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PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, 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. PolySearch2 supports a generalized 'Given X, find all associated Ys' query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: 'Find all diseases associated with Bisphenol A'. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

CFM-ID is a web server supporting three tasks associated with the interpretation of tandem mass spectra (MS/MS) for the purpose of automated metabolite identification: annotation of the peaks in a spectrum for a known chemical structure; prediction of spectra for a given chemical structure and putative metabolite identification--a predicted ranking of possible candidate structures for a target spectrum. The algorithms used for these tasks are based on Competitive Fragmentation Modeling (CFM), a recently introduced probabilistic generative model for the MS/MS fragmentation process that uses machine learning techniques to learn its parameters from data. These algorithms have been extensively tested on multiple datasets and have been shown to out-perform existing methods such as MetFrag and FingerId. This web server provides a simple interface for using these algorithms and a graphical display of the resulting annotations, spectra and structures. CFM-ID is made freely available at http://cfmid.wishartlab.com., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The BioTools Suite is a set of three comprehensive, platform-independent software packages (PepTool, GeneTool, and ChromaTool) developed for sequence assembly and analysis. In addition to supporting a large number of standard bioinformatics functions, these programs also incorporate a number of useful innovations including uniform graphical-user interface (GUI) design, direct internet connectivity, a novel approach to feature annotation, and a variety of enhanced algorithms for large scale proteome and genome analysis. This article describes the key features, recent changes, and general operation of all three programs.

Motivation: Current software tools are moderately effective in predicting genetic structure (exons, introns, intergenic regions, and complete genes) from raw DNA sequence data. Improvements in accuracy and speed are needed to deal with the increasing volume of data from large scale sequencing projects., Results: We present a two-stage computer program to predict genetic structure in eukaryotic DNA. The first stage makes use of a novel statistical technique, called reference point logistic (RPL) regression, to calculate scores for potential functional sites. These site scores are combined with interval content, length, and state scores, via a Generalized Hidden Markov Model, to determine a combined score for each possible parse of a given DNA sequence into exons, introns, and intergenic regions. An optimal parse is found using a dynamic programming algorithm. In the second stage, protein sequence alignment methods are applied to improve the accuracy of the initial parse. Computation in the first stage of the program is very fast (1 s on a 360 MHz CPU for a 16 kb sequence) and its predictive accuracy typically matches or exceeds the best results reported for other methods (Sensitivity = 0.93 and Specificity = 0.93 for the Burset/Guigótest set). Computation in the second stage is slower, but the final predictions are more accurate (Sn = 0.97, Sp = 0.97). The program (called GRPL) can handle partial, single, and multi-gene sequences. The program is also capable of predicting the genetic structure of vertebrate, invertebrate, and plant DNA with nearly equal accuracy. Statistical techniques have also been introduced to model the effects of varying C+G content in a continuous manner and to control overfitting of parameters for smaller training sets., Availability: An academic implementation of GRPL, compiled for SUN workstations, is available by anonymous ftp from snipe.pharmacy. ualberta.ca/pub. The training and test sets used in this work, together with supplementary material, can be found at the same location. A commercial implementation is available as a component of GeneTool (BioTools Inc., http://biotools.com).

A suite of programs called CAMRA (Computer Aided Magnetic Resonance Assignment) has been developed for computer assisted residue-specific assignments of proteins. CAMRA consists of three units: ORB, CAPTURE and PROCESS. ORB predicts NMR chemical shifts for unassigned proteins using a chemical shift database of previously assigned homologous proteins supplemented by a statistically derived chemical shift database in which the shifts are categorized according to their residue, atom and secondary structure type. CAPTURE generates a list of valid peaks from NMR spectra by filtering out noise peaks and other artifacts and then separating the derived peak list into distinct spin systems. PROCESS combines the chemical shift predictions from ORB with the spin systems identified by CAPTURE to obtain residue specific assignments. PROCESS ranks the top choices for an assignment along with scores and confidence values. In contrast to other auto-assignment programs, CAMRA does not use any connectivity information but instead is based solely on matching predicted shifts with observed spin systems. As such, CAMRA represents a new and unique approach for the assignment of protein NMR spectra. CAMRA will be particularly useful in conjunction with other assignment methods and under special circumstances, such as the assignment of flexible regions in proteins where sufficient NOE information is generally not available. CAMRA was tested on two medium-sized proteins belonging to the chemokine family. It was found to be effective in predicting the assignment providing a database of previously assigned proteins with at least 30% sequence identity is available. CAMRA is versatile and can be used to include and evaluate heteronuclear and three-dimensional experiments.

SEQSEE (SEQuence SEEker) is a multi-purpose, menu-driven suite of programs designed to provide a fully integrated, state-of-the-art package for the analysis and display of protein sequences and protein databases. It is currently configured to run on most UNIX-based machines including Sun, SGI and NeXT workstations with conversion to other architectures (e.g. Vax or Cray) being a relatively simple task. SEQSEE is capable of performing nearly all of the analytical and comparative tasks found in most comprehensive commercially available software packages. These include sequence/database searching, sequence retrieval, sequence entry and editing, statistical sequence analysis, multiple sequence alignment, flexible pattern matching, and secondary structure prediction. SEQSEE also integrates a number of unique databases which allow it to perform many additional functions such as structure-based sequence alignments and homology-based secondary structure prediction. Additional enhancements to many previously published algorithms have substantially improved the performance of SEQSEE over that found for most other commercial products. The source code, the documentation and all of the required databases for SEQSEE are freely available and may be obtained by anonymous ftp.