Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction.

Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of VHH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed.

Global analysis of VHHs framework regions with a structural alphabet.

The 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.

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

BACKGROUND: Microarray technologies produced large amount of data. In a previous study, we have shown the interest of k-Nearest Neighbour 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. RESULTS: We underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (EM_array). 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 k-means approach is more efficient to conserve gene associations. CONCLUSIONS: 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 EM_array 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.

[Gene regulation and bioinformatics]. / Bio-informatique et régulation des gènes.

Gene regulation networks control differentiation and function of hundreds of cell types. Dysfunctions of transcription factors, which are key elements in the regulation pathways, are involved in numerous pathologies. The recent development of genomics technologies allows the study of gene regulation mechanisms and help us understand their impact on the cells. Bioinformatic tools are needed to fully exploit data obtained by genomics approaches. Thus, bioinformatics play an essential role in the characterisation of the transcription factors binding sites and their target genes. In this review we will introduce the main breakthroughs in bioinformatics area for the comprehension of regulation mechanisms. We will insist on i) the approaches "with a priori" for the genome annotation based on known transcription factors binding sites, ii) the approaches "without a priori" for the discovery of new binding sites and iii) the functional annotation of the target genes of those transcription factors. Finally, we will present recent examples of the fruitful use of in silico studies for the comprehension of regulation mechanisms and of the consequences of their dysfunction.

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

BACKGROUND: 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.

Analysis of microsatellites in 13 hemiascomycetous yeast species: mechanisms involved in genome dynamics.

We have analyzed all di-, tri-, and tetranucleotide repeats in the partially sequenced genomes of 13 hemiascomycetous yeast species, and compared their sequences, lengths, and distributions to those observed in the genome of Saccharomyces cerevisiae. We found that most of the 13 species exhibit a unique distribution of microsatellites, not correlated to the base composition of their genome. Species close to S. cerevisiae exhibit a similar distribution, while species more distantly related show a more divergent distribution. We propose that de novo formation and continuous loss of microsatellites are active processes generating new DNA sequences. We also show that hemiascomycete-specific genes encoding transcription factors contain trinucleotide repeats more frequently than expected from their average frequency distribution. These transcription factors might play an important role in the speciation process, by regulating gene expression through DNA-protein or protein-protein interactions mediated by stretches of charged amino acids encoded by trinucleotide repeats.

The complete mitochondrial genome sequence of the pathogenic yeast Candida (Torulopsis) glabrata.

We report here the complete sequence of the mitochondrial (mt) genome of the pathogenic yeast Candida glabrata. This 20 kb mt genome is the smallest among sequenced hemiascomycetous yeasts. Despite its compaction, the mt genome contains the genes encoding the apocytochrome b (COB), three subunits of ATP synthetase (ATP6, 8 and 9), three subunits of cytochrome oxidase (COX1, 2 and 3), the ribosomal protein VAR1, 23 tRNAs, small and large ribosomal RNAs and the RNA subunit of RNase P. Three group I introns each with an intronic open reading frame are present in the COX1 gene. This sequence is available under accession number AJ511533.