Biosof LLC

New York City, NY, United States

Biosof LLC

New York City, NY, United States

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Corpas M.,The Genome Analysis Center | Jimenez R.,European Bioinformatics Institute | Carbon S.J.,Lawrence Berkeley National Laboratory | GarcIa A.,Florida State University | And 18 more authors.
F1000Research | Year: 2014

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 of 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. © 2014 Corpas M et al.


Yachdav G.,TUM | Yachdav G.,Biosof LLC | Hecht M.,TUM | Pasmanik-Chor M.,Tel Aviv University | And 3 more authors.
F1000Research | Year: 2014

The HeatMapViewer is a BioJS component that lays-out and 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 adapts 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. Availability: https://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7706. © 2014 Yachdav G et al.


Goldberg T.,Bioinformatics I12 | Hecht M.,Bioinformatics I12 | Hamp T.,Bioinformatics I12 | Karl T.,Bioinformatics I12 | And 35 more authors.
Nucleic Acids Research | Year: 2014

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 state-of-the-art method. Here, we report the 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. © 2014 The Author(s).


Yachdav G.,TU Munich | Yachdav G.,Biosof LLC | Kloppmann E.,TU Munich | Kloppmann E.,Columbia University | And 25 more authors.
Nucleic Acids Research | Year: 2014

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 (ConSurf), 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 mutations (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. © 2014 The Author(s).


Yachdav G.,TU Munich | Yachdav G.,Biosof LLC | Goldberg T.,TU Munich | Wilzbach S.,TU Munich | And 19 more authors.
eLife | Year: 2015

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects. © eLife Sciences Publications Ltd.


Kajan L.,TUM | Yachdav G.,TUM | Yachdav G.,Columbia University | Yachdav G.,Biosof LLC | And 14 more authors.
BioMed Research International | Year: 2013

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome. © 2013 László Kaján et al.

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