Global and local alignment in Bioinformatics
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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.
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Genome assembly
This document discusses different methods for genome sequencing and assembly, including restriction enzyme fingerprinting, marker sequences, and hybridization assays. It focuses on using marker sequences like sequence-tagged sites (STS), expressed sequence tags (ESTs), untranslated regions (UTRs), and single nucleotide polymorphisms (SNPs) to map genomes. Large-insert cloning vectors like BACs and PACs can be used with restriction enzyme fingerprinting and FPC software to assemble contigs and map genomes at a large scale. Marker sequences provide a dense set of physical markers to build accurate physical maps of genomes.
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Sequence Alignment
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.
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The Needleman-Wunsch algorithm is used to perform global sequence alignment to identify similarities between nucleotide or protein sequences. It is an example of dynamic programming that finds the optimal alignment between two sequences in quadratic time. The algorithm initializes a scoring matrix and then fills it using recursion relations, scoring matches, mismatches, and gaps. It then traces back through the matrix to find the highest scoring alignment path and deduce the optimal alignment between the sequences.
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Finding ORF
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
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blast bioinformatics
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3. Analyzing the BLAST results can provide information about the evolutionary relationship between the query sequence and matched sequences, such as whether they come from the same gene or protein family.
Genome annotation
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
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Instead of looking for entire sequence, S-W algorithm compares sequence of all possible lengths and optimizes similarity length.
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Multiple sequence alignment
- Multiple sequence alignment (MSA) is the alignment of three or more biological sequences, such as DNA, RNA, or protein sequences. It involves identifying regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
- Several algorithms and tools exist for generating MSAs, including ClustalW, T-Coffee, MUSCLE, MAFFT, and MSA. They employ different methods like progressive alignment and iterative refinement. The choice of algorithm depends on factors like number of sequences and computational time.
- MSAs aid in tasks like identifying conserved motifs and domains, phylogenetic analysis, and predicting protein structure and function. Formats like FASTA are used for representing MS
Phylogenetic analysis
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sequence alignment
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.
Sequence Analysis
The document discusses different types of sequence analysis and alignment methods. It describes analyzing DNA, RNA, and protein sequences to understand their features, functions, and evolution. Methods include aligning sequences globally or locally to identify similar regions. Pairwise alignment involves two sequences while multiple sequence alignment incorporates more sequences using techniques like dynamic programming, progressive alignment, and motif finding. Structural alignments also use 3D protein or RNA structure information.
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Sequence comparison techniques
The document discusses various sequence comparison techniques including pairwise alignment, local alignment, global alignment, and multiple alignment. It describes heuristic methods like FASTA and BLAST that are faster than dynamic programming but may miss optimal alignments. It provides details on the FASTA algorithm including finding identical words, re-scoring, joining segments, and dynamic programming. It also explains the BLAST algorithm and steps including query preprocessing, database scanning, and hit extension. Specialized BLAST databases and tools are also listed.
Gene prediction methods vijay
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
shotgun sequncing
Genome sequencing is the process of determining the order of nucleotide bases - A, C, G, and T - that make up an organism's DNA. Shotgun sequencing involves randomly breaking the genome into small fragments, sequencing those pieces, and reassembling the sequence by identifying overlapping regions. It was originally used by Sanger to sequence small genomes like viruses and bacteria. There are two main methods - hierarchical shotgun sequencing for larger genomes containing repeats, and whole genome shotgun sequencing for smaller genomes.
genome sequencing, types by kk sahu sir
INTRODUCTION
WHY SEQUENCING GENOME?
SHOTGUN SEQUENCING
HISTORY
PRINCIPLE
STEPS IN SHOTGUN SEQUENCING
STRATEGIES FOR SHOTGUN SEQUENCING
1. Hierarchical shotgun sequencing
2. Whole genome shotgun sequencing
MILESTONE IN GENOME SEQUENCING
CONCLUSION
REFERENCES
Global alignment
The document discusses global sequence alignment and the Needleman-Wunsch algorithm. Global alignment finds the optimal alignment of two sequences over their entire lengths by allowing gaps. It uses a dynamic programming approach to fill a matrix to find the highest scoring alignment based on matches, mismatches and gap penalties. The algorithm allows obtaining an alignment score that accounts for the total number of matches and mismatches as well as gap penalties.
2016.09.28TOPIC REVIEW• Exam • PS2 Sequence Alignment .docx
2016.09.28
TOPIC REVIEW
• Exam
• PS2 Sequence Alignment
• Command Line Blast
• PS1 Molecular Biology
• Personal Microbiome Project
CURRENTLY
LET’S NEGOTIATE
• Problem sets (4) - 10%
• Microbiome project - 20%
• Exam (1) - 20%
• Research project - 45%
• Participation - 5%
OR
• Problem sets (4) - 10%
• Microbiome project - 20%
• Exam 1 - 15%
• Exam 2 - 15%
• Research project - 35%
• Participation - 5%
PS2 SEQUENCE ALIGNMENT
PS2 SEQUENCE ALIGNMENT
RefSeqs, protein (experimentally supported)
On chromosome 17
Reverse strand
PRCD Progressive rod-cone degeneration
PS2: GLOBAL ALIGNMENT
BLOSUM62
• substitutions less penalized and are
preferred to gaps. There is also a
decrease in the level of identity.
BLOSUM80
• Substitutions more penalized and
gaps are favored.
PAM60
• Substitutions more penalized and gaps
are favored.
PAM250
• substitutions less penalized and are
preferred to gaps. There is also a
decrease in the level of identity.
PS2: LOCAL ALIGNMENT
SEQ1 A L S C V W M I P
SEQ2 A I S C M I P T
9 residues
8 residues
Create Matrix: length of seq1 + 1
x
length of seq2 + 1
Matrix 10 x 9
A L S C V W M I P
0 -2 -4 -6 -8 -10 -12 -14 -16 -18
-2
-4
-6
-8
-10
-12
-14
-16
A
I
S
C
M
I
P
T
Exercise: fill the scores of the alignment matrix
using the BLOSUM62 substitution matrix.
Gap opening penalty: -5
Gap extension penalty: -1
S V E T D
T
S
I
N
Q
E
T
Ala A 4
Arg R -1 5
Asn N -2 0 6
Asp D -2 -2 1 6
Cys C 0 -3 -3 -3 9
Gln Q -1 1 0 0 -3 5
Glu E -1 0 0 2 -4 2 5
Gly G 0 -2 0 -1 -3 -2 -2 6
His H -2 0 1 -1 -3 0 0 -2 8
Ile I -1 -3 -3 -3 -1 -3 -3 -4 -3 4
Leu L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4
Lys K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5
Met M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5
Phe F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6
Pro P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7
Ser S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4
Thr T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5
Trp W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11
Tyr Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7
Val V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4
A
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h
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rp
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a
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A R N D C Q E G H I L K M F P S T W Y V
Dynamical programming - global alignment
83
BLOSUM62
GAP COST: -2
At each cell, 3 scores are calculated:
• match score = diagonal cell score +
score from the substitution matrix.
• Vertical gap score = upper neighbor
+ gap cost
• Horizontal gap score = left neighbor
+ gap cost
• The highest score is retained and
the arrow is labelled
A L S C V W M I P
0 -2 -4 -6 -8 -10 -12 -14 -16 -18
-2
-4
-6
-8
-10
-12
-14
-16
A
I
S
C
M
I
P
T
Exercise: fill the scores of the alignment matrix
using the BLOSUM62 substitution matrix.
Gap opening penalty: -5
Gap extension penalty: -1
S V E T D
T
S
I
N
Q
E
T
A ...
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The Needleman-Wunsch algorithm is used to perform global sequence alignment to identify similarities between nucleotide or protein sequences. It is an example of dynamic programming that finds the optimal alignment between two sequences in quadratic time. The algorithm initializes a scoring matrix and then fills it using recursion relations, scoring matches, mismatches, and gaps. It then traces back through the matrix to find the highest scoring alignment path and deduce the optimal alignment between the sequences.
Global and local alignment (bioinformatics)
A general global alignment technique is the Needleman–Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context.
Sequence alignment global vs. local
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.
Blast and fasta
This document discusses sequence alignment methods. It describes global and local alignment, and algorithms used for alignment including dot matrix analysis, dynamic programming, and word/k-tuple methods as implemented in FASTA and BLAST programs. BLAST and FASTA are described as popular tools for sequence database searches that use heuristic methods and word matching to quickly identify regions of local similarity.
Finding ORF
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
FASTA
In bioinformatics and biochemistry, the FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences, in which nucleotides or amino acids are represented using single-letter codes. The format also allows for sequence names and comments to precede the sequences.
The Smith Waterman algorithm
The Smith-Waterman algorithm finds the best local alignment between two sequences. It involves filling a matrix using a recurrence relation to score matches, mismatches, and gaps. The highest scoring cell represents the best local alignment, which can be traced back through the matrix. For example, the best local alignment between sequences "TCAGTTGCC" and "AGGTTG" is "GTTG" with a score of 4.
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1. BLAST is a program that uses computer algorithms to compare a query DNA or protein sequence to sequence databases and identify sequences that resemble the query sequence above a certain threshold.
2. BLAST works by searching for short, exact matches between the query and database sequences, then extends the matches to find similar though not exact alignments.
3. Analyzing the BLAST results can provide information about the evolutionary relationship between the query sequence and matched sequences, such as whether they come from the same gene or protein family.
Genome annotation
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
smith - waterman algorithm.pptx
The S-W algorithm performs in local sequence alignment for determining two similar regions between two strings nucleotide sequences or protein sequence.
Instead of looking for entire sequence, S-W algorithm compares sequence of all possible lengths and optimizes similarity length.
methods for protein structure prediction
protein structure prediction methods. homology modelling, fold recognition, threading, ab initio methods. in short and easy form slides. after one time read you can easily understand methods for protein structure prediction.
Multiple sequence alignment
- Multiple sequence alignment (MSA) is the alignment of three or more biological sequences, such as DNA, RNA, or protein sequences. It involves identifying regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
- Several algorithms and tools exist for generating MSAs, including ClustalW, T-Coffee, MUSCLE, MAFFT, and MSA. They employ different methods like progressive alignment and iterative refinement. The choice of algorithm depends on factors like number of sequences and computational time.
- MSAs aid in tasks like identifying conserved motifs and domains, phylogenetic analysis, and predicting protein structure and function. Formats like FASTA are used for representing MS
Phylogenetic analysis
Mr. Nitin Maruti Naik presented on phylogenetic analysis. Phylogenetic analysis involves constructing phylogenetic trees that depict the evolutionary relationships between species or genes. There are two main types of methods for phylogenetic analysis - distance methods that construct trees based on sequence similarity, and character methods that consider evolutionary pathways. Popular tools for phylogenetic analysis include PHYLIP, which offers parsimony, distance, and likelihood methods, and ClustalW, which performs progressive multiple sequence alignments.
sequence alignment
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.
Sequence Analysis
The document discusses different types of sequence analysis and alignment methods. It describes analyzing DNA, RNA, and protein sequences to understand their features, functions, and evolution. Methods include aligning sequences globally or locally to identify similar regions. Pairwise alignment involves two sequences while multiple sequence alignment incorporates more sequences using techniques like dynamic programming, progressive alignment, and motif finding. Structural alignments also use 3D protein or RNA structure information.
BLAST
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Sequence comparison techniques
The document discusses various sequence comparison techniques including pairwise alignment, local alignment, global alignment, and multiple alignment. It describes heuristic methods like FASTA and BLAST that are faster than dynamic programming but may miss optimal alignments. It provides details on the FASTA algorithm including finding identical words, re-scoring, joining segments, and dynamic programming. It also explains the BLAST algorithm and steps including query preprocessing, database scanning, and hit extension. Specialized BLAST databases and tools are also listed.
Gene prediction methods vijay
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
shotgun sequncing
Genome sequencing is the process of determining the order of nucleotide bases - A, C, G, and T - that make up an organism's DNA. Shotgun sequencing involves randomly breaking the genome into small fragments, sequencing those pieces, and reassembling the sequence by identifying overlapping regions. It was originally used by Sanger to sequence small genomes like viruses and bacteria. There are two main methods - hierarchical shotgun sequencing for larger genomes containing repeats, and whole genome shotgun sequencing for smaller genomes.
genome sequencing, types by kk sahu sir
INTRODUCTION
WHY SEQUENCING GENOME?
SHOTGUN SEQUENCING
HISTORY
PRINCIPLE
STEPS IN SHOTGUN SEQUENCING
STRATEGIES FOR SHOTGUN SEQUENCING
1. Hierarchical shotgun sequencing
2. Whole genome shotgun sequencing
MILESTONE IN GENOME SEQUENCING
CONCLUSION
REFERENCES
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The document discusses global sequence alignment and the Needleman-Wunsch algorithm. Global alignment finds the optimal alignment of two sequences over their entire lengths by allowing gaps. It uses a dynamic programming approach to fill a matrix to find the highest scoring alignment based on matches, mismatches and gap penalties. The algorithm allows obtaining an alignment score that accounts for the total number of matches and mismatches as well as gap penalties.
2016.09.28TOPIC REVIEW• Exam • PS2 Sequence Alignment .docx
2016.09.28
TOPIC REVIEW
• Exam
• PS2 Sequence Alignment
• Command Line Blast
• PS1 Molecular Biology
• Personal Microbiome Project
CURRENTLY
LET’S NEGOTIATE
• Problem sets (4) - 10%
• Microbiome project - 20%
• Exam (1) - 20%
• Research project - 45%
• Participation - 5%
OR
• Problem sets (4) - 10%
• Microbiome project - 20%
• Exam 1 - 15%
• Exam 2 - 15%
• Research project - 35%
• Participation - 5%
PS2 SEQUENCE ALIGNMENT
PS2 SEQUENCE ALIGNMENT
RefSeqs, protein (experimentally supported)
On chromosome 17
Reverse strand
PRCD Progressive rod-cone degeneration
PS2: GLOBAL ALIGNMENT
BLOSUM62
• substitutions less penalized and are
preferred to gaps. There is also a
decrease in the level of identity.
BLOSUM80
• Substitutions more penalized and
gaps are favored.
PAM60
• Substitutions more penalized and gaps
are favored.
PAM250
• substitutions less penalized and are
preferred to gaps. There is also a
decrease in the level of identity.
PS2: LOCAL ALIGNMENT
SEQ1 A L S C V W M I P
SEQ2 A I S C M I P T
9 residues
8 residues
Create Matrix: length of seq1 + 1
x
length of seq2 + 1
Matrix 10 x 9
A L S C V W M I P
0 -2 -4 -6 -8 -10 -12 -14 -16 -18
-2
-4
-6
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-10
-12
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A
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Exercise: fill the scores of the alignment matrix
using the BLOSUM62 substitution matrix.
Gap opening penalty: -5
Gap extension penalty: -1
S V E T D
T
S
I
N
Q
E
T
Ala A 4
Arg R -1 5
Asn N -2 0 6
Asp D -2 -2 1 6
Cys C 0 -3 -3 -3 9
Gln Q -1 1 0 0 -3 5
Glu E -1 0 0 2 -4 2 5
Gly G 0 -2 0 -1 -3 -2 -2 6
His H -2 0 1 -1 -3 0 0 -2 8
Ile I -1 -3 -3 -3 -1 -3 -3 -4 -3 4
Leu L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4
Lys K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5
Met M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5
Phe F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6
Pro P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7
Ser S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4
Thr T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5
Trp W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11
Tyr Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7
Val V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4
A
la
A
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A
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sp
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T
h
r
T
rp
T
y
r
V
a
l
A R N D C Q E G H I L K M F P S T W Y V
Dynamical programming - global alignment
83
BLOSUM62
GAP COST: -2
At each cell, 3 scores are calculated:
• match score = diagonal cell score +
score from the substitution matrix.
• Vertical gap score = upper neighbor
+ gap cost
• Horizontal gap score = left neighbor
+ gap cost
• The highest score is retained and
the arrow is labelled
A L S C V W M I P
0 -2 -4 -6 -8 -10 -12 -14 -16 -18
-2
-4
-6
-8
-10
-12
-14
-16
A
I
S
C
M
I
P
T
Exercise: fill the scores of the alignment matrix
using the BLOSUM62 substitution matrix.
Gap opening penalty: -5
Gap extension penalty: -1
S V E T D
T
S
I
N
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20100515 bioinformatics kapushesky_lecture07
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.
Ch06 alignment
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.
Alg 1 ch. 3.5
This document contains notes on compound inequalities in algebra. It explains that for inequalities combined with "and", the values of x that satisfy both inequalities are graphed as the intersection. For inequalities combined with "or", the values of x that satisfy either inequality are graphed. Several examples of compound inequalities are given and practiced.
Puanumrahdalimon
The document discusses linear inequalities and their representations on number lines. It provides examples of writing inequalities based on number lines and situations. It also describes how to form new inequalities by performing operations such as addition, subtraction, multiplication, and division on both sides of a given inequality.
Video Compression, Part 2-Section 2, Video Coding Concepts
This document discusses video compression techniques. It begins by outlining the history of video compression and describing the basic components of a generic video encoder/decoder system. It then covers specific compression methods including differential pulse code modulation, transform coding using discrete cosine transform, quantization, and entropy coding. The document also discusses techniques for reducing both spatial and temporal redundancy in video, such as prediction coding. It provides examples of how quantization is used to control quality and compression ratio in both lossy and lossless compression systems.
Alg 1 ch. 3.5 Compound Inequalities
The document contains an agenda for November 1st including doing a notebook quiz, homework on pages 163-165, and a lesson on compound inequalities. Compound inequalities can use "and" or "or" to represent the intersection or union of two separate inequalities. Examples are shown of graphing inequalities and determining the values of x that satisfy the inequalities. Practice problems are provided to identify the inequality represented by number lines.
Sequence Alignment
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.
Similarity
The document discusses sequence similarity searching and comparison. It describes how programs like BLAST and FASTA are used to rapidly identify similarities between sequences and determine evolutionary relationships. BLAST and FASTA utilize word matching and heuristics to efficiently search large databases and return local or global alignments with scoring of matches. They provide a powerful method for functions prediction by comparing new sequences to known genes and proteins.
Genetic Algorithm (GA) Optimization - Step-by-Step Example
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
(SAC2020 SVT-2) Constrained Detecting Arrays for Fault Localization in Combin...
Authors:
Hao Jin, Osaka University
Ce Shi, Shanghai Lixin University of Accounting and Finance
Tatsuhiro Tsuchiya, Osaka University
Abstract:
Detecting Arrays (DAs) are mathematical objects that enable fault localization in combinatorial interaction testing. Each row of a DA serves as a test case, whereas a whole DA is treated as a test suite. In real-world testing problems, it is often the case that some constraints exist among test parameters. In this paper, we show that it may be impossible to construct a DA using only constraint-satisfying test cases. The reason for this is that a set of some faulty interactions may always mask the effect of other faulty interactions in the presence of constraints. Based on this observation, we propose the notion of Constrained Detecting Arrays (CDAs) to adapt DAs to practical situations. The definition of CDAs requires that all rows of a CDA must satisfy the constraints and the same fault localization capability as the DA must hold except for such inherently undetectable faults. We then propose a computational method for constructing CDAs. Experimental results obtained by using a program that implements the method show that the method was able to produce CDAs within a reasonable time for practical problem instances.
Computer Vision: Correlation, Convolution, and Gradient
The document provides an overview of computer vision techniques including correlation, convolution, and gradient filtering. It discusses how correlation can be used to match a template to an image region by calculating a similarity measure as the template is passed over the image. Convolution is explained as similar to correlation but with the signs of the variables flipped in the formula. Implementing these techniques in Python code is also covered generically for squared and odd-sized templates.
A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020
A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020NECST Lab @ Politecnico di Milano
Abstract inglese:
In recent years Graphic Processing Units have seen widespread adoption in many scientific fields, from Machine Learning (ML) to Genomics. Their use makes it possible to achieve significant speedups and improvements in power efficiency over computationally intensive algorithms compared to General Purpose Central Processing Units. However, algorithms require specific knowledge of the GPU architecture and expertise to achieve significant results. In this work, we describe a methodology for automatic GPU kernel optimization. Our methodology exploits the Berkeley Roofline Model to perform a performance analysis of the algorithm considered and aims to increase the accessibility of GPU programming automatizing the optimization process of the kernel. We provide an in-depth analysis of this methodology, an overview on the state of the art, and a description of a tool we developed that automatically applies our methodology to obtain a highly optimized GPU version of two of the most popular algorithms used in computational biology, the X-drop and Smith-Waterman algorithms. The Smith-Waterman algorithm is one of the most used algorithms in genomics pipelines. The algorithm finds the optimal local alignment between two genomic sequences, at the cost of being particularly compute-intensive. The popular X-drop algorithm reduces the time required by the alignment by searching only for high-quality alignments. The algorithms accelerated using our methodology achieve more than 6x and 3x speed-up, for the X-drop and Smith-Waterman algorithms respectively, with respect to the state of the art implementation of these algorithms.SUEC 高中 Adv Maths (Matrix) (Part 3).pptx
Visual - various maths sites (credits to original creator)
Questions - Dong Zong's Textbook
suitable for SUEC (Maths), SPM (Maths and Add Maths) too
Overview of sparse and low-rank matrix / tensor techniques
A short overview about sparse and low-rank matrix/tensor techniques, which could be used in spatio-temporal and environmental statistics
Bioinformatics life sciences_v2015
The document discusses bioinformatics and computational biology. It describes a lab with over 100 people from diverse backgrounds, including engineers, scientists, technicians, geneticists and clinicians. The lab applies information technology to analyze biological data, focusing on areas like sequence analysis, molecular modeling, phylogeny, medical applications, statistics and more. Specific applications mentioned include analyzing genomes to study genetic diseases and drug design, as well as using the same techniques in agriculture and animal health.
Trignometary
Trigonometry is a branch of mathematics that deals with relationships between sides and angles of triangles. The document defines trigonometric ratios like sine, cosine, and tangent that relate the sides and angles of a right triangle. It also discusses trigonometric identities, angles in different quadrants, radian measurement, and graphs of trigonometric functions.
Trignometary
Trigonometry is a branch of mathematics that deals with relationships between sides and angles of triangles. The document defines trigonometric ratios like sine, cosine, and tangent that relate the sides and angles of a right triangle. It also discusses trigonometric identities, angle measurement in degrees and radians, measuring angles in different quadrants, and the values of trigonometric functions for special angles.
Bioinfromatics - local alignment
The document discusses the Smith-Waterman algorithm for local sequence alignment. It describes how the algorithm uses dynamic programming to find the optimal local alignment between two sequences without allowing for negative scores. The key steps are initialization of a score matrix, filling the matrix using match/mismatch scores and a gap penalty, and tracing back through the matrix to determine the highest-scoring alignment. An example application of the algorithm aligns the sequences "GATGTAG" and "GAGATGTGC".
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Genetic Algorithm (GA) Optimization - Step-by-Step Example
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
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Chapter 4
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Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
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1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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Global and local alignment in Bioinformatics
- 1. 1
- 3. 3
- 4. 4 Why align sequences? • Useful for discovering • Functional • Structural and • Evolutionary relationship – For example • To find whether two (or more) genes or proteins are evolutionarily related to each other • Two proteins with similar sequences will probably be structurally or functionally similar
- 6. 6 Global Vs Local Alignment • Global Alignment – A general global alignment technique is the Needleman–Wunsch algorithm, which is based on dynamic programming. – Attempts to align the maximum of the entire sequence – Suitable for similar and equal length sequences • Local Alignment – Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. – Stretches of sequences with highest density of matches are aligned – Suitable for partially similar, different length and conserved region containing sequences
- 8. 8
- 9. 9 Allows obtaining the optimal alignment with linear gap cost has been proposed by Needleman and Wunsch by providing a score, for each position of the aligned sequences. Based on the dynamic programming technique. For two sequences of length m and n we define a matrix of dimensions m+1 and n+1. Global Alignment
- 10. 10 Global Alignment Three steps in dynamic programming Initialization Matrix fill (scoring) Traceback (alignment) Smith–Waterman algorithm
- 11. 11 Sequences: S: ATTATCT T: TTTCTA T S 0 _ A T T A T C T _ T T T C T A 0 -1 -2 -3 -4 -5 -6 -7 -1 -2 -3 -4 -5 -6 0 -1 -2 -3 -4 -5 1 2 1 0 -1 -2 0 3 4 3 2 1 -1 2 3 4 3 4 -2 1 4 3 6 5 -3 0 3 6 5 6 -4 -1 2 5 8 7 Match Score = +2 Mismatch Score = 0 Gap Penalty = -1
- 12. 12 0 _ A T T A T C T _ T T T C T A 0 -1 -2 -3 -4 -5 -6 -7 -1 -2 -3 -4 -5 -6 0 -1 -2 -3 -4 -5 1 2 1 0 -1 -2 0 3 4 3 2 1 -1 2 3 4 3 4 -2 1 4 3 6 5 -3 0 3 6 5 6 -4 -1 2 5 8 7 T S
- 13. 13 Optimal Alignment: S T No: of matches = 5 No: of gap = 2 (5 x 2) + (2 x -1) = 8 A T T A T C T - T T – T C T
- 15. 15
- 16. 16 S/T 0 A T G A T G T A G 0 0 0 0 0 0 0 0 0 0 0 G 0 0 0 A 0 G 0 A 0 T 0 G 0 T 0 G 0 C 0 0 + 2 0 +-2 0 + -2 2 0 + 2 = 2 0 + -2 = 0 0 + -2 = 0 Match : 2, Mismatch : -1, Gap : -2 0 + -1 0 + -2 0 + -2 0 0 + 2 = 0 0 + -2 = 0 0 + -2 = 0 Matrix fill (scoring)
- 17. 17 S/T 0 A T G A T G T A G 0 0 0 0 0 0 0 0 0 0 0 G 0 0 0 2 0 0 2 0 0 0 A 0 2 0 0 4 2 0 0 2 0 G 0 0 1 2 2 3 4 2 0 4 A 0 2 0 0 4 2 2 2 4 2 T 0 0 4 2 2 6 4 4 2 3 G 0 0 2 6 4 4 8 6 4 4 T 0 0 2 4 4 6 6 10 8 6 G 0 0 0 4 3 4 8 8 9 10 C 0 0 0 2 3 1 6 7 7 8 Match : 2, Mismatch : -1, Gap : -2 Matrix fill (scoring)
- 18. Trace back 18 S/T 0 A T G A T G T A G 0 0 0 0 0 0 0 0 0 0 0 G 0 0 0 2 0 0 2 0 0 0 A 0 2 0 0 4 2 0 0 2 0 G 0 0 1 2 2 3 4 2 0 4 A 0 2 0 0 4 2 2 2 4 2 T 0 0 4 2 2 6 4 4 2 3 G 0 0 2 6 4 4 8 6 4 4 T 0 0 2 4 4 6 6 10 8 6 G 0 0 0 4 3 4 8 8 9 10 C 0 0 0 2 3 1 6 7 7 8 Match : 2, Mismatch : -1, Gap : -2
- 19. Alignment 19 G A T G T A G | | | | | | | G A T G T - G 2 2 2 2 2 -2 2 6 X 2 = 12 1 X -2 = -2 10 G A T G T | | | | | G A T G T 2 2 2 2 2 5 X 2 = 10 10
- 20. 20
- 21. 21