DOCSLIB.ORG

Sequence Comparison Methods

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Sequence Comparison Methods Sequence Similarity Methods Gloria Rendon SC11 – Education June, 2011 Sequence Similarity Methods - caveats • Assumption1: genes of closely related species are more similar than genes of distantly related species. • Assumption2: Similar genes have similar sequences. • These methods predict the amount of evolution among species solely in terms of mutation events observed in the sequences of their genes. The General Algorithm... Step1. COLLECT. Sequences are gathered Step 2. COMPARE. Sequences are compared for similarity Step 3. SCORE. A score is computed to assess significance of results Step 4. CLUSTER. A matrix of sequence similarity is computed Step 5 (Opt). A phylogenetic tree is reconstructed with matrix Types of Similarity-Based Methods •Alignment-free Methods: oBased on k-word frequency oBased on Structural alignment oBased on Hidden markov models oOthers •Based on Sequence alignment Types of Similarity-Based Methods •Alignment-free Methods: oBased on k-word frequency oBased on Structural alignment oBased on Hidden markov models oOthers •Based on Sequence alignment Alignment-based Methods Alignment-based Methods A sequence alignment is a way of arranging the sequences of DNA, RNA, or proteins to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Alignment-based Methods A sequence alignment is a scheme of writing one sequence on top of another where the residues in one position are deemed to have a common evolutionary origin. If the same letter occurs in both sequences then this position has been conserved in evolution. If the letters differ it is assumed that the two derive from an ancestral letter (which could be one of the two or neither).. Alignment Representation Sequence Sequence Alignment Length Name Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Point Mutations •ONLY these types of point mutation events are considered by alignment-based methods: insertion, deletion, substitution. •Homologous sequences may have different length, though, which is generally explained through insertions or deletions in sequences. •Thus, a letter or a stretch of letters may be paired up with dashes in the other sequence to signify such an insertion or deletion. •The term given to those dashes is indel or gap. Gaps in Alignments One gap opening and two gap extensions Gaps may be are inserted between the residues so that identical or similar characters are aligned in successive columns. Gaps represent a) deletions or insertions events b) sites with missing information There are two types of Gaps (from the point of view of the aligning algorithm): gap opening and gap extension. Moreover, they are weighted differently by the algorithm. SNIPs are a special case of point mutations SNPs (single nucleotide polymorphism) •Copying errors during cell division result in variations in the DNA at a particular location. •These copying errors are point mutations called single nucleotide polymorphisms, or SNPs. •SNPs are passed on to the next generation through inheritance. Role of SNPs •In humans SNPs account for much of the genetic diversity. •Certain genetic diseases have been linked to SNPs. •However, much of the SNPs do not result in observable differences Point Mutation Analysis The reason for aligning sequences when trying to elucidate their evolutionary relationship is that algorithms can calculate an estimate of their evolutionary distance from the alignment. These methods are based on Levenshtein’s notion of edit distance between strings: “Edit distance is the minimum number of edit operations needed to transform one string into another.” “The more similar the sequences are, the smaller their edit distance is” Types of Alignment-based Methods •Global alignment is when matching is attempted on the entire length of the sequences. This is usually the choice when aligning very similar sequences •Local alignment is when matching is done for specific segments of the sequences. This is usually the choice when it is believed that sequences contained conserved regions. Types of Alignment-based Methods •Earlier we used BLAST to search for a sequence given a partial segment of it. •Blast will try both global as well as local alignments and will report the best matches of them all. •Re-examine the results page and find out which type of alignment performed best in this case Let us re-examine the portion of this page that displays the alignment --marked with 3 Let us re-examine the portion of this page that displays the alignment --marked with 3 There are three rows. The numbers on the left column specify the starting position The numbers on the right specify the ending position The first row is the partial sequence you typed, named Query The third row is the sequence it is being matched against; in this case P46098 The second row is the result of the alignment between the top and bottom seqs The match is exact at every position Types of Alignment-based Methods •Pair-wise alignment. Two sequences are aligned together •Multiple sequence alignment. Three or more sequences are aligned together Pairwise Alignment Illustrated with BLAST and 18s ribosomal RNA sequence Pair-wise Alignment 1.Collect the two sequences 2. Align the sequences 3. Count the mutations in the alignment 4. Score the alignments Pair-wise Alignment 1.Collect the >seq2|LemnaMinor_18S_rRNA two sequences CTCCTACCGATTGAATGGTCCGGTGAAGCGCTCGGATCGCGG CGACGAGGGCGGTCCCCCGCCCGCGACGTCGCGAGAAGTCCG TTGAACCTTATCATTTAGAGGAAGGAG 2. Align the sequences The first sequence is displayed above. 3. Count the To get the second sequence and perform the mutations in the alignment, we simply use BLAST. alignment Go to the BLAST page at NCBI 4. Score the blast.ncbi.nlm.nih.gov alignments Then click on nucleotide blast Pair-wise Alignment This is the nucleotide blast page at NCBI Paste the sequence in the box Select a database from the drop-down list; in this case, choose Nucleotide collection Scroll to the bottom of the page and click on the Blast button Pair-wise Alignment This is the results page of the Blast search. The top hit is our original sequence. It is listed in the table along with some statistics. Let’s see under the hood to understand what happened and how the stats were calculated.. Pair-wise Alignment 1.Collect the two sequences 2. Align the sequences 3. Count the mutations in the If you scroll down the same results page, you will alignment see the results of all the pairwise alignments that BLAST included in the report. 4. Score the They will be sorted from best alignment (first one in alignments the report) to worst alignment (last one in the report). This is the first one, therefore it is the best match. Pair-wise Alignment 1.Collect the two sequences 2. Align the sequences 3. Count the mutations in the Steps 3 and 4 are perform after the alignment is alignment performed in order to assess how good a match it is. First, we need to count mismatches in the alignment. 4. Score the alignments Counting Mismatches (mutations) Cell (T,T) = number of unchanged T residues = 1 Cell (T,G) = number of substitutions from T to G Cell (T, C) = number of substitutions from T to C Cell (T, A) = number of substitutions from T to A Cell (T, -) = number of deletions of T ... Cell (-, T) = number of insertions of T Cell (-, G) = number of insertions of G Cell (-, C) = number of insertions of C Cell (-, A) = number of insertions of A = 0 Pair-wise Alignment Not all mismatches are created equal. 1.Collect the two sequences Some substitutions are more likely than others; therefore we must use weight values, such as those in substitution matrices 2. Align the sequences 3. Count the mutations in the alignment 4. Score the alignments Scoring the alignments Note that the result is a single value, a score, obtained by performing dot product between the alignment matrix and the substitution matrix, and adding the values of the resulting matrix as shown here. So, now you have a clearer idea of what goes under the hood of pairwise-alignment tools like BLAST. Exercise2: Using BLAST to transfer annotation Sometimes we have a gene (or protein) for which an annotation (the description line in fasta format) is unknown; for example, when a new genome is being sequenced. The general ‘in-silico’ procedure for assigning an annotation to that newly sequenced gene (or protein) calls for using BLAST to find a similar gene (or protein) for which the annotation is known. If the match is close enough, we can then transfer the annotation from the known gene (or protein) to the new one. Exercise2: Using BLAST to transfer annotation •Open a web browser and go the UNIPROT url www.uniprot.org 1.Click on the Blast tab 2.In the box type the identifier: A7JKN7_FRANO 3.Then click on the BLAST button Exercise2: Using BLAST to transfer annotation Notice how the UniProt-Blast program fetches the corresponding sequence before launching the BLAST search. Also notice that the annotation (description line) is unknown Exercise2: Using BLAST to transfer annotation This is the BLAST result page. The first and second hits do not have annotations either. The third hit is annotated as Neurotransmitter-gated ion-channel. So, at first blush, we could transfer that annotation to the protein A7JKN7_FRANO Exercise3: GLOBAL Pairwise alignment program • Open a web browser and go to the MOBYLE portal: mobyle.pasteur.fr/ • Choose Programs/ Alignment /pairwise/global/needle from the Programs box (left) • Copy-paste any two sequences
Load more

Recommended publications
  • Comparative Analysis of Multiple Sequence Alignment Tools
    I.J. Information Technology and Computer Science, 2018, 8, 24-30 Published Online August 2018 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2018.08.04 Comparative Analysis of Multiple Sequence Alignment Tools Eman M. Mohamed Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected]. Hamdy M. Mousa, Arabi E. keshk Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected], [email protected]. Received: 24 April 2018; Accepted: 07 July 2018; Published: 08 August 2018 Abstract—The perfect alignment between three or more global alignment algorithm built-in dynamic sequences of Protein, RNA or DNA is a very difficult programming technique [1]. This algorithm maximizes task in bioinformatics. There are many techniques for the number of amino acid matches and minimizes the alignment multiple sequences. Many techniques number of required gaps to finds globally optimal maximize speed and do not concern with the accuracy of alignment. Local alignments are more useful for aligning the resulting alignment. Likewise, many techniques sub-regions of the sequences, whereas local alignment maximize accuracy and do not concern with the speed. maximizes sub-regions similarity alignment. One of the Reducing memory and execution time requirements and most known of Local alignment is Smith-Waterman increasing the accuracy of multiple sequence alignment algorithm [2]. on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the Table 1. Pairwise vs. multiple sequence alignment most well-known programs (CLUSTAL-OMEGA, PSA MSA MAFFT, BROBCONS, KALIGN, RETALIGN, and Compare two biological Compare more than two MUSCLE).
    [Show full text]
  • The ELIXIR Core Data Resources: ​Fundamental Infrastructure for The
    Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure ​ for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). ​ ​ Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences ​ 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that ​ ​ mention Core Data Resource by name or include specific data record accession numbers.
    [Show full text]
  • Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
    bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.
    [Show full text]
  • Sequence Motifs, Correlations and Structural Mapping of Evolutionary
    Talk overview • Sequence profiles – position specific scoring matrix • Psi-blast. Automated way to create and use sequence Sequence motifs, correlations profiles in similarity searches and structural mapping of • Sequence patterns and sequence logos evolutionary data • Bioinformatic tools which employ sequence profiles: PFAM BLOCKS PROSITE PRINTS InterPro • Correlated Mutations and structural insight • Mapping sequence data on structures: March 2011 Eran Eyal Conservations Correlations PSSM – position specific scoring matrix • A position-specific scoring matrix (PSSM) is a commonly used representation of motifs (patterns) in biological sequences • PSSM enables us to represent multiple sequence alignments as mathematical entities which we can work with. • PSSMs enables the scoring of multiple alignments with sequences, or other PSSMs. PSSM – position specific scoring matrix Assuming a string S of length n S = s1s2s3...sn If we want to score this string against our PSSM of length n (with n lines): n alignment _ score = m ∑ s j , j j=1 where m is the PSSM matrix and sj are the string elements. PSSM can also be incorporated to both dynamic programming algorithms and heuristic algorithms (like Psi-Blast). Sequence space PSI-BLAST • For a query sequence use Blast to find matching sequences. • Construct a multiple sequence alignment from the hits to find the common regions (consensus). • Use the “consensus” to search again the database, and get a new set of matching sequences • Repeat the process ! Sequence space Position-Specific-Iterated-BLAST • Intuition – substitution matrices should be specific to sites and not global. – Example: penalize alanine→glycine more in a helix •Idea – Use BLAST with high stringency to get a set of closely related sequences.
    [Show full text]
  • "Phylogenetic Analysis of Protein Sequence Data Using The
    Phylogenetic Analysis of Protein Sequence UNIT 19.11 Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program Antonis Rokas1 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee ABSTRACT Phylogenetic analysis is the study of evolutionary relationships among molecules, phenotypes, and organisms. In the context of protein sequence data, phylogenetic analysis is one of the cornerstones of comparative sequence analysis and has many applications in the study of protein evolution and function. This unit provides a brief review of the principles of phylogenetic analysis and describes several different standard phylogenetic analyses of protein sequence data using the RAXML (Randomized Axelerated Maximum Likelihood) Program. Curr. Protoc. Mol. Biol. 96:19.11.1-19.11.14. C 2011 by John Wiley & Sons, Inc. Keywords: molecular evolution r bootstrap r multiple sequence alignment r amino acid substitution matrix r evolutionary relationship r systematics INTRODUCTION the baboon-colobus monkey lineage almost Phylogenetic analysis is a standard and es- 25 million years ago, whereas baboons and sential tool in any molecular biologist’s bioin- colobus monkeys diverged less than 15 mil- formatics toolkit that, in the context of pro- lion years ago (Sterner et al., 2006). Clearly, tein sequence analysis, enables us to study degree of sequence similarity does not equate the evolutionary history and change of pro- with degree of evolutionary relationship. teins and their function. Such analysis is es- A typical phylogenetic analysis of protein sential to understanding major evolutionary sequence data involves five distinct steps: (a) questions, such as the origins and history of data collection, (b) inference of homology, (c) macromolecules, developmental mechanisms, sequence alignment, (d) alignment trimming, phenotypes, and life itself.
    [Show full text]
  • Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods
    International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods Arunima Mishra, B. K. Tripathi, S. S. Soam MSA is a well-known method of alignment of three or more Abstract: Protein Multiple sequence alignment (MSA) is a biological sequences. Multiple sequence alignment is a very process, that helps in alignment of more than two protein intricate problem, therefore, computation of exact MSA is sequences to establish an evolutionary relationship between the only feasible for the very small number of sequences which is sequences. As part of Protein MSA, the biological sequences are not practical in real situations. Dynamic programming as used aligned in a way to identify maximum similarities. Over time the sequencing technologies are becoming more sophisticated and in pairwise sequence method is impractical for a large number hence the volume of biological data generated is increasing at an of sequences while performing MSA and therefore the enormous rate. This increase in volume of data poses a challenge heuristic algorithms with approximate approaches [7] have to the existing methods used to perform effective MSA as with the been proved more successful. Generally, various biological increase in data volume the computational complexities also sequences are organized into a two-dimensional array such increases and the speed to process decreases. The accuracy of that the residues in each column are homologous or having the MSA is another factor critically important as many bioinformatics same functionality. Many MSA methods were developed over inferences are dependent on the output of MSA.
    [Show full text]
  • HMMER User's Guide
    HMMER User's Guide Biological sequence analysis using pro®le hidden Markov models http://hmmer.wustl.edu/ Version 2.1.1; December 1998 Sean Eddy Dept. of Genetics, Washington University School of Medicine 4566 Scott Ave., St. Louis, MO 63110, USA [email protected] With contributions by Ewan Birney ([email protected]) Copyright (C) 1992-1998, Washington University in St. Louis. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are retained on all copies. The HMMER software package is a copyrighted work that may be freely distributed and modi®ed under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Some versions of HMMER may have been obtained under specialized commercial licenses from Washington University; for details, see the ®les COPYING and LICENSE that came with your copy of the HMMER software. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Appendix for a copy of the full text of the GNU General Public License. 1 Contents 1 Tutorial 5 1.1 The programs in HMMER . 5 1.2 Files used in the tutorial . 6 1.3 Searching a sequence database with a single pro®le HMM . 6 HMM construction with hmmbuild . 7 HMM calibration with hmmcalibrate . 7 Sequence database search with hmmsearch . 8 Searching major databases like NR or SWISSPROT .
    [Show full text]
  • The Biogrid Interaction Database
    D470–D478 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 26 November 2014 doi: 10.1093/nar/gku1204 The BioGRID interaction database: 2015 update Andrew Chatr-aryamontri1, Bobby-Joe Breitkreutz2, Rose Oughtred3, Lorrie Boucher2, Sven Heinicke3, Daici Chen1, Chris Stark2, Ashton Breitkreutz2, Nadine Kolas2, Lara O’Donnell2, Teresa Reguly2, Julie Nixon4, Lindsay Ramage4, Andrew Winter4, Adnane Sellam5, Christie Chang3, Jodi Hirschman3, Chandra Theesfeld3, Jennifer Rust3, Michael S. Livstone3, Kara Dolinski3 and Mike Tyers1,2,4,* 1Institute for Research in Immunology and Cancer, Universite´ de Montreal,´ Montreal,´ Quebec H3C 3J7, Canada, 2The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA, 4School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK and 5Centre Hospitalier de l’UniversiteLaval´ (CHUL), Quebec,´ Quebec´ G1V 4G2, Canada Received September 26, 2014; Revised November 4, 2014; Accepted November 5, 2014 ABSTRACT semi-automated text-mining approaches, and to en- hance curation quality control. The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein in- INTRODUCTION teractions curated from the primary biomedical lit- Massive increases in high-throughput DNA sequencing erature for all major model organism species and technologies (1) have enabled an unprecedented level of humans. As of September 2014, the BioGRID con- genome annotation for many hundreds of species (2–6), tains 749 912 interactions as drawn from 43 149 pub- which has led to tremendous progress in the understand- lications that represent 30 model organisms.
    [Show full text]
  • The Interpro Database, an Integrated Documentation Resource for Protein
    The InterPro database, an integrated documentation resource for protein families, domains and functional sites R Apweiler, T K Attwood, A Bairoch, A Bateman, E Birney, M Biswas, P Bucher, L Cerutti, F Corpet, M D Croning, et al. To cite this version: R Apweiler, T K Attwood, A Bairoch, A Bateman, E Birney, et al.. The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Research, Oxford University Press, 2001, 29 (1), pp.37-40. 10.1093/nar/29.1.37. hal-01213150 HAL Id: hal-01213150 https://hal.archives-ouvertes.fr/hal-01213150 Submitted on 7 Oct 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. © 2001 Oxford University Press Nucleic Acids Research, 2001, Vol. 29, No. 1 37–40 The InterPro database, an integrated documentation resource for protein families, domains and functional sites R. Apweiler1,*, T. K. Attwood2,A.Bairoch3, A. Bateman4,E.Birney1, M. Biswas1, P. Bucher5, L. Cerutti4,F.Corpet6, M. D. R. Croning1,2, R. Durbin4,L.Falquet5,W.Fleischmann1, J. Gouzy6,H.Hermjakob1,N.Hulo3, I. Jonassen7,D.Kahn6,A.Kanapin1, Y. Karavidopoulou1, R.
    [Show full text]
  • Multiple Sequence Alignment
    ELB18S Entry Level Bioinformatics 05-09 November 2018 (Second 2018 run of this Course) Basic Bioinformatics Sessions Practical 6: Multiple Sequence Alignment Sunday 4 November 2018 Practical 6: Multiple Sequence Alignment Sunday 4 November 2018 Multiple Sequence Alignment Here we will look at some software tools to align some protein sequences. Before we can do that, we need some sequences to align. I propose we try all the human homeobox domains from the well annotated section of UniprotKB. Getting the sequences is a trifle clumsy, so concentrate now! There used to be a much easier way, but that was made redundant by foolish people intent on making the future ever more tricky!! So, begin by going to the home of Uniprot: http://www.uniprot.org/ Choose the option of the button. First specify that you are only interested in Human proteins. To do this, set the first field to Organism [OS] and Term to Human [9606]. Set the second field selector to Reviewed and the corresponding Term to Reviewed (that is, only SwissProt entries). If required, Click on the button to request a further field selection option. Set the new field to Function. Set the type of Function to DNA binding. Set the Term selection to Homeobox. From previous investigations, you should be aware that a Homeobox domain is generally 60 amino acids in length. To avoid partial and/or really weird Homeobox proteins, set the Length range settings to recognise only homeoboxs between 50 and 70 amino acids long. Leave the Evidence box as Any assertion method, one does not wish to be too fussy! Address the button with authority to get the search going.
    [Show full text]
  • Syntax Highlighting for Computational Biology Artem Babaian1†, Anicet Ebou2, Alyssa Fegen3, Ho Yin (Jeffrey) Kam4, German E
    bioRxiv preprint doi: https://doi.org/10.1101/235820; this version posted December 20, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. bioSyntax: Syntax Highlighting For Computational Biology Artem Babaian1†, Anicet Ebou2, Alyssa Fegen3, Ho Yin (Jeffrey) Kam4, German E. Novakovsky5, and Jasper Wong6. 5 10 15 Affiliations: 1. Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada. [[email protected]] 2. Departement de Formation et de Recherches Agriculture et Ressources Animales, Institut National Polytechnique Felix Houphouet-Boigny, Yamoussoukro, Côte d’Ivoire. [[email protected]] 20 3. Faculty of Science, University of British Columbia, Vancouver, BC, Canada [[email protected]] 4. Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada. [[email protected]] 5. Department of Medical Genetics, University of British Columbia, Vancouver, BC, 25 Canada. [[email protected]] 6. Genome Science and Technology, University of British Columbia, Vancouver, BC, Canada. [[email protected]] Correspondence†: 30 Artem Babaian Terry Fox Laboratory BC Cancer Research Centre 675 West 10th Avenue Vancouver, BC, Canada. V5Z 1L3. 35 Email: [[email protected]] bioRxiv preprint doi: https://doi.org/10.1101/235820; this version posted December 20, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors.
    [Show full text]
  • The Uniprot Knowledgebase BLAST
    Introduction to bioinformatics The UniProt Knowledgebase BLAST UniProtKB Basic Local Alignment Search Tool A CRITICAL GUIDE 1 Version: 1 August 2018 A Critical Guide to BLAST BLAST Overview This Critical Guide provides an overview of the BLAST similarity search tool, Briefly examining the underlying algorithm and its rise to popularity. Several WeB-based and stand-alone implementations are reviewed, and key features of typical search results are discussed. Teaching Goals & Learning Outcomes This Guide introduces concepts and theories emBodied in the sequence database search tool, BLAST, and examines features of search outputs important for understanding and interpreting BLAST results. On reading this Guide, you will Be aBle to: • search a variety of Web-based sequence databases with different query sequences, and alter search parameters; • explain a range of typical search parameters, and the likely impacts on search outputs of changing them; • analyse the information conveyed in search outputs and infer the significance of reported matches; • examine and investigate the annotations of reported matches, and their provenance; and • compare the outputs of different BLAST implementations and evaluate the implications of any differences. finding short words – k-tuples – common to the sequences Being 1 Introduction compared, and using heuristics to join those closest to each other, including the short mis-matched regions Between them. BLAST4 was the second major example of this type of algorithm, From the advent of the first molecular sequence repositories in and rapidly exceeded the popularity of FastA, owing to its efficiency the 1980s, tools for searching dataBases Became essential. DataBase searching is essentially a ‘pairwise alignment’ proBlem, in which the and Built-in statistics.
    [Show full text]