Evaluating the performance of a distributed database of repetitive elements in complete genomes

[[abstract]]this paper is organized as follows. In section 2, we briefly describe RSDB. In section 3, we give the design of experiments. Section 4 shows the performance evaluation of the experiments designed in section 3. Finally, we draw a summary in section

A machine learning based framework to identify and classify long terminal repeat retrotransposons

Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. Accurate identification and classification of TEs present in a genome is an important step towards understanding their effects on genes and their role in genome evolution. We introduce TE-LEARNER, a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them. We present an implementation of our framework towards LTR retrotransposons, a particular type of TEs characterized by having long terminal repeats (LTRs) at their boundaries. We evaluate the predictive performance of our framework on the well-annotated genomes of Drosophila melanogaster and Arabidopsis thaliana and we compare our results for three LTR retrotransposon superfamilies with the results of three widely used methods for TE identification or classification: REPEATMASKER, CENSOR and LTRDIGEST. In contrast to these methods, TE-LEARNER is the first to incorporate machine learning techniques, outperforming these methods in terms of predictive performance , while able to learn models and make predictions efficiently. Moreover, we show that our method was able to identify TEs that none of the above method could find, and we investigated TE-LEARNER'S predictions which did not correspond to an official annotation. It turns out that many of these predictions are in fact strongly homologous to a known TE

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution

The genome of Romanomermis culicivorax:revealing fundamental changes in the core developmental genetic toolkit in Nematoda

Background: The genetics of development in the nematode Caenorhabditis elegans has been described in exquisite detail. The phylum Nematoda has two classes: Chromadorea (which includes C. elegans) and the Enoplea. While the development of many chromadorean species resembles closely that of C. elegans, enoplean nematodes show markedly different patterns of early cell division and cell fate assignment. Embryogenesis of the enoplean Romanomermis culicivorax has been studied in detail, but the genetic circuitry underpinning development in this species has not been explored. Results: We generated a draft genome for R. culicivorax and compared its gene content with that of C. elegans, a second enoplean, the vertebrate parasite Trichinella spiralis, and a representative arthropod, Tribolium castaneum. This comparison revealed that R. culicivorax has retained components of the conserved ecdysozoan developmental gene toolkit lost in C. elegans. T. spiralis has independently lost even more of this toolkit than has C. elegans. However, the C. elegans toolkit is not simply depauperate, as many novel genes essential for embryogenesis in C. elegans are not found in, or have only extremely divergent homologues in R. culicivorax and T. spiralis. Our data imply fundamental differences in the genetic programmes not only for early cell specification but also others such as vulva formation and sex determination. Conclusions: Despite the apparent morphological conservatism, major differences in the molecular logic of development have evolved within the phylum Nematoda. R. culicivorax serves as a tractable system to contrast C. elegans and understand how divergent genomic and thus regulatory backgrounds nevertheless generate a conserved phenotype. The R. culicivorax draft genome will promote use of this species as a research model

Doctor of Philosophy

dissertationWhole genome sequencing projects have expanded our understanding of evolution, organism development, and human disease. Now advances in secondgeneration technologies are making whole genome sequencing routine even for small laboratories. However, advances in annotation technology have not kept pace with genome sequencing, and annotation has become the major bottleneck for many genome projects (especially those with limited bioinformatics expertise). At the same time, challenges associated with genomics research extend beyond merely annotating genomes, as annotations must be subjected to diverse downstream analyses, the complexities of which can confound smaller research groups. Additionally, with improvements in genome assembly and the wide availability of next generation transcriptome data (mRNA-seq), researchers have the opportunity to re-annotate previously published genomes, which creates new difficulties for data integration and management that are not well addressed by existing tools. In response to the challenges facing second-generation genome projects, I have developed the annotation pipeline MAKER2 together with accessory software for downstream analysis and data management. The MAKER2 annotation pipeline finds repeats within a genome, aligns ESTs and cDNAs, identifies sites of protein homology, and produces database-ready gene annotations in association with supporting evidence. However MAKER2 can go beyond structural annotation to identify and integrate functional annotations. MAKER2 also provides researchers iv with the capability to re-annotate legacy genome datasets and to incorporate mRNAseq. Additionally, MAKER2 supports distributed parallelization on computer clusters, thus providing a scalable solution for datasets of any size. Annotations produced by MAKER2 can be directly loaded into many popular downstream annotation analysis and management tools from the Generic Model Organism Database Project. By using MAKER2 with these tools, research groups can quickly build genome annotations, perform analyses, and distribute their data to the wider scientific community. Here I describe the internal architecture of MAKER2, and document its computational capabilities. I also describe my work to annotate and analyze eight emerging model organism genomes in collaboration with their associated genome projects. Thus, in the course of my thesis work, I have addressed a specific need within the scientific community for easy-to-use annotation and analysis tools while also expanding our understanding of evolution and biology

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLAS

Chromosomal-level assembly of the Asian Seabass genome using long sequence reads and multi-layered scaffolding

We report here the ~670 Mb genome assembly of the Asian seabass (Lates calcarifer), a tropical marine teleost. We used long-read sequencing augmented by transcriptomics, optical and genetic mapping along with shared synteny from closely related fish species to derive a chromosome-level assembly with a contig N50 size over 1 Mb and scaffold N50 size over 25 Mb that span ~90% of the genome. The population structure of L. calcarifer species complex was analyzed by re-sequencing 61 individuals representing various regions across the species' native range. SNP analyses identified high levels of genetic diversity and confirmed earlier indications of a population stratification comprising three clades with signs of admixture apparent in the South-East Asian population. The quality of the Asian seabass genome assembly far exceeds that of any other fish species, and will serve as a new standard for fish genomics