MetaboAnalyst is a comprehensive, web-based tool designed for
processing, analyzing, and interpreting metabolomic data. It
handles most of the common metabolomic data types including
compound concentration lists, spectral bin lists, peak lists,
and raw mass spectrometry (MS) spectra.
PubMed: 25897128, 27603023, 22553367, 21637195, 21633943,
19429898, 30909447, 29955821, 29762782
Bayesil is a web system that automatically identifies and
quantifies metabolites using 1D 1H NMR spectra of ultra-filtered
plasma, serum, or cerebrospinal fluid. Bayesil first performs
all spectral processing steps, including Fourier transformation,
phasing, solvent filtering, chemical shift referencing, baseline
correction, and reference line shape convolution automatically.
It then deconvolutes the resulting NMR spectrum using a
reference spectral library. This deconvolution process
determines both the identity and quantity of the compounds in
the biofluid mixture. Extensive testing shows that Bayesil meets
or exceeds the performance of highly-trained human experts.
PubMed: 26017271
BioTransformer 3.0 is a freely-available software package for
accurate, rapid, and comprehensive in silico metabolism
prediction and compound identification. BioTransformer combines
a machine learning-based approach with a knowledge-based
approach to predict small molecule metabolism in human tissues
(e.g. liver tissue), the human gut, as well as the environment
(soil and water microbiota).
PubMed: 3553652, 30612223
CFM-ID 4.0 provides a method for accurately and efficiently
identifying metabolites in spectra generated by electrospray
ionization tandem mass spectrometry (ESI-MS/MS). The program
uses competitive fragmentation modeling to produce a
probabilistic generative model for the MS/MS fragmentation
process and machine learning techniques to adapt the model
parameters from data.
PubMed: 24895432, 27381172, 31013937
Other references: Metabolomics 2015 Feb; 11(1): 98–110.
ClassyFire is a web-based application for automated structural
classification of chemical entities. This application uses a
rule-based approach that relies on a comprehensible,
comprehensive, and computable chemical taxonomy. ClassyFire
provides a hierarchical chemical classification of chemical
entities (mostly small molecules and short peptide sequences),
as well as a structure-based textual description, based on a
chemical taxonomy named ChemOnt, which covers 4825 chemical
classes of organic and inorganic compounds. It can be accessed
via the web interface or via the ClassyFire API. ClassyFire is
offered to the public as a freely-accessible web server. Use and
redistribution of the data, in whole or in part, for commercial
purposes requires explicit permission of the authors and
explicit acknowledgement of the source material (ClassyFire) and
the original publication (see below). We ask that users who
download portions of the database or use the service (via the
server or the API) cite the ClassyFire paper in any resulting
publications.
Pubmed: 27867422
GC–AutoFit is a web application that automatically identifies
and quantifies metabolites using gas chromatography mass
spectrometry (GC-MS) data. GC-AutoFit currently accepts .CDF and
.mzXML file formats. It uses alkane standards to calculate the
retention index (RI) of each peak in the sample. The extracted
EI-MS (electron ionization mass spectrometry) spectra from each
peak, along with the RIs, are then compared to reference spectra
(RIs and EI-MS) in the specified library to identify and
quantify the compounds. The inclusion of blank spectra is
optional, however, it is useful for removing noise effects from
the query spectra. Extensive testing shows that GC-AutoFit meets
or exceeds the performance of highly-trained human experts.
PubMed: 24895432, 27381172, 31013937 Other references:
Metabolomics 2015 Feb; 11(1): 98–110.
MAGMET is a web-based system that automatically identifies and
quantifies metabolites using 1D 1H NMR spectra of serum. MAGMET
first performs all spectral processing steps, including Fourier
transformation, phasing, solvent filtering, chemical shift
referencing, baseline correction, and reference line shape
convolution automatically. It then deconvolutes the resulting
NMR spectrum using a reference spectral library, which here
contains the signatures of more than 60 metabolites. This
deconvolution process determines both the identity and quantity
of the compounds in the biofluid mixture. Extensive testing
shows that MAGMET meets or exceeds the performance of
highly-trained human experts.
PubMed: 21360156
PHASTER (PHAge Search
Tool – Enhanced
Release) is a significant upgrade to the
popular PHAST web server for the rapid identification and
annotation of prophage sequences within bacterial genomes and
plasmids. While the steps in the phage identification pipeline
in PHASTER remain largely the same as in the original PHAST,
numerous software improvements and significant hardware
enhancements have now made PHASTER faster, more efficient, more
visually appealing and much more user-friendly. A number of
other optimizations have been implemented, including automated
algorithms to reduce the size and redundancy of PHASTER’s
databases, improvements in handling multiple (metagenomic)
queries and high user traffic, and the ability to perform
automated look-ups against >14,000 previously PHAST/PHASTER
annotated bacterial genomes (which can lead to complete phage
annotations in seconds as opposed to minutes). PHASTER’s web
interface has also been entirely rewritten. A new graphical
genome browser has been added, gene/genome visualization tools
have been improved, and the graphical interface is now more
modern, robust, and user-friendly.
PubMed: 27141966
PolySearch 2.0 is an online search engine and text-mining system
for identifying relationships between human diseases, genes,
proteins, drugs, metabolites, toxins, metabolic pathways,
organs, tissues, subcellular organelles, positive health
effects, negative health effects, drug actions, Gene Ontology
terms, MeSH terms, ICD-10 medical codes, biological taxonomies,
and chemical taxonomies. PolySearch 2.0 supports a generalized
‘given X, find all associated Ys’ query, where X and Y can be
selected from the aforementioned biomedical entities.
PubMed: 25925572, 18487273