Multiple sequence alignment (MSA) involves aligning more than two biological sequences, such as DNA, RNA, or protein sequences. There are several approaches to MSA, including progressive alignment and greedy algorithms. Progressive alignment works by first constructing pairwise alignments between the most similar sequences and then adding other sequences in order of similarity. This is guided by a phylogenetic tree. Greedy algorithms iteratively combine the most similar pair of sequences or profiles into a new sequence/profile to reduce the alignment problem size. Scoring MSA involves quantifying how well-conserved each column is, such as through matches, entropy, or sum-of-pairs scores.
This document provides an overview of pairwise sequence alignment and BLAST. It discusses how pairwise alignment works using substitution matrices to assign homology between sites. It demonstrates the dynamic programming approach to pairwise alignment calculation and describes how local alignments are identified. The document also introduces BLAST and how it uses word matching to rapidly identify similar sequences in a database and then performs local alignments on matching regions.
BLAST and FASTA are algorithms for searching sequence databases to find local alignments between a query sequence and database sequences, with BLAST providing faster searches and improved statistical analysis compared to FASTA. Both algorithms work by first identifying short exact matches between sequences and then extending these matches to identify longer regions of similarity. The algorithms model DNA and protein sequence alignments as coin tosses to determine the expected length of the longest matching region between random sequences.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
The document describes the quicksort algorithm. It covers the divide and conquer approach, partitioning the array around a pivot element, recursively sorting the subarrays, and combining the results. It provides pseudocode for quicksort and an example. It analyzes the time complexity in the best, worst, and average cases. The best case of O(n log n) occurs when the array is evenly partitioned on each recursive call. The worst case of O(n^2) happens when the partitioning produces very uneven subproblems. The average case is closer to the best case complexity of O(n log n).
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...ShuaiGao3
This assignment’s task is to analyze the egg depositions of Lake Huron Bloasters by using the analysis methods and choose the best model among a set of possible models for this data-set and give forecasts of egg depositions for the next 5 years. The data-set is collect from FSAdata package, we will directly use the eggs data provide by this data-set.
The document describes algorithms for sequence alignment, including the Needleman-Wunsch algorithm for global alignment and the Smith-Waterman algorithm for local alignment. It provides examples of filling in a scoring matrix according to the algorithms' recurrence relations and tracing back to find the optimal alignment between two sequences.
The document describes the Floyd-Warshall algorithm for finding shortest paths in a weighted graph. It discusses the all-pairs shortest path problem, previous solutions using Dijkstra's algorithm and dynamic programming, and then presents the Floyd-Warshall algorithm as another dynamic programming solution. The algorithm works by computing the shortest path between all pairs of vertices where intermediate vertices are restricted to a given set. It does this using a bottom-up computation in O(V^3) time and O(V^2) space.
The document discusses network modeling and analysis, specifically covering minimum spanning tree problems, shortest path problems, and maximum flow problems. It provides examples of Kruskal's algorithm to find minimum spanning trees and Floyd's algorithm to find shortest paths between nodes in a network. The document contains examples applying these algorithms to sample network graphs.
This document provides an overview of pairwise sequence alignment and BLAST. It discusses how pairwise alignment works using substitution matrices to assign homology between sites. It demonstrates the dynamic programming approach to pairwise alignment calculation and describes how local alignments are identified. The document also introduces BLAST and how it uses word matching to rapidly identify similar sequences in a database and then performs local alignments on matching regions.
BLAST and FASTA are algorithms for searching sequence databases to find local alignments between a query sequence and database sequences, with BLAST providing faster searches and improved statistical analysis compared to FASTA. Both algorithms work by first identifying short exact matches between sequences and then extending these matches to identify longer regions of similarity. The algorithms model DNA and protein sequence alignments as coin tosses to determine the expected length of the longest matching region between random sequences.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
The document describes the quicksort algorithm. It covers the divide and conquer approach, partitioning the array around a pivot element, recursively sorting the subarrays, and combining the results. It provides pseudocode for quicksort and an example. It analyzes the time complexity in the best, worst, and average cases. The best case of O(n log n) occurs when the array is evenly partitioned on each recursive call. The worst case of O(n^2) happens when the partitioning produces very uneven subproblems. The average case is closer to the best case complexity of O(n log n).
Time Series Analysis on Egg depositions (in millions) of age-3 Lake Huron Blo...ShuaiGao3
This assignment’s task is to analyze the egg depositions of Lake Huron Bloasters by using the analysis methods and choose the best model among a set of possible models for this data-set and give forecasts of egg depositions for the next 5 years. The data-set is collect from FSAdata package, we will directly use the eggs data provide by this data-set.
The document describes algorithms for sequence alignment, including the Needleman-Wunsch algorithm for global alignment and the Smith-Waterman algorithm for local alignment. It provides examples of filling in a scoring matrix according to the algorithms' recurrence relations and tracing back to find the optimal alignment between two sequences.
The document describes the Floyd-Warshall algorithm for finding shortest paths in a weighted graph. It discusses the all-pairs shortest path problem, previous solutions using Dijkstra's algorithm and dynamic programming, and then presents the Floyd-Warshall algorithm as another dynamic programming solution. The algorithm works by computing the shortest path between all pairs of vertices where intermediate vertices are restricted to a given set. It does this using a bottom-up computation in O(V^3) time and O(V^2) space.
The document discusses network modeling and analysis, specifically covering minimum spanning tree problems, shortest path problems, and maximum flow problems. It provides examples of Kruskal's algorithm to find minimum spanning trees and Floyd's algorithm to find shortest paths between nodes in a network. The document contains examples applying these algorithms to sample network graphs.
This document discusses the inverse Laplace transform. It defines the inverse Laplace transform and provides some key properties and theorems. It then gives examples of taking the inverse Laplace transform of various functions and using Laplace transforms to solve initial value problems involving differential equations. It also defines the unit step function and discusses its properties. Finally, it presents a convolution theorem relating the inverse Laplace transform of a product of functions to a convolution integral.
Merge sort is a sorting technique based on divide and conquer technique. With worst-case time complexity being Ο(n log n), it is one of the most respected algorithms.
Merge sort first divides the array into equal halves and then combines them in a sorted manner.
PROPERTIES OF LAPLACE TRANSFORM part 2Ranit Sarkar
Contents
SECOND SHIFTING THROEM and its example.
DERIVATIVE PROPERTY (multiplication by t property) with examples.
INTEGRAL PROPERTY (division by t property) with examples.
Partial fraction decomposition for inverse laplace transformVishalsagar657
This document discusses partial fraction decomposition for inverse Laplace transforms. It begins with an introduction to partial fraction decomposition and why it is useful for integration. It then covers various cases for partial fraction decomposition of inverse Laplace transforms, including when the denominator is a quadratic with two real roots, a double root, or complex conjugate roots. It also covers the case when the denominator is a cubic with one real and two complex conjugate roots. The goal is to decompose the function into simpler forms that can be easily inverted using the Laplace transform table.
John von Neumann invented the merge sort algorithm in 1945. Merge sort follows the divide and conquer paradigm by dividing the unsorted list into halves, recursively sorting each half through merging, and then merging the sorted halves back into a single sorted list. The time complexity of merge sort is O(n log n) in all cases (best, average, worst) due to its divide and conquer approach, while its space complexity is O(n) to store the temporary merged list.
Bioinformatics uses databases and computers to answer biological questions through activities like annotating biological data and manipulating information. It involves fields like molecular biology, genomics, systems biology, and more. The history of bioinformatics began in the pre-1970s with early sequence alignment algorithms and studies on protein structure and evolution. It expanded in the 1970s and 1980s with more advanced algorithms and resources for tasks like sequence analysis, molecular databases, and protein structure prediction. The course will cover topics like biological databases, nucleotide sequence analysis, statistical methods, protein structure modeling, and applications like genomics, proteomics, and drug design.
1) The document describes steps in a bioinformatics analysis pipeline that uses BLAST and CLUSTALW to perform sequence alignment and clustering on a query protein sequence from Homo sapiens (hsa:6469).
2) Each step is contained in a separate Ruby script (step10.rb through step60.rb) that retrieves data through REST/SOAP calls and passes output through text files.
3) The final step performs a multiple sequence alignment of BLAST hits passing the E-value threshold using CLUSTALW through a SOAP call and outputs the result.
Proteins are polymers made of amino acids linked together, where the 20 common amino acids differ in their side chains. Amino acids can have different configurations and proteins have multiple levels of structure from primary to quaternary. Alignment methods like CLUSTALW are used to compare protein sequences in an iterative fashion.
Clustal X help to the Bioinformatics candidate to predicts the Multiple Sequence Alignment and Phylogenetic Analysis for given a nuber of Gene Sequences of varrious organism,and find the evolutionary relationship.
This document provides an overview of multiple sequence alignment (MSA). MSA is used to align biological sequences, such as DNA, RNA, or proteins, to find similarities and differences between sequences. The document outlines the goal of MSA as finding structural, functional, and evolutionary relationships. It describes the general considerations and steps of MSA, including pairwise comparison of sequences, cluster analysis to generate a hierarchy, and progressive alignment. Common MSA software and applications are also summarized.
The document discusses multiple sequence alignment methods. It describes ClustalW, a commonly used progressive alignment method that first performs pairwise alignments of sequences and constructs a guide tree before progressively aligning sequences based on the tree. ClustalW is fast but has limitations as it is a heuristic that may not find the optimal alignment and provides no way to quantify alignment accuracy.
This document discusses multiple sequence alignment and compares it to pairwise sequence alignment. It introduces the concept of multiple alignment and describes how algorithms like CLUSTAL extend pairwise alignment approaches to align three or more sequences simultaneously. CLUSTAL is highlighted as a popular heuristic algorithm that uses progressive alignment to build a multiple sequence alignment in an efficient manner.
Multiple sequence alignment is used to determine evolutionary relationships and structural relationships between sequences. It provides information on the most similar regions between sequences and can predict specific probes. Multiple sequence alignment extends pairwise sequence alignment through dynamic programming to align three or more sequences simultaneously. Popular multiple sequence alignment programs like CLUSTALW use progressive alignment methods that first align closely related sequences, then progressively align more distantly related sequences.
This document discusses multiple sequence alignment (MSA), which involves aligning more than two biological sequences, such as DNA, RNA, or protein sequences. MSA can reveal subtle similarities between sequences that pairwise alignment cannot by identifying conserved regions present in many sequences. The document describes different approaches to MSA, including optimal global alignments using dynamic programming, progressive alignments, and iterative alignments. It notes challenges like computational expense and difficulty of scoring and identifying ancestry relationships with more divergent sequences. The dynamic programming approach to aligning three sequences using a 3D "Manhattan cube" representation is also explained.
This document discusses multiple sequence alignment (MSA), which involves aligning more than two biological sequences, such as DNA, RNA, or protein sequences. MSA can reveal subtle similarities between sequences that pairwise alignment does not show. The document describes different approaches to MSA, including optimal global alignments using dynamic programming, progressive alignments, and iterative alignments. It notes challenges like computational expense and difficulty of scoring and identifying ancestry relationships with more divergent sequences. The dynamic programming approach to aligning three sequences is also explained.
The document provides an overview of sequence alignment, including:
- The task of sequence alignment is to determine the correspondences between substrings in sequences that maximize a similarity score.
- Alignment allows inference of homology and function based on sequence similarity.
- Key issues include variable sequence lengths, small matching regions, and modeling substitutions and gaps.
- Dynamic programming is used to find optimal global and local alignments in quadratic time by solving subproblems and reusing results.
This document discusses sequence alignment and contains four sections:
1) Global alignment which finds the highest scoring alignment between entire sequences using dynamic programming.
2) Scoring matrices which generalize alignment scoring by assigning scores to individual character matches/mismatches based on biological evidence.
3) Local alignment which finds the best scoring alignment between substrings of sequences to identify conserved regions, as global alignment may miss these.
4) Ways to solve the local alignment problem efficiently in quadratic time instead of quartic time by computing alignments from each vertex in the grid.
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 Needleman-Wunsch Algorithm for Sequence Alignment Parinda Rajapaksha
Presentation based on the following research paper.
“A General method applicable to the search for similarities in the amino acid sequence of two proteins”
Presentation structured as,
1. Introduction
-Sequence Alignment
-Approaches
-Needleman-Wunch Algorithm vs. Dynamic Programming
2. Example
-Optimal Alignment Score
-Optimal Alignment
-Algorithm Cost
3. Workout
4. Application
-Results & Discussion
-Methodology
-Usefulness
With thanks to Contributors; Mr. Kasun Bandara and Ms. Kavindri Dilshani
This document discusses dot plots, which are graphical methods for comparing biological sequences and identifying similar regions. It provides background on dot plots, including their history, principles, examples, interpretation, and applications. Key points covered include how dot plots represent sequences along axes and plot dots to indicate identities, different features that can be observed in dot plots like direct repeats and palindromes, and how dot plots can be used to find sequence alignments and gene locations. Limitations and different scoring schemes for dot plots are also summarized.
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.
This document discusses the inverse Laplace transform. It defines the inverse Laplace transform and provides some key properties and theorems. It then gives examples of taking the inverse Laplace transform of various functions and using Laplace transforms to solve initial value problems involving differential equations. It also defines the unit step function and discusses its properties. Finally, it presents a convolution theorem relating the inverse Laplace transform of a product of functions to a convolution integral.
Merge sort is a sorting technique based on divide and conquer technique. With worst-case time complexity being Ο(n log n), it is one of the most respected algorithms.
Merge sort first divides the array into equal halves and then combines them in a sorted manner.
PROPERTIES OF LAPLACE TRANSFORM part 2Ranit Sarkar
Contents
SECOND SHIFTING THROEM and its example.
DERIVATIVE PROPERTY (multiplication by t property) with examples.
INTEGRAL PROPERTY (division by t property) with examples.
Partial fraction decomposition for inverse laplace transformVishalsagar657
This document discusses partial fraction decomposition for inverse Laplace transforms. It begins with an introduction to partial fraction decomposition and why it is useful for integration. It then covers various cases for partial fraction decomposition of inverse Laplace transforms, including when the denominator is a quadratic with two real roots, a double root, or complex conjugate roots. It also covers the case when the denominator is a cubic with one real and two complex conjugate roots. The goal is to decompose the function into simpler forms that can be easily inverted using the Laplace transform table.
John von Neumann invented the merge sort algorithm in 1945. Merge sort follows the divide and conquer paradigm by dividing the unsorted list into halves, recursively sorting each half through merging, and then merging the sorted halves back into a single sorted list. The time complexity of merge sort is O(n log n) in all cases (best, average, worst) due to its divide and conquer approach, while its space complexity is O(n) to store the temporary merged list.
Bioinformatics uses databases and computers to answer biological questions through activities like annotating biological data and manipulating information. It involves fields like molecular biology, genomics, systems biology, and more. The history of bioinformatics began in the pre-1970s with early sequence alignment algorithms and studies on protein structure and evolution. It expanded in the 1970s and 1980s with more advanced algorithms and resources for tasks like sequence analysis, molecular databases, and protein structure prediction. The course will cover topics like biological databases, nucleotide sequence analysis, statistical methods, protein structure modeling, and applications like genomics, proteomics, and drug design.
1) The document describes steps in a bioinformatics analysis pipeline that uses BLAST and CLUSTALW to perform sequence alignment and clustering on a query protein sequence from Homo sapiens (hsa:6469).
2) Each step is contained in a separate Ruby script (step10.rb through step60.rb) that retrieves data through REST/SOAP calls and passes output through text files.
3) The final step performs a multiple sequence alignment of BLAST hits passing the E-value threshold using CLUSTALW through a SOAP call and outputs the result.
Proteins are polymers made of amino acids linked together, where the 20 common amino acids differ in their side chains. Amino acids can have different configurations and proteins have multiple levels of structure from primary to quaternary. Alignment methods like CLUSTALW are used to compare protein sequences in an iterative fashion.
Clustal X help to the Bioinformatics candidate to predicts the Multiple Sequence Alignment and Phylogenetic Analysis for given a nuber of Gene Sequences of varrious organism,and find the evolutionary relationship.
This document provides an overview of multiple sequence alignment (MSA). MSA is used to align biological sequences, such as DNA, RNA, or proteins, to find similarities and differences between sequences. The document outlines the goal of MSA as finding structural, functional, and evolutionary relationships. It describes the general considerations and steps of MSA, including pairwise comparison of sequences, cluster analysis to generate a hierarchy, and progressive alignment. Common MSA software and applications are also summarized.
The document discusses multiple sequence alignment methods. It describes ClustalW, a commonly used progressive alignment method that first performs pairwise alignments of sequences and constructs a guide tree before progressively aligning sequences based on the tree. ClustalW is fast but has limitations as it is a heuristic that may not find the optimal alignment and provides no way to quantify alignment accuracy.
This document discusses multiple sequence alignment and compares it to pairwise sequence alignment. It introduces the concept of multiple alignment and describes how algorithms like CLUSTAL extend pairwise alignment approaches to align three or more sequences simultaneously. CLUSTAL is highlighted as a popular heuristic algorithm that uses progressive alignment to build a multiple sequence alignment in an efficient manner.
Multiple sequence alignment is used to determine evolutionary relationships and structural relationships between sequences. It provides information on the most similar regions between sequences and can predict specific probes. Multiple sequence alignment extends pairwise sequence alignment through dynamic programming to align three or more sequences simultaneously. Popular multiple sequence alignment programs like CLUSTALW use progressive alignment methods that first align closely related sequences, then progressively align more distantly related sequences.
This document discusses multiple sequence alignment (MSA), which involves aligning more than two biological sequences, such as DNA, RNA, or protein sequences. MSA can reveal subtle similarities between sequences that pairwise alignment cannot by identifying conserved regions present in many sequences. The document describes different approaches to MSA, including optimal global alignments using dynamic programming, progressive alignments, and iterative alignments. It notes challenges like computational expense and difficulty of scoring and identifying ancestry relationships with more divergent sequences. The dynamic programming approach to aligning three sequences using a 3D "Manhattan cube" representation is also explained.
This document discusses multiple sequence alignment (MSA), which involves aligning more than two biological sequences, such as DNA, RNA, or protein sequences. MSA can reveal subtle similarities between sequences that pairwise alignment does not show. The document describes different approaches to MSA, including optimal global alignments using dynamic programming, progressive alignments, and iterative alignments. It notes challenges like computational expense and difficulty of scoring and identifying ancestry relationships with more divergent sequences. The dynamic programming approach to aligning three sequences is also explained.
The document provides an overview of sequence alignment, including:
- The task of sequence alignment is to determine the correspondences between substrings in sequences that maximize a similarity score.
- Alignment allows inference of homology and function based on sequence similarity.
- Key issues include variable sequence lengths, small matching regions, and modeling substitutions and gaps.
- Dynamic programming is used to find optimal global and local alignments in quadratic time by solving subproblems and reusing results.
This document discusses sequence alignment and contains four sections:
1) Global alignment which finds the highest scoring alignment between entire sequences using dynamic programming.
2) Scoring matrices which generalize alignment scoring by assigning scores to individual character matches/mismatches based on biological evidence.
3) Local alignment which finds the best scoring alignment between substrings of sequences to identify conserved regions, as global alignment may miss these.
4) Ways to solve the local alignment problem efficiently in quadratic time instead of quartic time by computing alignments from each vertex in the grid.
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 Needleman-Wunsch Algorithm for Sequence Alignment Parinda Rajapaksha
Presentation based on the following research paper.
“A General method applicable to the search for similarities in the amino acid sequence of two proteins”
Presentation structured as,
1. Introduction
-Sequence Alignment
-Approaches
-Needleman-Wunch Algorithm vs. Dynamic Programming
2. Example
-Optimal Alignment Score
-Optimal Alignment
-Algorithm Cost
3. Workout
4. Application
-Results & Discussion
-Methodology
-Usefulness
With thanks to Contributors; Mr. Kasun Bandara and Ms. Kavindri Dilshani
This document discusses dot plots, which are graphical methods for comparing biological sequences and identifying similar regions. It provides background on dot plots, including their history, principles, examples, interpretation, and applications. Key points covered include how dot plots represent sequences along axes and plot dots to indicate identities, different features that can be observed in dot plots like direct repeats and palindromes, and how dot plots can be used to find sequence alignments and gene locations. Limitations and different scoring schemes for dot plots are also summarized.
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.
Phylogenetics is the study of evolutionary history and relationships between taxa. Phylogenetic trees present relationships as a collection of nodes and branches, with closely related taxa appearing near each other. Multiple sequence alignment (MSA) is used to reveal biological facts about sequences and to construct phylogenetic trees. However, MSA is computationally complex due to the exponential growth in possible alignments as more sequences are added.
This document discusses global and local sequence alignment. Global alignment aims to align the entire sequences, treating gaps equally across the sequences. It is useful for closely related sequences of similar length. Local alignment finds locally similar regions, allowing gaps to be treated differently. It is useful for more distantly related sequences that may contain similar subsequences. Both use dynamic programming, with global alignment using Needleman-Wunsch and local using Smith-Waterman. Dynamic programming breaks the problem into subproblems by filling a matrix to find the highest scoring alignment.
The document discusses pairwise sequence alignment methods. It defines key concepts like homology and orthology. It explains that dynamic programming is used to find optimal alignments through building a score matrix and backtracking. Global alignment finds the best match over full sequences while local alignment identifies regions of local similarity. Scoring systems like PAM matrices assign values based on substitutions and penalties for gaps.
This document summarizes key concepts in sequence alignment including:
1) Sequence alignment involves finding the linear correspondence between symbols in one sequence to another that maximizes similarity. Dynamic programming is commonly used to compute optimal alignments.
2) BLAST is an extremely fast database search tool that uses heuristics like word matching to find local alignments and statistical analysis to assess significance.
3) Multiple sequence alignments make conserved features more apparent but are more difficult to compute than pairwise alignments. Progressive alignment gradually merges pairwise alignments based on a phylogenetic tree.
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.
1) Pairwise sequence alignment is a method to compare two biological sequences like DNA, RNA, or proteins. It involves arranging the sequences in columns to highlight their similarities and differences.
2) There are many possible alignments between two sequences, but most imply too many mutations. The best alignment minimizes the number of mutations needed to explain the differences between the sequences.
3) For short protein sequences like "QKGSYPVRSTC" and "QKGSGPVRSTC", the optimal alignment implies one single mutation occurred since the sequences diverged from a common ancestor.
The W-curve converts DNA character sequences to 3D geometry. This geometry can be stored in PostGIS and queried to perform fuzzy matching on the sequences. The alignments can be multi-pass to handle sub-sequences.
Sequence alignment involves comparing two or more sequences to identify regions of similarity. It can be used to determine if genes or proteins are evolutionarily related or identify structurally similar regions. The alignment process involves placing the sequences side by side and introducing gaps to maximize matches. Dynamic programming algorithms like Needleman-Wunsch calculate the optimal global alignment through matrix initialization, scoring, and traceback steps.
The document discusses various clustering techniques. It begins by introducing clustering and some common techniques, including partitioning algorithms like k-means, hierarchical algorithms, and density-based algorithms. It then focuses on explaining the k-means algorithm in detail, providing pseudocode and an example application with 16 data points. Key aspects of k-means discussed include initializing cluster centroids, calculating distances between data points and centroids, assigning points to clusters, updating centroids, and determining convergence. The document concludes by analyzing pros and cons of k-means, such as the need to specify the number of clusters k and properly select initial centroids to avoid local optima.
The document discusses genome assembly from sequencing reads. It describes how reads can be aligned to a reference genome if available, but for a new genome the reads must be assembled without a reference. Two main assembly approaches are described: overlap-layout-consensus which builds an overlap graph, and de Brujin graph assembly which constructs a de Brujin graph from k-mers. Both approaches aim to find contiguous sequences (contigs) from the reads but face challenges from computational complexity and sequencing errors in the reads.
Sequence analysis involves subjecting DNA, RNA, or peptide sequences to analytical methods to understand their features, functions, structures, or evolution. The most basic sequence analysis is sequence alignment, which involves aligning two sequences and measuring their similarity to determine if they are related. Sequence alignment is important for biological inferences like predicting the function and structure of unknown proteins from similar sequences or determining evolutionary relationships between sequences from different species.
The document describes string comparison techniques using matrix algebra and seaweed matrices. It introduces the concept of semi-local string comparison, which involves comparing a whole string to substrings of another string. The key idea is representing string comparison matrices implicitly using seaweed matrices, which represent unit-Monge matrices. This allows developing algebraic techniques for efficiently multiplying such matrices using the algebra of braids and the seaweed monoid. These multiplication techniques can then be applied to problems like dynamic programming string comparison and comparing compressed strings.
The document provides an overview of the KNIME analytics platform and its capabilities. It discusses:
- KNIME's origins, offices, codebase, and application areas including pharma, healthcare, finance, retail, and more.
- The key components of the KNIME platform including data access, transformation, analysis, visualization, and deployment capabilities.
- Integrations with tools like R, Weka, databases, and file formats.
- Community contributions expanding KNIME's functionality in areas like bioinformatics, chemistry, image processing, and more.
Ядерный век прошел, и становится все понятнее, что в фокусе науки 21-го века будут живые системы, медицина, и человек во всех его проявлениях. Здесь осуществляются самые масштабные финансовые вливания, и на эту отрасль человечество возлагает самые большие надежды. Все чаще слышатся предметные обсуждения тем, казавшихся еще недавно научной фантастикой: сможет ли человечество победить старение, рак, и другие смертельные заболевания? Сможет ли менять свой геном по собственному желанию? Будем ли мы хозяевами своим телам в той же мере, как мы хозяйничаем на Земле?
Многие десятилетия биология и медицина развивались как описательные науки. Однако по мере созревания и накопления информации, любая наука рано или поздно переходит на более точный язык - язык математики. Проект "Геном человека" обеспечил технологический прорыв, который будет питать науку о живом еще много лет - но который также поставил много новых глобальных вопросов перед современными учеными.
Иммунотерапия раковых опухолей: взгляд со стороны системной биологии. Максим ...BioinformaticsInstitute
This document summarizes recent advances in cancer immunotherapy from the perspective of systems biology. It discusses how checkpoint blockade immunotherapy works by addressing the second co-inhibitory checkpoint signal needed for T cell activation. Computational methods are now able to identify tumor-specific neoantigens that can be targeted by immunotherapy. Mouse model studies showed that certain tumors are naturally rejected due to expression of a mutant antigen recognized by T cells, and that antigen-specific T cells are present before immunotherapy treatment. The high mutational load in melanoma makes it particularly responsive to checkpoint blockade. Early work in the 19th century by William Coley observed tumor regression following bacterial infection, which led to development of a toxin mixture that resembled modern vaccine formulations. Members of
http://bioinformaticsinstitute.ru/guests
В пятницу 10 октября в 19.00 Мария Шутова (ИоГЕН РАН) выступала в Институте биоинформатики с открытой лекцией, посвященной изучению рака.
Рак -- одна из наиболее распространенных причин смерти по всему миру. В лекции рассматривается, как знания об эволюции, работе генома, репрограммировании, а также использование биоинформатических методов помогли лучше понять, как развивается раковая опухоль и предложить новые методы лечения разнообразных типов рака. Рассмотрены мышиные модели развития рака и интересные результаты, которые были получены с их помощью.
http://bioinformaticsinstitute.ru/lectures
Гостевая лекция Института биоинформатики, 9 октября 2014. Лектор -- Мария Шутова (ИоГЕН РАН).
За последние десять лет плюрипонтентные клетки стали героями двух Нобелевских премий и многих тысяч научных и научно-популярных статей. Их уникальная возможность превращаться в любую клетку взрослого организма до сих пор дает пищу для ума как биологам развития, так и ученым, ищущим способы лечения генетических заболеваний. В лекции будет рассказано о двух типах плюрипотентных клеток: "естественных" (эмбриональные стволовые клетки) и "искусственных" (индуцированные плюрипотентные стволовые клетки). Отдельно мы остановимся на том, как знания о работе транскрипционных факторов помогли репрограммировать клетки, и как эти "искусственные" плюрипотентные клетки можно использовать в медицине.
Секвенирование как инструмент исследования сложных фенотипов человека: от ген...BioinformaticsInstitute
This document summarizes genetic analyses of complex human phenotypes. It describes whole genome sequencing of individuals from bipolar disorder families and finding an association between genetic variation in a chromosome 6 region and amygdala volume. It also discusses rare variant sequencing of metabolic syndrome-related genes in Finnish cohorts, identifying new signals beyond existing GWAS hits. Additionally, it outlines exome and targeted sequencing of Tourette syndrome pedigrees, with a genome-wide significant result in a long non-coding RNA gene linked to the trait.
В своей лекции Андрей Афанасьев рассказал о стартапах в биотехе и биоинформатике и своем биоинформатическом проекте iBinom, разобрал несколько биотехнологических проектов глазами инноваторов и инвесторов, а также коснулся вопроса поиска инвестиций и поделился личным опытом взаимодействия с венчурными фондами и институтами развития.
This document provides an overview of the ENCODE project and how its data can be accessed through the UCSC Genome Browser. It discusses the different types of ENCODE data available, including mapping data, gene annotations, expression data, regulatory information, and genetic variation. It also explains how to find, view, and download ENCODE tracks from the Genome Browser and where to get more information about ENCODE. The overall goal of the ENCODE project is to identify all functional elements in the human genome.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
[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.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
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/
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
5. Multiple Sequence Alignment (MSA)
• Up until now we have only
tried to align two sequences.
• What about aligning more
than two sequences?
6. Multiple Sequence Alignment (MSA)
• Up until now we have only
tried to align two sequences.
• What about aligning more
than two sequences?
• A faint similarity between
two sequences becomes
significant if it is present in
many other sequences.
7. Multiple Sequence Alignment (MSA)
• Up until now we have only
tried to align two sequences.
• What about aligning more
than two sequences?
• A faint similarity between
two sequences becomes
significant if it is present in
many other sequences.
• Therefore multiple
alignments can reveal subtle
similarities that pairwise
alignments do not reveal.
8. Generalizing Pairwise to Multiple Alignment
• Alignment of 2 sequences is represented as a 2-row matrix.
• In a similar way, we represent alignment of 3 sequences as a
3-row matrix
• Example:
A T - G C G -
A - C G T - A
A T C A C - A
• Our scoring function should score alignments with conserved
columns higher.
9. A A T -- C
A -- T G C
-- A T G C
Alignments = Paths in 3-Space
• Say we have 3 sequences to align: ATGC, AATC, ATGC
10. 0 1 1 2 3 4
A A T -- C
A -- T G C
-- A T G C
x coordinate
Alignments = Paths in 3-Space
• Say we have 3 sequences to align: ATGC, AATC, ATGC
11. 0 1 1 2 3 4
0 1 2 3 3 4
A A T -- C
A -- T G C
-- A T G C
y coordinate
Alignments = Paths in 3-Space
• Say we have 3 sequences to align: ATGC, AATC, ATGC
x coordinate
12. 0 1 1 2 3 4
0 1 2 3 3 4
A A T -- C
A -- T G C
0 0 1 2 3 4
-- A T G C
x coordinate
y coordinate
z coordinate
Alignments = Paths in 3-Space
• Say we have 3 sequences to align: ATGC, AATC, ATGC
• Plotting the coordinates gives a path in 3-space:
13. 0 1 1 2 3 4
0 1 2 3 3 4
A A T -- C
A -- T G C
0 0 1 2 3 4
-- A T G C
x coordinate
y coordinate
z coordinate
Alignments = Paths in 3-Space
• Say we have 3 sequences to align: ATGC, AATC, ATGC
• Plotting the coordinates gives a path in 3-space:
• (0,0,0)(1,1,0)(1,2,1) (2,3,2) (3,3,3) (4,4,4)
14. Source
Sink
Alignments = Paths in 3-Space
• Same strategy as aligning two
sequences.
• Use a 3-D “Manhattan Cube”,
with each axis representing a
sequence to align.
• For global alignments, go
from source to sink.
15. 2-D Alignment Cell versus 3-D Alignment Cell
• In 2-D, 3 edges in
each unit square
• In 3-D, 7 edges in
each unit cube
3-D Unit Cube2-D Unit Square
17. • si,j,k = max
• (x, y, z) is an entry in the 3-D scoring matrix.
Cube diagonal: no indels
Face diagonal: one indel
Edge diagonal: two indels
Multiple Alignment: Dynamic Programming
si-1,j-1,k-1 + (vi, wj, uk)
si-1,j-1,k + (vi, wj, _ )
si-1,j,k-1 + (vi, _, uk)
si,j-1,k-1 + (_, wj, uk)
si-1,j,k + (vi, _ , _)
si,j-1,k + (_, wj, _)
si,j,k-1 + (_, _, uk)
18. Multiple Alignment: Running Time
• For 3 sequences of length n, the run time is 7n3 = O(n3)
• For generalization to k sequences, build a k-dimensional
Manhattan graph:
• There are nk vertices in this graph.
• Each vertex has 2k – 1 edges coming into it.
• Therefore, run time = (2k – 1)(nk) = O(2knk)
• Conclusion: The dynamic programming approach for
alignment between two sequences is easily extended to k
sequences but it is impractical due to a run time that is
exponential in the number of sequences.
19. Multiple Alignment Induces Pairwise
Alignments
• Every multiple alignment induces pairwise alignments
• Example: The alignment
x: A C - G C G G - C
y: A C - G C - G A G
z: G C C G C - G A G
induces the following three pairwise alignments:
x: ACGCGG-C x: AC-GCGG-C y: AC-GCGAG
y: ACGC-GAC z: GCCGC-GAG z: GCCGCGAG
20. Idea: Construct Multiple from Pairwise Alignments
• Given k arbitrary pairwise alignments, can we construct a
multiple alignment that induces them?
• Example: 3 sequence alignment
• x = ACGCTGGC, y = ACGCGAC, z = GCCGCAGAG
• Say we have optimal pairwise alignments as follows:
• Can we construct a multiple alignment that induces them?
x: ACGCTGG-C x: AC-GCTGG-C y: AC-GC-GAG
y: ACGC--GAC z: GCCGCA-GAG z: GCCGCAGAG
21. Idea: Construct Multiple from Pairwise Alignments
• Given k arbitrary pairwise alignments, can we construct a
multiple alignment that induces them?
• Example: 3 sequence alignment
• x = ACGCTGGC, y = ACGCGAC, z = GCCGCAGAG
• Say we have optimal pairwise alignments as follows:
• Can we construct a multiple alignment that induces them?
• Answer: Not always!
x: ACGCTGG-C x: AC-GCTGG-C y: AC-GC-GAG
y: ACGC--GAC z: GCCGCA-GAG z: GCCGCAGAG
22. Idea: Construct Multiple from Pairwise Alignments
• From an optimal multiple alignment, we can infer pairwise
alignments between all pairs of sequences, but they are not
necessarily optimal.
• Likewise, it is difficult to infer a “good” multiple alignment
from optimal pairwise alignments between all sequences.
23. Idea: Construct Multiple from Pairwise Alignments
• Example 1: Can combine
pairwise alignments into
optimal multiple alignment.
• Example 2: Can not combine
pairwise alignments into
optimal multiple alignment.
24. - A G G C T A T C A C C T G
T A G – C T A C C A - - - G
C A G – C T A C C A - - - G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G
A 1 1 .8
C .6 1 .4 1 .6 .2
G 1 .2 .2 .4 1
T .2 1 .6 .2
- .2 .8 .4 .8 .4
Profile Representation of Multiple Alignment
25. Profile Representation of Multiple Alignment
• In the past we were aligning a sequence against a sequence.
- A G G C T A T C A C C T G
T A G – C T A C C A - - - G
C A G – C T A C C A - - - G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G
A 1 1 .8
C .6 1 .4 1 .6 .2
G 1 .2 .2 .4 1
T .2 1 .6 .2
- .2 .8 .4 .8 .4
26. Profile Representation of Multiple Alignment
• In the past we were aligning a sequence against a sequence.
• Can we align a sequence against a profile?
- A G G C T A T C A C C T G
T A G – C T A C C A - - - G
C A G – C T A C C A - - - G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G
A 1 1 .8
C .6 1 .4 1 .6 .2
G 1 .2 .2 .4 1
T .2 1 .6 .2
- .2 .8 .4 .8 .4
27. Profile Representation of Multiple Alignment
• In the past we were aligning a sequence against a sequence.
• Can we align a sequence against a profile?
• Can we align a profile against a profile?
- A G G C T A T C A C C T G
T A G – C T A C C A - - - G
C A G – C T A C C A - - - G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G
A 1 1 .8
C .6 1 .4 1 .6 .2
G 1 .2 .2 .4 1
T .2 1 .6 .2
- .2 .8 .4 .8 .4
28. • Choose the most similar pair of strings and combine them into
a profile, thereby reducing alignment of k sequences to an
alignment of of k – 1 sequences/profiles.
• Then repeat.
• This is a heuristic (greedy) method.
u1= ACGTACGTACGT…
u2 = TTAATTAATTAA…
u3 = ACTACTACTACT…
…
uk = CCGGCCGGCCGG
u1= ACg/tTACg/tTACg/cT…
u2 = TTAATTAATTAA…
…
uk = CCGGCCGGCCGG…
k
k – 1
Multiple Alignment: Greedy Approach
31. • s2 and s4 are closest, so we consolidate these sequences into
one by using the profile matrix:
• New set of 3 sequences to align:
• We can choose either of the nucleotides in question for s2, 4.
s2 GTCTGA
s4 GTCAGC
s2,4 = GTCt/aGa/cA
s1 GATTCA
s3 GATATT
s2,4 GTCt/aGa/c
Greedy Approach: Example
33. Progressive Alignment
• Progressive alignment: A variation of the greedy algorithm
for multiple alignment with a somewhat more intelligent
strategy for choosing the order of alignments.
• Progressive alignment works well for close sequences, but
deteriorates for distant sequences.
• Gaps in consensus string are permanent.
• Use profiles to compare sequences.
34. ClustalW
• Popular multiple alignment tool today.
• „W‟ stands for „weighted‟ (different parts of alignment are
weighted differently).
• Three-step process:
1. Construct pairwise alignments.
2. Build guide tree.
3. Progressive alignment guided by the tree.
35. v1 v2 v3 v4
v1 -
v2 .17 -
v3 .87 .28 -
v4 .59 .33 .62 - (.17 means 17 % identical)
Step 1: Pairwise Alignment
• Aligns each sequence against each other, giving a similarity
matrix.
• Similarity = exact matches / sequence length (percent identity).
36. Step 2: Guide Tree
• Create guide tree using the similarity matrix.
• ClustalW uses the neighbor-joining method,
• Guide tree roughly reflects evolutionary relations.
v1 v2 v3 v4
v1 -
v2 .17 -
v3 .87 .28 -
v4 .59 .33 .62 -
v1
v3
v4
v2
37. FOS_RAT PEEMSVTS-LDLTGGLPEATTPESEEAFTLPLLNDPEPK-PSLEPVKNISNMELKAEPFD
FOS_MOUSE PEEMSVAS-LDLTGGLPEASTPESEEAFTLPLLNDPEPK-PSLEPVKSISNVELKAEPFD
FOS_CHICK SEELAAATALDLG----APSPAAAEEAFALPLMTEAPPAVPPKEPSG--SGLELKAEPFD
FOSB_MOUSE PGPGPLAEVRDLPG-----STSAKEDGFGWLLPPPPPPP-----------------LPFQ
FOSB_HUMAN PGPGPLAEVRDLPG-----SAPAKEDGFSWLLPPPPPPP-----------------LPFQ
. . : ** . :.. *:.* * . * **:
Dots and stars show how well-conserved a column is
Step 3: Progressive Alignment
• Start by aligning the two most similar sequences.
• Following the guide tree, add in the next sequences, aligning
to the existing alignment.
• Insert gaps as necessary.
39. Multiple Alignments: Scoring
• We will discuss three possible scoring systems:
1. Number of matches (multiple longest common
subsequence score)
2. Entropy score
3. Sum of pairs (SP-Score)
40. • A column is a “match” if all the letters in the column are the
same.
• Example: Only the first column in the following matching
represents a “match:”
• The Multiple LCS score is the total number of matches.
• This score is good for very similar sequences.
A A A
A A A
A A T
A T C
Score # 1: Multiple LCS Score
41. Score # 2: Entropy Score
• Define frequencies px for the occurrence of each letter x in each
column of the multiple alignment.
• Then, compute “entropy” of each column.
• The entropy score is then given by the sum of the entropies of all
the columns.
Entropy of Column =
CGTAX
XX
pp
,,,
log
48. Inferring Pairwise from Multiple Alignments
• Recall: Every multiple alignment induces pairwise alignments.
• From a multiple alignment, we can infer pairwise alignments
between all sequences, but they are not necessarily optimal.
• We can view reducing multiple alignments to pairwise
alignments as projecting a 3-D multiple alignment path onto a
2-D face of the cube
• Our third scoring function for MSA will be based off the
projections.
49. Multiple Alignment Projections: Illustration
• A 3-D alignment can be
projected onto the 2-D plane
to represent an alignment
between a pair of sequences.
• Example: Figure at right.
• Solid line: represents a 3-D
alignment path.
• Dashed lines: represent the
three induced pairwise
alignments that are projected
onto the cube‟s faces.
50. • Consider the pairwise alignment of sequences ai and aj
implied from a multiple alignment of k sequences.
• Denote the score of this (not necessarily optimal) pairwise
alignment as s*(ai, aj).
• Sum of Pairs (SP) Score: Obtained by summing the pairwise
scores:
s(a1,…,ak) = Σi,j s*(ai, aj)
Score 3: Sum of Pairs Score (SP-Score)
51. Multiple Alignment: History
• 1975: Sankoff formulates multiple
alignment problem and gives the
dynamic programming solution.
• 1988: Carrillo and Lipman provide
branch and bound approach for Multiple
Alignment.
David Sankoff
David Lipman
52. Multiple Alignment: History
• 1990: Feng and Doolittle develop
progressive alignment.
• 1994: Thompson,
Higgins, and Gibson
create ClustalW, which
is the most popular
multiple alignment
program in the world. Des HigginsJulie Thompson Toby Gibson
Russell Doolittle
53. Multiple Alignment: History
• 1998: Morgenstern et al. create
DIALIGN, an algorithm for
segment-based multiple
alignment.
• 2000: Notredame, Des Higgins,
and Heringa develop T-Coffee,
which aligns multiple sequences
based off a library of pairwise
alignments.
Burkhard Morgenstern
Cedric Notredame JaapHeringa
54. Multiple Alignment: History
• 2004: Robert Edgar formulates
MUSCLE, a faster and more efficient
algorithm than ClustalW.
• 201X: What is next?
55. Problems with Multiple Alignment
• Multidomain proteins evolve not only through point mutations
but also through domain duplications and domain
recombinations.
• Although Multiple Alignment is a 30 year old problem, there
were no approaches for aligning rearranged sequences (i.e.,
multi-domain proteins with shuffled domains) prior to 2002.
• It is often impossible to align all protein sequences throughout
their entire length.
57. Alignment as a Graph
• Conventional Alignment
• Protein sequence as a path
• Two protein sequence paths
• Combination of two protein
graphs into one graph
58. Representing Sequences as Paths in a Graph
• Each protein sequence is
represented by a path.
• Dashed edges connect
“equivalent” positions.
• Vertices with identical
labels are fused.
59. • Two objectives:
1. Find a graph that represents domain structure
2. Find mapping of each sequence to this graph
• Partial Order Alignment (POA): A graph such that every
sequence in the given set is a path in G.
Partial Order Multiple Alignment
60. • Aligns sequences onto a directed acyclic graph (DAG)
• Outline:
1. Guide Tree Construction
2. Progressive Alignment Following Guide Tree
3. Dynamic Programming Algorithm to align two POAs
(POA-POA Alignment).
• We learned how to align one sequence (path) against
another sequence (path).
• We need to develop an algorithm for aligning a directed
graph against a directed graph.
POA Algorithm
61. Dynamic Programming for Aligning Two
Graphs
• S(n, o) = optimal score for n = node in G, o = node in G‟
• Match/mismatch: Aligning two nodes with score s(n,o)
• Deletion/insertion:
• Omitting node n from the alignment with score ∆(n)
• Omitting node o from the alignment with score ∆(o)
• Dynamic formula for S(n, o):
63. • POA is more flexible: standard methods force sequences to
align linearly.
• POA representation handles gaps more naturally and retains
(and uses) all information in the MSA (unlike linear profiles).
POA Advantages
65. A-Bruijn Alignment
• A-Bruijn Alignment (ABA): Represents alignment as
directed graph that may contains cycles.
• This is in contrast to POA, which represents alignment as an
acyclic directed graph.
67. MSA vs. POA vs. ABA
MSAOriginal Sequences
ABAPOA
68. Advantages of ABA
1. More flexible than POA: allows larger class of evolutionary
relationships between aligned sequences
2. Can align proteins with shuffled and/or repeated domain
structure
3. Can align proteins with domains present in multiple copies in
some proteins
4. Handles:
• Domains not present in all proteins.
• Domains present in different orders in different proteins.
69. • Chris Lee, POA, UCLA
http://www.bioinformatics.ucla.edu/poa/Poa_Tutorial.html
Credits