Authors: Tominaga D
Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike’s information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics.We show that this method has h…

Understanding mechanisms of protein flexibility is of great importance to structural biology. The ability to detect similarities between proteins and their patterns is vital in discovering new information about unknown protein functions. A Distance Constraint Model (DCM) provides a means to generate a variety of flexibility measures based on a given protein structure. Although information about mechanical properties of flexibility is critical for understanding protein function for a given protein, the question of whether certain characteristics are shared across homologous proteins is difficult to assess. For a proper assessment, a quantified measure of similarity is necessary. This paper begins to explore image processing techniques to quantify similarities in signals and images that char…

Conclusions:
Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected. (Source: BMC Bioinformatics – Latest articles)

Conclusions:
The time efficiency of the algorithm and its genome-wide applications makes this work of broad interest to scientists interested in transcriptional regulation, motif discovery, and comparative genomics. (Source: BMC Bioinformatics – Latest articles)

Conclusions:
Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets. (Source: BMC Bioinformatics – Latest articles)

Conclusions:
Our proposed methods can simultaneously identify survival associated prognostic factors and predict survival outcomes with ultra-high dimensional genomic data. We have demonstrated the performance of our methods with both simulation and real data. The proposed method performs superbly with limited computational studies. (Source: BMC Bioinformatics – Latest articles)

A new genetic biomarker that indicates an increased risk for developing breast cancer can be found in an individual’s “junk” (non-coding) DNA, according to a new study featuring work from researchers at the Virginia Bioinformatics Institute (VBI) at Virginia Tech and their colleagues. The multidisciplinary team found that longer DNA sequences of a repetitive microsatellite were much more likely to be present in breast cancer patients than healthy volunteers… (Source: Health News from Medical News Today)

Conclusions:
An abundant 16-nt RNA sequence is sourced from a spliceosomal RNA, lies in a stem of a predicted RNA hairpin, and includes reverse complements of subsequences of the 3′UTR of a gene coding for a spliceosome protein. Thus RNU1 could function both as a component of spliceosome assembly and as inhibitor of production of the essential, spliceosome protein coded by SFRS1. Beyond this example, a general procedure is needed for systematic discovery of multiple alignments of sequencing, splicing, and RNA folding data. (Source: BMC Bioinformatics – Latest articles)

Conclusion: Our study provides novel genome-wide transcriptional profiling data on the mouse colon mucosa’s response to the Salmonella typhimurium infection. Building the pathways and networks of interactions between these genes help us to understand the complex interplay in the mice colon during Salmonella infection, and further provide new insights into the molecular cascade, which is mobilized to combat Salmonella-associated colon infection in vivo. (Source: BMC Genomics – Latest articles)

Motivation: Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs).
Results: We introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus cap…