Structural elucidation and physicochemical properties of litchi polysaccharide with the promoting effect on exopolysaccharide production by Weissella confusa.

Exopolysaccharide (EPS), as a secondary metabolite of microorganisms, has been commonly used in the dairy industry to replace the traditional stabilizers. However, the EPS production by microorganism is generally low, which limits its application. A litchi polysaccharide (Lzp2-2) with the promoting effect on EPS production by Weissella confusa was purified. The SEM and FT-IR analysis indicated that Lzp2-2 displayed a compact netlike structure and typical bands of carbohydrates. The structure of Lzp2-2 was further elucidated, which was comprised of a major backbone structure [→3)-ß-D-Galp-(1→6)-ß-D-Galp-(1 â†’ 6)-ß-D-Galp-(1 â†’ 3)-ß-D-Glcp-(1 â†’ 6)-α-D-Glcp-(1 â†’ 3)-α-D-Glcp-(1→] linked with two side chains [α-L-Araf-(1 â†’ 5)-α-L-Araf-(1→, and ß-D-Glcp-(1 â†’ or α-L-Araf-(1→] at the O-3 and O-6) of ß-D-Galp-(1→, respectively. Finally, Lzp2-2 was applied as an additive to the medium of yoghurt fermented by W. confusa. The results indicated Lzp2-2 not only promoted the EPS production to improve the viscosity, texture and mouthfeel of yoghurt, but also facilitated the generation of other secondary metabolites (volatile organic compounds), thus elevating the flavor of yoghurt.

MAnorm2 for quantitatively comparing groups of ChIP-seq samples.

Eukaryotic gene transcription is regulated by a large cohort of chromatin-associated proteins, and inferring their differential binding sites between cellular contexts requires a rigorous comparison of the corresponding ChIP-seq data. We present MAnorm2, a new computational tool for quantitatively comparing groups of ChIP-seq samples. MAnorm2 uses a hierarchical strategy for normalization of ChIP-seq data and assesses within-group variability of ChIP-seq signals based on an empirical Bayes framework. In this framework, MAnorm2 allows for abundant differential ChIP-seq signals between groups of samples as well as very different global within-group variability between groups. Using a number of real ChIP-seq data sets, we observed that MAnorm2 clearly outperformed existing tools for differential ChIP-seq analysis, especially when the groups of samples being compared had distinct global within-group variability.

MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes.

Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.