Numerous recursive sites contribute to accuracy of splicing of long introns in flies [preprint]

Recursive splicing, a process by which a single intron is removed from pre-mRNA transcripts in multiple distinct segments, has been observed in a small subset of Drosophila melanogaster introns. However, detection of recursive splicing requires observation of splicing intermediates which are inherently unstable, making it difficult to study. Here we developed new computational approaches to identify recursively spliced introns and applied them, in combination with existing methods, to nascent RNA sequencing data from Drosophila S2 cells. These approaches identified hundreds of novel sites of recursive splicing, expanding the catalog of recursively spliced fly introns by 4-fold. Recursive sites occur in most very long (\u3e 40 kb) fly introns, including many genes involved in morphogenesis and development, and tend to occur near the midpoints of introns. Suggesting a possible function for recursive splicing, we observe that fly introns with recursive sites are spliced more accurately than comparably sized non-recursive introns

Finding the missing honey bee genes: lessons learned from a genome upgrade

BACKGROUND: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. RESULTS: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. CONCLUSIONS: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.Funding for the project was provided by a grant to RG from the National Human Genome Research Institute, National Institutes of Health (NHGRI, NIH) U54 HG003273. Contributions from members of the CGE lab were supported by Agriculture and Food Research Initiative Competitive grant no. 2010- 65205-20407 from the USDA National Institute of Food Agriculture. AKB was supported by a Clare Luce Booth Fellowship at Georgetown University

Finding the Missing Honey Bee Genes: Lessons Learned from a Genome Upgrade

Background: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes.Results: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data.Conclusions: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination

A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection

Numerous high-throughput sequencing studies have focused on detecting conventionally spliced mRNAs in RNA-seq data. However, non-standard RNAs arising through gene fusion, circularization or trans-splicing are often neglected. We introduce a novel, unbiased algorithm to detect splice junctions from single-end cDNA sequences. In contrast to other methods, our approach accommodates multi-junction structures. Our method compares favorably with competing tools for conventionally spliced mRNAs and, with a gain of up to 40% of recall, systematically outperforms them on reads with multiple splits, trans-splicing and circular products

NOVEL COMPUTATIONAL METHODS FOR SEQUENCING DATA ANALYSIS: MAPPING, QUERY, AND CLASSIFICATION

Over the past decade, the evolution of next-generation sequencing technology has considerably advanced the genomics research. As a consequence, fast and accurate computational methods are needed for analyzing the large data in different applications. The research presented in this dissertation focuses on three areas: RNA-seq read mapping, large-scale data query, and metagenomics sequence classification. A critical step of RNA-seq data analysis is to map the RNA-seq reads onto a reference genome. This dissertation presents a novel splice alignment tool, MapSplice3. It achieves high read alignment and base mapping yields and is able to detect splice junctions, gene fusions, and circular RNAs comprehensively at the same time. Based on MapSplice3, we further extend a novel lightweight approach called iMapSplice that enables personalized mRNA transcriptional profiling. As huge amount of RNA-seq has been shared through public datasets, it provides invaluable resources for researchers to test hypotheses by reusing existing datasets. To meet the needs of efficiently querying large-scale sequencing data, a novel method, called SeqOthello, has been developed. It is able to efficiently query sequence k-mers against large-scale datasets and finally determines the existence of the given sequence. Metagenomics studies often generate tens of millions of reads to capture the presence of microbial organisms. Thus efficient and accurate algorithms are in high demand. In this dissertation, we introduce MetaOthello, a probabilistic hashing classifier for metagenomic sequences. It supports efficient query of a taxon using its k-mer signatures

Applied Bioinformatics for ncRNA Characterization - Case Studies Combining Next Generation Sequencing & Genomics

Non-coding RNAs (ncRNAs) present a diverse class of functional molecules inherent in virtually all forms of cellular life. Besides the canonical protein-encoding mRNAs the role of these abundant transcripts has been overlooked for decades. Defined by their highly conserved structure ncRNAs are resistant to degradation and perform various regulatory functions. Despite the poor sequence conservation, comparative genomics can be employed to identify homologous ncRNAs based on their structure in related species. Through the availability of next generation sequencing techniques, a rich corpus of datasets is available which grants a detailed look into cellular processes. The combination of genomic and transcriptomic data allows for a detailed understanding of molecular mechanism as well as characterization of individual gene functions and their evolution. However, analytical processing of modern high-throughput data is only made viable through optimized bioinformatic algorithms and reproducible automation pipelines. This thesis consists of four major parts highlighting the diverse roles of ncRNAs concerning the transcription process viewed from different vantage points. The first part concerns an unusually long untranslated region in Rhodobacter which harbors a ncRNA that regulates the expression of the downstream division cell wall cluster. Second, the degradation of 6S RNA in Bacillus subtilis is experimentally reconstructed to shed light on this final part of the RNA life cycle. This ncRNA is ubiquitous among bacteria and known to be a global transcription regulator itself. Next, the focus moves to the eukaryotic system and RNase P, an ancient ribozyme that is involved in tRNA maturation. Due to differences in composition with an optional RNA and multiple protein subunits, its phylogenetic distribution and deviant characteristics throughout the eukaryotic lineage are examined in order to trace its evolution. Finally, a diverse subgroup of non-translated RNAs are circRNAs which recently received increased attention due to their abundance in neural tissue. Resulting from post-transcriptional back-splicing events circRNAs compete with their host gene for expression. In a zoological study of social insects circRNA were for the first time identified in honeybees. The goal was to find task-related differences in circRNA expression between nurse bees and foragers and thus pinpoint potential functions of these elusive ncRNAs. The combination of genomic methods and transcriptomic data makes in-depth functional analysis of ncRNAs possible and enables us to understand the molecular mechanisms on multiple levels. Through structural predictions a riboswitch like transcriptional control of UpsM was revealed that is unique to Rhodobacteraceae. Transcriptomic analysis exposed that 6S RNA is primarily processed by RNase J1 for maturation and degraded at internal loops by RNase Y. Evolutionary comparison of organellar RNase P revealed that the RNA subunit is potentially less conserved than thought while organellar proteinonly variants are widespread potentially due to horizontal gene transfer. In the case of circRNA, an entire group of ncRNAs was characterized in the social model organism of honeybees and evidence of at least one gene where circRNA levels are significantly reduced during nurse-to-forager transition could be shown. Moreover, an unexpected link between elevated DNA methylation and RNA circularization was discovered. The bioinformatic findings in all of these cases provide a foundation for further experimental research and illustrate how scientific endeavors cannot be automated completely but require rigorous investigation with customized tools

Paired is better: local assembly algorithms for NGS paired reads and applications to RNA-Seq

The analysis of biological sequences is one of the main research areas of Bioinformatics. Sequencing data are the input for almost all types of studies concerning genomic as well as transcriptomic sequences, and sequencing experiments should be conceived specifically for each type of application. The challenges posed by fundamental biological questions are usually addressed by firstly aligning or assemblying the reads produced by new sequencing technologies. Assembly is the first step when a reference sequence is not available. Alignment of genomic reads towards a known genome is fundamental, e.g., to find the differences among organisms of related species, and to detect mutations proper of the so-called "diseases of the genome". Alignment of transcriptomic reads against a reference genome, allows to detect the expressed genes as well as to annotate and quantify alternative transcripts. In this thesis we overview the approaches proposed in literature for solving the above mentioned problems. In particular, we deeply analyze the sequence assembly problem, with particular emphasys on genome reconstruction, both from a more theoretical point of view and in light of the characteristics of sequencing data produced by state-of-the-art technologies. We also review the main steps in a pipeline for the analysis of the transcriptome, that is, alignment, assembly, and transcripts quantification, with particular emphasys on the opportunities given by RNA-Seq technologies in enhancing precision. The thesis is divided in two parts, the first one devoted to the study of local assembly methods for Next Generation Sequencing data, the second one concerning the development of tools for alignment of RNA-Seq reads and transcripts quantification. The permanent theme is the use of paired reads in all fields of applications discussed in this thesis. In particular, we emphasyze the benefits of assemblying inserts from paired reads in a wide range of applications, from de novo assembly, to the analysis of RNA. The main contribution of this thesis lies in the introduction of innovative tools, based on well-studied heuristics fine tuned on the data. Software is always tested to specifically assess the correctness of prediction. The aim is to produce robust methods, that, having low false positives rate, produce a certified output characterized by high specificity.openDottorato di ricerca in InformaticaopenNadalin, Francesc

Lessons from non-canonical splicing

Recent improvements in experimental and computational techniques that are used to study the transcriptome have enabled an unprecedented view of RNA processing, revealing many previously unknown non-canonical splicing events. This includes cryptic events located far from the currently annotated exons and unconventional splicing mechanisms that have important roles in regulating gene expression. These non-canonical splicing events are a major source of newly emerging transcripts during evolution, especially when they involve sequences derived from transposable elements. They are therefore under precise regulation and quality control, which minimizes their potential to disrupt gene expression. We explain how non-canonical splicing can lead to aberrant transcripts that cause many diseases, and also how it can be exploited for new therapeutic strategies

Computational analysis of expressed sequence tags for understanding gene regulation.

High-throughput sequencing has provided a myriad of genetic data for thousands of organisms. Computational analysis of one data type, expressed sequence tags (ESTs) yields insight into gene expression, alternative splicing, tissue specificity gene functionality and the detection and differentiation of pseudogenes. Two computational methods have been developed to analyze alternative splicing events and to detect and characterize pseudogenes using ESTs. A case study of rat phosphodiesterase 4 (PDE4) genes yielded more than twenty-five previously unreported isoforms. These were experimentally verified through wet lab collaboration and found to be tissue specific. In addition, thirteen cytochrome-like gene and pseudogene sequences from the human genome were analyzed for pseudogene properties. Of the thirteen sequences, one was identified as the actual cytochrome gene, two were found to be non-cytochrome-related sequences, and eight were determined to be pseudogenes. The remaining two sequences were identified to be duplicates. As a precursor to applying the two new methods, the efficiency of three BLAST algorithms (NCBI BLAST, WU BLAST and mpiBLAST) were examined for comparing large numbers of short sequences (ESTs) to fewer large sequences (genomic regions). In general, WU BLAST was found to be the most efficient sequence comparison tool. These approaches illustrate the power of ESTs in understanding gene expression. Efficient computational analysis of ESTs (such as the two tools described) will be vital to understanding the complexity of gene expression as more high-throughput EST data is made available via advances in molecular sequencing technologies, such as the current next-generation approaches

Leveraging EST Evidence to Automatically Predict Alternatively Spliced Genes, Master\u27s Thesis, December 2006

Current methods for high-throughput automatic annotation of newly sequenced genomes are largely limited to tools which predict only one transcript per gene locus. Evidence suggests that 20-50% of genes in higher eukariotic organisms are alternatively spliced. This leaves the remainder of the transcripts to be annotated by hand, an expensive time-consuming process. Genomes are being sequenced at a much higher rate than they can be annotated. We present three methods for using the alignments of inexpensive Expressed Sequence Tags in combination with HMM-based gene prediction with N-SCAN EST to recreate the vast majority of hand annotations in the D.melanogaster genome. In our first method, we “piece together” N-SCAN EST predictions with clustered EST alignments to increase the number of transcripts per locus predicted. This is shown to be a sensitve and accurate method, predicting the vast majority of known transcripts in the D.melanogaster genome. We present an approach of using these clusters of EST alignments to construct a Multi-Pass gene prediction phase, again, piecing it together with clusters of EST alignments. While time consuming, Multi-Pass gene prediction is very accurate and more sensitive than single-pass. Finally, we present a new Hidden Markov Model instance, which augments the current N-SCAN EST HMM, that predicts multiple splice forms in a single pass of prediction. This method is less time consuming, and performs nearly as well as the multi-pass approach