Homology HOMOLOGY SEARCHES: BLAST (Basic Local Alignment Search Tool) & FASTA BACKGROUND INFORMATION: The three BLAST programs that one will commonly use are BLASTN, BLASTP and BLASTX. BLASTN will compare your DNA sequence with all the DNA sequences in the nonredundant database (nr). BLASTP will compare your protein sequence with all the protein sequences in nr. In BLASTX your nucleotide sequence will be translated in all six reading frames and the products compared with the nr protein database. Several online tutorial are available including and from NCBI and a. - (NCBI) Nucleotide BLAST ( ) N.B. The default database is the 'nucleotide collection (nt/nr).' Comparatively recently NCBI offers the ability to conduct Batch BLAST searches. Protein BLAST ( ) N.B. This program is also coupled with a motif search. If you suspect that your pprotein may only show weak sequence similarity to other proteins, I would suggest clicking on the PSI-BLAST (Position-Specific Iterated BLAST) feature. NCBI provides a, and a. Since interpretation of the results requires scientific tact you might want to check the potential homologs using. Simple Is Beautiful is a novel methodology that aims to improve the statistical assessment of hits returned from a PSI-BLAST search to yield better delineation of true and false positives.( Reference: Lee, M.M. et al. Bioinformatics 24:1339-1343). SIB-BLAST can also be accessed. Translated BLAST ( ) - runs an input DNA sequence againnst the protein databases. Searches translated nucleotide databases using a translated nucleotide query; while searches translated nucleotide databases using a protein query. These are useful resources if you are interested in homologs in unfinished genomes. Under 'Databases' select 'genomic survey sequences', 'High throughput genomic sequences' or 'whole-genome shotgun reads' (BLASTN, TBLASTN, TBLASTX etc.). Permits one to compare a nucleic acid or protein sequence against finished archaeal and bacterial genomes. Depending upon the time of day your results may appear almost immediately or your search may be delayed or not accepted at all. Check if your website is mobile-friendly. Get list of recommendations on how to improve your website mobile usability and performance scores. The latest Tweets from BlastStation (@BlastStation): 'Our customers include United States federal agencies such as NIH and USDA. In bioinformatics, BLAST for Basic Local Alignment Search Tool is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences. External Coating – External blast station – External Blaster – 02; External Coating – External blast station – External gas pre heat; External Coating – External blast station – External gas pre heater; External Coating – External blast station – External pre heat; External Coating – External blast station – 0- ø100” 3L. Be prepared for plenty of results. You may only want to print the first few pages (e.g.1-5). Alternatively under 'Algorithm Parameters' change the 'Maximum targets' from 100 (default) to 10 or 50. For PSI-BLAST, and other searches I frequently enter information in the 'Entrez Query' section e.g. Escherichia coli[organism] or Viruses[organism] to see 'hits' specifically to E. Coli or viruses/bacteriophages (see for details) 3. It is adviseable to always select ' Show results in a new window' - (European Molecular Biology network - Swiss node). Very convenient since it permits one to specifically search databases such as prokaryote, bacteriophage, fungal, & 16S rRNA using BLASTN, and specific bacterial genomes or SwissProt using BLASTX or BLASTN. (TM Software, Inc.; founder: Takashi Miyajima) is a web-based 64-bit local BlastStation running on the Cloud computer. It supports megablast, blastn, blastp, and blastx searches; allows easy database creation from your FASTA or FASTQ file, which can be compressed in.gz,.Z, or.zip format. A graphical display of search results and a summary table display of search results. The latter can be exported in CSV format, while the hit sequences can be exported in FASTA format. Also available for download in Mac or PC format. (Laboratory for Gene-Product Informatics, National Institute of Genetics, Japan) - offers BLASTP search capability against individual Archaea, Bacteria, Eukaryota, and viruses. (Graham Hatfull, U.S.A.) - allows BLASTN and BLASTP analyses against a growing list of phages that infect bacterial hosts within the phylum Actinobacteria. Homology detection & structure prediction by HMM-HMM comparison - is a method for database searching and structure prediction that is as easy to use as BLAST but is much more sensitive in finding remote homologs. HHpred is the first server that is based on the pairwise comparison of profile hidden Markov models (HMMs). Whereas most conventional sequence search methods search sequence databases such as UniProt or the NR, HHpred searches alignment databases, like Pfam or SMART. This greatly simplifies the list of hits to a number of sequence families instead of a clutter of single sequences. HHpred accepts a single query sequence or a multiple alignment as input. ( Reference: Söding J et al. 33, W244-W248 (Web Server issue) - can be used to analyze immunoglobulin (Ig) sequences and T cell receptor (TR) sequences: Finding primers specific to your PCR template (using Primer3 and BLAST). Is a system that quickly finds segments of a nucleic acid sequence that may be of vector origin. It helps researchers identify and remove any segments of vector origin before they analyze or submit sequences. For more sophisticated studies you might want to employ: (Domain Enhanced Lookup Time Accelerated BLAST) search - ( NCBI) Position-Specific Iterative BLAST creates a profile after the initial search. - ( NCBI) BLAST two sequences against one another. This utilizes BLASTN, P, X as well as TBLASTN and TBLASTX. - is an incredible tool for visualizing the genome context of a gene or group of genes (synteny). In the following diagram an RpoN (Sigma54) protein was analyzed. ( Reference: R. (2004) Bioinformatics 20: 2307-2308). - Server for Synteny Identification and Analysis of Genome Rearrangement using reversal distance as a measure. You may create a project and upload your own data by following the links below or work with pre-loaded data by selecting the genomes below ( Reference: Sinha, A.U. BMC Bioinformatics 8: 82) Other search engines include: Protein Similarity Search - (EBI) This tool provides sequence similarity searching against protein databases using the FASTA suite of programs. FASTA provides a heuristic search with a protein query. FASTX and FASTY translate a DNA query. Optimal searches are available with SSEARCH (local), GGSEARCH (global) and GLSEARCH (global query, local database). (Saier Laboratory Bioinformatics Grp, Univ. San Diego, U.S.A.) - Scans the transport protein database (TC-DB) producing alignments and phylogenetic trees. The TC-DB details a comprehensive classification system for membrane transport proteins known as the Transport Commission (TC) system. - permits one to screen protein sequences against an extensive database of characterized peptidases ( Reference: Rawlings, N.D et al. 2002. Nucleic Acids Res. 30: 343-346). - is a profile-based method for the detection of remote sequence similarity and the prediction of protein structure. The server features three major developments: (i) improved statistical accuracy; (ii) increased speed from parallel implementation; and (iii) new functional features facilitating structure prediction. These features include visualization tools that allow the user to quickly and effectively analyze specific local structural region predictions suggested by COMPASS alignments.( Reference: R.I. Sadreyev et al. 37(Web Server issue:W90-W94): interactive homology search against Uniprot - the webserver provides protein sequence database searches with immediate response and professional alignment visualization by third-party software. The output is a list, pairwise alignment or stacked alignment of sequence-similar proteins from Uniprot, UniRef90/50, Swissprot or Protein Data Bank. The stacked alignments are viewed in Jalview or as sequence logos. The database search uses the suffix array neighborhood search (SANS) method, which has been re-implemented as a client-server, improved and parallelized. The method is extremely fast and as sensitive as BLAST above 50% sequence identity. ( Reference: P. Somervuo & L. 43 (W1): W24-W29). Detect bacterial toxins through text and homology searches: Database of Bacterial ExoToxins for Human - is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins from 26 different human pathogenic bacterial genera. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. ( Reference: Chakraborty, A. 40(Database issue): D615-620). Orthologous genes/proteins COG analysis - Clusters of Orthologous Groups - COG protein database was generated by comparing predicted and known proteins in all completely sequenced microbial genomes to infer sets of orthologs. Each COG consists of a group of proteins found to be orthologous across at least three lineages and likely corresponds to an ancient conserved domain (). Sites which offer this analysis include: ( Reference: S. BMC Genomics 12:444), ( Reference: Aziz RK et al. BMC Genomics 9:75), and ( Bacterial Annotation System; Reference: Van Domselaar GH et al. Nucleic Acids Res. 33(Web Server issue):W455-459.) and ( Integrated Microbial Genomes; Reference: Markowitz VM et al. 42: D560-D567. ) Other sites: - A database of orthologous groups and functional annotation that derives Nonsupervised Orthologous Groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. ( Reference: Powell S et al. Nucleic Acids Res. 42 (D1): D231-D239 - is another algorithm for grouping proteins into ortholog groups based on their sequence similarity. The process usually takes between 6 and 72 hours.( Reference: Fischer S et al. Curr Protoc Bioinformatics; Chapter 6:Unit 6.12.1-19). (KEGG Automatic Annotation Server) provides functional annotation of genes by BLAST or GHOST comparisons against the manually curated KEGG GENES database. The result contains KO (KEGG Orthology) assignments and automatically generated KEGG pathways. ( Reference: Moriya Y et al. Nucleic Acids Res. 35(Web Server issue):W182-185). Unique search engine: - performs BLASTP searches in UniProt to identify names and synonyms based on homologous proteins and subsequently queries PubMed, using combined search terms in order to find and present relevant literature. This tool only allows max. 100 queries per user per day. ( Reference: G. Dieterich et al. Bioinformatics 21: 3450-3451). Comparison of homology between two small genomes: helps you create interactive visualizations of BLAST results from your web browser. Find your most interesting alignments, list detailed parametersfor each, and export a publication-ready vector image. Incredibly easy to use - here are the results for a BLASTN comparison to Escherichia phages T1 (query)and ADB-2. ( Reference: Wintersinger JA et al. Bioinformatics 31:1305-1306). (Softberry.com) provides one with a colour-coded graphical alignment of genome length DNAs in Java. In the top panel regions of high sequence identity are presented in red. By highlighting the gray, yellow, green, black boxes one can select specific regions for examination of the sequence alignment. For additional information on the output see. This site appears to work best with Internet Explorer. ( Schwartz et al. 10, Issue 4, 577-586, April 2000 ) aligns two DNA sequences and returns a percent identity plot of that alignment, together with a traditional textual form of the alignment. You might want to download ( Penn State - Bioinformatics Group, U.S.A. ) for viewing and manipulating the output from pairwise alignment programs such as PipMaker representations of the alignments.: A Java Dot Plot Viewer ( Viral Bioinformatics Resource Center, University of Victoria, Canada) - a dot matrix plotter for Java. Produces similar diagrams to the above mentioned programs, but with better control on output. Also available.: multiple sequence alignment tool (Comparative Genomics Center, Lawrence Livermore National Laboratory, U.S.A.) - provides nice dotplot graphs and dynamic visualizations. If simple gene locations are provided in the form (e.g. > 2000 5000 RNA_polymerase; indicates the the RNA polymerase gene is found on the plus strand between bases 2000 and 5000) this data will be added to the dynamic visualization. ZPicture alignments can be automatically submitted to rVista to identify conserved transcription factor binding sites. For more than two genomes go. CoreGenes (D. Seto, Bioinformatics & Computational Biology, George Mason Univ., U.S.A.) is designed to analyze two to five genomes simultaneously, generating a table of related genes - orthologs and putative orthologs. These entries are linked to their GenBank data. I have used this suite of programs extensively in the classification of bacterial viruses. It has proved exctremely useful in determining unique genes in comparisons between large Myoviridae. Is the batch CoreGenes server. The 1.4 version of BlastStation-Local is provided as a free download on our software library. This download was checked by our built-in antivirus and was rated as malware free. The software belongs to System Utilities. The size of the latest downloadable setup file is 4.2 MB. Bslocal.exe and _18be6784.exe are the most common filenames for this program's installer. This program is a product of TM Software. The following versions: 1.5, 1.4 and 1.1 are the most frequently downloaded ones by the program users. BlastStation-Local is specialized to local NCBI-Blast search on your PC. BlastStation-Local provides NCBI-Blast search capability at affordable price with the same easy-to-use interface of BlastStation2. You can use databases available from the NCBI ftp server and/or create databases from your FASTA files. BlastStation-Local is available for Mac OS X and Windows platforms. You may want to check out more software, such as Hibernate Trigger, MobilePhoto or Word Key, which might be to BlastStation-Local. 2.7.0+ / 10 October 2016; 13 months ago ( 2016-10-10),,, tool Website In, BLAST for Basic Local Alignment Search Tool is an for comparing biological sequence information, such as the sequences of or the of. A BLAST search enables a researcher to compare a query sequence with a library or of sequences, and identify library sequences that resemble the query sequence above a certain threshold. Different types of BLASTs are available according to the query sequences. For example, following the discovery of a previously unknown gene in the, a scientist will typically perform a BLAST search of the to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. The BLAST algorithm and program were designed by,,,, and at the and was published in the in 1990 and cited over 50,000 times. Contents • • • • • • • • • • • • • • • • • Background [ ] BLAST is one of the most widely used bioinformatics programs for sequence searching. It addresses a fundamental problem in bioinformatics research. The algorithm it uses is much faster than other approaches, such as calculating an optimal alignment. This emphasis on speed is vital to making the algorithm practical on the huge genome databases currently available, although subsequent algorithms can be even faster. Before BLAST, was developed by David J. Lipman and William R. Pearson in 1985. Before fast algorithms such as BLAST and were developed, doing database searches for protein or nucleic sequences was very time consuming because a full alignment procedure (e.g., the ) was used. While BLAST is faster than any Smith-Waterman implementation for most cases, it cannot 'guarantee the optimal alignments of the query and database sequences' as Smith-Waterman algorithm does. The optimality of Smith-Waterman 'ensured the best performance on accuracy and the most precise results' at the expense of time and computer power. BLAST is more time-efficient than FASTA by searching only for the more significant patterns in the sequences, yet with comparative sensitivity. This could be further realized by understanding the algorithm of BLAST introduced below. Examples of other questions that researchers use BLAST to answer are: • Which have a protein that is related in lineage to a certain protein with known • What other genes encode proteins that exhibit structures or such as ones that have just been determined BLAST is also often used as part of other algorithms that require approximate sequence matching. The BLAST algorithm and the that implements it were developed by,, and at the U.S. (NCBI), at the, and at the. It is available on the web on. Alternative implementations include (formerly known as ), (last updated in 2006), and. The original paper by Altschul, et al. Was the most highly cited paper published in the 1990s. Input [ ] Input sequences (in or format) and weight matrix. Output [ ] BLAST output can be delivered in a variety of formats. These formats include,, and formatting. For NCBI's web-page, the default format for output is HTML. When performing a BLAST on NCBI, the results are given in a graphical format showing the hits found, a table showing sequence identifiers for the hits with scoring related data, as well as alignments for the sequence of interest and the hits received with corresponding BLAST scores for these. The easiest to read and most informative of these is probably the table. If one is attempting to search for a proprietary sequence or simply one that is unavailable in databases available to the general public through sources such as NCBI, there is a BLAST program available for download to any computer, at no cost. This can be found. There are also commercial programs available for purchase. Databases can be found from the NCBI site, as well as from (FTP). Process [ ] Using a method, BLAST finds similar sequences, by locating short matches between the two sequences. This process of finding similar sequences is called seeding. It is after this first match that BLAST begins to make local alignments. While attempting to find similarity in sequences, sets of common letters, known as words, are very important. For example, suppose that the sequence contains the following stretch of letters, GLKFA. If a was being conducted under normal conditions, the word size would be 3 letters. In this case, using the given stretch of letters, the searched words would be GLK, LKF, KFA. The heuristic algorithm of BLAST locates all common three-letter words between the sequence of interest and the hit sequence or sequences from the database. This result will then be used to build an alignment. After making words for the sequence of interest, the rest of the words are also assembled. These words must satisfy a requirement of having a score of at least the threshold T, when compared by using a scoring matrix. One commonly used scoring matrix for BLAST searches is, although the optimal scoring matrix depends on sequence similarity. Once both words and neighborhood words are assembled and compiled, they are compared to the sequences in the database in order to find matches. The threshold score T determines whether or not a particular word will be included in the alignment. Once seeding has been conducted, the alignment which is only 3 residues long, is extended in both directions by the algorithm used by BLAST. Each extension impacts the score of the alignment by either increasing or decreasing it. If this score is higher than a pre-determined T, the alignment will be included in the results given by BLAST. However, if this score is lower than this pre-determined T, the alignment will cease to extend, preventing the areas of poor alignment from being included in the BLAST results. Note that increasing the T score limits the amount of space available to search, decreasing the number of neighborhood words, while at the same time speeding up the process of BLAST. Algorithm [ ] To run the software, BLAST requires a query sequence to search for, and a sequence to search against (also called the target sequence) or a sequence database containing multiple such sequences. BLAST will find sub-sequences in the database which are similar to sub sequences in the query. In typical usage, the query sequence is much smaller than the database, e.g., the query may be one thousand nucleotides while the database is several billion nucleotides. The main idea of BLAST is that there are often High-scoring Segment Pairs (HSP) contained in a statistically significant alignment. BLAST searches for high scoring between the query sequence and the existing sequences in the database using a heuristic approach that approximates the. However, the exhaustive Smith-Waterman approach is too slow for searching large genomic databases such as. Therefore, the BLAST algorithm uses a approach that is less accurate than the Smith-Waterman algorithm but over 50 times faster. [8] The speed and relatively good accuracy of BLAST are among the key technical innovations of the BLAST programs. An overview of the BLAST algorithm (a protein to protein search) is as follows: and • Remove low-complexity region or sequence repeats in the query sequence. 'Low-complexity region' means a region of a sequence composed of few kinds of elements. These regions might give high scores that confuse the program to find the actual significant sequences in the database, so they should be filtered out. The regions will be marked with an X (protein sequences) or N (nucleic acid sequences) and then be ignored by the BLAST program. To filter out the low-complexity regions, the program is used for protein sequences and the program is used for DNA sequences. On the other hand, the program is used to mask off the tandem repeats in protein sequences. • Make a k-letter word list of the query sequence. Take k=3 for example, we list the words of length 3 in the query protein sequence ( k is usually 11 for a DNA sequence) 'sequentially', until the last letter of the query sequence is included. The method is illustrated in figure 1. 1 The method to establish the k-letter query word list. Adapted from Biological Sequence Analysis I, Current Topics in Genome Analysis. • List the possible matching words. This step is one of the main differences between BLAST and FASTA. FASTA cares about all of the common words in the database and query sequences that are listed in step 2; however, BLAST only cares about the high-scoring words. The scores are created by comparing the word in the list in step 2 with all the 3-letter words. By using the scoring matrix (substitution matrix) to score the comparison of each residue pair, there are 20^3 possible match scores for a 3-letter word. For example, the score obtained by comparing PQG with PEG and PQA is respectively 15 and 12 with the weighting scheme. For DNA words, a match is scored as +5 and a mismatch as -4, or as +2 and -3. After that, a neighborhood word score threshold T is used to reduce the number of possible matching words. The words whose scores are greater than the threshold T will remain in the possible matching words list, while those with lower scores will be discarded. For example, PEG is kept, but PQA is abandoned when T is 13. • Organize the remaining high-scoring words into an efficient search tree. This allows the program to rapidly compare the high-scoring words to the database sequences. • Repeat step 3 to 4 for each k-letter word in the query sequence. • Scan the database sequences for exact matches with the remaining high-scoring words. The BLAST program scans the database sequences for the remaining high-scoring word, such as PEG, of each position. If an exact match is found, this match is used to seed a possible un-gapped alignment between the query and database sequences. • Extend the exact matches to high-scoring segment pair (HSP). • The original version of BLAST stretches a longer alignment between the query and the database sequence in the left and right directions, from the position where the exact match occurred. The extension does not stop until the accumulated total score of the HSP begins to decrease. A simplified example is presented in figure 2. • ^;;;; (1990).. Journal of Molecular Biology. 215 (3): 403–410... Business Intelligence Network. • Lipman, DJ; Pearson, WR (1985). 'Rapid and sensitive protein similarity searches'. 227 (4693): 1435–41... • Oehmen, C.; Nieplocha, J. 'ScalaBLAST: A Scalable Implementation of BLAST for High-Performance Data-Intensive Bioinformatics Analysis'. IEEE Transactions on Parallel and Distributed Systems. 17 (8): 740.. S.; Baxter, D. 29 (6): 797–798.... July–August 2000. Archived from on October 7, 2007. Cold Spring Harbor Press.. • Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T. BMC Bioinformatics. D.; Sahinidis, N. 27 (2): 182–8.... • Zhao, K.; Chu, X. 30 (10): 1384–91... James (2002-04-01).. Genome Research. 12 (4): 656–664..... • Lavenier, D.; Lavenier, Dominique (2009).. BMC Bioinformatics. • Lavenier, D. 'Ordered index seed algorithm for intensive DNA sequence comparison'. • Buchfink, Xie and Huson (2015). 'Fast and sensitive protein alignment using DIAMOND'. Nature Methods. • Steinegger, Martin; Soeding, Johannes (2017-10-16). 'MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets'. Nature Biotechnology.. • Neumann, Kumar and Shalchian-Tabrizi (2014). 'BLAST output visualization in the new sequencing era'. Briefings in Bioinformatics. 15 (4): 484–503... External links [ ] about Sequence alignment • • • • — free source downloads •: Andy Baxevanis' lecture from NHGRI's series, covering contemporary areas in genomics and bioinformatics •: talk by Gene Myers (slides and video) Tutorials [ ] • Baxevanis, Andreas D. 'Chapter 11: Assessing Pairwise Sequence Similarity: BLAST and FASTA'. In Andreas D. Baxevanis; B. Francis Ouellette. New York: John Wiley & Sons.. • Wheeler, David; Bhagwat, Medha (2007). 'Chapter 9: BLAST QuickStart'. In Bergman, Nicholas H.. Methods in Molecular Biology. Totowa, NJ: Humana Press.. • Mount DW (1 Jul 2007).. Cold Spring Harbor Protocols. 2007 (14): pdb.top17...
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