below
design
www.bio-rad.com/genomics/pcrsupport
37. How did they fare?
AMPLIFICATION
CCl26 amplified using Bio-Rad iQ SYBR Green Supermix: 5ul Assay 95oC 60sec / 50x95oC 10 sec 55-70oC 60 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
39. Primer Titration
AMPLIFICATION
• Primer concentration plays an important role in qPCR
amplification.
• Typical concentrations go from 200nM to 500nM but can vary
from 50nM to 800nM and sometimes higher.
• High primer concentrations dramatically increase the incidence
of non specific amplification and primer-dimers.
• Reasonably well designed assays work best at normal primer
concentrations
www.bio-rad.com/genomics/pcrsupport
40. 100nM each Primer
AMPLIFICATION
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
41. 100nM each Primer
AMPLIFICATION
Replicates Mean C(t) : 27.24
Standard Deviation : 0.284
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
42. 200nM each Primer
AMPLIFICATION
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
43. 200nM each Primer
AMPLIFICATION
Replicates Mean C(t) : 26.59
Standard Deviation : 0.184
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
44. 300nM each Primer
AMPLIFICATION
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
45. 300nM each Primer
AMPLIFICATION
Replicates Mean C(t) : 26.54
Standard Deviation : 0.185
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
46. 400nM each Primer
AMPLIFICATION
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
47. 400nM each Primer
AMPLIFICATION
Replicates Mean C(t) : 26.51
Standard Deviation : 0.269
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
48. 600nM each Primer
AMPLIFICATION
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
49. 600nM each Primer
AMPLIFICATION
Replicates Mean C(t) : 26.49
Standard Deviation : 0.233
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
50. 800nM each Primer
AMPLIFICATION
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
51. 800nM each Primer
AMPLIFICATION
Replicates Mean C(t) : 26.58
Standard Deviation : 0.193
CCl26 amplified using Bio-Rad SsoFast EVAGreen Supermix: 5ul Assay 98oC 30sec / 50x 95oC 1 sec 55-70oC 5 sec / melt analysis
www.bio-rad.com/genomics/pcrsupport
72. Large amplicons
AMPLIFICATION
• Classic qPCR rules dictate that amplification products be
between 75 and 200 bp in length.
• New “ultra fast” reagents allow much larger amplicons to be
used in qPCR.
• Extending the size of the amplicon should be considered when
trying to circumvent secondary structures, sequence homology
and unfavorable regions.
• Proper validation is required.
www.bio-rad.com/genomics/pcrsupport
73. Large amplicons – dynamic range
AMPLIFICATION
•B-Actin 1076 bp amplicon from plasmid
•109 to 10 copy per well 10 fold dilution
109 copies series
•5 ul asay run on CFX384 using Bio-
Rad’s SsoFast EVA Green Supermix
10 copies
•Protocol : 98oC 3 min
45 x 95oC 1 sec 66oC 5 sec
melt curve
www.bio-rad.com/genomics/pcrsupport
74. Large amplicons - sensitivity
AMPLIFICATION
•B-Actin 1076 bp amplicon from plasmid
•105 to 200 copy per well 2 fold dilution
series
105 copies
•5 ul asay run on CFX384 using Bio-
Rad’s SsoFast EVA Green Supermix
200 copies
•Protocol : 98oC 3 min
45 x 95oC 1 sec 66oC 5 sec
melt curve
www.bio-rad.com/genomics/pcrsupport
75. Sequence Homology
AMPLIFICATION
• Designing primers on a region of template sequence
homologous to another gene should be avoided if possible.
• When inevitable, a single primer can be designed to anneal on a
homologous region for a series of genes. The other primer
should annealing on a clean region or one that has no homology
with genes annealed by the first primer.
• Multiple primers should be designed and tested.
• If a single primer anneals multiple targets, it will generate a
linear amplification of DNA where as if both primers anneal, the
amplification will be exponential.
www.bio-rad.com/genomics/pcrsupport
80. Throughput
AMPLIFICATION
• The CFX384 real-time PCR
detection system brings flexibility
and ease of use to researchers
performing high-throughput real-
time PCR in a 384-well format.
• With up to 4-target detection,
unsurpassed thermal cycler
performance, and powerful, yet
easy-to-use software, the CFX384
system has been designed for the
way you work.
– FAST – shorten the time from
experiment setup to results
– FRIENDLY – a new standard for
ease of use, delivering data you
can trust with no maintenance
– FLEXIBLE – customize a set up
that fits individual laboratory needs
www.bio-rad.com/genomics/pcrsupport
81. Speed
AMPLIFICATION
SsoFast EvaGreen Supermix
Sso7d from Sulfolobus solfataricus
– 7kD, 63 aa.
– Thermostable (Tm >90°C)
– No sequence preference
– Binds to dsDNA (3-6 bp/protein molecule)
– Monomeric
• Minimal inhibition of PCR by use of
EvaGreen
• Higher activity
• Tolerant to PCR inhibitors
www.bio-rad.com/genomics/pcrsupport
82. Conclusions
AMPLIFICATION
• The key to speeding up any screening process begins with
proper design and optimization.
• qPCR assay optimization and dynamic range validation require
very little time and effort and help guarantee that the results will
be reproducible and comparable form experiment to experiment.
• If potentially interfering elements are discovered at the design
and optimization phases, they can be accounted for and
possible corrected.
• As demands for shorter run times increase, proper care in the
selection of reagents and instruments is required.
www.bio-rad.com/genomics/pcrsupport