Apis Mellifera)
Total Page：16
File Type：pdf, Size：1020Kb
COMPUTATIONAL ANALYSIS OF DIFFICULT-TO-PREDICT GENES AND DETECTION OF LINEAGE-SPECIFIC GENES IN HONEY BEE (APIS MELLIFERA) A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biology. By Anna K. Bennett, M.S. Washington, DC July 29, 2013 Copyright 2013 by Anna K. Bennett All Rights Reserved ii COMPUTATIONAL ANALYSIS OF DIFFICULT-TO-PREDICT GENES AND DETECTION OF LINEAGE-SPECIFIC GENES IN HONEY BEE (APIS MELLIFERA) Anna K. Bennett, M.S. Thesis Advisor: Christine G. Elsik, Ph.D. ABSTRACT Honey bees are key agricultural pollinators and a research model for social behavior and the evolution of eusociality. Generation of reliable gene predictions is critical to the success of laboratory experiments and comparative analyses for sequenced genomes. The honey bee genome, published in 2006 by the Honey Bee Genome Sequencing Consortium, had fewer predicted genes than expected, partially due to a lack of transcriptome data and protein homologs from closely related species. As part of ongoing Consortium efforts, genomes of two closely related taxa were sequenced, dwarf honey bee (Apis florea) and buff-tailed bumble bee (Bombus terrestris), the draft A. mellifera genome was improved with additional sequence, and multiple A. mellifera tissue transcriptomes were sequenced. The Consortium predicted genes using seven methods and used these data to produce an improved official gene set (OGSv3.2) with ~5000 more protein-coding genes than the first set (OGSv1.0). We present our approach to detect previously unknown genes in A. mellifera. We found new genes through both improvements in genome assembly completeness, and changes to prediction pipelines and additional transcript and homology evidence. The later set of new genes were shorter, less likely to overlap evidence alignments, and had a different surrounding genome GC content than previously predicted genes, which made them challenging to predict. Based on these findings, new genome projects will benefit from more effective gene prediction strategies. iii We focused on the N-SCAN method specifically, to determine if it predicted more genes than were in OGSv1.0. N-SCAN leveraged the sequence conservation between A. mellifera and the two additional bee genomes to predict genes not found by other prediction pipelines. Therefore, N-SCAN proved useful and is a worthwhile tool to include in gene prediction efforts where closely related sequenced species are available. We identified 4,277 genes specific to A. mellifera or to lineages within the insect order Hymenoptera. We detected lineage-specific genes using homology to other species’ genomes and proteins, and propose mechanisms for their emergence. We identified genes with tentative roles in brood care, immunity, defense and other processes important to hive health, and thus associated with the evolution of eusociality in Apis. iv I dedicate this thesis to my family and friends who were always there to support me, even and especially, on the longest days. I would also like to thank everyone who helped me along the way, including my advisor Dr. Christine Elsik and my dissertation committee members Dr. Laura Elnitski, Dr. Matthew Hamilton, and Dr. Anne Rosenwald. And to the humble bees, may we never know a world without them. Many thanks, Anna K. Bennett v TABLE OF CONTENTS Introduction and Literature Review .................................................................................. 1 Research Chapter One: Investigating Characteristics of Difficult-to-Predict Genes in the Honey Bee (Apis mellifera) ....................................................................................................... 17 Introduction ........................................................................................................ 17 Methods .............................................................................................................. 21 Results and Discussion ...................................................................................... 30 Conclusion ......................................................................................................... 40 Research Chapter Two: Characteristics of Novel Exons Predicted Using Genome Conservation ......................................................................................................................................... 43 Introduction ........................................................................................................ 43 Methods .............................................................................................................. 45 Results ................................................................................................................ 51 Discussion .......................................................................................................... 61 Research Chapter Three: Identification of Genes Specific to the Lineage of the Hymenopteran Insect, Honey Bee (Apis mellifera) ................................................................................. 65 Introduction ........................................................................................................ 65 Methods............................................................................................................... 67 Results ................................................................................................................ 75 Discussion .......................................................................................................... 85 Conclusion ......................................................................................................... 92 Bibliography .................................................................................................................. 93 vi INTRODUCTION AND LITERATURE REVIEW Lack of quality protein-coding gene predictions hinders research progress on newly sequenced genomes. High quality protein-coding gene predictions are those that accurately encode real proteins with correct start codons, splice sites, and stop codons. Without dependable gene predictions, primer design for candidate genes investigated in everyday laboratory experiments will not be successful, nor will development of technologies for targeted exome sequencing, epigenetic studies and other genetic tools. Additionally, comparative genomic analyses aimed at understanding evolutionary patterns across taxa require reliable gene predictions in order to accurately search and discover homologous sequences in other genomes or protein databases. It is essential that accurate gene predictions be generated for every genome lest the experiments based on them fail or are not undertaken. However, there are numerous challenges to the production of high quality gene predictions including data availability, genome and transcriptome assembly quality, and computational gene prediction accuracy. Computational gene prediction is known to produce errors including false positive predictions, incomplete predictions and predictions that correspond to incorrectly split or merged genes. Because computational gene predictions are not always correct, the best way to produce a high quality gene prediction set is to manually annotate gene predictions. This requires viewing each prediction along with all available transcript and protein homolog evidence, often in a graphical environment such as Apollo [1]. The annotator can then inspect each gene structure and compare it to the available evidence to make sure it has a complete sequence with correct intron and exon boundaries. This work takes a considerable amount of time per gene, so it is impractical to manually annotate each gene to produce a final gene set in most cases. 1 In addition, as the volume of data to annotate continues to increase, there simply are not enough people to individually vet gene predictions. New sequencing technologies, with decreasing costs and increasing efficiency, are leading to an explosion in the number of genome and transcriptome sequencing projects. The decreasing sequencing costs have lowered the barrier to genome sequencing for small research groups, many of whom lack the bioinformatics expertise found in larger sequencing consortiums to perform sequence analysis and the staffing to manually annotate individual assembly or gene prediction issues. Because trained annotators are at a premium, researchers must still rely heavily on computational sequence analysis algorithms. Despite the drastic improvement in sequencing technology, the development of improved algorithms has not kept pace and bioinformaticians who specialize in gene prediction are also at a premium. These limitations are inhibiting research of non-model organisms, which have previously received less attention and funding toward the development of genetic and genomic tools. Therefore, this motivates improvement of computational gene prediction algorithms in order to reduce the error rate of gene prediction and also to decrease the need for manual annotation. To further understand the challenges in computational gene prediction, we have taken advantage of the efforts of the Honey Bee Genome Sequencing Consortium, which undertook an entire gene prediction effort for Apis mellifera seven years ago [2] and recently repeated the process with new data [3]. The combined data from these two gene prediction efforts represents a valuable resource because