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WebFEATURE

Automated function prediction in protein structures. Results of a FEATURE scan for metal binding sites in ribosomal protein S2. Putative sites are shown as red spheres within a 7 Angstrom radius. Charged, acidic residues are highlighted in blue. We have extended FEATURE to generate models automatically from 1D sequence motifs, a method we call SeqFEATURE, and implemented a web tool ( WebFEATURE. To allow scanning and analysis of protein structures for over 250 functions. See our publications.

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Automated function prediction in protein structures. Results of a FEATURE scan for metal binding sites in ribosomal protein S2. Putative sites are shown as red spheres within a 7 Angstrom radius. Charged, acidic residues are highlighted in blue. We have extended FEATURE to generate models automatically from 1D sequence motifs, a method we call SeqFEATURE, and implemented a web tool ( WebFEATURE. To allow scanning and analysis of protein structures for over 250 functions. See our publications.

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5 downloads

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7 publications

8 projects

9 helix group

10 the feature system

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WebFEATURE | feature.stanford.edu Reviews

https://feature.stanford.edu

Automated function prediction in protein structures. Results of a FEATURE scan for metal binding sites in ribosomal protein S2. Putative sites are shown as red spheres within a 7 Angstrom radius. Charged, acidic residues are highlighted in blue. We have extended FEATURE to generate models automatically from 1D sequence motifs, a method we call SeqFEATURE, and implemented a web tool ( WebFEATURE. To allow scanning and analysis of protein structures for over 250 functions. See our publications.

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feature.stanford.edu

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WebFEATURE

http://feature.stanford.edu/webfeature/data.php

Automated function prediction in protein structures. SeqFEATURE is a method to:. Identify active sites using PROSITE functional classes as the basis for ground truth. Compute microenvironments at known active sites and known non-active sites to develop training data for a protein functional class. Use machine learning algorithms such as Naive Bayes and Support Vector Machine to train predictive models based on the training data. Choose a model for which to download data:. 2FE2S FER 1.1.CYS.SG. A DEAMINAS...

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WebFEATURE

http://feature.stanford.edu/downloads.php

Automated function prediction in protein structures. For installing the latest command-line FEATURE tools:. FEATURE 3.1 source code. 2016 Helix Lab, Stanford University in collaboration with SFSU. WebFEATURE is supported by NIH LM05652 and GM102365. How to cite FEATURE.

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WebFEATURE

http://feature.stanford.edu/publications.php

Automated function prediction in protein structures. If you use the SeqFEATURE models or data from the PDB scan in your research, please cite the following paper:. Wu S, Liang MP, Altman RB. The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation. Genome Biology. 2008, 9:R8 PMID: 18197987. If you use any of the other models or the interactive results pages in your research, please also cite this paper:. J R Soc Interface. Identif...

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WebFEATURE

http://feature.stanford.edu/webfeature/index.php

Automated function prediction in protein structures. Scan a structure for function. To scan your own data, follow the steps below. First time users must register. Step 1: Choose a structure ( PDB. Please enter a valid PDB ID or File. Upload a PDB file. Step 2: Choose a function. The search may take a few minutes). Please select a model. Select One- - - - -. All functional models below. 2FE2S FER 1.1.CYS.SG. 2FE2S FER 1.6.CYS.SG. 2FE2S FER 1.9.CYS.SG. 4FE4S FER 1.11.CYS.SG. 4FE4S FER 1.1.CYS.SG. ALPHA 2 M...

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WebFEATURE

http://feature.stanford.edu/methods.php

Automated function prediction in protein structures. WebFEATURE is a web-based implementation of a machine learning algorithm for functional site recognition in protein structures. A few of the models provided are built from hand-curated training sets, but the majority come from the SeqFEATURE library of models [ Wu et al. 2008. WebFEATURE 4.0 employs both Naive Bayes [ Liang et al. 2003. And Support Vector Machines (SVM) [ Buturović et al. 2014. Score = Sum(i) log [P(Site v. A score cutoff for classifyi...

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konradjkarczewski.com

Blog | Konrad J. Karczewski

https://konradjkarczewski.com/blog

Konrad J. Karczewski. User-friendly, open-source personal genome pipeline. Today, GenomeWeb wrote a piece on. The STORMSeq pipeline (Scalable Tools for Open source Read Mapping),. Our newest project in the goal of enabling the public to explore their own personal genetic data. In this pipeline, users upload reads to Amazon S3 and start a webserver in Amazon EC2, where they can set parameters for read mapping and variant calling, all in a graphical user interface. Once they click “GO! The privacy discussi...

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Konrad Karczewski | Konrad J. Karczewski

https://konradjkarczewski.com/author/konradjkarczewski

Konrad J. Karczewski. Author Archive for Konrad Karczewski. User-friendly, open-source personal genome pipeline. Today, GenomeWeb wrote a piece on. The STORMSeq pipeline (Scalable Tools for Open source Read Mapping),. Our newest project in the goal of enabling the public to explore their own personal genetic data. And the code is available on github.com/konradjk/stormseq. Personal Genomics, in wordle form. But I thought I’d share the wordle for the entire book:. PSB 2012: Biocomputing and Bioethics.

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feature.stanford.edu

WebFEATURE

Automated function prediction in protein structures. Results of a FEATURE scan for metal binding sites in ribosomal protein S2. Putative sites are shown as red spheres within a 7 Angstrom radius. Charged, acidic residues are highlighted in blue. We have extended FEATURE to generate models automatically from 1D sequence motifs, a method we call SeqFEATURE, and implemented a web tool ( WebFEATURE. To allow scanning and analysis of protein structures for over 250 functions. See our publications.

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