Defining window-boundaries for genomic analyses using smoothing spline techniques

Background

High-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise, and reduce the total number of tests performed. However, use of adjacent marker information can result in over- or under-smoothing, undesirable window boundary specifications, or highly correlated test statistics. We introduce a method for defining windows based on statistically guided breakpoints in the data, as a foundation for the analysis of multiple adjacent data points. This method involves first fitting a cubic smoothing spline to the data and then identifying the inflection points of the fitted spline, which serve as the boundaries of adjacent windows. This technique does not require prior knowledge of linkage disequilibrium, and therefore can be applied to data collected from individual or pooled sequencing experiments. Moreover, in contrast to existing methods, an arbitrary choice of window size is not necessary, since these are determined empirically and allowed to vary along the genome.

Results

Simulations applying this method were performed to identify selection signatures from pooled sequencing F_ST data, for which allele frequencies were estimated from a pool of individuals. The relative ratio of true to false positives was twice that generated by existing techniques. A comparison of the approach to a previous study that involved pooled sequencing F_ST data from maize suggested that outlying windows were more clearly separated from their neighbors than when using a standard sliding window approach.

Conclusions

We have developed a novel technique to identify window boundaries for subsequent analysis protocols. When applied to selection studies based on F_ST data, this method provides a high discovery rate and minimizes false positives. The method is implemented in the R package GenWin, which is publicly available from CRAN.     

Single cell studies of the cell cycle and some models

Analysis of growth and division often involves measurements made on cell populations, which tend to average data. The value of single cell analysis needs to be appreciated, and models based on findings from single cells should be taken into greater consideration in our understanding of the way in which cell size and division are co-ordinated. Examples are given of some single cell analyses in mammalian cells, yeast and other microorganisms. There is also a short discussion on how far the results are in accord with simple models.     

Novel Rath peptide for intracellular delivery of protein and nucleic acids

In the present study, a novel cell penetrating peptide (CPP) named as Rath, has been identified from the avian infectious bursal disease virus. It has the potential to penetrate and translocate cargo molecules into cells independent of temperature. Additionally, it can deliver oligonucleotide in 30 min and antibodies within an hour intracellular to chicken embryonic fibroblast primary cells. As an ideal delivery vehicle, it has the ability to protect the cargo molecules in the presence of serum, nucleases and has minimal or no cytotoxicity at even higher peptide concentrations studied. The biophysical characterizations showed that Rath has a dominant β structure with a small α helix and has remarkable binding ability with protein and DNA. Thus, the characterization of unique Rath peptide to deliver protein or nucleic acid into the cells with non-covalent interaction could be used as an effective delivery method for various cell based assays.     

Methods for peptide identification by spectral comparison

Background  

Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies.     

Removing celiac disease-related gluten proteins from bread wheat while retaining technological properties: a study with Chinese Spring deletion lines

Background  

Gluten proteins can induce celiac disease (CD) in genetically susceptible individuals. In CD patients gluten-derived peptides are presented to the immune system, which leads to a CD4⁺ T-cell mediated immune response and inflammation of the small intestine. However, not all gluten proteins contain T-cell stimulatory epitopes. Gluten proteins are encoded by multigene loci present on chromosomes 1 and 6 of the three different genomes of hexaploid bread wheat (Triticum aestivum) (AABBDD).