FASTA is a sequence alignment tool that was developed before BLAST. It uses a hashing strategy to find matches between k-tuples, or short stretches of identical residues, in query and target sequences. FASTA breaks sequences down into k-tuples and searches target databases to find similarities. While faster than dynamic programming, FASTA and BLAST may not find optimal alignments or true homologs.
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
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
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
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
INTRODUCTION OF BIOINFORMATICS
HISTORY
WHAT IS DATABASE
NEED FOR DATABASE
TYPES OF DATABASE
PRIMARY DATABASE
NUCLEIC ACID SEQUENCE DATABASE
GENE BANK
INTRODUCTION
GENE BANK SUBMISSION TOOL
GENE BANK SUBMISSION TYPE
HOW TO RETRIEVE DATA FROM GENEBANK
APPLICATION
CONCLUSION
REFERENCE
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.
INTRODUCTION OF BIOINFORMATICS
HISTORY
WHAT IS DATABASE
NEED FOR DATABASE
TYPES OF DATABASE
PRIMARY DATABASE
NUCLEIC ACID SEQUENCE DATABASE
GENE BANK
INTRODUCTION
GENE BANK SUBMISSION TOOL
GENE BANK SUBMISSION TYPE
HOW TO RETRIEVE DATA FROM GENEBANK
APPLICATION
CONCLUSION
REFERENCE
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.
Sequence homology search and multiple sequence alignment(1)AnkitTiwari354
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
Bioinformatics involves the analysis of biological information using computers and statistical techniques,
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
Bioinformatics is a fast-growing field of study that is providing major solutions to global challenges. It has its applications in the fields of medicine, pharmacology, agriculture, evolution, and environmental management. This document discusses one of the key tools in the field of Bioinformatics - the FastA Homology search algorithm. This document is for academic purposes and does not attempt to exhaust the subject. However, if you would like to discuss the subject in more depth, write to me on my email and we will surely have a discussion. Enjoy the read!
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.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Basic Civil Engineering Notes of Chapter-6, Topic- Ecosystem, Biodiversity Green house effect & Hydrological cycle
Types of Ecosystem
(1) Natural Ecosystem
(2) Artificial Ecosystem
component of ecosystem
Biotic Components
Abiotic Components
Producers
Consumers
Decomposers
Functions of Ecosystem
Types of Biodiversity
Genetic Biodiversity
Species Biodiversity
Ecological Biodiversity
Importance of Biodiversity
Hydrological Cycle
Green House Effect
This presentation provides an introduction to quantitative trait loci (QTL) analysis and marker-assisted selection (MAS) in plant breeding. The presentation begins by explaining the type of quantitative traits. The process of QTL analysis, including the use of molecular genetic markers and statistical methods, is discussed. Practical examples demonstrating the power of MAS are provided, such as its use in improving crop traits in plant breeding programs. Overall, this presentation offers a comprehensive overview of these important genomics-based approaches that are transforming modern agriculture.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2. FASTA stands for fast-all” or “FastA”.
It was the first database similarity search tool developed, preceding the development of
BLAST.
FASTA is another sequence alignment tool which is used to search similarities between
sequences of DNA and proteins.
FASTA uses a “hashing” strategy to find matches for a short stretch of identical residues
with a length of k. The string of residues is known as ktuples or ktups, which are
equivalent to words in BLAST, but are normally shorter than the words.
Typically, a ktup is composed of two residues for protein sequences and six residues for
DNA sequences.
The query sequence is thus broken down into sequence patterns or words known as k-
tuples and the target sequences are searched for these k-tuples in order to find the
similarities between the two.
FASTA is a fine tool for similarity searches.
These methods are not guaranteed to find the optimal alignment or true homologs, but are
50–100 times faster than dynamic programming.
3. FastA - Compares a DNA query sequence to a DNA
database, or a protein query to a protein database,
detecting the sequence type automatically.
Versions 2 and 3 are in common use, version 3
having a highly improved score normalization
method. It significantly reduces the overlap between
the score distributions.
FASTX - Compares a DNA query to a protein
database. It may introduce gaps only between
codons.
FASTY - Compares a DNA query to a protein
database, optimizing gap location, even within
codons.
TFASTA - Compares a protein query to a DNA
database.
4.
5.
6. • It is used for the identification of the species.
• Used for the establishment of the phylogeny
• For DNA mapping
• FASTA is also used for understanding the
biochemical functions of the protein.
• Study the evolution of the species, from where
that specific species evolved, or identify the
ancestors.
• Calculation of the molecular weight
• Identification of mutations in the sequences by
comparing those sequences with the reference
sequences.
7. Basic steps Step1: Set a word size, usually 6 for DNA and 2 for protein. Hashing: FASTA
locates regions of the query sequence and matching regions in the database sequences
that have high densities of exact word matches (without gaps). The length of the
matched word is called the k-tuple parameter.
Step 2: Scoring: The ten highest scoring regions are rescored using the BLOSUM50
scoring matrix. The score for such a pair of regions is saved as the init1 score.
Step 3: Introduction of Gaps: FASTA determines if any of the initial regions from
different diagonals may be joined together to form an approximate alignment with gaps.
Only non-overlapping regions may be joined. The score for the joined regions is the
sum of the scores of the initial regions minus a joining penalty for each gap. The score
of the highest scoring region, at the end of this step, is saved as the init n. FASTA
(4) Step 4: Alignment: After computing the initial scores, FASTA determines the best
segment of similarity between the query sequence and the search set sequence, using a
variation of the SmithWaterman algorithm. The score for this alignment is the opt score.
Step 5: Random Sequence Simulation: In order to evaluate the significance of such
alignment FASTA empirically estimates the score distribution from the alignment of
many random pairs of sequences. More precisely, the characters of the query sequences
are reshuffled (to maintain bias due to length and character composition) and searched
against a random subset of the database. This empirical distribution is extrapolated,
assuming it is an extreme value distribution, and each alignment to the real query is
assigned a Z-score and an E-score. Modifications: In step4, use a band around init1
8. FASTA calculates significance “on the fly”.
This can be problematic if the dataset is
small. To identify an unknown protein
sequence use either of these: FastA3,
Ssearch3 or tFastX3. FASTA3 has improved
methods of aligning sequences and of
calculating the statistical significance of
alignment.
9. There is no standard filename extension for a
text file containing FASTA formatted
sequences. The table below shows each
extension and its respective meaning.
10. Developed by Steven Altschul and Samuel
Karlin in 1990.
• Compares nucleotide/aminoacid
sequences
• Is a heuristic method.
• Is a fast but approximate method of
alignment.
• Locates local alignments/short matches
called words
11.
12. blastp: compares a protein sequence against a
protein sequence database.
blastn: compares a nucleotide sequence against a
nucleotide sequence database.
blastx: compares a six frame translation of a
nucleotide sequence against a protein database
tblastn: compares a protein sequence against a
six frame translation of a nucleotide database
tblastx: compares a six frame translation of a
nucleotide sequence against a six frame
translation of a nucleotide database
13. Blast searches begin with a query sequence
that will be matched against sequence
databases specified by the user.
•Begins by breaking down the query sequence
into a series of short overlapping “words”
•Default word size for BLAST N is 28 nucleotides
•Default word size for BLAST P is 3 amino acids
•Results obtained depend on the scoring matrix
used.
•BLOSUM 62 matrix is the default scoring matrix
for BLASTP
14. Basic steps Step1: Set a word size, usually 11 for DNA and
3 for protein. Given query sequence, compile the list of
possible words, which form with words in high scoring
word pairs (Filter out low complexity regions)
Step 2: Scan database for exact matching with the list of
words complied in step 1. e.g. qlnfsagw -> (ql, ln, nf, fs,
sa, ag, gw) Extend the list (using some threshold T) Step 3:
Scan through the string and whenever a word in the list is
found try to extend it in both directions (no gaps) to get to
a score beyond a threshold S. While extending use a
parameter L that defines how long an extension will be
tried to raise the score over S.
Modification of step 3: -Original BLAST: Extension is
continued as long as the score continued to increase. -
Another version -BLAST2 (gapped BLAST): - Lower value of
T is used. - After extension try to combine (allowing gaps)
- Find maximal scoring segment. This program uses the
BLASTP or BLASTN algorithms for aligning two sequences.
15. BLAST calculates probabilities and this can fail if
some assumptions are invalid for that search. There
are versions of BLAST for searching nucleic acid and
protein databases, which can be used to translate
DNA sequences prior to comparing them to protein
sequence databases in 1997. Recent improvement in
BLAST is GAPPED-BLAST (three times faster than the
original BLAST) and PSI-BLAST (position-specific-
iterated BLAST). The GAPPED-BLAST algorithm allows
gaps to be introduced into the alignments. That
means that similar regions are not broken into
several segments (as in the older versions). This
method reflects biological relationships much better
than ordinary BLAST.