Ranked Gene Lists Research Articles

A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

BackgroundSeveral computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.ResultsThe gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods.ConclusionsThe combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).

Exploration of Potential Genomic Portraits Associated with Intrahepatic Recurrence in Human Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous disease with recognized variability in virus infection, genetic features, and clinical outcome. To date, transcriptional profilings of HCC have been used to predict recurrence or survival/prognosis. However, there remains a challenge to identify specific genomic prints associated with HCC recurrence, which could lead to novel therapies or effective treatment. Here we examine the association between biological signals and intrahepatic recurrence using global gene expression profiles and powerful analytical methods. Gene expression profiles were generated in 24 HCC patients with hepatitis B infections (B-type HCC) and 60 HCC patients with hepatitis C infections (C-type HCC). Gene set enrichment analysis (GSEA) was applied to the entire ranked gene lists related to early intrahepatic recurrence, based on "ideal discriminator method." GSEA revealed Ribosomal Proteins as a common regulatory pathway in B-type (P < .001) and C-type (P = .003) HCC recurrence. In addition, Proteasome (P < .001) and Pentose Phosphate Pathway (P = .01) were identified as specific pathways in each type of HCC recurrence, respectively. Understanding these biologically common and different mechanisms related to intrahepatic recurrence in B-type and C-type HCC could be useful in the development of new therapeutic strategies in our fight against HCC.

Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays

BackgroundReproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.ResultsThe same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage of overlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees.ConclusionsUsual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stability estimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniques is observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets.

Stability of Ranked Gene Lists in Large Microarray Analysis Studies

This paper presents an empirical study that aims to explain the relationship between the number of samples and stability of different gene selection techniques for microarray datasets. Unlike other similar studies where number of genes in a ranked gene list is variable, this study uses an alternative approach where stability is observed at different number of samples that are used for gene selection. Three different metrics of stability, including a novel metric in bioinformatics, were used to estimate the stability of the ranked gene lists. Results of this study demonstrate that the univariate selection methods produce significantly more stable ranked gene lists than the multivariate selection methods used in this study. More specifically, thousands of samples are needed for these multivariate selection methods to achieve the same level of stability any given univariate selection method can achieve with only hundreds.

Abstract A30: Integrated analysis of genomic data and annotations in cancer research

Abstract Many cancer researchers are currently generating heterogeneous genomic data sets containing mRNA expression, array based comparative genomic hybridization (aCGH), and other information from multiple patients. In the near future, data sets will likely include proteomics, methylomics, and high throughput sequencing data. Analytical tools that integrate these different data types to prioritize genes for further study have lagged, however. With this in mind, we have implemented a standalone software application that integrates CGH and mRNA expression profiling data. This application implements several different methods to generate ranked lists of genes and genomic regions based on breakpoint frequency, aberration frequency, and the impact of copy number changes on mRNA expression. The software is Java GUI based, with abstracted object models that can be extended as new genomic data types and annotations become available. We used this application to analyze aCGH datasets generated from 22 primary pancreatic tumor xenografts and mRNA expression datasets from 16 of the 22 samples. Genes which were located near a putative breakpoint and whose expression levels were high in a majority of the xenografts samples were considered putative targets. Preliminary IHC analysis of pancreas primary tumor tissue microarrays confirms one of these putative targets, Chemokine (CXC motif) Ligand 5 (CXCL5). By allowing cancer researchers to correlate genomic aberrations with expression data, we believe that this application will generate more meaningful targets for further biomarker identification and/or drug target discovery. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A30.

Stability and aggregation of ranked gene lists

Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector.

A Cross-Study Transcriptional Analysis of Parkinson's Disease

The study of Parkinson's disease (PD), like other complex neurodegenerative disorders, is limited by access to brain tissue from patients with a confirmed diagnosis. Alternatively the study of peripheral tissues may offer some insight into the molecular basis of disease susceptibility and progression, but this approach still relies on brain tissue to benchmark relevant molecular changes against. Several studies have reported whole-genome expression profiling in post-mortem brain but reported concordance between these analyses is lacking. Here we apply a standardised pathway analysis to seven independent case-control studies, and demonstrate increased concordance between data sets. Moreover data convergence increased when the analysis was limited to the five substantia nigra (SN) data sets; this highlighted the down regulation of dopamine receptor signaling and insulin-like growth factor 1 (IGF1) signaling pathways. We also show that case-control comparisons of affected post mortem brain tissue are more likely to reflect terminal cytoarchitectural differences rather than primary pathogenic mechanisms. The implementation of a correction factor for dopaminergic neuronal loss predictably resulted in the loss of significance of the dopamine signaling pathway while axon guidance pathways increased in significance. Interestingly the IGF1 signaling pathway was also over-represented when data from non-SN areas, unaffected or only terminally affected in PD, were considered. Our findings suggest that there is greater concordance in PD whole-genome expression profiling when standardised pathway membership rather than ranked gene list is used for comparison.

Automated Gene-Retrieval System for Biological Information Needs

In this day and age, conducting a biological experiment is presumably a very expensive procedure largely owing to the highly sophisticated and expensive equipment necessitated by the process. Conceivably, being capable of isolating and focusing on a smaller set of imperative genes or gene products that are of high relevance to the experiment, pathway, or biological system under investigation is very desirable largely owing to the potential savings in experimental costs. In this work, we propose an intelligent information system capable of generating a ranked list of genes and gene products that are most pertinent to a given biological pathway, experiment or system (referred to as a biological context henceforth). We assume that the biological context of interest can be described by various textual query terms and phrases from the biological domain which, in turn, relate to various molecular functions, biological processes and cellular components of genes and their products. Intelligent text-based analyses and mining are utilised for this purpose by using the published literature, in the form of publication abstracts downloaded from PubMed, with the intention of ranking genes and gene products having identified relationships to the specified description terms based on the gene ontology (GO) standard. At this stage, our approach is capable of producing promising results given all surrounding restrictions, one of which is the lack of similar work in the literature. For demonstration purposes, we report experimental results on the molting regulation pathway in Drosophila melanogaster (fruit fly).

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

BackgroundSince the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.ResultsGOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression). GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. Building on a complete theoretical characterization of the underlying distribution, called mHG, GOrilla computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations. This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds. The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms.ConclusionGOrilla is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools. GOrilla's unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation. GOrilla is publicly available at:

An evaluation framework for statistical tests on microarray data

Microarray analysis has become a popular and routine method in functional genomics. It is typical for such experiments to involve a small number of replicates, which causes unreliable estimates of the sample variance. Microarrays have fostered the development of new statistical methods to analyze data resulting from experiments with small sample sizes. In this study, we tackle the problem of evaluating the performance of statistical tests for generating ranked gene lists from two-channel direct comparisons. We propose an evaluation method based on a oligonucleotide microarray with a large number of replicate spots yielding a maximum of 400 replicates per gene. We apply Spearman’s rank correlation coefficient to ranked gene-lists generated by eight widely used microarray specific test statistics, which are applied to small random samples. We could show that variance stabilizing methods such as Cyber-T, SAM, and LIMMA can be beneficial for very small sample sizes and that SAM and the t-test provide stronger control of the type I error rate than the other methods. Specifically, we report that for four replicates all methods reach a high to very high correlation with our reference standard.

Detecting microRNA binding and siRNA off-target effects from expression data

Sylamer is a method for detecting microRNA target and small interfering (si)RNA off-target signals from expression data. The input is a ranked genelist from up to downregulated 3′ untranslated regions (UTRs) following an miRNA or RNAi experiment. The output is a landscape plot that tracks occurrence biases using hypergeometric P-values for all words across the gene ranking. The utility, speed, and accuracy of the approach on several miRNA and siRNA datasets are demonstrated.

M-BISON: Microarray-based integration of data sources using networks

BackgroundThe accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes.ResultsM-BISON improves signal detection on a range of simulated data, particularly when using very noisy microarray data. We also applied the method to the task of predicting heat shock-related differentially expressed genes in S. cerevisiae, using an hsf1 mutant microarray dataset and conserved yeast DNA sequence motifs. Our results demonstrate that M-BISON improves the analysis quality and makes predictions that are easy to interpret in concert with incorporated knowledge. Specifically, M-BISON increases the AUC of DE gene prediction from .541 to .623 when compared to a method using only microarray data, and M-BISON outperforms a related method, GeneRank. Furthermore, by analyzing M-BISON predictions in the context of the background knowledge, we identified YHR124W as a potentially novel player in the yeast heat shock response.ConclusionThis work provides a solid foundation for the principled integration of imperfect biological knowledge with gene expression data and other high-throughput data sources.

Automatic mining of the literature to generate new hypotheses for the possible link between periodontitis and atherosclerosis: lipopolysaccharide as a case study

The aim of the current report was to generate and explore new hypotheses into how, in a pathophysiological sense, atherosclerosis and periodontitis could be linked. Two different biomedical informatics techniques were used: an association-based technique that generated a ranked list of genes associated with the diseases, and a natural language processing tool that extracted the relationships between the retrieved genes and lipopolysaccharide (LPS). This combined approach of association-based and natural language processing-based literature mining identified a hit list of 16 candidate genes, with PON1 as the primary candidate. Further study of the literature prompted the hypothesis that PON1 might connect periodontitis with atherosclerosis in both an LPS-dependent and a non-LPS-dependent manner. Furthermore, the resulting genes not only confirmed already known associations between the two diseases, but also provided genes or gene products that have only been investigated separately in the two disease states, and genes or gene products previously reported to be involved in atherosclerosis. These findings remain to be investigated through clinical studies. This example of multidisciplinary research illustrates how collaborative efforts of investigators from different fields of expertise can result in the discovery of new hypotheses.

G:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments.

g:Profiler (http://biit.cs.ut.ee/gprofiler/) is a public web server for characterising and manipulating gene lists resulting from mining high-throughput genomic data. g:Profiler has a simple, user-friendly web interface with powerful visualisation for capturing Gene Ontology (GO), pathway, or transcription factor binding site enrichments down to individual gene levels. Besides standard multiple testing corrections, a new improved method for estimating the true effect of multiple testing over complex structures like GO has been introduced. Interpreting ranked gene lists is supported from the same interface with very efficient algorithms. Such ordered lists may arise when studying the most significantly affected genes from high-throughput data or genes co-expressed with the query gene. Other important aspects of practical data analysis are supported by modules tightly integrated with g:Profiler. These are: g:Convert for converting between different database identifiers; g:Orth for finding orthologous genes from other species; and g:Sorter for searching a large body of public gene expression data for co-expression. g:Profiler supports 31 different species, and underlying data is updated regularly from sources like the Ensembl database. Bioinformatics communities wishing to integrate with g:Profiler can use alternative simple textual outputs.

Reliable gene signatures for microarray classification: assessment of stability and performance

Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. R package at http://www.bio.ifi.lmu.de/~davis/edaprakt

A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates

BackgroundStudies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial considerations and limits on the available amount of RNA for sample preparation. In addition, failed hybridizations are not uncommon leading to a further reduction in the number of replicates available for analysis. Most existing methods for studying differential expression rely on the availability of replicates and the demand for alternative methods that require few or no replicates is high.ResultsWe describe a statistical procedure for performing differential expression analysis without replicates. The procedure relies on a Bayesian integrated approach (BGX) to the analysis of Affymetrix GeneChips. The BGX method estimates a posterior distribution of expression for each gene and condition, from a simultaneous consideration of the available probe intensities representing the gene in a condition. Importantly, posterior distributions of expression are obtained regardless of the number of replicates available. We exploit these posterior distributions to create ranked gene lists that take into account the estimated expression difference as well as its associated uncertainty. We estimate the proportion of non-differentially expressed genes empirically, allowing an informed choice of cut-off for the ranked gene list, adapting an approach proposed by Efron. We assess the performance of the method, and compare it to those of other methods, on publicly available spike-in data sets, as well as in a proper biological setting.ConclusionThe method presented is a powerful tool for extracting information on differential expression from GeneChip expression studies with limited or no replicates.

Comparing functional annotation analyses with Catmap

BackgroundRanked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis.ResultsWe analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at ), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes.ConclusionsIn gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.