An SVD-based comparison of nine whole eukaryotic genomes supports a coelomate rather than ecdysozoan lineage

BACKGROUND: Eukaryotic whole genome sequences are accumulating at an impressive rate. Effective methods for comparing multiple whole eukaryotic genomes on a large scale are needed. Most attempted solutions involve the production of large scale alignments, and many of these require a high stringency pre-screen for putative orthologs in order to reduce the effective size of the dataset and provide a reasonably high but unknown fraction of correctly aligned homologous sites for comparison. As an alternative, highly efficient methods that do not require the pre-alignment of operationally defined orthologs are also being explored. RESULTS: A non-alignment method based on the Singular Value Decomposition (SVD) was used to compare the predicted protein complement of nine whole eukaryotic genomes ranging from yeast to man. This analysis resulted in the simultaneous identification and definition of a large number of well conserved motifs and gene families, and produced a species tree supporting one of two conflicting hypotheses of metazoan relationships. CONCLUSIONS: Our SVD-based analysis of the entire protein complement of nine whole eukaryotic genomes suggests that highly conserved motifs and gene families can be identified and effectively compared in a single coherent definition space for the easy extraction of gene and species trees. While this occurs without the explicit definition of orthologs or homologous sites, the analysis can provide a basis for these definitions

CO-phylum: An Assembly-Free Phylogenomic Approach for Close Related Organisms

Phylogenomic approaches developed thus far are either too time-consuming or lack a solid evolutionary basis. Moreover, no phylogenomic approach is capable of constructing a tree directly from unassembled raw sequencing data. A new phylogenomic method, CO-phylum, is developed to alleviate these flaws. CO-phylum can generate a high-resolution and highly accurate tree using complete genome or unassembled sequencing data of close related organisms, in addition, CO-phylum distance is almost linear with p-distance.Comment: 21 pages, 6 figure

Molecular Distance Maps: An alignment-free computational tool for analyzing and visualizing DNA sequences\u27 interrelationships

In an attempt to identify and classify species based on genetic evidence, we propose a novel combination of methods to quantify and visualize the interrelationships between thousand of species. This is possible by using Chaos Game Representation (CGR) of DNA sequences to compute genomic signatures which we then compare by computing pairwise distances. In the last step, the original DNA sequences are embedded in a high dimensional space using Multi-Dimensional Scaling (MDS) before everything is projected on a Euclidean 3D space. To start with, we apply this method to a mitochondrial DNA dataset from NCBI containing over 3,000 species. The analysis shows that the oligomer composition of full mtDNA sequences can be a source of taxonomic information, suggesting that this method could be used for unclassified species and taxonomic controversies. Next, we test the hypothesis that CGR-based genomic signature is preserved along a species\u27 genome by comparing inter- and intra-genomic signatures of nuclear DNA sequences from six different organisms, one from each kingdom of life. We also compare six different distances and we assess their performance using statistical measures. Our results support the existence of a genomic signature for a species\u27 genome at the kingdom level. In addition, we test whether CGR-based genomic signatures originating only from nuclear DNA can be used to distinguish between closely-related species and we answer in the negative. To overcome this limitation, we propose the concept of ``composite signatures\u27\u27 which combine information from different types of DNA and we show that they can effectively distinguish all closely-related species under consideration. We also propose the concept of ``assembled signatures\u27\u27 which, among other advantages, do not require a long contiguous DNA sequence but can be built from smaller ones consisting of ~100-300 base pairs. Finally, we design an interactive webtool MoDMaps3D for building three-dimensional Molecular Distance Maps. The user can explore an already existing map or build his/her own using NCBI\u27s accession numbers as input. MoDMaps3D is platform independent, written in Javascript and can run in all major modern browsers

The evolution of protein kinase specificity

All research conducted at EMBL-EBI under the supervision of Dr. Pedro Beltrao. Work on the PhD project was paused temporarily in the Spring of 2017 for me to undertake a 3-month internship at EMBO Press (in Heidelberg).Protein phosphorylation represents one of the most important post-translational modifica- tions (PTMs) for cell signalling, and is is catalysed by a group of enzymes called protein kinases. Through this activity they serve as key regulators of almost all cellular processes. This is achieved at any time by a network of different kinases that are transiently active. The fidelity of cell systems control therefore requires that each kinase targets only a restricted set of substrates. This specificity is achieved partly by contextual factors that separate kinases spatially and temporally, but also by sequence features that are encoded in the kinase domain itself. For this thesis I focus on elements of kinase specificity that are encoded in the the active site of the enzyme. During these investigations I have tried to address three main questions: 1) How is specificity for residues surrounding the phosphorylation site determined in the kinase? 2) How did these specificities evolve? and 3) To what extent does kinase evolution correlate with the evolution of its substrates? First, I developed a sequence-based method for the automated detection of kinase speci- ficity determining residues (SDRs). The putative determinants were then rationalised using available structural data, and in two specific cases were validated experimentally. I also used mutation data from The Cancer Genome Atlas (TCGA) to demonstrate that kinase SDRs are often targeted during cancer. Second, a global analysis of SDR evolution was performed for kinases following gene duplication and speciation, revealing that SDRs often diverge between paralogues but not between orthologues. This global analysis is followed by a detailed case study of G-protein coupled receptor kinase (GRKs) evolution using ancestral sequence reconstructions. Third, I inferred global substrate preferences in a taxonomically broad range of species using phosphoproteome data. I then related the evolution of substrate motif sequences to that of their cognate effector kinases where possible. The results strongly suggest that many of the motifs emerged in a universal eukaryotic ancestor. I finish by summarising the major findings of this doctoral research, which to my knowl- edge represents the most comprehensive analysis to date of protein kinase specificity and its evolution.BBSR

Genomics as a tool for natural product structure elucidation

Natural product discovery is in the midst of a transition from a largely serendipity-based effort to an informatics-driven one. For most of the 20th century, natural product discovery relied on genome-blind bioassay-guided isolation. This was initially exceptionally productive, yielding the golden age of antibiotics. The fact that a majority of all medicines—especially antibiotics—are in some way derived from or inspired by natural products is a testament to the importance of understanding and harnessing the chemical strategies for biological interaction that have evolved over millions of years. Unfortunately, the overwhelmingly frequent rediscovery rate of known compounds among screened natural extracts meant that what was initially a life-saving torrent of new drugs eventually dried up into a costly trickle. Unfortunately, this has coincided with the rise of drug-resistance superbugs as our initial stockpiles of antibiotics have become overdeployed. Fortunately, we are now poised to enact an antibiotic renaissance powered by the ease and affordability of large-scale genomic analysis. The ability to genome-gaze has not only revealed hundreds of thousands of yet-untapped secondary metabolites in sequenced organisms but also can facilitate strain prioritization, novelty determination (dereplication), structure elucidation, three principal bottlenecks in the discovery process, as reviewed in Chapter 1. We report here progress in the use of genomics to facilitate the discovery and contextualization of new chemical matter. In Chapter 2, we report the discovery, isolation, and structural elucidation of streptomonomicin (STM), an antibiotic lasso peptide from Streptomonospora alba, and report the genome for its producing organism. STM-resistant clones of Bacillus anthracis harbor mutations to walR, the gene encoding a response regulator for the only known widely-distributed and essential two-component signal transduction system in Firmicutes. Our results demonstrate that understudied microbes remain fruitful reservoirs for the rapid discovery of novel, bioactive natural product and also highlight the usefulness of genomics in combination with NMR and HR-MS/MS for determining the structure of ribosomal natural products. In Chapter 3, we use HR-MS/MS, reactivity-based screening, NMR, and bioinformatic analysis to identify Streptomyces varsoviensis as a novel producer of JBIR-100, a fumarate-containing hygrolide. Using a combination of NMR and bioinformatic analysis, we elucidated the stereochemistry of the natural product. We investigated the antimicrobial activity of JBIR-100, with preliminary insight into mode of action indicating that it perturbs the membrane of Bacillus subtilis. S. varsoviensis is known to produce compounds from multiple hygrolide sub-families, namely hygrobafilomycins (JBIR-100 and hygrobafilomycin) and bafilomycins (bafilomycin C1 and D). In light of this, we identified the biosynthetic gene cluster for JBIR-100, which, to our knowledge, represents the first reported for a hygrobafilomycin. Finally, we performed a bioinformatic analysis of the hygrolide family using our RODEO algorithm from Chapter 4, describing clusters from known and predicted producers. Our results indicate that potential remains for the Actinobacteria to yield novel hygrolide congeners and provides a survey of the hygrolide landscape. In Chapter 4, we report RODEO (Rapid ORF Description and Evaluation Online), an algorithm which combines hidden Markov model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future high-throughput genome mining. In addition to lasso peptides, RODEO provides the ability to analyze local genomic regions using custom profile hidden Markov models (pHMMs) and is suitable for RiPP, polyketide (PKS), nonribosomal peptide (NRPS), and other natural product biosynthetic gene cluster types; as part of an effort to make it available as a community resource we have created a web portal with its code and tutorials

Novel algorithms for protein sequence analysis

Each protein is characterized by its unique sequential order of amino acids, the so-called protein sequence. Biology__s paradigm is that this order of amino acids determines the protein__s architecture and function. In this thesis, we introduce novel algorithms to analyze protein sequences. Chapter 1 begins with the introduction of amino acids, proteins and protein families. Then fundamental techniques from computer science related to the thesis are briefly described. Making a multiple sequence alignment (MSA) and constructing a phylogenetic tree are traditional means of sequence analysis. Information entropy, feature selection and sequential pattern mining provide alternative ways to analyze protein sequences and they are all from computer science. In Chapter 2, information entropy was used to measure the conservation on a given position of the alignment. From an alignment which is grouped into subfamilies, two types of information entropy values are calculated for each position in the MSA. One is the average entropy for a given position among the subfamilies, the other is the entropy for the same position in the entire multiple sequence alignment. This so-called two-entropies analysis or TEA in short, yields a scatter-plot in which all positions are represented with their two entropy values as x- and y-coordinates. The different locations of the positions (or dots) in the scatter-plot are indicative of various conservation patterns and may suggest different biological functions. The globally conserved positions show up at the lower left corner of the graph, which suggests that these positions may be essential for the folding or for the main functions of the protein superfamily. In contrast the positions neither conserved between subfamilies nor conserved in each individual subfamily appear at the upper right corner. The positions conserved within each subfamily but divergent among subfamilies are in the upper left corner. They may participate in biological functions that divide subfamilies, such as recognition of an endogenous ligand in G protein-coupled receptors. The TEA method requires a definition of protein subfamilies as an input. However such definition is a challenging problem by itself, particularly because this definition is crucial for the following prediction of specificity positions. In Chapter 3, we automated the TEA method described in Chapter 2 by tracing the evolutionary pressure from the root to the branches of the phylogenetic tree. At each level of the tree, a TEA plot is produced to capture the signal of the evolutionary pressure. A consensus TEA-O plot is composed from the whole series of plots to provide a condensed representation. Positions related to functions that evolved early (conserved) or later (specificity) are close to the lower left or upper left corner of the TEA-O plot, respectively. This novel approach allows an unbiased, user-independent, analysis of residue relevance in a protein family. We tested the TEA-O method on a synthetic dataset as well as on __real__ data, i.e., LacI and GPCR datasets. The ROC plots for the real data showed that TEA-O works perfectly well on all datasets and much better than other considered methods such as evolutionary trace, SDPpred and TreeDet. While positions were treated independently from each other in Chapter 2 and 3 in predicting specificity positions, in Chapter 4 multi-RELIEF considers both sequence similarity and distance in 3D structure in the specificity scoring function. The multi-RELIEF method was developed based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected __local__ functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we showed that multi-RELIEF identifies specificity residues for the seven test sets used. In addition, incorporating structural information improved the prediction for specificity of interaction with small molecules. Comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. In Chapter 2, 3 and 4, we heavily relied on multiple sequence alignment to identify conserved and specificity positions. As mentioned before, the construction of such alignment is not self-evident. Following the principle of sequential pattern mining, in Chapter 5, we proposed a new algorithm that directly identifies frequent biologically meaningful patterns from unaligned sequences. Six algorithms were designed and implemented to mine three different pattern types from either one or two datasets using a pattern growth approach. We compared our approach to PRATT2 and TEIRESIAS in efficiency, completeness and the diversity of pattern types. Compared to PRATT2, our approach is faster, capable of processing large datasets and able to identify the so-called type III patterns. Our approach is comparable to TEIRESIAS in the discovery of the so-called type I patterns but has additional functionality such as mining the so-called type II and type III patterns and finding discriminating patterns between two datasets. From Chapter 2 to 5, we aimed to identify functional residues from either aligned or unaligned protein sequences. In Chapter 6, we introduce an alignment-independent procedure to cluster protein sequences, which may be used to predict protein function. Traditionally phylogeny reconstruction is usually based on multiple sequence alignment. The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In cheminformatics, constructing a similarity tree of ligands is usually alignment free. Feature spaces are routine means to convert compounds into binary fingerprints. Then distances among compounds can be obtained and similarity trees are constructed via clustering techniques. We explored building feature spaces for phylogeny reconstruction either using the so-called k-mer method or via sequential pattern mining with additional filtering and combining operations. Satisfying trees were built from both approaches compared with alignment-based methods. We found that when k equals 3, the phylogenetic tree built from the k-mer fingerprints is as good as one of the alignment-based methods, in which PAM and Neighborhood joining are used for computing distance and constructing a tree, respectively (NJ-PAM). As for the sequential pattern mining approach, the quality of the phylogenetic tree is better than one of the alignment-based method (NJ-PAM), if we set the support value to 10% and used maximum patterns only as descriptors. Finally in Chapter 7, general conclusions about the research described in this thesis are drawn. They are supplemented with an outlook on further research lines. We are convinced that the described algorithms can be useful in, e.g., genomic analyses, and provide further ideas for novel algorithms in this respect.Leiden University, NWO (Horizon Breakthrough project 050-71-041) and the Dutch Top Institute Pharma (D1-105)UBL - phd migration 201

Conservative route to genome compaction in a miniature annelid

The causes and consequences of genome reduction in animals are unclear because our understanding of this process mostly relies on lineages with often exceptionally high rates of evolution. Here, we decode the compact 73.8-megabase genome of Dimorphilus gyrociliatus, a meiobenthic segmented worm. The D. gyrociliatus genome retains traits classically associated with larger and slower-evolving genomes, such as an ordered, intact Hox cluster, a generally conserved developmental toolkit and traces of ancestral bilaterian linkage. Unlike some other animals with small genomes, the analysis of the D. gyrociliatus epigenome revealed canonical features of genome regulation, excluding the presence of operons and trans-splicing. Instead, the gene-dense D. gyrociliatus genome presents a divergent Myc pathway, a key physiological regulator of growth, proliferation and genome stability in animals. Altogether, our results uncover a conservative route to genome compaction in annelids, reminiscent of that observed in the vertebrate Takifugu rubripes