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HeatMapViewer: interactive display of 2D data in biology

Guy Yachdav, Maximilian Hecht, Metsada Pasmanik-Chor, Adva Yeheskel, Burkhard Rost
2014 F1000Research  
The HeatMapViewer is a BioJS component that lays-out and Summary: renders two-dimensional (2D) plots or heat maps that are ideally suited to visualize matrix formatted data in biology such as for the display of microarray experiments or the outcome of mutational studies and the study of SNP-like sequence variants. It can be easily integrated into documents and provides a powerful, interactive way to visualize heat maps in web applications. The software uses a scalable graphics technology that
more » ... apts the visualization component to any required resolution, a useful feature for a presentation with many different data-points. The component can be applied to present various biological data types. Here, we present two such cases -showing gene expression data and visualizing mutability landscape analysis.
doi:10.12688/f1000research.3-48.v1 pmid:24860644 pmcid:PMC4023661 fatcat:ggmp2dfjkrdo7hffppeubmam5e

FeatureViewer, a BioJS component for visualization of position-based annotations in protein sequences

Leyla Garcia, Guy Yachdav, Maria-Jesus Martin
2014 F1000Research  
FeatureViewer is a BioJS component that lays out, maps, orients, Summary: and renders position-based annotations for protein sequences. This component is highly flexible and customizable, allowing the presentation of annotations by rows, all centered, or distributed in non-overlapping tracks. It uses either lines or shapes for sites and rectangles for regions. The result is a powerful visualization tool that can be easily integrated into web applications as well as documents as it provides an export-to-image functionality.
doi:10.12688/f1000research.3-47.v1 pmid:24741440 pmcid:PMC3983936 fatcat:phthggfvpzcurnjhyzl7kxqqbu

MSAViewer: interactive JavaScript visualization of multiple sequence alignments

Guy Yachdav, Sebastian Wilzbach, Benedikt Rauscher, Robert Sheridan, Ian Sillitoe, James Procter, Suzanna E. Lewis, Burkhard Rost, Tatyana Goldberg
2016 Bioinformatics  
Filled red rectangles indicate sequence annotations provided by the user [here: secondary structure predictions of PredictProtein (Yachdav et al., 2014) ].  ... 
doi:10.1093/bioinformatics/btw474 pmid:27412096 pmcid:PMC5181560 fatcat:cfhka6nwgvfqdkfq26y3jqk4n4

Improved Disorder Prediction by Combination of Orthogonal Approaches

Avner Schlessinger, Marco Punta, Guy Yachdav, Laszlo Kajan, Burkhard Rost, Joseph P. R. O. Orgel
2009 PLoS ONE  
Availability: http://www.rostlab.org/services/md/ Citation: Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B (2009) Improved Disorder Prediction by Combination of Orthogonal Approaches.  ... 
doi:10.1371/journal.pone.0004433 pmid:19209228 pmcid:PMC2635965 fatcat:efrioji5ozhr3ktltkwvfd6lwa

SNAP predicts effect of mutations on protein function

Yana Bromberg, Guy Yachdav, Burkhard Rost
2008 Computer applications in the biosciences : CABIOS  
Many non-synonymous single nucleotide polymorphisms (nsSNPs) in humans are suspected to impact protein function. Here, we present a publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions. SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold. Each prediction is associated with a
more » ... iability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions. Availability: Web-server: http://www.rostlab.org/services/SNAP; downloadable program available upon request.
doi:10.1093/bioinformatics/btn435 pmid:18757876 pmcid:PMC2562009 fatcat:pwowtimxrjgeleyukzydsoouyq

FeatureViewer, a BioJS component for visualization of position-based annotations in protein sequences

Leyla Garcia, Guy Yachdav, Maria-Jesus Martin
2014 F1000Research  
FeatureViewer is a BioJS component that lays out, maps, orients, Summary: and renders position-based annotations for protein sequences. This component is highly flexible and customizable, allowing the presentation of annotations by rows, all centered, or distributed in non-overlapping tracks. It uses either lines or shapes for sites and rectangles for regions. The result is a powerful visualization tool that can be easily integrated into web applications as well as documents as it provides an export-to-image functionality.
doi:10.12688/f1000research.3-47.v2 pmid:24741440 pmcid:PMC3983936 fatcat:nfu3fgkil5axzelginjj2im4le

New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'

Yana Bromberg, Guy Yachdav, Yanay Ofran, Reinhard Schneider, Burkhard Rost
2009 Current opinion in drug discovery & development  
ISIS interaction sites identified from sequence (Adapted with permission from Bromberg Y, Yachdav G, Ofran Y, Schneider R and Rost B © 2009 Bromberg Y, Yachdav G, Ofran Y, Schneider R and Rost B) A E  ...  SNAP screening for non-acceptable polymorphisms (Adapted with permission from Bromberg Y, Yachdav G, Ofran Y, Schneider R and Rost B © 2009 Bromberg Y, Yachdav G, Ofran Y, Schneider R and Rost B) 0  ... 
pmid:19396742 fatcat:oymcw4ptejggxlvsjl7a6c7ydu

Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

László Kaján, Guy Yachdav, Esmeralda Vicedo, Martin Steinegger, Milot Mirdita, Christof Angermüller, Ariane Böhm, Simon Domke, Julia Ertl, Christian Mertes, Eva Reisinger, Cedric Staniewski (+1 others)
2013 BioMed Research International  
Yachdav). M. Steinegger and M. Mirdita received funding from Amazon and Cycle Computing for case study 2. The authors thank all those who funded our research.  ...  Yachdav (equal contributors) have redesigned "predictprotein, " performed initial software packaging, and wrote the paper; E. Vicedo, M. Steinegger and M.  ... 
doi:10.1155/2013/398968 pmid:23971032 pmcid:PMC3732596 fatcat:qx5dh4rp3vajhgsjcnhti2x7gy

BioJS: an open source standard for biological visualisation – its status in 2014

Manuel Corpas, Rafael Jimenez, Seth J Carbon, Alex García, Leyla Garcia, Tatyana Goldberg, John Gomez, Alexis Kalderimis, Suzanna E Lewis, Ian Mulvany, Aleksandra Pawlik, Francis Rowland (+8 others)
2014 F1000Research  
BioJS is a community-based standard and repository of functional components to represent biological information on the web. The development of BioJS has been prompted by the growing need for bioinformatics visualisation tools to be easily shared, reused and discovered. Its modular architecture makes it easy for users to find a specific functionality without needing to know how it has been built, while components can be extended or created for implementing new functionality. The BioJS community
more » ... f developers currently provides a range of functionality that is open access and freely available. A registry has been set up that categorises and provides installation instructions and testing facilities at http://www.ebi.ac.uk/tools/biojs/. The source code for all components is available for ready use at https://github.com/biojs/biojs.
doi:10.12688/f1000research.3-55.v1 pmid:25075290 pmcid:PMC4103492 fatcat:d355hvzzujgilggkszqvbowyea

Anatomy of BioJS, an open source community for the life sciences

Guy Yachdav, Tatyana Goldberg, Sebastian Wilzbach, David Dao, Iris Shih, Saket Choudhary, Steve Crouch, Max Franz, Alexander García, Leyla J García, Björn A Grüning, Devasena Inupakutika (+9 others)
2015 eLife  
DOI: 10.7554/eLife.07009.002 Yachdav et al. eLife 2015;4:e07009. DOI: 10.7554/eLife.07009 Feature articleCutting edge | Anatomy of BioJS, an open source community for the life sciences  ...  Several recognized projects and institutions have already shown commitment to BioJS by utilizing and developing components: examples of this include PredictProtein (Yachdav et al., 2014) , CATH (Sillitoe  ... 
doi:10.7554/elife.07009 pmid:26153621 pmcid:PMC4495654 fatcat:7dwu6xkfk5gzdp44olw3ti3foe

PredictProtein - Predicting Protein Structure and Function for 29 Years [article]

Michael Bernhofer, Christian Dallago, Tim Karl, Venkata Satagopam, Michael Heinzinger, Maria Littmann, Tobias Olenyi, Jiajun Qiu, Konstantin Schuetze, Guy Yachdav, Haim Ashkenazy, Nir Ben-Tal (+16 others)
2021 bioRxiv   pre-print
Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein
more » ... re in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and second-ary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. Pre-dictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
doi:10.1101/2021.02.23.432527 fatcat:5tdh5vgujjbataxvwidial7fee

Structural genomics reveals EVE as a new ASCH/PUA-related domain

Claudia Bertonati, Marco Punta, Markus Fischer, Guy Yachdav, Farhad Forouhar, Weihong Zhou, Alexander P. Kuzin, Jayaraman Seetharaman, Mariam Abashidze, Theresa A. Ramelot, Michael A. Kennedy, John R. Cort (+5 others)
2009 Proteins: Structure, Function, and Bioinformatics  
Guy Yachdav and Burkhard Rost were additionally supported by the grants R01-GM079767 and R01-LM07329 from the NIH; Claudia Bertonati was supported by Istituto Pasteur -Fondazione Cenci Bolognetti Universita  ... 
doi:10.1002/prot.22287 pmid:19191354 pmcid:PMC4080787 fatcat:lidszpwqafamzlsuz5qibcu5uy

PredictProtein—an open resource for online prediction of protein structural and functional features

Guy Yachdav, Edda Kloppmann, Laszlo Kajan, Maximilian Hecht, Tatyana Goldberg, Tobias Hamp, Peter Hönigschmid, Andrea Schafferhans, Manfred Roos, Michael Bernhofer, Lothar Richter, Haim Ashkenazy (+8 others)
2014 Nucleic Acids Research  
PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions
more » ... rf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein-protein binding sites (ISIS2), protein-polynucleotide binding sites (SomeNA) and predictions of the effect of point mu-tations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.
doi:10.1093/nar/gku366 pmid:24799431 pmcid:PMC4086098 fatcat:c5pmgwyx7zg4jjncj4eywixmf4

LocTree3 prediction of localization

Tatyana Goldberg, Maximilian Hecht, Tobias Hamp, Timothy Karl, Guy Yachdav, Nadeem Ahmed, Uwe Altermann, Philipp Angerer, Sonja Ansorge, Kinga Balasz, Michael Bernhofer, Alexander Betz (+22 others)
2014 Nucleic Acids Research  
The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other stateof-the-art method. Here, we report
more » ... he availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.
doi:10.1093/nar/gku396 pmid:24848019 pmcid:PMC4086075 fatcat:elitvr465zfr5oylrfegv6g4oi

PredictProtein - Predicting Protein Structure and Function for 29 Years

Michael Bernhofer, Christian Dallago, Tim Karl, Venkata Satagopam, Michael Heinzinger, Maria Littmann, Tobias Olenyi, Jiajun Qiu, Konstantin Schütze, Guy Yachdav, Haim Ashkenazy, Nir Ben-Tal (+16 others)
2021 Nucleic Acids Research  
Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein
more » ... re in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
doi:10.1093/nar/gkab354 pmid:33999203 pmcid:PMC8265159 fatcat:3ozsvwjgmze35lzdfp46ortxjq
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