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.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
Module 2 Sequence similarity.
Part of bioinformatics training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
Global and local alignment (bioinformatics)Pritom Chaki
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.
Protein Structure Analysis (University of Cambridge)Bronwyn Barker
Topics:
1) Public repositories of structural data (Protein Data Bank and Electron Microscopy Data Bank)
2) Resources for protein analysis and classification (Pfam, InterPro and HMMER)
3) Retrieving information about the structure and function of your protein sequence using CATH,
4) Principles of modern state-of-the-art protein modelling with Phyre2
5) Methods for predicting the effects of mutations on protein structure and function using the SAAP family of tools
Outcome:
1) Use HMMER, Pfam and InterPro to annotate protein sequences
2) Discover protein and nucleic acid structures related to your area of work and understand how this information could be used to further your own research
3) Scan a protein sequence against CATH and interpret the significance and biological meaning of results
4) Create your own protein models and analyse their features and assess their reliability
5) Use mutation prediction methods and evaluate their performance.
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
Module 2 Sequence similarity.
Part of bioinformatics training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
Global and local alignment (bioinformatics)Pritom Chaki
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.
Protein Structure Analysis (University of Cambridge)Bronwyn Barker
Topics:
1) Public repositories of structural data (Protein Data Bank and Electron Microscopy Data Bank)
2) Resources for protein analysis and classification (Pfam, InterPro and HMMER)
3) Retrieving information about the structure and function of your protein sequence using CATH,
4) Principles of modern state-of-the-art protein modelling with Phyre2
5) Methods for predicting the effects of mutations on protein structure and function using the SAAP family of tools
Outcome:
1) Use HMMER, Pfam and InterPro to annotate protein sequences
2) Discover protein and nucleic acid structures related to your area of work and understand how this information could be used to further your own research
3) Scan a protein sequence against CATH and interpret the significance and biological meaning of results
4) Create your own protein models and analyse their features and assess their reliability
5) Use mutation prediction methods and evaluate their performance.
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.
Discovery of one of the most important driving forces in protein folding and, probably, protein conformational change. This was presented in 2015. More on this work here: https://doi.org/10.1021/jacs.5b00660
The MSc defense ceremony was held on 6-7-2017 in Mansoura University, Faculty of Engineering. This presentation is shared to help MSc students in Faculty of Engineering prepare their thesis presentation and ease their tension before their presentation time
(SAC2020 SVT-2) Constrained Detecting Arrays for Fault Localization in Combin...Hao Jin
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.
Jogging While Driving, and Other Software Engineering Research Problems (invi...David Rosenblum
invited talk presented for the Distinguished Lecturer Series of the Department of Computer Science at the University of Illinois at Chicago, 10 April 2014
Representations for large-scale (Big) Sequence Data MiningVijay Raghavan
Analyzing and classifying sequence data based on structural similarities and differences is a mathematical problem of escalating relevance. Indeed, a primary challenge in designing machine learning algorithms to analyzing se-quence data is the extraction and representation of significant features. This paper introduces a generalized sequence feature extraction model, referred to as the Generalized Multi-Layered Vector Spaces (GMLVS) model. Unlike most models that represent sequence data based on subsequences frequency, the GMLVS model represents a given sequence as a collection of features, where each individual feature captures the spatial relationships between two subse-quences and can be mapped into a feature vector. The utility of this approach is demonstrated via two special cases of the GMLVS model, namely, Lossless Decomposition (LD) and the Multi-Layered Vector Spaces (MLVS).
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2. Pairwise Alignment
Global Local
• Best score from among • Best score from among
alignments of full-length alignments of partial
sequences sequences
• Needelman-Wunch • Smith-Waterman
algorithm algorithm
2
3. Why do we need local alignments?
• To compare a short sequence to a large one.
• To compare a single sequence to an entire
database
• To compare a partial sequence to the whole.
3
4. Why do we need local alignments?
• Identify newly determined sequences
• Compare new genes to known ones
• Guess functions for entire genomes full of
ORFs of unknown function
4
5. Mathematical Basis
for Local Alignment
• Model matches as a sequence of coin
tosses
• Let p be the probability of “head”
– For a “fair” coin, p = 0.5
• According to Paul Erdös-Alfréd Rényi
law:
If there are n throws, then the expected
length, R, of the longest run of “heads”
is
R = log1/p (n). Paul Erdös
5
6. Mathematical Basis
for Local Alignment
• Example: Suppose n = 20 for a “fair” coin
R=log2(20)=4.32
• Problem: How does one model DNA (or
amino acid) alignments as coin tosses.
6
7. Modeling Sequence Alignments
• To model random sequence alignments, replace a match by
“head” (H) and mismatch by “tail” (T).
AATCAT
HTHHHT
ATTCAG
• For ungapped DNA alignments, the probability of a “head”
is 1/4.
• For ungapped amino acid alignments, the probability of a
“head” is 1/20.
7
8. Modeling Sequence Alignments
• Thus, for any one particular alignment, the Erdös-
Rényi law can be applied
• What about for all possible alignments?
– Consider that sequences can being shifted back and
forth in the dot matrix plot
• The expected length of the longest match is
R = log1/p(mn)
where m and n are the lengths of the two
sequences.
8
9. Modeling Sequence Alignments
• Suppose m = n = 10, and we deal with DNA
sequences
R = log4(100) = 3.32
• This analysis assumes that the base
composition is uniform and the alignment is
ungapped. The result is approximate, but
not bad.
9
11. Heuristic Methods: FASTA and BLAST
FASTA
• First fast sequence searching algorithm for
comparing a query sequence against a database.
BLAST
• Basic Local Alignment Search Technique
improvement of FASTA: Search speed, ease of
use, statistical rigor.
11
12. FASTA and BLAST
• Basic idea: a good alignment contains
subsequences of absolute identity (short lengths
of exact matches):
– First, identify very short exact matches.
– Next, the best short hits from the first step are
extended to longer regions of similarity.
– Finally, the best hits are optimized.
12
13. FASTA
Derived from logic of the dot plot
– compute best diagonals from all frames of
alignment
The method looks for exact matches between
words in query and test sequence
– DNA words are usually 6 nucleotides long
– protein words are 2 amino acids long
13
20. FASTA on the Web
• Many websites offer
FASTA searches
• Each server has its limits
• Be aware that you
depend “on the kindness
of strangers.”
20
21. Institut de Génétique Humaine, Montpellier France, GeneStream server
http://www2.igh.cnrs.fr/bin/fasta-guess.cgi
Oak Ridge National Laboratory GenQuest server
http://avalon.epm.ornl.gov/
European Bioinformatics Institute, Cambridge, UK
http://www.ebi.ac.uk/htbin/fasta.py?request
EMBL, Heidelberg, Germany
http://www.embl-heidelberg.de/cgi/fasta-wrapper-free
Munich Information Center for Protein Sequences (MIPS)
at Max-Planck-Institut, Germany
http://speedy.mips.biochem.mpg.de/mips/programs/fasta.html
Institute of Biology and Chemistry of Proteins Lyon, France
http://www.ibcp.fr/serv_main.html
Institute Pasteur, France
http://central.pasteur.fr/seqanal/interfaces/fasta.html
GenQuest at The Johns Hopkins University
http://www.bis.med.jhmi.edu/Dan/gq/gq.form.html
National Cancer Center of Japan
http://bioinfo.ncc.go.jp
21
22. FASTA Format
• simple format used by almost all programs
• >header line with a [return] at end
• Sequence (no specific requirements for line
length, characters, etc)
>URO1 uro1.seq Length: 2018 November 9, 2000 11:50 Type: N Check: 3854 ..
CGCAGAAAGAGGAGGCGCTTGCCTTCAGCTTGTGGGAAATCCCGAAGATGGCCAAAGACA
ACTCAACTGTTCGTTGCTTCCAGGGCCTGCTGATTTTTGGAAATGTGATTATTGGTTGTT
GCGGCATTGCCCTGACTGCGGAGTGCATCTTCTTTGTATCTGACCAACACAGCCTCTACC
CACTGCTTGAAGCCACCGACAACGATGACATCTATGGGGCTGCCTGGATCGGCATATTTG
TGGGCATCTGCCTCTTCTGCCTGTCTGTTCTAGGCATTGTAGGCATCATGAAGTCCAGCA
GGAAAATTCTTCTGGCGTATTTCATTCTGATGTTTATAGTATATGCCTTTGAAGTGGCAT
CTTGTATCACAGCAGCAACACAACAAGACTTTTTCACACCCAACCTCTTCCTGAAGCAGA
TGCTAGAGAGGTACCAAAACAACAGCCCTCCAAACAATGATGACCAGTGGAAAAACAATG
GAGTCACCAAAACCTGGGACAGGCTCATGCTCCAGGACAATTGCTGTGGCGTAAATGGTC
CATCAGACTGGCAAAAATACACATCTGCCTTCCGGACTGAGAATAATGATGCTGACTATC
CCTGGCCTCGTCAATGCTGTGTTATGAACAATCTTAAAGAACCTCTCAACCTGGAGGCTT 22
23. Assessing Alignment Significance
• Generate random alignments and
calculate their scores
• Compute the mean and the standard
deviation (SD) for random scores
• Compute the deviation of the actual score
from the mean of random scores
Z = (meanX)/SD
• Evaluate the significance of the alignment
• The probability of a Z value is called the E
score
23
24. E scores or E values
E scores are not equivalent to p
values where
p < 0.05
are generally considered
statistically significant.
24
25. E values (rules of thumb)
E values below 10-6 are most probably
statistically significant.
E values above 10-6 but below 10-3
deserve a second look.
E values above 10-3 should not be
tossed aside lightly; they should be
thrown out with great force. 25
26. BLAST
• Basic Local Alignment Search Tool
– Altschul et al. 1990,1994,1997
• Heuristic method for local alignment
• Designed specifically for database searches
• Based on the same assumption as FASTA
that good alignments contain short lengths
of exact matches
26
27. BLAST
• Both BLAST and FASTA search for local
sequence similarity - indeed they have exactly
the same goals, though they use somewhat
different algorithms and statistical approaches.
• BLAST benefits
– Speed
– User friendly
– Statistical rigor
– More sensitive
27
28. Input/Output
• Input:
– Query sequence Q
– Database of sequences DB
– Minimal score S
• Output:
– Sequences from DB (Seq), such that Q and Seq
have scores > S
28
29. BLAST Searches GenBank
[BLAST= Basic Local Alignment Search Tool]
The NCBI BLAST web server lets you compare your
query sequence to various sections of GenBank:
– nr = non-redundant (main sections)
– month = new sequences from the past few weeks
– refseq_rna
– RNA entries from NCBI's Reference Sequence project
– refseq_genomic
– Genomic entries from NCBI's Reference Sequence project
– ESTs
– Taxon = e.g., human, Drososphila, yeast, E. coli
– proteins (by automatic translation)
– pdb = Sequences derived from the 3-dimensional structure
from Brookhaven Protein Data Bank
29
30. BLAST
• Uses word matching like FASTA
• Similarity matching of words (3 amino acids, 11
bases)
– does not require identical words.
• If no words are similar, then no alignment
– Will not find matches for very short sequences
• Does not handle gaps well
• “gapped BLAST” is somewhat better
30
33. Find locations of matching words
in database sequences
ELEPRRPRYRVPDVLVADPPIARLSVSGRDENSVELT MEAT
MEA
EAA TDVRWMSETGIIDVFLLLGPSISDVFRQYASLTGTQALPPLFSLGYHQSRWNY
AAV IWLDIEEIHADGKRYFTWDPSRFPQPRTMLERLASKRRV KLVAIVDPH
AVK
KLV
KEE
EEI
EIS
ISV
33
35. Seq_XYZ: HVTGRSAF_FSYYGYGCYCGLGTGKGLPVDATDRCCWA
Query: QSVFDYIYYGCYCGWGLG_GK__PRDA
E-val=10-13
•Use two word matches as anchors to build an alignment
between the query and a database sequence.
•Then score the alignment.
35
36. HSPs are Aligned Regions
• The results of the word matching and
attempts to extend the alignment are
segments
- called HSPs (High-Scoring Segment
Pairs)
• BLAST often produces several short HSPs
rather than a single aligned region
36
63. More on BLAST
NCBI Blast Glossary
http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/glossary2.html
Education: Blast Information
http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html
Steve Altschul's Blast Course
http://www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-1.html
63