A Brief History of Sequence Logos
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DNA Sequencing and Sorting: Identifying Genetic Variations
BioMath DNA Sequencing and Sorting: Identifying Genetic Variations Student Edition Funded by the National Science Foundation, Proposal No. ESI-06-28091 This material was prepared with the support of the National Science Foundation. However, any opinions, findings, conclusions, and/or recommendations herein are those of the authors and do not necessarily reflect the views of the NSF. At the time of publishing, all included URLs were checked and active. We make every effort to make sure all links stay active, but we cannot make any guaranties that they will remain so. If you find a URL that is inactive, please inform us at [email protected]. DIMACS Published by COMAP, Inc. in conjunction with DIMACS, Rutgers University. ©2015 COMAP, Inc. Printed in the U.S.A. COMAP, Inc. 175 Middlesex Turnpike, Suite 3B Bedford, MA 01730 www.comap.com ISBN: 1 933223 71 5 Front Cover Photograph: EPA GULF BREEZE LABORATORY, PATHO-BIOLOGY LAB. LINDA SHARP ASSISTANT This work is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties. DNA Sequencing and Sorting: Identifying Genetic Variations Overview Each of the cells in your body contains a copy of your genetic inheritance, your DNA which has been passed down to you, one half from your biological mother and one half from your biological father. This DNA determines physical features, like eye color and hair color, and can determine susceptibility to medical conditions like hypertension, heart disease, diabetes, and cancer. -
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. -
Sequence Motifs, Information Content, and Sequence Logos Morten
Sequence motifs, information content, and sequence logos Morten Nielsen, CBS, Depart of Systems Biology, DTU Objectives • Visualization of binding motifs – Construction of sequence logos • Understand the concepts of weight matrix construction – One of the most important methods of bioinformatics • How to deal with data redundancy • How to deal with low counts Outline • Pattern recognition • Weight matrix – Regular expressions construction and probabilities – Sequence weighting • Information content – Low (pseudo) counts • Examples from the real – Sequence logos world • Multiple alignment and • Sequence profiles sequence motifs Binding Motif. MHC class I with peptide Anchor positions Sequence information SLLPAIVEL YLLPAIVHI TLWVDPYEV GLVPFLVSV KLLEPVLLL LLDVPTAAV LLDVPTAAV LLDVPTAAV LLDVPTAAV VLFRGGPRG MVDGTLLLL YMNGTMSQV MLLSVPLLL SLLGLLVEV ALLPPINIL TLIKIQHTL HLIDYLVTS ILAPPVVKL ALFPQLVIL GILGFVFTL STNRQSGRQ GLDVLTAKV RILGAVAKV QVCERIPTI ILFGHENRV ILMEHIHKL ILDQKINEV SLAGGIIGV LLIENVASL FLLWATAEA SLPDFGISY KKREEAPSL LERPGGNEI ALSNLEVKL ALNELLQHV DLERKVESL FLGENISNF ALSDHHIYL GLSEFTEYL STAPPAHGV PLDGEYFTL GVLVGVALI RTLDKVLEV HLSTAFARV RLDSYVRSL YMNGTMSQV GILGFVFTL ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CLGGLLTMV FIAGNSAYE KLGEFYNQM KLVALGINA DLMGYIPLV RLVTLKDIV MLLAVLYCL AAGIGILTV YLEPGPVTA LLDGTATLR ITDQVPFSV KTWGQYWQV TITDQVPFS AFHHVAREL YLNKIQNSL MMRKLAILS AIMDKNIIL IMDKNIILK SMVGNWAKV SLLAPGAKQ KIFGSLAFL ELVSEFSRM KLTPLCVTL VLYRYGSFS YIGEVLVSV CINGVCWTV VMNILLQYV ILTVILGVL KVLEYVIKV FLWGPRALV GLSRYVARL FLLTRILTI -
Seq2logo: a Method for Construction and Visualization of Amino Acid Binding Motifs and Sequence Profiles Including Sequence Weig
Downloaded from orbit.dtu.dk on: Dec 20, 2017 Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion Thomsen, Martin Christen Frølund; Nielsen, Morten Published in: Nucleic Acids Research Link to article, DOI: 10.1093/nar/gks469 Publication date: 2012 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Thomsen, M. C. F., & Nielsen, M. (2012). Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion. Nucleic Acids Research, 40(W1), W281-W287. DOI: 10.1093/nar/gks469 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Published online 25 May 2012 Nucleic Acids Research, 2012, Vol. -
Interpreting a Sequence Logo When Initiating Translation, Ribosomes Bind to an Mrna at a Ribosome Binding Site Upstream of the AUG Start Codon
Interpreting a Sequence Logo When initiating translation, ribosomes bind to an mRNA at a ribosome binding site upstream of the AUG start codon. Because mRNAs from different genes all bind to a ribosome, the genes encoding these mRNAs are likely to have a similar base sequence where the ribosomes bind. Therefore, candidate ribosome binding sites on mRNA can be identified by comparing DNA sequences (and thus the mRNA sequences) of several genes in a species, searching the region upstream of the start codon for shared (conserved) base sequences. The DNA sequences of 149 genes from the E. coli genome were aligned with the aim to identify similar base sequences as potential ribosome binding sites. Rather than presenting the data as a series of 149 sequences aligned in a column (a sequence alignment), the researchers used a sequence logo. The potential ribosome binding regions from 10 E. coli genes are shown in the sequence alignment in Figure 1. The sequence logo derived from the aligned sequences is shown in Figure 2. Note that the DNA shown is the nontemplate (coding) strand, which is how DNA sequences are typically presented. Figure 1 Sequence alignment for 10 E. coli genes. Figure 2 Sequence logo derived from sequence alignment 1) In the sequence logo, the horizontal axis shows the primary sequence of the DNA by nucleotide position. Letters for each base are stacked on top of each other according to their relative frequency at that position among the aligned sequences, with the most common base as the largest letter at the top of the stack. -
A New Sequence Logo Plot to Highlight Enrichment and Depletion
bioRxiv preprint doi: https://doi.org/10.1101/226597; this version posted November 29, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A new sequence logo plot to highlight enrichment and depletion. Kushal K. Dey 1, Dongyue Xie 1, Matthew Stephens 1, 2 1 Department of Statistics, University of Chicago 2 Department of Human Genetics, University of Chicago * Corresponding Email : [email protected] Abstract Background : Sequence logo plots have become a standard graphical tool for visualizing sequence motifs in DNA, RNA or protein sequences. However standard logo plots primarily highlight enrichment of symbols, and may fail to highlight interesting depletions. Current alternatives that try to highlight depletion often produce visually cluttered logos. Results : We introduce a new sequence logo plot, the EDLogo plot, that highlights both enrichment and depletion, while minimizing visual clutter. We provide an easy-to-use and highly customizable R package Logolas to produce a range of logo plots, including EDLogo plots. This software also allows elements in the logo plot to be strings of characters, rather than a single character, extending the range of applications beyond the usual DNA, RNA or protein sequences. We illustrate our methods and software on applications to transcription factor binding site motifs, protein sequence alignments and cancer mutation signature profiles. Conclusion : Our new EDLogo plots, and flexible software implementation, can help data analysts visualize both enrichment and depletion of characters (DNA sequence bases, amino acids, etc) across a wide range of applications. -
A Tool for Detecting Base Mis-Calls in Multiple Sequence Alignments by Semi-Automatic Chromatogram Inspection
ChromatoGate: A Tool for Detecting Base Mis-Calls in Multiple Sequence Alignments by Semi-Automatic Chromatogram Inspection Nikolaos Alachiotis Emmanouella Vogiatzi∗ Scientific Computing Group Institute of Marine Biology and Genetics HITS gGmbH HCMR Heidelberg, Germany Heraklion Crete, Greece [email protected] [email protected] Pavlos Pavlidis Alexandros Stamatakis Scientific Computing Group Scientific Computing Group HITS gGmbH HITS gGmbH Heidelberg, Germany Heidelberg, Germany [email protected] [email protected] ∗ Affiliated also with the Department of Genetics and Molecular Biology of the Democritian University of Thrace at Alexandroupolis, Greece. Corresponding author: Nikolaos Alachiotis Keywords: chromatograms, software, mis-calls Abstract Automated DNA sequencers generate chromatograms that contain raw sequencing data. They also generate data that translates the chromatograms into molecular sequences of A, C, G, T, or N (undetermined) characters. Since chromatogram translation programs frequently introduce errors, a manual inspection of the generated sequence data is required. As sequence numbers and lengths increase, visual inspection and manual correction of chromatograms and corresponding sequences on a per-peak and per-nucleotide basis becomes an error-prone, time-consuming, and tedious process. Here, we introduce ChromatoGate (CG), an open-source software that accelerates and partially automates the inspection of chromatograms and the detection of sequencing errors for bidirectional sequencing runs. To provide users full control over the error correction process, a fully automated error correction algorithm has not been implemented. Initially, the program scans a given multiple sequence alignment (MSA) for potential sequencing errors, assuming that each polymorphic site in the alignment may be attributed to a sequencing error with a certain probability. -
Bioinformatics Manual Sequence Data Analysis with CLC Main Workbench
Introduction to Molecular Biology and Bioinformatics Workshop Biosciences eastern and central Africa - International Livestock Research Institute Hub (BecA-ILRI Hub), Nairobi, Kenya May 5-16, 2014 Bioinformatics Manual Sequence data analysis with CLC Main Workbench Written and compiled by Joyce Njuguna, Mark Wamalwa, Rob Skilton 1 Getting started with CLC Main Workbench A. Quality control of sequenced data B. Assembling sequences C. Nucleotide and protein sequence manipulation D. BLAST sequence search E. Aligning sequences F. Building phylogenetic trees Welcome to CLC Main Workbench -- a user-friendly sequence analysis software package for analysing Sanger sequencing data and for supporting your daily bioinformatics work. Definition Sequence: the order of nucleotide bases [or amino acids] in a DNA [or protein] molecule DNA Sequencing: Biochemical methods used to determine the order of nucleotide bases, adenine(A), guanine(G),cytosine(C)and thymine(T) in a DNA strand TIP CLC Main Workbench includes an extensive Help function, which can be found in the Help menu of the program’s Menu bar. The Help can also be shown by pressing F1. The help topics are sorted in a table of contents and the topics can be searched. Also, it is recommended that you view the Online presentations where a product specialist from CLC bio demonstrates the software. This is a very easy way to get started using the program. Read more about online presentations here: http://clcbio.com/presentation. A. Getting Started with CLC Main Workbench 1. Download the trial version of CLC Main Workbench 6.8.1 from the following URL: http://www.clcbio.com/products/clc-main-workbench-direct-download/ 2. -
A STAT Protein Domain That Determines DNA Sequence Recognition Suggests a Novel DNA-Binding Domain
Downloaded from genesdev.cshlp.org on September 25, 2021 - Published by Cold Spring Harbor Laboratory Press A STAT protein domain that determines DNA sequence recognition suggests a novel DNA-binding domain Curt M. Horvath, Zilong Wen, and James E. Darnell Jr. Laboratory of Molecular Cell Biology, The Rockefeller University, New York, New York 10021 Statl and Stat3 are two members of the ligand-activated transcription factor family that serve the dual functions of signal transducers and activators of transcription. Whereas the two proteins select very similar (not identical) optimum binding sites from random oligonucleotides, differences in their binding affinity were readily apparent with natural STAT-binding sites. To take advantage of these different affinities, chimeric Statl:Stat3 molecules were used to locate the amino acids that could discriminate a general binding site from a specific binding site. The amino acids between residues -400 and -500 of these -750-amino-acid-long proteins determine the DNA-binding site specificity. Mutations within this region result in Stat proteins that are activated normally by tyrosine phosphorylation and that dimerize but have greatly reduced DNA-binding affinities. [Key Words: STAT proteins; DNA binding; site selection] Received January 6, 1995; revised version accepted March 2, 1995. The STAT (signal transducers and activators if transcrip- Whereas oligonucleotides representing these selected se- tion) proteins have the dual purpose of, first, signal trans- quences exhibited slight binding preferences, the con- duction from ligand-activated receptor kinase com- sensus sites overlapped sufficiently to be recognized by plexes, followed by nuclear translocation and DNA bind- both factors. However, by screening different natural ing to activate transcription (Darnell et al. -
Bioinformatics I Sanger Sequence Analysis
Updated: October 2020 Lab 5: Bioinformatics I Sanger Sequence Analysis Project Guide The Wolbachia Project Page Contents 3 Activity at a Glance 4 Technical Overview 5-7 Activity: How to Analyze Sanger Sequences 8 DataBase Entry 9 Appendix I: Illustrated BLAST Alignment 10 Appendix II: Sanger Sequencing Quick Reference Content is made available under the Creative Commons Attribution-NonCommercial-No Derivatives International License. Contact ([email protected]) if you would like to make adaptations for distribution beyond the classroom. The Wolbachia Project: Discover the Microbes Within! was developed by a collaboration of scientists, educators, and outreach specialists. It is directed by the Bordenstein Lab at Vanderbilt University. https://www.vanderbilt.edu/wolbachiaproject 2 Activity at a Glance Goals To analyze and interpret the quality of Sanger sequences To generate a consensus DNA sequence for bioinformatics analyses Learning Objectives Upon completion of this activity, students will (i) understand the Sanger method of sequencing, also known as the chain-termination method; (ii) be able to interpret chromatograms; (iii) evaluate sequencing Quality Scores; and (iv) generate a consensus DNA sequence based on forward and reverse Sanger reactions. Prerequisite Skills While no computer programming skills are necessary to complete this work, prior exposure to personal computers and the Internet is assumed. Teaching Time: One class period Recommended Background Tutorials • DNA Learning Center Animation: Sanger Method of -
Identification of Factors That Interact with the E IA-Inducible Adenovirus E3 Promoter
Downloaded from genesdev.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Identification of factors that interact with the E IA-inducible adenovirus E3 promoter Helen C. Hurst and Nicholas C. Jones Gene Regulation Group, Imperial Cancer Research Fund, London, England, WC2A 3PX We have investigated the E1A-inducible E3 promoter of adenovirus type 5 with respect to its ability to bind specific nuclear proteins. Four distinct nucleoprotein-binding sites were detected, located between positions - 7 to -33, -44 to -68, -81 to - 103, and - 154 to - 183, relative to the E3 cap site. These sites contain sequences previously shown to be functionally important for efficient E3 transcription. No major qualitative or quantitative differences were found in the binding pattern between nucleoprotein extracts prepared from uninfected or adenovirus-infected HeLa cells. Competition experiments suggest that the factors binding to the - 154 to - 183 and -81 to - 103 sites are the previously identified nucleoproteins, NFI and AP1, respectively. The factor binding to the -44 to -68 site, which we term ATF, also interacts with other E1A-inducible promoters and is very similar and probably identical to the factor that binds to the cAMP-responsive element of somatostatin. We have purified this factor, which is a protein of 43 kD in size. [Key Words: Trans-activation; gel retardation; footprinting; nuclear factors] Received June 12, 1987; revised version accepted October 5, 1987. The control of the rate of transcription initiation is an (Borrelli et al. 1984; Velcich and Ziff 1985), whereas the important element in the regulation of eukaryotic gene protein of 243 amino acids, although it represses very expression. -
Lecture 7: Sequence Motif Discovery
Sequence motif: definitions COSC 348: Computing for Bioinformatics • In Bioinformatics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has Lecture 7: been proven or assumed to have a biological significance. Sequence Motif Discovery • Once we know the sequence pattern of the motif, then we can use the search methods to find it in the sequences (i.e. Lubica Benuskova Boyer-Moore algorithm, Rabin-Karp, suffix trees, etc.) • The problem is to discover the motifs, i.e. what is the order of letters the particular motif is comprised of. http://www.cs.otago.ac.nz/cosc348/ 1 2 Examples of motifs in DNA Sequence motif: notations • The TATA promoter sequence is an example of a highly • An example of a motif in a protein: N, followed by anything but P, conserved DNA sequence motif found in eukaryotes. followed by either S or T, followed by anything but P − One convention is to write N{P}[ST]{P} where {X} means • Another example of motifs: binding sites for transcription any amino acid except X; and [XYZ] means either X or Y or Z. factors (TF) near promoter regions of genes, etc. • Another notation: each ‘.’ signifies any single AA, and each ‘*’ Gene 1 indicates one member of a closely-related AA family: Gene 2 − WDIND*.*P..*...D.F.*W***.**.IYS**...A.*H*S*WAMRN Gene 3 Gene 4 • In the 1st assignment we have motifs like A??CG, where the Gene 5 wildcard ? Stands for any of A,U,C,G. Binding sites for TF 3 4 Sequence motif discovery from conservation Motif discovery based on alignment • profile analysis is another word for this.