The Subjective Index for Physical and Social Outcome (SIPSO) in Stroke: investigation of its subscale structure

Background  

Short and valid measures of the impact of a stroke on integration are required in health and social settings. The Subjective Index of Physical and Social Outcome (SIPSO) is one such measure. However, there are questions whether scores can be summed into a total score or whether subscale scores should be calculated. This paper aims to provide clarity on the internal construct validity of the subscales and the total scale.     

Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models

Background  

When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model. While there are several techniques for the fitting of sparse high-dimensional survival models by penalized parameter estimation, none allows for explicit consideration of such mandatory covariates.     

Revealing and avoiding bias in semantic similarity scores for protein pairs

Background  

Semantic similarity scores for protein pairs are widely applied in functional genomic researches for finding functional clusters of proteins, predicting protein functions and protein-protein interactions, and for identifying putative disease genes. However, because some proteins, such as those related to diseases, tend to be studied more intensively, annotations are likely to be biased, which may affect applications based on semantic similarity measures. Thus, it is necessary to evaluate the effects of the bias on semantic similarity scores between proteins and then find a method to avoid them.     

A database of phylogenetically atypical genes in archaeal and bacterial genomes,identified using the <Emphasis Type="Italic">DarkHorse</Emphasis> algorithm

Background  

The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not.     

Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics

Background  

In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.     

ABC: software for interactive browsing of genomic multiple sequence alignment data

Background  

Alignment and comparison of related genome sequences is a powerful method to identify regions likely to contain functional elements. Such analyses are data intensive, requiring the inclusion of genomic multiple sequence alignments, sequence annotations, and scores describing regional attributes of columns in the alignment. Visualization and browsing of results can be difficult, and there are currently limited software options for performing this task.     

Associations between duration of first trimester intrauterine exposure to genocide against the Tutsi and health outcomes in adulthood

Objectives

Hundreds of thousands of Rwandans were conceived during the 1994 genocide against the Tutsi, including thousands conceived by genocidal rape. We explore whether the duration of first trimester exposure to the genocide is associated with variation in adult mental health outcomes in individuals exposed to varying degrees of genocide-related stress in utero.

Materials and Methods

We recruited 30 Rwandans conceived via genocidal rape, 31 Rwandans conceived by genocide survivors not raped, and 30 individuals of Rwandan-descent who were conceived outside of Rwanda at the time of the genocide (control group). Individuals were age- and sex-matched across groups. Adult mental health was assessed through standardized questionnaires for vitality, anxiety, and depression.

Results

Among the genocide only group, a longer duration of first trimester prenatal exposure was associated with higher anxiety scores and lower vitality (both p < 0.010), and higher depression scores (p = 0.051). Duration of first trimester exposure was not associated with any measures of mental health among the genocidal rape or control group.

Discussion

Duration of exposure to genocide in the first trimester of gestation was associated with variation in adult mental health among the genocide only group. The lack of association between duration of first trimester exposure to genocide and adult mental health in the genocidal rape group may reflect the fact that stress associated with conception through rape persisted beyond the genocide period itself, encompassing all of gestation and likely beyond. Geopolitical and community interventions are needed in the context of extreme events during pregnancy to mitigate adverse intergenerational outcomes.     

Natural history and clinical significance of MRI-detected bone marrow lesions at the knee: a prospective study in community dwelling older adults

Introduction  

There are conflicting data on the natural history and clinical significance of bone marrow lesions (BMLs). The aims of this study were to describe the natural history of MRI-detected BMLs at the knee using a quantitative measure and examine the association of BMLs with pain, function and stiffness scores, and total knee replacement (TKR) surgery.     

Identification of recurrent regions of copy-number variants across multiple individuals

Background  

Algorithms and software for CNV detection have been developed, but they detect the CNV regions sample-by-sample with individual-specific breakpoints, while common CNV regions are likely to occur at the same genomic locations across different individuals in a homogenous population. Current algorithms to detect common CNV regions do not account for the varying reliability of the individual CNVs, typically reported as confidence scores by SNP-based CNV detection algorithms. General methodologies for identifying these recurrent regions, especially those directed at SNP arrays, are still needed.     

The distance-profile representation and its application to detection of distantly related protein families

Background  

Detecting homology between remotely related protein families is an important problem in computational biology since the biological properties of uncharacterized proteins can often be inferred from those of homologous proteins. Many existing approaches address this problem by measuring the similarity between proteins through sequence or structural alignment. However, these methods do not exploit collective aspects of the protein space and the computed scores are often noisy and frequently fail to recognize distantly related protein families.     

Improved protein structure selection using decoy-dependent discriminatory functions

Background  

A key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations.     

Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments

Background  

While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.     

Ancestral sequence alignment under optimal conditions

Background  

Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms.     

Improved results in proteomics by use of local and peptide-class specific false discovery rates

Background  

Proteomic protein identification results need to be compared across laboratories and platforms, and thus a reliable method is needed to estimate false discovery rates. The target-decoy strategy is a platform-independent and thus a prime candidate for standardized reporting of data. In its current usage based on global population parameters, the method does not utilize individual peptide scores optimally.     

PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination

Background  

We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by 'Pairwise Identity and Cost Scores Ordination' (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.     

ASPDock: protein-protein docking algorithm using atomic solvation parameters model

Background  

Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock).     

BranchClust: a phylogenetic algorithm for selecting gene families

Background  

Automated methods for assembling families of orthologous genes include those based on sequence similarity scores and those based on phylogenetic approaches. The first are easy to automate but usually they do not distinguish between paralogs and orthologs or have restriction on the number of taxa. Phylogenetic methods often are based on reconciliation of a gene tree with a known rooted species tree; a limitation of this approach, especially in case of prokaryotes, is that the species tree is often unknown, and that from the analyses of single gene families the branching order between related organisms frequently is unresolved.     

Semi-supervised discovery of differential genes

Background  

Various statistical scores have been proposed for evaluating the significance of genes that may exhibit differential expression between two or more controlled conditions. However, in many clinical studies to detect clinical marker genes for example, the conditions have not necessarily been controlled well, thus condition labels are sometimes hard to obtain due to physical, financial, and time costs. In such a situation, we can consider an unsupervised case where labels are not available or a semi-supervised case where labels are available for a part of the whole sample set, rather than a well-studied supervised case where all samples have their labels.     

Island method for estimating the statistical significance of profile-profile alignment scores

Background  

In the last decade, a significant improvement in detecting remote similarity between protein sequences has been made by utilizing alignment profiles in place of amino-acid strings. Unfortunately, no analytical theory is available for estimating the significance of a gapped alignment of two profiles. Many experiments suggest that the distribution of local profile-profile alignment scores is of the Gumbel form. However, estimating distribution parameters by random simulations turns out to be computationally very expensive.