Advanced Search and Flexible grammar tool for biologists to locate non functional coding sequence - cis regulatory modules in a genome along with the display of annotation
AgBioData: Complexity and Diversity of the Pan-Genome AgBioData
A pan-genome represents the full complement of diversity within a clade, or the union of all genes or SNPs across a representative selection of genomes. One of the first pan-genomes was that of Streptococcus agalactiae, introduced in 2005. Since then, with the acceleration of whole-genome sequencing technology, pan-genomes have been generated across a wide range of multicellular eukaryotes. This presentation will outline the history of pan-genomes, the categories of pan-genomes, advances in pan-genome assessment, and the challenges of representing the diversity of a taxonomic clade in complex eukaryotes.
This document discusses correlated data structures and methods for analyzing correlated binary outcome data, specifically generalized estimating equations (GEE) and generalized linear mixed models (GLMM). It begins with examples of correlated data and an overview of GEE and GLMM. It then compares GEE and GLMM, noting that GEE makes population-level inferences while GLMM allows for individual-level inferences. The document concludes by stating that both GEE and GLMM can be applied to genome-wide association studies (GWAS) to account for genetic correlations.
this is a presentation on molecular markers that include what is molecular marker, it's types, biochemical markets (alloenzyme), it's classification, data analysis and it's applications
This document provides an overview of sequence alignment methods. It discusses pairwise sequence alignment, including global alignment using Needleman-Wunsch and local alignment using Smith-Waterman. It also covers multiple sequence alignment, comparing progressive and iterative methods. Challenges with multiple sequence alignment are noted, such as computational expense and difficulty in scoring and placing gaps for distant sequences.
Omics related approaches for higher productivity and improved quality.pptxAnirudhTV
The document discusses using omics approaches to improve crop productivity and quality. It covers various omics fields including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. Examples are provided on applying these approaches in crops like rice, tomato, groundnut, and brassica to traits such as drought tolerance, nutrient enrichment, and reduced anti-nutrients. A case study on analyzing protein abundance changes in wheat cultivars under drought stress using proteomics is also mentioned.
Presentation carried out by Casandra Riera, researcher from the Translational Bioinformatics group at VHIR, for the course "Identification and analysis of sequence variants in sequencing
projects: fundamentals and tools"
AgBioData: Complexity and Diversity of the Pan-Genome AgBioData
A pan-genome represents the full complement of diversity within a clade, or the union of all genes or SNPs across a representative selection of genomes. One of the first pan-genomes was that of Streptococcus agalactiae, introduced in 2005. Since then, with the acceleration of whole-genome sequencing technology, pan-genomes have been generated across a wide range of multicellular eukaryotes. This presentation will outline the history of pan-genomes, the categories of pan-genomes, advances in pan-genome assessment, and the challenges of representing the diversity of a taxonomic clade in complex eukaryotes.
This document discusses correlated data structures and methods for analyzing correlated binary outcome data, specifically generalized estimating equations (GEE) and generalized linear mixed models (GLMM). It begins with examples of correlated data and an overview of GEE and GLMM. It then compares GEE and GLMM, noting that GEE makes population-level inferences while GLMM allows for individual-level inferences. The document concludes by stating that both GEE and GLMM can be applied to genome-wide association studies (GWAS) to account for genetic correlations.
this is a presentation on molecular markers that include what is molecular marker, it's types, biochemical markets (alloenzyme), it's classification, data analysis and it's applications
This document provides an overview of sequence alignment methods. It discusses pairwise sequence alignment, including global alignment using Needleman-Wunsch and local alignment using Smith-Waterman. It also covers multiple sequence alignment, comparing progressive and iterative methods. Challenges with multiple sequence alignment are noted, such as computational expense and difficulty in scoring and placing gaps for distant sequences.
Omics related approaches for higher productivity and improved quality.pptxAnirudhTV
The document discusses using omics approaches to improve crop productivity and quality. It covers various omics fields including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. Examples are provided on applying these approaches in crops like rice, tomato, groundnut, and brassica to traits such as drought tolerance, nutrient enrichment, and reduced anti-nutrients. A case study on analyzing protein abundance changes in wheat cultivars under drought stress using proteomics is also mentioned.
Presentation carried out by Casandra Riera, researcher from the Translational Bioinformatics group at VHIR, for the course "Identification and analysis of sequence variants in sequencing
projects: fundamentals and tools"
Visualizing the pan genome - Australian Society for Microbiology - tue 8 jul ...Torsten Seemann
Invited talk at the Australian Society for Microbiology Annual Conference 2014 on "FriPan" our tool for visualizing bacterial pan genomes across 10-100s of isolates.
This document provides an overview of 16S rRNA amplicon sequencing methodology. It discusses how environmental samples are used to extract mixed genomic DNA which is then amplified via PCR using primers targeting the 16S rRNA gene. The resulting PCR products are sequenced via next-generation sequencing. Sequence reads are processed, clustered into OTUs, and classified by matching to reference databases. Analysis of amplicon data can provide information on alpha and beta diversity of bacterial communities. While providing phylogenetic information, amplicon sequencing is limited compared to metagenomics in functional data obtained.
Gene pyramiding involves combining multiple genes from different parents to develop elite varieties with improved traits. Marker assisted selection can facilitate gene pyramiding by allowing breeders to select plants with desired gene combinations at an early stage. The document discusses strategies for gene pyramiding such as iterative hybridization and co-transformation, and how molecular markers can aid in selecting plants with target genes and pyramiding genes into a single variety more effectively.
The document describes the BLAST algorithm for comparing biological sequences. BLAST stands for Basic Local Alignment Search Tool. It allows for fast comparison of a query sequence against large databases. BLAST uses heuristics to find locally similar regions between sequences and scores alignments based on identities without considering gaps. This rapid approximation allows BLAST to be applied to search large databases on common computers, providing a significant improvement over previous algorithms. The document outlines the methods used in BLAST, including compiling high-scoring words from the query, scanning the database for hits, and extending hits to determine significant alignments. It also discusses evaluating the statistical significance of results and how parameters like word length and score thresholds can impact BLAST's speed and accuracy.
The quality of data is very important for various downstream analyses, such as sequence assembly, single nucleotide polymorphisms identification this ppt show parameters for
NGS Data quality check and Dataformat of top sequencing machine
Gene Action for Yield and its Attributes by Generation Mean Analysis in Brinj...AI Publications
Genetic studies assist the breeder in understanding the inheritance mechanism and enhance the efficiency of a breeding programme. Knowledge of gene action and their relative contribution in expression of character is of great importance. Eggplant yield depends on two components viz., fruit weight and number of fruits per plant. These traits are quantitative and therefore influenced by multiple genes. The objective of this study was to estimate the main gene effects (additive, dominance and digenic epistasis) and to determine the mode of inheritance for fruit Yield and its components. The generation mean analysis was employed in three crosses viz., Ac-2 x Annamalai, EP-45 x Annamalai and EP-89 X Annamalai to partition the genetic variance. Among the three crosses studied, the cross Ac-2 x Annamalai had complimentary type of epistasis along with significant additive gene effects and additive x additive interaction gene effects for all the three traits. Considering fruit yield per plant and its attributes, this cross was judged as the best cross for further selection programme.
This document provides an introduction to genomic selection for crop improvement. It discusses how genomic selection works and the steps involved, including creating a training population, genotyping and phenotyping the training population, model training, genotyping the breeding population, calculating genomic estimated breeding values, and making selection decisions. Some advantages of genomic selection are greater genetic gains per unit of time compared to phenotypic selection and the ability to select for low heritability traits. Factors that can affect the accuracy of genomic predicted breeding values include the prediction model used, population size, marker density and type, trait heritability, and number of causal variants. Genomic selection is being applied to plant breeding programs for traits like disease resistance and yield to help meet future food
The pET bacterial recombinant protein vector uses the T7 promoter system to drive strong but tightly controlled expression of recombinant proteins in E. coli, utilizing the lac operon and lac repressor to repress expression until IPTG is added, and it exists at a low copy number in E. coli to reduce leaky expression and optimize protein yield.
This document contains information about converting regular expressions to finite automata. It discusses Kleene's theorem, which states that any language that can be defined by a regular expression can also be defined by a finite automaton and vice versa. It then provides steps for converting a regular expression to an NFA-Λ and converting an NFA-Λ to a finite automaton. The document concludes by recommending reviewing a textbook chapter on these topics.
Global and local alignment in BioinformaticsMahmudul Alam
1. Global alignment finds the optimal alignment over the entire sequence length trying to match as many elements as possible, while local alignment finds the region of highest similarity between two sequences that may be of different lengths.
2. The Needleman-Wunsch algorithm is commonly used for global alignment using dynamic programming to find the optimal full sequence alignment with linear gap costs.
3. The Smith-Waterman algorithm is used for local sequence alignment to identify similar regions by calculating similarity scores and only retaining alignments with scores higher than a threshold.
Genome-wide association mapping identifies genomic regions associated with phenotypes by analyzing phenotypic and genotypic data. Phenotypic data includes traits like flowering time and yield, while genotypic data consists of genetic markers spanning the genome. Single nucleotide polymorphisms (SNPs) are commonly used markers. Association mapping fits statistical models to test for association between each SNP and the phenotype. Accounting for population structure and relatedness through mixed models reduces false positives. Significant associations between SNPs and traits suggest the SNP directly affects the trait or is linked to a causal variant. Results are visualized through Manhattan plots and QQ-plots.
De Bruijn graphs are directed graphs used to represent overlaps between sequences of symbols. They are constructed by splitting a DNA sequence into k-mers (subsequences of length k), creating nodes for each k-mer, and connecting nodes with edges where the k-mers overlap by k-1 nucleotides. De Bruijn graphs are commonly used for genome assembly from next-generation sequencing data by reconstructing the original sequence from the k-mers. They allow mapping short reads onto a reference genome despite the reads being shorter than the repeats within the genome.
Presentation about how to perform data processing for genomics data in population genetics and quantitative genetics studies. It explains how to process the reads, map them, get variants and quantify them. It also presents 25 common Linux commands that are required in order to interact with the Linux system and be able to run different tools.
Association mapping, GWAS, Mapping, natural population mappingMahesh Biradar
This document discusses association mapping for crop improvement. It explains that association mapping exploits historical recombination events in populations to map quantitative trait loci with greater precision than family-based linkage analysis. Association mapping can be applied to diverse populations and detect more alleles than bi-parental mapping. Genome-wide association studies allow for high-resolution mapping of traits down to the sequence level by leveraging linkage disequilibrium. Statistical methods must account for population structure and kinship to avoid false positives in association analyses.
This document discusses various methods for aligning and comparing biological sequences like DNA and proteins, including local vs global alignment, exact vs heuristic algorithms, and tools like BLAST, PSI-BLAST, and statistical tests for assessing the significance of sequence similarities. Local alignment finds short similar regions, while global alignment considers the full sequence length. Exact methods are rigorous but slow, while heuristics sacrifice completeness for speed. BLAST uses ungapped segment pairs to identify regions for gapped extension and alignment. PSI-BLAST iteratively reweights sequences based on multiple alignments to identify more distant homologs. Statistical tests compare observed sequence similarities to random expectations to assess biological significance.
Bioinformatics emerged from the marriage of computer science and molecular biology to analyze massive amounts of biological data, like that produced by the Human Genome Project. It uses algorithms and techniques from computer science to solve problems in molecular biology, like comparing genomic sequences to understand evolution. As genomic data exploded publicly, bioinformatics was needed to efficiently store, analyze, and make sense of this information, which has applications in molecular medicine, drug development, agriculture, and more.
Sequence alignment involves arranging biological sequences like DNA, RNA, or proteins to identify similar regions that may indicate functional, structural, or evolutionary relationships. There are two main types of sequence alignment: local alignment, which finds short, locally similar regions; and global alignment, which tries to match the full sequences. Sequence alignment is performed using algorithms like Needleman-Wunsch for global alignment and Smith-Waterman for local alignment. It can provide information about sequence homology and evolutionary relationships between sequences.
Visualizing the pan genome - Australian Society for Microbiology - tue 8 jul ...Torsten Seemann
Invited talk at the Australian Society for Microbiology Annual Conference 2014 on "FriPan" our tool for visualizing bacterial pan genomes across 10-100s of isolates.
This document provides an overview of 16S rRNA amplicon sequencing methodology. It discusses how environmental samples are used to extract mixed genomic DNA which is then amplified via PCR using primers targeting the 16S rRNA gene. The resulting PCR products are sequenced via next-generation sequencing. Sequence reads are processed, clustered into OTUs, and classified by matching to reference databases. Analysis of amplicon data can provide information on alpha and beta diversity of bacterial communities. While providing phylogenetic information, amplicon sequencing is limited compared to metagenomics in functional data obtained.
Gene pyramiding involves combining multiple genes from different parents to develop elite varieties with improved traits. Marker assisted selection can facilitate gene pyramiding by allowing breeders to select plants with desired gene combinations at an early stage. The document discusses strategies for gene pyramiding such as iterative hybridization and co-transformation, and how molecular markers can aid in selecting plants with target genes and pyramiding genes into a single variety more effectively.
The document describes the BLAST algorithm for comparing biological sequences. BLAST stands for Basic Local Alignment Search Tool. It allows for fast comparison of a query sequence against large databases. BLAST uses heuristics to find locally similar regions between sequences and scores alignments based on identities without considering gaps. This rapid approximation allows BLAST to be applied to search large databases on common computers, providing a significant improvement over previous algorithms. The document outlines the methods used in BLAST, including compiling high-scoring words from the query, scanning the database for hits, and extending hits to determine significant alignments. It also discusses evaluating the statistical significance of results and how parameters like word length and score thresholds can impact BLAST's speed and accuracy.
The quality of data is very important for various downstream analyses, such as sequence assembly, single nucleotide polymorphisms identification this ppt show parameters for
NGS Data quality check and Dataformat of top sequencing machine
Gene Action for Yield and its Attributes by Generation Mean Analysis in Brinj...AI Publications
Genetic studies assist the breeder in understanding the inheritance mechanism and enhance the efficiency of a breeding programme. Knowledge of gene action and their relative contribution in expression of character is of great importance. Eggplant yield depends on two components viz., fruit weight and number of fruits per plant. These traits are quantitative and therefore influenced by multiple genes. The objective of this study was to estimate the main gene effects (additive, dominance and digenic epistasis) and to determine the mode of inheritance for fruit Yield and its components. The generation mean analysis was employed in three crosses viz., Ac-2 x Annamalai, EP-45 x Annamalai and EP-89 X Annamalai to partition the genetic variance. Among the three crosses studied, the cross Ac-2 x Annamalai had complimentary type of epistasis along with significant additive gene effects and additive x additive interaction gene effects for all the three traits. Considering fruit yield per plant and its attributes, this cross was judged as the best cross for further selection programme.
This document provides an introduction to genomic selection for crop improvement. It discusses how genomic selection works and the steps involved, including creating a training population, genotyping and phenotyping the training population, model training, genotyping the breeding population, calculating genomic estimated breeding values, and making selection decisions. Some advantages of genomic selection are greater genetic gains per unit of time compared to phenotypic selection and the ability to select for low heritability traits. Factors that can affect the accuracy of genomic predicted breeding values include the prediction model used, population size, marker density and type, trait heritability, and number of causal variants. Genomic selection is being applied to plant breeding programs for traits like disease resistance and yield to help meet future food
The pET bacterial recombinant protein vector uses the T7 promoter system to drive strong but tightly controlled expression of recombinant proteins in E. coli, utilizing the lac operon and lac repressor to repress expression until IPTG is added, and it exists at a low copy number in E. coli to reduce leaky expression and optimize protein yield.
This document contains information about converting regular expressions to finite automata. It discusses Kleene's theorem, which states that any language that can be defined by a regular expression can also be defined by a finite automaton and vice versa. It then provides steps for converting a regular expression to an NFA-Λ and converting an NFA-Λ to a finite automaton. The document concludes by recommending reviewing a textbook chapter on these topics.
Global and local alignment in BioinformaticsMahmudul Alam
1. Global alignment finds the optimal alignment over the entire sequence length trying to match as many elements as possible, while local alignment finds the region of highest similarity between two sequences that may be of different lengths.
2. The Needleman-Wunsch algorithm is commonly used for global alignment using dynamic programming to find the optimal full sequence alignment with linear gap costs.
3. The Smith-Waterman algorithm is used for local sequence alignment to identify similar regions by calculating similarity scores and only retaining alignments with scores higher than a threshold.
Genome-wide association mapping identifies genomic regions associated with phenotypes by analyzing phenotypic and genotypic data. Phenotypic data includes traits like flowering time and yield, while genotypic data consists of genetic markers spanning the genome. Single nucleotide polymorphisms (SNPs) are commonly used markers. Association mapping fits statistical models to test for association between each SNP and the phenotype. Accounting for population structure and relatedness through mixed models reduces false positives. Significant associations between SNPs and traits suggest the SNP directly affects the trait or is linked to a causal variant. Results are visualized through Manhattan plots and QQ-plots.
De Bruijn graphs are directed graphs used to represent overlaps between sequences of symbols. They are constructed by splitting a DNA sequence into k-mers (subsequences of length k), creating nodes for each k-mer, and connecting nodes with edges where the k-mers overlap by k-1 nucleotides. De Bruijn graphs are commonly used for genome assembly from next-generation sequencing data by reconstructing the original sequence from the k-mers. They allow mapping short reads onto a reference genome despite the reads being shorter than the repeats within the genome.
Presentation about how to perform data processing for genomics data in population genetics and quantitative genetics studies. It explains how to process the reads, map them, get variants and quantify them. It also presents 25 common Linux commands that are required in order to interact with the Linux system and be able to run different tools.
Association mapping, GWAS, Mapping, natural population mappingMahesh Biradar
This document discusses association mapping for crop improvement. It explains that association mapping exploits historical recombination events in populations to map quantitative trait loci with greater precision than family-based linkage analysis. Association mapping can be applied to diverse populations and detect more alleles than bi-parental mapping. Genome-wide association studies allow for high-resolution mapping of traits down to the sequence level by leveraging linkage disequilibrium. Statistical methods must account for population structure and kinship to avoid false positives in association analyses.
This document discusses various methods for aligning and comparing biological sequences like DNA and proteins, including local vs global alignment, exact vs heuristic algorithms, and tools like BLAST, PSI-BLAST, and statistical tests for assessing the significance of sequence similarities. Local alignment finds short similar regions, while global alignment considers the full sequence length. Exact methods are rigorous but slow, while heuristics sacrifice completeness for speed. BLAST uses ungapped segment pairs to identify regions for gapped extension and alignment. PSI-BLAST iteratively reweights sequences based on multiple alignments to identify more distant homologs. Statistical tests compare observed sequence similarities to random expectations to assess biological significance.
Bioinformatics emerged from the marriage of computer science and molecular biology to analyze massive amounts of biological data, like that produced by the Human Genome Project. It uses algorithms and techniques from computer science to solve problems in molecular biology, like comparing genomic sequences to understand evolution. As genomic data exploded publicly, bioinformatics was needed to efficiently store, analyze, and make sense of this information, which has applications in molecular medicine, drug development, agriculture, and more.
Sequence alignment involves arranging biological sequences like DNA, RNA, or proteins to identify similar regions that may indicate functional, structural, or evolutionary relationships. There are two main types of sequence alignment: local alignment, which finds short, locally similar regions; and global alignment, which tries to match the full sequences. Sequence alignment is performed using algorithms like Needleman-Wunsch for global alignment and Smith-Waterman for local alignment. It can provide information about sequence homology and evolutionary relationships between sequences.
Machine learning techniques can help address several unsolved problems in structural bioinformatics, including predicting protein flexibility and binding sites. The document discusses using machine learning models like SVMs trained on structural data to predict flexibility regions and protein-protein interaction sites from sequence alone. It also presents challenges in defining protein domain boundaries and predicting other structural features from sequence.
This document discusses different types of sequence alignment methods used in bioinformatics to identify similarities between DNA, RNA, and protein sequences. It describes global and local alignment, which aim to identify conserved regions across entire or local subsequences. Pairwise alignment methods like dot matrix, dynamic programming, and word methods are used to compare two sequences. Multiple sequence alignment extends this to three or more sequences, using progressive, iterative, or dynamic programming approaches to infer evolutionary relationships.
The document provides an overview of computational methods for sequence alignment. It discusses different types of sequence alignment including global and local alignment. It also describes various methods for sequence alignment, such as dot matrix analysis, dynamic programming algorithms (e.g. Needleman-Wunsch, Smith-Waterman), and word/k-tuple methods. Scoring matrices like PAM and BLOSUM that are used for sequence alignments are also explained.
2008: Applied AIS - A Roadmap of AIS Research in Brazil and Sample ApplicationsLeandro de Castro
The document summarizes several artificial immune system (AIS) research projects from groups in Brazil. It describes applications of AIS for (1) text clustering using an immune-inspired biclustering algorithm, (2) spam detection using an innate and adaptive AIS, and (3) optimal power flow optimization using a cluster gradient-based AIS.
Performance Improvement of BLAST with Use of MSA Techniques to Search Ancesto...journal ijrtem
process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
Performance Improvement of BLAST with Use of MSA Techniques to Search Ancesto...IJRTEMJOURNAL
BLAST is most popular sequence alignment tool used to align bioinformatics patterns. It uses
local alignment process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
Bioinformatic tools analyze biological sequences to find similarities, domains, and coding regions. BLAST is a widely used tool that compares a query sequence to database sequences to find regions of similarity, helping scientists determine sequence function. Sequence alignment identifies similar character patterns between two or more sequences and can provide information about function, structure, and evolutionary relationships. CpG islands are regions of DNA where cytosine and guanine nucleotides frequently occur next to each other. Methylation of cytosines within CpG islands can regulate gene expression and is an epigenetic mechanism studied in cancer diagnosis.
Sequence alignment involves arranging DNA, RNA, or protein sequences to identify similar regions and discover functional, structural, and evolutionary relationships. It compares a reference sequence to a query sequence. Alignments reveal regions of similarity that unlikely occurred by chance and may indicate common ancestry. Global alignment looks for conserved regions across full sequences while local alignment finds local matches between subsequences. Pairwise alignment involves two sequences while multiple sequence alignment handles three or more. Dynamic programming and word methods are common algorithmic approaches to sequence alignment.
B.sc biochem i bobi u 3.1 sequence alignmentRai University
This document provides an outline of basic concepts in bioinformatics including sequence alignment, scoring alignments, inserting gaps, dynamic programming, and database searches. It discusses comparing biological sequences to determine similarity and homology for predicting gene/protein function and constructing phylogenies. Scoring matrices like BLOSUM and PAM are described for quantifying sequence similarity. Dynamic programming algorithms like Needleman-Wunsch and Smith-Waterman are summarized for global and local sequence alignment. Database search tools like FASTA and BLAST are introduced for searching sequence databases.
B.sc biochem i bobi u 3.1 sequence alignmentRai University
This document provides an outline of basic concepts in bioinformatics including sequence alignment, scoring alignments, inserting gaps, dynamic programming, and database searches. It discusses comparing biological sequences to determine similarity and homology for predicting gene and protein function, constructing phylogeny, and finding motifs. It describes scoring matrices, gap penalties, global and local alignment, and algorithms for database searches including FASTA and BLAST.
The document describes a probabilistic relational model called Pro Bic for identifying overlapping biclusters in gene expression data. Pro Bic models the relationships between genes, conditions, and expression levels using a probabilistic graphical model. It uses an expectation-maximization algorithm to estimate model parameters and assign genes and conditions to biclusters in an unsupervised manner. The model can handle noise, missing values, and identify multiple overlapping biclusters of various shapes without prior knowledge of their number or structure.
This document discusses Bayesian networks and their application in bioinformatics. It begins with an introduction to Bayesian networks, including how they can represent joint probability distributions and be used for classification. It then discusses learning Bayesian network structures from data and performing probabilistic inference. The document applies these concepts to analyzing microarray gene expression and drug activity data from cancer cell lines. It describes preprocessing the NCI60 dataset and learning a Bayesian network to model dependencies between genes, drugs and cancer types for purposes of target discovery.
Verify3D is a web-based tool that evaluates the correctness of a protein structure model based on its 3D structural profile. It works by assigning structural classes to residues based on their location and environment, then comparing the results to profiles of good protein structures. The tool generates plots representing the average and raw scores for each residue, with higher average scores across residues indicating a more correct model structure. Verify3D is useful for protein structure prediction as it can verify models based on how well their 3D profiles match their amino acid sequences.
This document discusses sequence alignment, which involves arranging biological sequences like DNA, RNA, or proteins to identify regions of similarity. It covers the basic concepts of sequence alignment including global versus local alignment and different methods like dot matrix, dynamic programming, and word-based approaches. Dynamic programming is highlighted as the most common algorithm that uses a scoring system to find the optimal alignment between two sequences.
5.4 mining sequence patterns in biological dataKrish_ver2
This document discusses methods for mining sequence patterns in biological data, including alignment algorithms and hidden Markov models. It covers pairwise and multiple sequence alignment algorithms like Needleman-Wunsch, Smith-Waterman, BLAST, and FASTA. Hidden Markov models are introduced as a method to find conserved patterns or features in long biological sequences, such as CpG islands. The document outlines how hidden Markov models incorporate states, transitions, and emission probabilities to represent probabilistic sequences and can be used for tasks like evaluation, decoding, and learning on biological sequence data.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Similar to Advanced Search Grammar Tool for locating non functional coding sequences in a genome (20)
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
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"Choosing proper type of scaling", Olena SyrotaFwdays
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Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
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The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
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Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
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Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
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AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
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From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
4. 5 to 20 ≈ 3KB Gene Sequences of length 5-20 bases (exist on either side of the gene) control the gene transcription. Such sequences often are over represented near the gene they regulate. They co-ordinate in controlling the gene transcription. It is believed such short motifs are highly preserved due to their functionality and are transferred across organisms with minor changes.
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20. Smith Waterman Algorithm Where D ij denotes the element in the matrix S ij represents the similarity score between two amino acids. The similarity value is obtained by the number of properties common between two amino acids. (32 bit vector is use with 32 nd bit denoting the gap bit. w k and w l represents penalty for introducing gap