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HPM / Human Proteome Map
An interactive resource to the scientific community by integrating the massive peptide sequencing result from the draft map of the human proteome project. The Human Proteome Map was based on LC-MS/MS by utilizing of high resolution and high accuracy Fourier transform mass spectrometry. This includes 17 adult tissues, 6 primary hematopoietic cells and 7 fetal tissues. The HPM portal provides an interactive web resource by reorganizing the label-free quantitative proteomic data set in a simple graphical view. In addition, the portal provides selected reaction monitoring (SRM) information for all peptides identified.
Determines the monoisotopic mass and charge state of parent ions from high resolution tandem mass spectrometry data. DeconMSn offers significant improvement for LTQ_FT and LTQ_Orbitrap instruments over the commercially delivered Thermo Fisher Scientific’s extract_msn tool. The software calculates the monoisotopic mass of the parent ion for each MSn spectrum by applying a modified THRASH approach to the parent isotopic distribution. For datasets with low-resolution MS1 spectra, it uses a support-vector machine based charge-detection algorithm to determine parent mass.
Simplifies the construction of high quality custom spectral libraries. Curatr is complementary to mass spectrometry visualization tools, spectral matching software, online data analysis and sharing platforms, and repositories. It has several features: (1) it collects information about standards analyzed, (2) it automatically selects candidate tandem mass spectrometry (MS/MS) spectra for standards, (3) it facilitates manual curation of MS/MS spectra through the web-browser, (4) it has a searchable catalogue of all spectra contained in the library, and (5) it provides export compatibility with major repositories and external software applications.
MiCId / Microorganism Classification and Identification
A software tool that processes mass spectrometry tandem spectra (MS/MS) data for identification and classification of microorganisms. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms.
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