This document discusses the Basic Local Alignment Search Tool (BLAST), which allows users to compare a query DNA or protein sequence against sequence databases to find regions of local similarity. BLAST breaks the query into short words that are then searched for in database sequences. When words are found in common, BLAST extends the alignment in both directions to find higher-scoring matches. BLAST outputs include a graphical display of alignments, a hit list ranking matches by similarity score, and detailed alignments. BLAST has many applications, such as identifying species, establishing evolutionary relationships, DNA mapping, and locating protein domains.
This document discusses sequence database searching algorithms. It describes the key criteria of sensitivity, selectivity, and speed. Heuristic algorithms like BLAST and FASTA use word searches to rapidly identify similar sequences. BLAST finds ungapped segments while FASTA joins diagonals to produce gapped alignments. Both use substitution matrices and E-values to statistically evaluate results. The document also discusses different BLAST and FASTA variants and output formats.
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 .
BLAST works by finding short matches ("words") between query and database sequences. It uses these matches to seed ungapped local alignments, which are then extended and evaluated. Significant alignments above a score threshold are reported, along with a statistical analysis to assess their reliability. The process removes low-complexity regions before alignment and uses substitution matrices to score matches between residues.
BLAST is a program that compares a query DNA or protein sequence against a database to find sequences that resemble the query above a certain threshold. It works by breaking the query into short words and searching the database for those words, then extending any matches. The output includes alignments of high-scoring segment pairs and statistical measures like E-values and bit scores to indicate match significance. BLAST is faster than FASTA and more specific due to low complexity filtering.
BLAST is a sequence comparison algorithm that is used to compare a query sequence against databases to find optimal local alignments. It breaks sequences into fragments and seeks matches between them, which is faster than full sequence alignment. The main types of BLAST programs are blastn, blastp, psi-blast, blastx, tblastx, tblastn, and megablast. BLAST's algorithm involves removing repeats, making a word list, finding matches, and extending matches to report significant alignments. It outputs alignments and statistics like E-values and bit scores. BLAST can identify species, locate domains, establish phylogeny, and enable comparison of sequences.
The document provides an introduction to BLAST (Basic Local Alignment Search Tool), which is an algorithm used to compare gene and protein sequences to those in public databases. It discusses the types of BLAST programs, the BLAST algorithm, input/output, how to perform a BLAST search, and the functions and objectives of BLAST. Specifically, BLAST is faster than previous sequence comparison methods, it outputs alignments and statistical values to evaluate matches, and its main objectives are to identify related sequences and locate domains through local alignments.
BLAST (Basic Local Alignment Search Tool) is a program that detects sequence similarities between a query sequence and database sequences. It uses statistical analysis to determine if alignments between sequences are statistically significant. BLAST is helpful for identifying known genes in a novel sequence and determining relationships between genes/proteins. Users select a query sequence, database, and BLAST program, then receive output including alignments and statistics describing matches.
This document discusses the Basic Local Alignment Search Tool (BLAST), which allows users to compare a query DNA or protein sequence against sequence databases to find regions of local similarity. BLAST breaks the query into short words that are then searched for in database sequences. When words are found in common, BLAST extends the alignment in both directions to find higher-scoring matches. BLAST outputs include a graphical display of alignments, a hit list ranking matches by similarity score, and detailed alignments. BLAST has many applications, such as identifying species, establishing evolutionary relationships, DNA mapping, and locating protein domains.
This document discusses sequence database searching algorithms. It describes the key criteria of sensitivity, selectivity, and speed. Heuristic algorithms like BLAST and FASTA use word searches to rapidly identify similar sequences. BLAST finds ungapped segments while FASTA joins diagonals to produce gapped alignments. Both use substitution matrices and E-values to statistically evaluate results. The document also discusses different BLAST and FASTA variants and output formats.
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 .
BLAST works by finding short matches ("words") between query and database sequences. It uses these matches to seed ungapped local alignments, which are then extended and evaluated. Significant alignments above a score threshold are reported, along with a statistical analysis to assess their reliability. The process removes low-complexity regions before alignment and uses substitution matrices to score matches between residues.
BLAST is a program that compares a query DNA or protein sequence against a database to find sequences that resemble the query above a certain threshold. It works by breaking the query into short words and searching the database for those words, then extending any matches. The output includes alignments of high-scoring segment pairs and statistical measures like E-values and bit scores to indicate match significance. BLAST is faster than FASTA and more specific due to low complexity filtering.
BLAST is a sequence comparison algorithm that is used to compare a query sequence against databases to find optimal local alignments. It breaks sequences into fragments and seeks matches between them, which is faster than full sequence alignment. The main types of BLAST programs are blastn, blastp, psi-blast, blastx, tblastx, tblastn, and megablast. BLAST's algorithm involves removing repeats, making a word list, finding matches, and extending matches to report significant alignments. It outputs alignments and statistics like E-values and bit scores. BLAST can identify species, locate domains, establish phylogeny, and enable comparison of sequences.
The document provides an introduction to BLAST (Basic Local Alignment Search Tool), which is an algorithm used to compare gene and protein sequences to those in public databases. It discusses the types of BLAST programs, the BLAST algorithm, input/output, how to perform a BLAST search, and the functions and objectives of BLAST. Specifically, BLAST is faster than previous sequence comparison methods, it outputs alignments and statistical values to evaluate matches, and its main objectives are to identify related sequences and locate domains through local alignments.
BLAST (Basic Local Alignment Search Tool) is a program that detects sequence similarities between a query sequence and database sequences. It uses statistical analysis to determine if alignments between sequences are statistically significant. BLAST is helpful for identifying known genes in a novel sequence and determining relationships between genes/proteins. Users select a query sequence, database, and BLAST program, then receive output including alignments and statistics describing matches.
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.
This presentation introduces BLAST (Basic Local Alignment Search Tool) which is used to compare gene and protein sequences against databases. It discusses the types of BLAST programs including blastn, blastp, and PSI-BLAST. The BLAST algorithm works by removing repeats, making a word list from the query, searching the database for matches, and extending matches. BLAST output includes alignments and statistical values. Key functions of BLAST are locating domains, establishing phylogeny, DNA mapping, and comparison. The objectives are to enable comparison of a query to database sequences and identify related matches above a threshold.
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.
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.
Bioinformatic tools analyze biological sequences to find similarities, domains, and coding regions. BLAST is a widely used tool that compares a query sequence to database sequences to find regions of similarity, helping scientists determine sequence function. Sequence alignment identifies similar character patterns between two or more sequences and can provide information about function, structure, and evolutionary relationships. CpG islands are regions of DNA where cytosine and guanine nucleotides frequently occur next to each other. Methylation of cytosines within CpG islands can regulate gene expression and is an epigenetic mechanism studied in cancer diagnosis.
The document discusses similarity searches of sequence databases using BLAST and FASTA. It describes the importance of identifying similar sequences to infer shared biological function. BLAST and FASTA use heuristic algorithms to rapidly identify local similarities between query and database sequences. The statistical significance of alignments is assessed using an extreme value distribution to calculate p-values and e-values, which estimate the probability of observing a given alignment score by chance. This allows filtering of random matches and identification of biologically meaningful similarities.
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.
BLAST AND FASTA.pptx12345789999987544321234alizain9604
- BLAST and FASTA are two commonly used bioinformatics tools for analyzing biological sequences like DNA and proteins.
- BLAST was introduced in 1990 and uses a heuristic algorithm to identify regions of local similarity between query and database sequences. It is fast, sensitive, and widely used.
- FASTA, introduced in 1985, also uses a heuristic algorithm to search databases and identify similar matches to a query sequence. It works by breaking sequences into smaller words, identifying regions of similarity, and performing alignments using substitution matrices.
BLAST is a tool that compares a query DNA or protein sequence against sequence databases to find regions of similarity. It uses a heuristic algorithm that scans databases quickly to find matching sequences. BLAST helps scientists infer functional and evolutionary relationships between sequences and identify members of gene families. It was developed as an improvement over previous alignment tools as it is faster and can process the large volumes of sequence data that were accumulating. There are several variants of BLAST that can search nucleotide or protein databases using different query types.
Sequence alignment involves arranging biological sequences like DNA, RNA, or proteins to identify similar regions that may indicate functional, structural, or evolutionary relationships. There are two main types of sequence alignment: local alignment, which finds short, locally similar regions; and global alignment, which tries to match the full sequences. Sequence alignment is performed using algorithms like Needleman-Wunsch for global alignment and Smith-Waterman for local alignment. It can provide information about sequence homology and evolutionary relationships between sequences.
This document provides an overview of the BLAST algorithm used for comparing biological sequences and identifying sequence similarities. It describes how BLAST works by generating words from a query sequence and searching a database for exact matches. Significant matches are extended locally to identify Maximal Segment Pairs (MSPs) based on scoring. MSPs are evaluated and ranked using E-values, which estimate the statistical significance of matches. The document also discusses different BLAST programs and provides examples of running BLAST searches and interpreting results. Homework assignments are included applying BLAST to specific sequence analysis tasks.
The document discusses BLAST (Basic Local Alignment Search Tool), an algorithm used to compare a query DNA or protein sequence against a database of sequences. BLAST works by identifying exact or approximate matches between words of 3-11 letters in the query and database sequences. Matches are extended to find local alignments with high scores. Significant alignments are identified based on their score and the expected number of matches by chance (E-value). The document provides examples of how BLAST finds local alignments and calculates E-values. It also describes different BLAST programs and suggestions for using BLAST.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
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.
The document discusses several sequence similarity tools: BLAST, FASTA, and CLUSTAL. It describes BLAST as an algorithm that finds locally and globally alignable sequences by calculating segment pairs between a query and database sequences above a scoring threshold. FASTA is described as a fast protein or nucleotide comparison tool that is more sensitive than BLAST but slower. CLUSTAL W is introduced as a tool that produces meaningful multiple sequence alignments of divergent sequences and can reveal evolutionary relationships.
This document discusses biological database searching. It begins by defining biological databases as organized collections of persistent biological data that can be queried and retrieved. Examples are provided such as GenBank and SwissProt. Database searching involves using a query sequence to find related sequences in the database based on homology. Parameters like E-values and bit scores are used to define the significance of matches. Popular search algorithms like BLAST and FASTA first use heuristics to find high-scoring regions before applying dynamic programming for alignment.
BLAST is an algorithm for comparing biological sequences like protein or DNA sequences to identify sequences that resemble a query sequence. It was developed in 1990 and allows researchers to compare a query to a database of sequences. BLAST searches for similar subsequences between the query and database sequences using heuristics to approximate the Smith-Waterman algorithm in a faster way. There are different types of BLAST programs that can compare nucleotide to nucleotide, protein to protein, or translate sequences for comparison. BLAST output is typically delivered in HTML or text formats showing scoring data and alignments of matching sequences.
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.
This presentation introduces BLAST (Basic Local Alignment Search Tool) which is used to compare gene and protein sequences against databases. It discusses the types of BLAST programs including blastn, blastp, and PSI-BLAST. The BLAST algorithm works by removing repeats, making a word list from the query, searching the database for matches, and extending matches. BLAST output includes alignments and statistical values. Key functions of BLAST are locating domains, establishing phylogeny, DNA mapping, and comparison. The objectives are to enable comparison of a query to database sequences and identify related matches above a threshold.
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.
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.
Bioinformatic tools analyze biological sequences to find similarities, domains, and coding regions. BLAST is a widely used tool that compares a query sequence to database sequences to find regions of similarity, helping scientists determine sequence function. Sequence alignment identifies similar character patterns between two or more sequences and can provide information about function, structure, and evolutionary relationships. CpG islands are regions of DNA where cytosine and guanine nucleotides frequently occur next to each other. Methylation of cytosines within CpG islands can regulate gene expression and is an epigenetic mechanism studied in cancer diagnosis.
The document discusses similarity searches of sequence databases using BLAST and FASTA. It describes the importance of identifying similar sequences to infer shared biological function. BLAST and FASTA use heuristic algorithms to rapidly identify local similarities between query and database sequences. The statistical significance of alignments is assessed using an extreme value distribution to calculate p-values and e-values, which estimate the probability of observing a given alignment score by chance. This allows filtering of random matches and identification of biologically meaningful similarities.
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.
BLAST AND FASTA.pptx12345789999987544321234alizain9604
- BLAST and FASTA are two commonly used bioinformatics tools for analyzing biological sequences like DNA and proteins.
- BLAST was introduced in 1990 and uses a heuristic algorithm to identify regions of local similarity between query and database sequences. It is fast, sensitive, and widely used.
- FASTA, introduced in 1985, also uses a heuristic algorithm to search databases and identify similar matches to a query sequence. It works by breaking sequences into smaller words, identifying regions of similarity, and performing alignments using substitution matrices.
BLAST is a tool that compares a query DNA or protein sequence against sequence databases to find regions of similarity. It uses a heuristic algorithm that scans databases quickly to find matching sequences. BLAST helps scientists infer functional and evolutionary relationships between sequences and identify members of gene families. It was developed as an improvement over previous alignment tools as it is faster and can process the large volumes of sequence data that were accumulating. There are several variants of BLAST that can search nucleotide or protein databases using different query types.
Sequence alignment involves arranging biological sequences like DNA, RNA, or proteins to identify similar regions that may indicate functional, structural, or evolutionary relationships. There are two main types of sequence alignment: local alignment, which finds short, locally similar regions; and global alignment, which tries to match the full sequences. Sequence alignment is performed using algorithms like Needleman-Wunsch for global alignment and Smith-Waterman for local alignment. It can provide information about sequence homology and evolutionary relationships between sequences.
This document provides an overview of the BLAST algorithm used for comparing biological sequences and identifying sequence similarities. It describes how BLAST works by generating words from a query sequence and searching a database for exact matches. Significant matches are extended locally to identify Maximal Segment Pairs (MSPs) based on scoring. MSPs are evaluated and ranked using E-values, which estimate the statistical significance of matches. The document also discusses different BLAST programs and provides examples of running BLAST searches and interpreting results. Homework assignments are included applying BLAST to specific sequence analysis tasks.
The document discusses BLAST (Basic Local Alignment Search Tool), an algorithm used to compare a query DNA or protein sequence against a database of sequences. BLAST works by identifying exact or approximate matches between words of 3-11 letters in the query and database sequences. Matches are extended to find local alignments with high scores. Significant alignments are identified based on their score and the expected number of matches by chance (E-value). The document provides examples of how BLAST finds local alignments and calculates E-values. It also describes different BLAST programs and suggestions for using BLAST.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
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.
The document discusses several sequence similarity tools: BLAST, FASTA, and CLUSTAL. It describes BLAST as an algorithm that finds locally and globally alignable sequences by calculating segment pairs between a query and database sequences above a scoring threshold. FASTA is described as a fast protein or nucleotide comparison tool that is more sensitive than BLAST but slower. CLUSTAL W is introduced as a tool that produces meaningful multiple sequence alignments of divergent sequences and can reveal evolutionary relationships.
This document discusses biological database searching. It begins by defining biological databases as organized collections of persistent biological data that can be queried and retrieved. Examples are provided such as GenBank and SwissProt. Database searching involves using a query sequence to find related sequences in the database based on homology. Parameters like E-values and bit scores are used to define the significance of matches. Popular search algorithms like BLAST and FASTA first use heuristics to find high-scoring regions before applying dynamic programming for alignment.
BLAST is an algorithm for comparing biological sequences like protein or DNA sequences to identify sequences that resemble a query sequence. It was developed in 1990 and allows researchers to compare a query to a database of sequences. BLAST searches for similar subsequences between the query and database sequences using heuristics to approximate the Smith-Waterman algorithm in a faster way. There are different types of BLAST programs that can compare nucleotide to nucleotide, protein to protein, or translate sequences for comparison. BLAST output is typically delivered in HTML or text formats showing scoring data and alignments of matching sequences.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)eitps1506
Description:
Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
Key topics covered include:
Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties.
Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties.
Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications.
Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism.
Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena.
With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.
1. BLAST
(Basic Local Alignment Search Tool)
• 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
2. Uses of BLAST:
• Search a database for sequences similar
to an input sequence.
• Identify previously characterized
sequences.
• Find phylogenetically related sequences.
• Identify possible functions based on
similarities to known sequences.
4. Types of BLAST:
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.
5. How BLAST works
•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
7. The BLASTP algorithm
• Query sequence is broken into all possible 3-letter
words using a moving window
• Numerical score is calculated for each word by adding
up the values for the amino acids from the BLOSUM62
matrix
• Words with a score of 12 or more are collected into the
initial BLASTP search set.
• The search set is broadened by adding synonyms that
differ from the words at one position.
• Only synonyms with scores above a threshold value are
added to the search set. NCBI BLASTP uses a default
threshold of 10 for synonyms
8.
9. Contd….
Using this search set, BLAST scans a database
and identifies word hits/matches that score
above the threshold.
These short matches serve as seeds. The BLAST
algorithm attempts to extend the match in the
immediate sequence neighborhood
BLAST keeps a running raw score, using scoring
matrices, as it extends the matches. Each new
amino acid either increases or decreases the raw
score
Penalties are assigned for mismatches and for
gaps between the two alignments.
10. • In the NCBI default settings, a gap brings an initial
penalty of 11, which increases by 1 for each missing
amino acid.
• Once the score falls below a set level, the
alignment ceases and blast stops trying to extend
the alignment.
• An extended sequence alignment that was initially
seeded by a word hit is produced -called an hsp, or
high-scoring segment pair
Contd….
11. Contd….
All HSPs that have a cumulative score above the
threshold score are reported in BLAST results.
Raw scores are then converted into bit scores by
correcting for the scoring matrix used
12.
13. The Blast output
Includes a table with the bit scores (S) for each alignment and its E-
value, or “expect score”
the score (S) is a measure of the quality of an alignment (calculated as
the sum of substitution and gap scores for each aligned residue)
E-value (E), or expectation value is a measure of the significance of the
alignment. The E-value is the number of different alignments, with
scores equivalent to or better than S, that are expected to occur in a
database search by chance.
The lower the E-value, the more significant the alignment result.
Alignments with the highest bit scores and lowest E-values are listed at
the top of the table.
15. The query sequence - numbered red bar at the top of the figure. Database hits are
shown aligned to the query, below the red bar. Of the aligned sequences, the most
similar are shown closest to the query. In this case, there are three high scoring
database matches that align to most of the query sequence. The next twelve bars
represent lower-scoring matches that align to two regions of the query, from about
residues 3–60 and residues 220–500. The cross-hatched parts of the these bars
indicate that the two regions of similarity are on the same protein, but that this
intervening region does not match. The remaining bars show lower-scoring
alignments. Mousing over the bars displays the definition line for that sequence to
be shown in the window above the graphic.
16. One-line descriptions in the BLAST report
Each line is composed of four fields: (a) the gi number, database designation, accession
number, and locus name for the matched sequence, separated by vertical bars (appendix 1);
(b) a brief textual description of the sequence, the definition. This usually includes
information on the organism from which the sequence was derived, the type of sequence
(e.G., mRNA or DNA), and some information about function or phenotype. The definition
line is often truncated in the one-line descriptions to keep the display compact; (c) the
alignment score in bits. Higher scoring hits are found at the top of the list; and (d) the e-
value, which provides an estimate of statistical significance. For the first hit in the list, the
gi number is 116365, the database designation is sp (for SWISS-PROT), the accession
number is P26374, the locus name is RAE2_HUMAN, the definition line is rab proteins, the
17. A pairwise sequence alignment from a BLAST report
The alignment is preceded by the sequence identifier, the full definition line, and the length of the matched
sequence, in amino acids. Next comes the bit score (the raw score is in parentheses) and then the E-value. The
following line contains information on the number of identical residues in this alignment (Identities), the
number of conservative substitutions (Positives), and if applicable, the number of gaps in the alignment.
Finally, the actual alignment is shown, with the query on top, and the database match is labeled as Sbjct,
below. The numbers at left and right refer to the position in the amino acid sequence. One or more dashes (–)
within a sequence indicate insertions or deletions. Amino acid residues in the query sequence that have been
masked because of low complexity are replaced by Xs (see, for example, the fourth and last blocks). The line
between the two sequences indicates the similarities between the sequences. If the query and the subject