A generally applicable validation scheme for the assessment of factors involved in reproducibility and quality of DNA-microarray data

BACKGROUND: In research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed. RESULTS: A number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance. CONCLUSION: Even in cases where 90 % of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25 %. Clustering experiments showed that trends can be reliably detected also from genes with very low expression levels. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality

Improved analysis of bacterial CGH data beyond the log-ratio paradigm

<p>Abstract</p> <p>Background</p> <p>Existing methods for analyzing bacterial CGH data from two-color arrays are based on log-ratios only, a paradigm inherited from expression studies. We propose an alternative approach, where microarray signals are used in a different way and sequence identity is predicted using a supervised learning approach.</p> <p>Results</p> <p>A data set containing 32 hybridizations of sequenced versus sequenced genomes have been used to test and compare methods. A ROC-analysis has been performed to illustrate the ability to rank probes with respect to Present/Absent calls. Classification into Present and Absent is compared with that of a gaussian mixture model.</p> <p>Conclusion</p> <p>The results indicate our proposed method is an improvement of existing methods with respect to ranking and classification of probes, especially for multi-genome arrays.</p

Phylogenetic comparative assembly

Husemann P, Stoye J. Phylogenetic Comparative Assembly. Algorithms for Molecular Biology. 2010;5(1): 3.BACKGROUND:Recent high throughput sequencing technologies are capable of generating a huge amount of data for bacterial genome sequencing projects. Although current sequence assemblers successfully merge the overlapping reads, often several contigs remain which cannot be assembled any further. It is still costly and time consuming to close all the gaps in order to acquire the whole genomic sequence. RESULTS:Here we propose an algorithm that takes several related genomes and their phylogenetic relationships into account to create a graph that contains the likelihood for each pair of contigs to be adjacent. Subsequently, this graph can be used to compute a layout graph that shows the most promising contig adjacencies in order to aid biologists in finishing the complete genomic sequence. The layout graph shows unique contig orderings where possible, and the best alternatives where necessary. CONCLUSIONS:Our new algorithm for contig ordering uses sequence similarity as well as phylogenetic information to estimate adjacencies of contigs. An evaluation of our implementation shows that it performs better than recent approaches while being much faster at the same tim

An overview of the recent developments on fructooligosaccharide production and applications

Over the past years, many researchers have suggested that deficiencies in the diet can lead to disease states and that some diseases can be avoided through an adequate intake of relevant dietary components. Recently, a great interest in dietary modulation of the human gut has been registered. Prebiotics, such as fructooligosaccharides (FOS), play a key role in the improvement of gut microbiota balance and in individual health. FOS are generally used as components of functional foods, are generally regarded as safe (generally recognized as safe status—from the Food and Drug Administration, USA), and worth about 150€ per kilogram. Due to their nutrition- and health-relevant properties, such as moderate sweetness, low carcinogenicity, low calorimetric value, and low glycemic index, FOS have been increasingly used by the food industry. Conventionally, FOS are produced through a two-stage process that requires an enzyme production and purification step in order to proceed with the chemical reaction itself. Several studies have been conducted on the production of FOS, aiming its optimization toward the development of more efficient production processes and their potential as food ingredients. The improvement of FOS yield and productivity can be achieved by the use of different fermentative methods and different microbial sources of FOS producing enzymes and the optimization of nutritional and culture parameter; therefore, this review focuses on the latest progresses in FOS research such as its production, functional properties, and market data.Agencia de Inovacao (AdI)-Project BIOLIFE reference PRIME 03/347. Ana Dominguez acknowledges Fundacao para a Ciencia e a Tecnologia, Portugal, for her PhD grant reference SFRH/BD/23083/2005

Transcriptome Analysis Reveals Mechanisms by Which Lactococcus lactis Acquires Nisin Resistance

Nisin, a posttranslationally modified antimicrobial peptide produced by Lactococcus lactis, is widely used as a food preservative. Yet, the mechanisms leading to the development of nisin resistance in bacteria are poorly understood. We used whole-genome DNA microarrays of L. lactis IL1403 to identify the factors underlying acquired nisin resistance mechanisms. The transcriptomes of L. lactis IL1403 and L. lactis IL1403 Nis(r), which reached a 75-fold higher nisin resistance level, were compared. Differential expression was observed in genes encoding proteins that are involved in cell wall biosynthesis, energy metabolism, fatty acid and phospholipid metabolism, regulatory functions, and metal and/or peptide transport and binding. These results were further substantiated by showing that several knockout and overexpression mutants of these genes had strongly altered nisin resistance levels and that some knockout strains could no longer become resistant to the same level of nisin as that of the wild-type strain. The acquired nisin resistance mechanism in L. lactis is complex, involving various different mechanisms. The four major mechanisms are (i) preventing nisin from reaching the cytoplasmic membrane, (ii) reducing the acidity of the extracellular medium, thereby stimulating the binding of nisin to the cell wall, (iii) preventing the insertion of nisin into the membrane, and (iv) possibly transporting nisin across the membrane or extruding nisin out of the membrane

Binding Interactions Between α-glucans from Lactobacillus reuteri and Milk Proteins Characterised by Surface Plasmon Resonance

Interactions between milk proteins and α-glucans at pH 4.0–5.5 were investigated by use of surface plasmon resonance. The α-glucans were synthesised with glucansucrase enzymes from Lactobacillus reuteri strains ATCC-55730, 180, ML1 and 121. Variations in the molecular characteristics of the α-glucans, such as molecular weight, linkage type and degree of branching, influenced the interactions with native and denatured β-lactoglobulin and κ-casein. The highest overall binding levels were reached with α-(1,4) compared to α-(1,3) linked glucans. Glucans withmany α-(1,6) linkages demonstrated the highest binding levels to κ-casein, whereas the interaction with native β-lactoglobulin was suppressed by α-(1,6) linkages. Glucans with a higher degree of branching generally displayed lower protein binding levels whereas a higher molecular weight resulted in increased binding to κ-casein. The interactions with κ-casein were not pH dependent, whereas binding to denatured β-lactoglobulin was highest at pH 4.0 and binding to native β-lactoglobulin was optimal at pH 4.5–5.0. This study shows that molecular weight, linkage type and degree of branching of α-glucans highly influence the binding interactions with milk proteins.