Family classification without domain chaining

Motivation: Classification of gene and protein sequences into homologous families, i.e. sets of sequences that share common ancestry, is an essential step in comparative genomic analyses. This is typically achieved by construction of a sequence homology network, followed by clustering to identify dense subgraphs corresponding to families. Accurate classification of single domain families is now within reach due to major algorithmic advances in remote homology detection and graph clustering. However, classification of multidomain families remains a significant challenge. The presence of the same domain in sequences that do not share common ancestry introduces false edges in the homology network that link unrelated families and stymy clustering algorithms

Ultra-fast sequence clustering from similarity networks with SiLiX

<p>Abstract</p> <p>Background</p> <p>The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time.</p> <p>Results</p> <p>We present the software package <monospace>SiLiX</monospace> that implements a novel method which reconsiders single linkage clustering with a graph theoretical approach. A parallel version of the algorithms is also presented. As a demonstration of the ability of our software, we clustered more than 3 millions sequences from about 2 billion BLAST hits in 7 minutes, with a high clustering quality, both in terms of sensitivity and specificity.</p> <p>Conclusions</p> <p>Comparing state-of-the-art software, <monospace>SiLiX</monospace> presents the best up-to-date capabilities to face the problem of clustering large collections of sequences. <monospace>SiLiX</monospace> is freely available at <url>http://lbbe.univ-lyon1.fr/SiLiX</url>.</p

Automatic identification of highly conserved family regions and relationships in genome wide datasets including remote protein sequences

Identifying shared sequence segments along amino acid sequences generally requires a collection of closely related proteins, most often curated manually from the sequence datasets to suit the purpose at hand. Currently developed statistical methods are strained, however, when the collection contains remote sequences with poor alignment to the rest, or sequences containing multiple domains. In this paper, we propose a completely unsupervised and automated method to identify the shared sequence segments observed in a diverse collection of protein sequences including those present in a smaller fraction of the sequences in the collection, using a combination of sequence alignment, residue conservation scoring and graph-theoretical approaches. Since shared sequence fragments often imply conserved functional or structural attributes, the method produces a table of associations between the sequences and the identified conserved regions that can reveal previously unknown protein families as well as new members to existing ones. We evaluated the biological relevance of the method by clustering the proteins in gold standard datasets and assessing the clustering performance in comparison with previous methods from the literature. We have then applied the proposed method to a genome wide dataset of 17793 human proteins and generated a global association map to each of the 4753 identified conserved regions. Investigations on the major conserved regions revealed that they corresponded strongly to annotated structural domains. This suggests that the method can be useful in predicting novel domains on protein sequences

Graph-based methods for large-scale protein classification and orthology inference

The quest for understanding how proteins evolve and function has been a prominent and costly human endeavor. With advances in genomics and use of bioinformatics tools, the diversity of proteins in present day genomes can now be studied more efficiently than ever before. This thesis describes computational methods suitable for large-scale protein classification of many proteomes of diverse species. Specifically, we focus on methods that combine unsupervised learning (clustering) techniques with the knowledge of molecular phylogenetics, particularly that of orthology. In chapter 1 we introduce the biological context of protein structure, function and evolution, review the state-of-the-art sequence-based protein classification methods, and then describe methods used to validate the predictions. Finally, we present the outline and objectives of this thesis. Evolutionary (phylogenetic) concepts are instrumental in studying subjects as diverse as the diversity of genomes, cellular networks, protein structures and functions, and functional genome annotation. In particular, the detection of orthologous proteins (genes) across genomes provides reliable means to infer biological functions and processes from one organism to another. Chapter 2 evaluates the available computational tools, such as algorithms and databases, used to infer orthologous relationships between genes from fully sequenced genomes. We discuss the main caveats of large-scale orthology detection in general as well as the merits and pitfalls of each method in particular. We argue that establishing true orthologous relationships requires a phylogenetic approach which combines both trees and graphs (networks), reliable species phylogeny, genomic data for more than two species, and an insight into the processes of molecular evolution. Also proposed is a set of guidelines to aid researchers in selecting the correct tool. Moreover, this review motivates further research in developing reliable and scalable methods for functional and phylogenetic classification of large protein collections. Chapter 3 proposes a framework in which various protein knowledge-bases are combined into unique network of mappings (links), and hence allows comparisons to be made between expert curated and fully-automated protein classifications from a single entry point. We developed an integrated annotation resource for protein orthology, ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap), to help researchers and database annotators who often need to assess the coherence of proposed annotations and/or group assignments, as well as users of high throughput methodologies (e.g., microarrays or proteomics) who deal with partially annotated genomic data. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240,000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF using a fast and fully automated sequence-based mapping approach. The ProGMap database is equipped with a web interface that enables queries to be made using synonymous sequence identifiers, gene symbols, protein functions, and amino acid or nucleotide sequences. It incorporates also services, namely BLAST similarity search and QuickMatch identity search, for finding sequences similar (or identical) to a query sequence, and tools for presenting the results in graphic form. Graphs (networks) have gained an increasing attention in contemporary biology because they have enabled complex biological systems and processes to be modeled and better understood. For example, protein similarity networks constructed of all-versus-all sequence comparisons are frequently used to delineate similarity groups, such as protein families or orthologous groups in comparative genomics studies. Chapter 4.1 presents a benchmark study of freely available graph software used for this purpose. Specifically, the computational complexity of the programs is investigated using both simulated and biological networks. We show that most available software is not suitable for large networks, such as those encountered in large-scale proteome analyzes, because of the high demands on computational resources. To address this, we developed a fast and memory-efficient graph software, netclust (http://www.bioinformatics.nl/netclust/), which can scale to large protein networks, such as those constructed of millions of proteins and sequence similarities, on a standard computer. An extended version of this program called Multi-netclust is presented in chapter 4.2. This tool that can find connected clusters of data presented by different network data sets. It uses user-defined threshold values to combine the data sets in such a way that clusters connected in all or in either of the networks can be retrieved efficiently. Automated protein sequence clustering is an important task in genome annotation projects and phylogenomic studies. During the past years, several protein clustering programs have been developed for delineating protein families or orthologous groups from large sequence collections. However, most of these programs have not been benchmarked systematically, in particular with respect to the trade-off between computational complexity and biological soundness. In chapter 5 we evaluate three best known algorithms on different protein similarity networks and validation (or 'gold' standard) data sets to find out which one can scale to hundreds of proteomes and still delineate high quality similarity groups at the minimum computational cost. For this, a reliable partition-based approach was used to assess the biological soundness of predicted groups using known protein functions, manually curated protein/domain families and orthologous groups available in expert-curated databases. Our benchmark results support the view that a simple and computationally cheap method such as netclust can perform similar to and in cases even better than more sophisticated, yet much more costly methods. Moreover, we introduce an efficient graph-based method that can delineate protein orthologs of hundreds of proteomes into hierarchical similarity groups de novo. The validity of this method is demonstrated on data obtained from 347 prokaryotic proteomes. The resulting hierarchical protein classification is not only in agreement with manually curated classifications but also provides an enriched framework in which the functional and evolutionary relationships between proteins can be studied at various levels of specificity. Finally, in chapter 6 we summarize the main findings and discuss the merits and shortcomings of the methods developed herein. We also propose directions for future research. The ever increasing flood of new sequence data makes it clear that we need improved tools to be able to handle and extract relevant (orthological) information from these protein data. This thesis summarizes these needs and how they can be addressed by the available tools, or be improved by the new tools that were developed in the course of this research. <br/

Identification and Characterization of Cold-Tolerance Associated Genes in Wheat

The low temperature remains as one of the major limiting factors of crop productivity in the temperate region, and identification of cold tolerance related genes is crucial for developing cold tolerant crop plants to increase agricultural productivity. The objective of my thesis is to identify cold tolerance related candidate genes in wheat, one of the major crops in the temperate region. In Chapter 2, I have reviewed the literature pertaining to the mechanisms of cold tolerance in plants with specific emphasis on Wheat. In Chapter 3, forty candidate genes with increased expression under cold exposure based on published microarray data were selected and further characterized. These genes belonging to four categories namely defense-related regulators; transcriptional and epigenetic regulators; post-transcriptional and post-translational regulators; and genes of unknown functions revealed many differentially expressed genes including Remorin – upregulated in response to cold; a novel gene in wheat homologous to RD29B of Arabidopsis-upregulated in response to cold and ABA; and another novel gene regulated by both ABA and MetJA. In chapter 4, the results of genome-wide identification and characterization of the wheat remorin family and its association with cold tolerance are presented. A search of the wheat database revealed the existence of twenty different remorin genes that we classified into six groups sharing a common structure and phylogenetic origin. Promoter analysis of TaREM genes revealed the presence of putative cis-elements related to diverse functions like development, hormonal regulation, biotic and abiotic stress responsiveness. Expression levels of TaREM genes were measured in plants grown under in field and laboratory conditions and in response to hormone treatment. Our analyses revealed twelve members of the remorin family that are regulated during cold acclimation of wheat in four different tissues (root, crown, stem and leaves), with the highest expression in roots. Differential gene expression was found between wheat cultivars with contrasting degree of cold tolerance suggesting the implication of TaREM genes in cold response and tolerance. Additionally, eight genes were induced in response to ABA and MetJA treatment. This genome-wide analysis of TaREM genes provides valuable resources for functional analysis aimed at understanding their role in stress adaptation. The chapter 5 is focused on gaining insights into the evolutionary history and in-silico functional characterization of a novel cold-responsive gene in wheat. This gene in wheat has distant homology to known abiotic stress-related genes in other plants including CAP160 in Spinacia oleracea, RD29B in Arabidopsis and CDeT11-24 in Craterostigma plantagineum. The results show that these genes are homologous and may have evolved from a common ancestor. The Bayesian phylogenetic analyses of the protein sequences of this gene from various plant species revealed three distinctive clades. Further analyses revealed that this gene has predominantly evolved through neutral processes with some regions experiencing signatures of negative selections and some regions showing signatures of episodic positive selections. These genes contained common K-like segments and function predictions revealed that these protein-coding genes may share at least two functions related to abiotic stress conditions. One function is similar to the cryoprotective function of LEA protein, and the second function as a signalling molecule by binding specifically to phosphatidic acid

Family classification without domain chaining.

MOTIVATION: Classification of gene and protein sequences into homologous families, i.e. sets of sequences that share common ancestry, is an essential step in comparative genomic analyses. This is typically achieved by construction of a sequence homology network, followed by clustering to identify dense subgraphs corresponding to families. Accurate classification of single domain families is now within reach due to major algorithmic advances in remote homology detection and graph clustering. However, classification of multidomain families remains a significant challenge. The presence of the same domain in sequences that do not share common ancestry introduces false edges in the homology network that link unrelated families and stymy clustering algorithms. RESULTS: Here, we investigate a network-rewiring strategy designed to eliminate edges due to promiscuous domains. We show that this strategy can reduce noise in and restore structure to artificial networks with simulated noise, as well as to the yeast genome homology network. We further evaluate this approach on a hand-curated set of multidomain sequences in mouse and human, and demonstrate that classification using the rewired network delivers dramatic improvement in Precision and Recall, compared with current methods. Families in our test set exhibit a broad range of domain architectures and sequence conservation, demonstrating that our method is flexible, robust and suitable for high-throughput, automated processing of heterogeneous, genome-scale data.</p