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Motivation: DNA and protein patterns are usefully represented by sequence logos. However, the methods for logo generation in common use lack a proper statistical basis, and are non-optimal for recognizing functionally relevant alignment columns., Results: We redefine the information at a logo position as a per-observation multiple alignment log-odds score. Such scores are positive or negative, depending on whether a column's observations are better explained as arising from relatedness or chance. Within this framework, we propose distinct normalized maximum likelihood and Bayesian measures of column information. We illustrate these measures on High Mobility Group B (HMGB) box proteins and a dataset of enzyme alignments. Particularly in the context of protein alignments, our measures improve the discrimination of biologically relevant positions., Availability and Implementation: Our new measures are implemented in an open-source Web-based logo generation program, which is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/logoddslogo/index.html. A stand-alone version of the program is also available from this site., Contact: altschul@ncbi.nlm.nih.gov, Supplementary Information: Supplementary data are available at Bioinformatics online., (Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.)

Motivation: Term-enrichment analysis facilitates biological interpretation by assigning to experimentally/computationally obtained data annotation associated with terms from controlled vocabularies. This process usually involves obtaining statistical significance for each vocabulary term and using the most significant terms to describe a given set of biological entities, often associated with weights. Many existing enrichment methods require selections of (arbitrary number of) the most significant entities and/or do not account for weights of entities. Others either mandate extensive simulations to obtain statistics or assume normal weight distribution. In addition, most methods have difficulty assigning correct statistical significance to terms with few entities., Results: Implementing the well-known Lugananni-Rice formula, we have developed a novel approach, called SaddleSum, that is free from all the aforementioned constraints and evaluated it against several existing methods. With entity weights properly taken into account, SaddleSum is internally consistent and stable with respect to the choice of number of most significant entities selected. Making few assumptions on the input data, the proposed method is universal and can thus be applied to areas beyond analysis of microarrays. Employing asymptotic approximation, SaddleSum provides a term-size-dependent score distribution function that gives rise to accurate statistical significance even for terms with few entities. As a consequence, SaddleSum enables researchers to place confidence in its significance assignments to small terms that are often biologically most specific., Availability: Our implementation, which uses Bonferroni correction to account for multiple hypotheses testing, is available at http://www.ncbi.nlm.nih.gov/CBBresearch/qmbp/mn/enrich/. Source code for the standalone version can be downloaded from ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/SaddleSum/.

Summary: Founded upon diffusion with damping, ITM Probe is an application for modeling information flow in protein interaction networks without prior restriction to the sub-network of interest. Given a context consisting of desired origins and destinations of information, ITM Probe returns the set of most relevant proteins with weights and a graphical representation of the corresponding sub-network. With a click, the user may send the resulting protein list for enrichment analysis to facilitate hypothesis formation or confirmation., Availability: ITM Probe web service and documentation can be found at www.ncbi.nlm.nih.gov/CBBresearch/qmbp/mn/itm_probe.

Motivation: The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments., Results: We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program., Availability: The scripts for performing evaluations are available upon request from the authors.