Testing gene set enrichment for subset of genes: Sub-GSE

Background  

Many methods have been developed to test the enrichment of genes related to certain phenotypes or cell states in gene sets. These approaches usually combine gene expression data with functionally related gene sets as defined in databases such as GeneOntology (GO), KEGG, or BioCarta. The results based on gene set analysis are generally more biologically interpretable, accurate and robust than the results based on individual gene analysis. However, while most available methods for gene set enrichment analysis test the enrichment of the entire gene set, it is more likely that only a subset of the genes in the gene set may be related to the phenotypes of interest.     

A meta-data based method for DNA microarray imputation

Background  

DNA microarray experiments are conducted in logical sets, such as time course profiling after a treatment is applied to the samples, or comparisons of the samples under two or more conditions. Due to cost and design constraints of spotted cDNA microarray experiments, each logical set commonly includes only a small number of replicates per condition. Despite the vast improvement of the microarray technology in recent years, missing values are prevalent. Intuitively, imputation of missing values is best done using many replicates within the same logical set. In practice, there are few replicates and thus reliable imputation within logical sets is difficult. However, it is in the case of few replicates that the presence of missing values, and how they are imputed, can have the most profound impact on the outcome of downstream analyses (e.g. significance analysis and clustering). This study explores the feasibility of imputation across logical sets, using the vast amount of publicly available microarray data to improve imputation reliability in the small sample size setting.     

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets

Background  

Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn.     

The Gene Set Builder: collation,curation, and distribution of sets of genes

Background  

In bioinformatics and genomics, there are many applications designed to investigate the common properties for a set of genes. Often, these multi-gene analysis tools attempt to reveal sequential, functional, and expressional ties. However, while tremendous effort has been invested in developing tools that can analyze a set of genes, minimal effort has been invested in developing tools that can help researchers compile, store, and annotate gene sets in the first place. As a result, the process of making or accessing a set often involves tedious and time consuming steps such as finding identifiers for each individual gene. These steps are often repeated extensively to shift from one identifier type to another; or to recreate a published set. In this paper, we present a simple online tool which – with the help of the gene catalogs Ensembl and GeneLynx – can help researchers build and annotate sets of genes quickly and easily.     

Clustering metagenomic sequences with interpolated Markov models

Background  

Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.     

Multiclass classification of microarray data samples with a reduced number of genes

Background  

Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.     

Representative transcript sets for evaluating a translational initiation sites predictor

Background  

Translational initiation site (TIS) prediction is a very important and actively studied topic in bioinformatics. In order to complete a comparative analysis, it is desirable to have several benchmark data sets which can be used to test the effectiveness of different algorithms. An ideal benchmark data set should be reliable, representative and readily available. Preferably, proteins encoded by members of the data set should also be representative of the protein population actually expressed in cellular specimens.     

Effects of red bull energy drink on repeated sprint performance in women athletes

Energy drinks are frequently consumed by athletes prior to competition to improve performance. This study examined the effect of Red Bull™ on repeated sprint performance in women athletes. Fifteen collegiate soccer players participated, with mean age, height, and body mass equal to 19.5 ± 1.1 year, 168.4 ± 5.8 cm, and 63.4 ± 6.1 kg, respectively. After performing a familiarization trial, subjects performed three sets of eight bouts of the modified t test after ingestion of 255 mL of placebo or Red Bull 1 h pre-exercise in a randomized, placebo-controlled crossover design. Throughout testing, sprint time, heart rate (HR), and rating of perceived exertion (RPE) were continuously obtained. Repeated measures analysis of variance was used to examine differences in variables between drink conditions. Across athletes, t test time ranged from 10.4 to 12.7 s. Mean sprint time was similar (p > 0.05) between Red Bull (11.31 ± 0.61 s) and placebo (11.35 ± 0.61 s). HR and RPE increased (p < 0.05) during the bouts, but there was no effect (p > 0.05) of Red Bull on either variable versus placebo. Findings indicate that 255 mL of Red Bull containing 1.3 mg/kg of caffeine and 1 g of taurine does not alter repeated sprint performance, RPE, or HR in women athletes versus placebo. One serving of this energy drink provides no ergogenic benefit for women athletes engaging in sprint-based exercise.     

Gene set analysis exploiting the topology of a pathway

Background  

Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it.     

Class prediction for high-dimensional class-imbalanced data

Background  

The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance.     

Unsupervised reduction of random noise in complex data by a row-specific,sorted principal component-guided method

Background  

Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but it tends to remove small features as well as random noise.     

Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

Background  

The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set.     

Incorporating functional inter-relationships into protein function prediction algorithms

Background  

Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.     

A cis-regulatory logic simulator

Background  

A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence.     

Stratification bias in low signal microarray studies

Background  

When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated.     

Improved Repeated Confidence Bounds in Trials with a Maximal Goal

Repeated confidence intervals can be computed at every interim analysis of a flexible group sequential design without the need to stop the trial with a pre‐planned stopping rule. Often, however, there is a maximal goal such that the trial is surely stopped if this goal is reached. This induces a maximal stopping rule, and repeated confidence intervals are strictly conservative, when adhering to it. A modification is proposed which uniformly improves the one sided repeated confidence interval in such a situation. It preserves the monitoring character, and leads to uniformly smaller intervals, when reaching the maximal goal at an interim analysis. The modification is worked out for two stage designs and is indicated for multi‐stage trials. The extent of the improvement is quantified for two simple scenarios.     

The CRISPRdb database and tools to display CRISPRs and to generate dictionaries of spacers and repeats

Background  

In Archeae and Bacteria, the repeated elements called CRISPRs for "clustered regularly interspaced short palindromic repeats" are believed to participate in the defence against viruses. Short sequences called spacers are stored in-between repeated elements. In the current model, motifs comprising spacers and repeats may target an invading DNA and lead to its degradation through a proposed mechanism similar to RNA interference. Analysis of intra-species polymorphism shows that new motifs (one spacer and one repeated element) are added in a polarised fashion. Although their principal characteristics have been described, a lot remains to be discovered on the way CRISPRs are created and evolve. As new genome sequences become available it appears necessary to develop automated scanning tools to make available CRISPRs related information and to facilitate additional investigations.     

PatentMatrix: an automated tool to survey patents related to large sets of genes or proteins

Background  

The enormity of the information contained in large data sets makes it difficult to develop intuitive understanding. It would be useful to have software that allows visualization of possible correlations between properties that can be associated with a core data set. In the case of bacterial genomes, existing visualization tools focus on either global properties such as variations in composition or detailed local displays of the features that comprise the annotation. It is not easy to visualize other information in the context of this core information.     

The structure of pyogenecin immunity protein, a novel bacteriocin-like immunity protein from Streptococcus pyogenes

Background  

Many Gram-positive lactic acid bacteria (LAB) produce anti-bacterial peptides and small proteins called bacteriocins, which enable them to compete against other bacteria in the environment. These peptides fall structurally into three different classes, I, II, III, with class IIa being pediocin-like single entities and class IIb being two-peptide bacteriocins. Self-protective cognate immunity proteins are usually co-transcribed with these toxins. Several examples of cognates for IIa have already been solved structurally. Streptococcus pyogenes, closely related to LAB, is one of the most common human pathogens, so knowledge of how it competes against other LAB species is likely to prove invaluable.