Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

<p>Abstract</p> <p>Background</p> <p>Microarray technologies produced large amount of data. In a previous study, we have shown the interest of <it>k-Nearest Neighbour </it>approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human.</p> <p>Results</p> <p>We underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (<it>EM_array</it>). These improvements have been observed also on the imputation of extreme values, the most difficult predictable values. We showed that the imputed MVs have still important effects on the stability of the gene clusters. The improvement on the clustering obtained by hierarchical clustering remains limited and, not sufficient to restore completely the correct gene associations. However, a common tendency can be found between the quality of the imputation method and the gene cluster stability. Even if the comparison between clustering algorithms is a complex task, we observed that <it>k-means </it>approach is more efficient to conserve gene associations.</p> <p>Conclusions</p> <p>More than 6.000.000 independent simulations have assessed the quality of 12 imputation methods on five very different biological datasets. Important improvements have so been done since our last study. The <it>EM_array </it>approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level. Nonetheless, the presence of MVs even at a low rate is a major factor of gene cluster instability. Our study highlights the need for a systematic assessment of imputation methods and so of dedicated benchmarks. A noticeable point is the specific influence of some biological dataset.</p

A Re-Annotation of the Saccharomyces Cerevisiae Genome

Discrepancies in gene and orphan number indicated by previous analyses suggest that S. cerevisiae would benefit from a consistent re-annotation. In this analysis three new genes are identified and 46 alterations to gene coordinates are described. 370 ORFs are defined as totally spurious ORFs which should be disregarded. At least a further 193 genes could be described as very hypothetical, based on a number of criteria. It was found that disparate genes with sequence overlaps over ten amino acids (especially at the N-terminus) are rare in both S. cerevisiae and Sz. pombe. A new S. cerevisiae gene number estimate with an upper limit of 5804 is proposed, but after the removal of very hypothetical genes and pseudogenes this is reduced to 5570. Although this is likely to be closer to the true upper limit, it is still predicted to be an overestimate of gene number. A complete list of revised gene coordinates is available from the Sanger Centre (S. cerevisiae reannotation: ftp://ftp/pub/yeast/SCreannotation)

Megasatellites: a peculiar class of giant minisatellites in genes involved in cell adhesion and pathogenicity in Candida glabrata

Minisatellites are DNA tandem repeats that are found in all sequenced genomes. In the yeast Saccharomyces cerevisiae, they are frequently encountered in genes encoding cell wall proteins. Minisatellites present in the completely sequenced genome of the pathogenic yeast Candida glabrata were similarly analyzed, and two new types of minisatellites were discovered: minisatellites that are composed of two different intermingled repeats (called compound minisatellites), and minisatellites containing unusually long repeated motifs (126–429 bp). These long repeat minisatellites may reach unusual length for such elements (up to 10 kb). Due to these peculiar properties, they have been named ‘megasatellites’. They are found essentially in genes involved in cell–cell adhesion, and could therefore be involved in the ability of this opportunistic pathogen to colonize the human host. In addition to megasatellites, found in large paralogous gene families, there are 93 minisatellites with simple shorter motifs, comparable to those found in S. cerevisiae. Most of the time, these minisatellites are not conserved between C. glabrata and S. cerevisiae, although their host genes are well conserved, raising the question of an active mechanism creating minisatellites de novo in hemiascomycetes

Unravelling the ORFan Puzzle

ORFans are open reading frames (ORFs) with no detectable sequence similarity to any other sequence in the databases. Each newly sequenced genome contains a significant number of ORFans. Therefore, ORFans entail interesting evolutionary puzzles. However, little can be learned about them using bioinformatics tools, and their study seems to have been underemphasized. Here we present some of the questions that the existence of so many ORFans have raised and review some of the studies aimed at understanding ORFans, their functions and their origins. These works have demonstrated that ORFans are an untapped source of research, requiring further computational and experimental studies

Genomic Exploration of the Hemiascomycetous Yeasts: 1. A set of yeast species for molecular evolution studies11Sequences and annotations are accessible at: Génoscope (http://www.genoscope.cns.fr), FEBS Letters Website (http://www.elsevier.nl/febs/show/), Bordeaux (http://cbi.genopole-bordeaux.fr/Genolevures) and were deposited into the EMBL database (accession number from AL392203 to AL441602).

AbstractThe identification of molecular evolutionary mechanisms in eukaryotes is approached by a comparative genomics study of a homogeneous group of species classified as Hemiascomycetes. This group includes Saccharomyces cerevisiae, the first eukaryotic genome entirely sequenced, back in 1996. A random sequencing analysis has been performed on 13 different species sharing a small genome size and a low frequency of introns. Detailed information is provided in the 20 following papers. Additional tables available on websites describe the ca. 20 000 newly identified genes. This wealth of data, so far unique among eukaryotes, allowed us to examine the conservation of chromosome maps, to identify the ‘yeast-specific’ genes, and to review the distribution of gene families into functional classes. This project conducted by a network of seven French laboratories has been designated ‘Génolevures’

Genomic Exploration of the Hemiascomycetous Yeasts: 19. Ascomycetes-specific genes

AbstractComparisons of the 6213 predicted Saccharomyces cerevisiae open reading frame (ORF) products with sequences from organisms of other biological phyla differentiate genes commonly conserved in evolution from ‘maverick’ genes which have no homologue in phyla other than the Ascomycetes. We show that a majority of the ‘maverick’ genes have homologues among other yeast species and thus define a set of 1892 genes that, from sequence comparisons, appear ‘Ascomycetes-specific’. We estimate, retrospectively, that the S. cerevisiae genome contains 5651 actual protein-coding genes, 50 of which were identified for the first time in this work, and that the present public databases contain 612 predicted ORFs that are not real genes. Interestingly, the sequences of the ‘Ascomycetes-specific’ genes tend to diverge more rapidly in evolution than that of other genes. Half of the ‘Ascomycetes-specific’ genes are functionally characterized in S. cerevisiae, and a few functional categories are over-represented in them

Migraine et contraception orale

AIX-MARSEILLE2-BU Pharmacie (130552105) / SudocSudocFranceF

Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering.

12 pages + sup. dataBACKGROUND: Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs. RESULTS: In this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods (e.g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs (e.g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression. CONCLUSION: The presence of MVs (even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation

Global analysis of VHHs framework regions with a structural alphabet: VHH FRs structures

International audienceThe VHHs are antigen-binding region/domain of camelid heavy chain antibodies (HCAb). They have many interesting biotechnological and biomedical properties due to their small size, high solubility and stability, and high affinity and specificity for their antigens. HCAb and classical IgGs are evolutionary related and share a common fold. VHHs are composed of regions considered as constant, called the frameworks (FRs) connected by Complementarity Determining Regions (CDRs), a highly variable region that provide interaction with the epitope. Actually, no systematic structural analyses had been performed on VHH structures despite a significant number of structures. This work is the first study to analyse the structural diversity of FRs of VHHs. Using a structural alphabet that allows approximating the local conformation, we show that each of the four FRs do not have a unique structure but exhibit many structural variant patterns. Moreover, no direct simple link between the local conformational change and amino acid composition can be detected. These results indicate that long-range interactions affect the local conformation of FRs and impact the building of structural models

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries