Context dependent substitution biases vary within the human genome

Background  

Models of sequence evolution typically assume that different nucleotide positions evolve independently. This assumption is widely appreciated to be an over-simplification. The best known violations involve biases due to adjacent nucleotides. There have also been suggestions that biases exist at larger scales, however this possibility has not been systematically explored.     

Microarray and EST database estimates of mRNA expression levels differ: The protein length versus expression curve forC. elegans

Background  

Various methods for estimating protein expression levels are known. The level of correlation between these methods is only fair, and systematic biases in each of the methods cannot be ruled out. We here investigate systematic biases in the estimation of gene expression rates from microarray data and from abundance within the Expressed Sequence Tag (EST) database. We suggest that length is a significant factor in biases to measured gene expression rates.     

Calibration and assessment of channel-specific biases in microarray data with extended dynamical range

Background  

Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software.     

Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data

Background  

Normalization is essential in dual-labelled microarray data analysis to remove non-biological variations and systematic biases. Many normalization methods have been used to remove such biases within slides (Global, Lowess) and across slides (Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice.     

Hypervigilance or avoidance of trigger related cues in migraineurs? - A case-control study using the emotional stroop task

Background  

"Negative affect" is one of the major migraine triggers. The aim of the study was to assess attentional biases for negative affective stimuli that might be related to migraine triggers in migraine patients with either few or frequent migraine and healthy controls.     

Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein–protein interactions data sets

Background  

Several studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein–protein interaction studies could lead to a spurious correlation.     

Global features of sequences of bacterial chromosomes,plasmids and phages revealed by analysis of oligonucleotide usage patterns

Background  

Oligonucleotide frequencies were shown to be conserved signatures for bacterial genomes, however, the underlying constraints have yet not been resolved in detail. In this paper we analyzed oligonucleotide usage (OU) biases in a comprehensive collection of 155 completely sequenced bacterial chromosomes, 316 plasmids and 104 phages.     

Spotting effect in microarray experiments

Background  

Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array.     

Evaluation of normalization methods for cDNA microarray data by <Emphasis Type="Italic">k</Emphasis>-NN classification

Background  

Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification.     

Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays

Background  

Microarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique way on each chip. It is likely that such artifacts affect many SNP arrays, but the normalization methods used in currently available genotyping algorithms make no attempt at spatial bias correction. Here, we propose an effective single-chip spatial bias removal procedure for Affymetrix 6.0 SNP arrays or platforms with similar design features. This procedure deals with both extreme and subtle biases and is intended to be applied before standard genotype calling algorithms.     

Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring

Background  

Since the transfer and application of modern sequencing technologies to the analysis of amplified fragment-length polymorphisms (AFLP), evolutionary biologists have included an increasing number of samples and markers in their studies. Although justified in this context, the use of automated scoring procedures may result in technical biases that weaken the power and reliability of further analyses.     

Quality assessment of microarrays: Visualization of spatial artifacts and quantitation of regional biases

Background  

Quality-control is an important issue in the analysis of gene expression microarrays. One type of problem is regional bias, in which one region of a chip shows artifactually high or low intensities (or ratios in a two-channel array) relative to the majority of the chip. Current practice in quality assessment for microarrays does not address regional biases.     

Gene identification and protein classification in microbial metagenomic sequence data via incremental clustering

Background  

The identification and study of proteins from metagenomic datasets can shed light on the roles and interactions of the source organisms in their communities. However, metagenomic datasets are characterized by the presence of organisms with varying GC composition, codon usage biases etc., and consequently gene identification is challenging. The vast amount of sequence data also requires faster protein family classification tools.     

GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest

Background  

Microarray data are often used for patient classification and gene selection. An appropriate tool for end users and biomedical researchers should combine user friendliness with statistical rigor, including carefully avoiding selection biases and allowing analysis of multiple solutions, together with access to additional functional information of selected genes. Methodologically, such a tool would be of greater use if it incorporates state-of-the-art computational approaches and makes source code available.     

A stepwise framework for the normalization of array CGH data

Background  

In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.     

Extension of Lander-Waterman theory for sequencing filtered DNA libraries

Background  

The degree to which conventional DNA sequencing techniques will be successful for highly repetitive genomes is unclear. Investigators are therefore considering various filtering methods to select against high-copy sequence in DNA clone libraries. The standard model for random sequencing, Lander-Waterman theory, does not account for two important issues in such libraries, discontinuities and position-based sampling biases (the so-called "edge effect"). We report an extension of the theory for analyzing such configurations.     

Differences in the selection response of serially repeated color pattern characters: Standing variation,development, and evolution

Background  

There is spectacular morphological diversity in nature but lineages typically display a limited range of phenotypes. Because developmental processes generate the phenotypic variation that fuels natural selection, they are a likely source of evolutionary biases, facilitating some changes and limiting others. Although shifts in developmental regulation are associated with morphological differences between taxa, it is unclear how underlying mechanisms affect the rate and direction of evolutionary change within populations under selection.     

Accounting for horizontal gene transfers explains conflicting hypotheses regarding the position of aquificales in the phylogeny of Bacteria

Background  

Despite a large agreement between ribosomal RNA and concatenated protein phylogenies, the phylogenetic tree of the bacterial domain remains uncertain in its deepest nodes. For instance, the position of the hyperthermophilic Aquificales is debated, as their commonly observed position close to Thermotogales may proceed from horizontal gene transfers, long branch attraction or compositional biases, and may not represent vertical descent. Indeed, another view, based on the analysis of rare genomic changes, places Aquificales close to epsilon-Proteobacteria.