A Tool for Creating Informative, Interactive Logos
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RDA COVID-19 Recommendations and Guidelines on Data Sharing
RDA COVID-19 Recommendations and Guidelines on Data Sharing DOI: 10.15497/RDA00052 Authors: RDA COVID-19 Working Group Published: 30th June 2020 Abstract: This is the final version of the Recommendations and Guidelines from the RDA COVID19 Working Group, and has been endorsed through the official RDA process. Keywords: RDA; Recommendations; COVID-19. Language: English License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication RDA webpage: https://www.rd-alliance.org/group/rda-covid19-rda-covid19-omics-rda-covid19- epidemiology-rda-covid19-clinical-rda-covid19-1 Related resources: - RDA COVID-19 Guidelines and Recommendations – preliminary version, https://doi.org/10.15497/RDA00046 - Data Sharing in Epidemiology, https://doi.org/10.15497/RDA00049 - RDA COVID-19 Zotero Library, https://doi.org/10.15497/RDA00051 Citation and Download: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/RDA00052 RDA COVID-19 Recommendations and Guidelines on Data Sharing RDA Recommendation (FINAL Release) Produced by: RDA COVID-19 Working Group, 2020 Document Metadata Identifier DOI: https://doi.org/10.15497/rda00052 Citation To cite this document please use: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/rda00052 Title RDA COVID-19; Recommendations and Guidelines on Data Sharing, Final release 30 June 2020 Description This is the final version of the Recommendations and Guidelines -
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. -
Downloaded Were Considered to Be True Positive While Those from the from UCSC Databases on 14Th September 2011 [70,71]
Basu et al. BMC Bioinformatics 2013, 14(Suppl 7):S14 http://www.biomedcentral.com/1471-2105/14/S7/S14 RESEARCH Open Access Examples of sequence conservation analyses capture a subset of mouse long non-coding RNAs sharing homology with fish conserved genomic elements Swaraj Basu1, Ferenc Müller2, Remo Sanges1* From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS) Catania, Sicily. 2-4 May 2012 Abstract Background: Long non-coding RNAs (lncRNA) are a major class of non-coding RNAs. They are involved in diverse intra-cellular mechanisms like molecular scaffolding, splicing and DNA methylation. Through these mechanisms they are reported to play a role in cellular differentiation and development. They show an enriched expression in the brain where they are implicated in maintaining cellular identity, homeostasis, stress responses and plasticity. Low sequence conservation and lack of functional annotations make it difficult to identify homologs of mammalian lncRNAs in other vertebrates. A computational evaluation of the lncRNAs through systematic conservation analyses of both sequences as well as their genomic architecture is required. Results: Our results show that a subset of mouse candidate lncRNAs could be distinguished from random sequences based on their alignment with zebrafish phastCons elements. Using ROC analyses we were able to define a measure to select significantly conserved lncRNAs. Indeed, starting from ~2,800 mouse lncRNAs we could predict that between 4 and 11% present conserved sequence fragments in fish genomes. Gene ontology (GO) enrichment analyses of protein coding genes, proximal to the region of conservation, in both organisms highlighted similar GO classes like regulation of transcription and central nervous system development. -
Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic Acids
This page intentionally left blank Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modern molecular genetics. Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. The need to understand the data is becoming ever more pressing. Demands for sophisticated analyses of biological sequences are driving forward the newly-created and explosively expanding research area of computational molecular biology, or bioinformatics. Many of the most powerful sequence analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference of phylogenetic trees using maximum likelihood approaches. This book provides the first unified, up-to-date, and tutorial-level overview of sequence analysis methods, with particular emphasis on probabilistic modelling. Pairwise alignment, hidden Markov models, multiple alignment, profile searches, RNA secondary structure analysis, and phylogenetic inference are treated at length. Written by an interdisciplinary team of authors, the book is accessible to molecular biologists, computer scientists and mathematicians with no formal knowledge of each others’ fields. It presents the state-of-the-art in this important, new and rapidly developing discipline. Richard Durbin is Head of the Informatics Division at the Sanger Centre in Cambridge, England. Sean Eddy is Assistant Professor at Washington University’s School of Medicine and also one of the Principle Investigators at the Washington University Genome Sequencing Center. -
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. -
Genomic and Transcriptomic Surveys for the Study of Ncrnas with a Focus on Tropical Parasites
PhD Thesis PROGRAMA DE PÓS-GRADUAÇÃO EM BIOINFORMÁTICA UNIVERSIDADE FEDERAL DE MINAS GERAIS Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites Mainá Bitar Belo Horizonte February 2015 Universidade Federal de Minas Gerais PhD Thesis PROGRAMA DE PÓS-GRADUAÇÃO EM BIOINFORMÁTICA Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites PhD candidate: Mainá Bitar Advisor: Glória Regina Franco Co-advisor: Martin Alexander Smith Mainá Bitar Lourenço Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites Versão final Tese apresentada ao Programa Interunidades de Pós-Graduação em Bioinformática do Instituto de Ciências Biológicas da Universidade Federal de Minas Gerais como requisito parcial para a obtenção do título de Doutor em Bioinformática. Orientador: Profa. Dra. Glória Regina Franco BELO HORIZONTE 2015 043 Bitar, Mainá. Genomic and transcriptomic surveys for the study of ncRNAs with a focus on tropical parasites [manuscrito] / Mainá Bitar. – 2015. 134 f. : il. ; 29,5 cm. Orientador: Glória Regina Franco. Coorientador: Martin Alexander Smith. Tese (doutorado) – Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas. Programa de Pós-Graduação em Bioinformática. 1. Bioinformática - Teses. 2. Trypanosoma cruzi. 3. Schistosoma mansoni. 4. Genômica. 5. Transcriptoma. 6. Trans-Splicing. I. Franco, Glória Regina. II. Smith, Martin Alexander. III. Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. IV. Título. CDU: 573:004 Ficha catalográfica elaborada por Fabiane C. M. Reis – CRB 6/2680 Esta tese é dedicada à minha mãe, que me deu a liberdade para sonhar e a força para viver a realidade. -
On the Necessity of Dissecting Sequence Similarity Scores Into
Wong et al. BMC Bioinformatics 2014, 15:166 http://www.biomedcentral.com/1471-2105/15/166 METHODOLOGY ARTICLE Open Access On the necessity of dissecting sequence similarity scores into segment-specific contributions for inferring protein homology, function prediction and annotation Wing-Cheong Wong1*, Sebastian Maurer-Stroh1,2, Birgit Eisenhaber1 and Frank Eisenhaber1,3,4* Abstract Background: Protein sequence similarities to any types of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc. where either positional sequence conservation is the result of a very simple, physically induced pattern or rather integral sequence properties are critical) are pertinent sources for mistaken homologies. Regretfully, these considerations regularly escape attention in large-scale annotation studies since, often, there is no substitute to manual handling of these cases. Quantitative criteria are required to suppress events of function annotation transfer as a result of false homology assignments. Results: The sequence homology concept is based on the similarity comparison between the structural elements, the basic building blocks for conferring the overall fold of a protein. We propose to dissect the total similarity score into fold-critical and other, remaining contributions and suggest that, for a valid homology statement, the fold-relevant score contribution should at least be significant on its own. As part of the article, we provide the DissectHMMER software program for dissecting HMMER2/3 scores into segment-specific contributions. We show that DissectHMMER reproduces HMMER2/3 scores with sufficient accuracy and that it is useful in automated decisions about homology for instructive sequence examples. To generalize the dissection concept for cases without 3D structural information, we find that a dissection based on alignment quality is an appropriate surrogate. -
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B NY RA DY BARRETT ILLUSTRATION BY MIKE PERRY TE H NEW JANELIA COMPUTING CLUSTER PUTS A PREMIUM ON EXPANDABILITY AND SPEED. ple—it’s pretty obvious to anyone which words are basically the same. That would be like two genes from humans and apes.” But in organisms that are more diver- gent, Eddy needs to understand how DNA sequences tend to change over time. “And it becomes a difficult specialty, with seri- ous statistical analysis,” he says. From a computational standpoint, that means churning through a lot of opera- tions. Comparing two typical-sized protein sequences, to take a simple example, would require a whopping 10200 opera- Computational biologists have a need for to help investigators conduct genome tions. Classic algorithms, available since speed. The computing cluster at HHMI’s searches and catalog the inner workings the 1960s, can trim that search to 160,000 Janelia Farm Research Campus delivers and structures of the brain. computations—a task that would take the performance they require—at a mind- only a millisecond or so on any modern boggling 36 trillion operations per second. F ASTER Answers processor. But in the genome business, In the course of their work, Janelia A group leader at Janelia Farm, Eddy deals people routinely do enormous numbers researchers generate millions of digitized in the realm of millions of computations of these sequence comparisons—trillions images and gigabytes of data files, and they daily as he compares sequences of DNA. and trillions of them. These “routine” cal- run algorithms daily that demand robust He is a rare breed, both biologist and code culations could take years if they had to be computational horsepower. -
A Brief History of Sequence Logos
Biostatistics and Biometrics Open Access Journal ISSN: 2573-2633 Mini-Review Biostat Biometrics Open Acc J Volume 6 Issue 3 - April 2018 Copyright © All rights are reserved by Kushal K Dey DOI: 10.19080/BBOAJ.2018.06.555690 A Brief History of Sequence Logos Kushal K Dey* Department of Statistics, University of Chicago, USA Submission: February 12, 2018; Published: April 25, 2018 *Corresponding author: Kushal K Dey, Department of Statistics, University of Chicago, 5747 S Ellis Ave, Chicago, IL 60637, USA. Tel: 312-709- 0680; Email: Abstract For nearly three decades, sequence logo plots have served as the standard tool for graphical representation of aligned DNA, RNA and protein sequences. Over the years, a large number of packages and web applications have been developed for generating these logo plots and using them handling and the overall scope of these plots in biological applications and beyond. Here I attempt to review some popular tools for generating sequenceto identify logos, conserved with a patterns focus on in how sequences these plots called have motifs. evolved Also, over over time time, since we their have origin seen anda considerable how I view theupgrade future in for the these look, plots. flexibility of data Keywords : Graphical representation; Sequence logo plots; Standard tool; Motifs; Biological applications; Flexibility of data; DNA sequence data; Python library; Interdependencies; PLogo; Depletion of symbols; Alphanumeric strings; Visualizes pairwise; Oligonucleotide RNA sequence data; Visualize succinctly; Predictive power; Initial attempts; Widespread; Stylistic configurations; Multiple sequence alignment; Introduction based comparisons and predictions. In the next section, we The seeds of the origin of sequence logos were planted in review the modeling frameworks and functionalities of some of early 1980s when researchers, equipped with large amounts of these tools [5].