846 resultados para Whole genome sequencing
Resumo:
Stylonychia lemnae is a classical model single-celled eukaryote, and a quintessential ciliate typified by dimorphic nuclei: A small, germline micronucleus and a massive, vegetative macronucleus. The genome within Stylonychia's macronucleus has a very unusual architecture, comprised variably and highly amplified "nanochromosomes," each usually encoding a single gene with a minimal amount of surrounding noncoding DNA. As only a tiny fraction of the Stylonychia genes has been sequenced, and to promote research using this organism, we sequenced its macronuclear genome. We report the analysis of the 50.2-Mb draft S. lemnae macronuclear genome assembly, containing in excess of 16,000 complete nanochromosomes, assembled as less than 20,000 contigs. We found considerable conservation of fundamental genomic properties between S. lemnae and its close relative, Oxytricha trifallax, including nanochromosomal gene synteny, alternative fragmentation, and copy number. Protein domain searches in Stylonychia revealed two new telomere-binding protein homologs and the presence of linker histones. Among the diverse histone variants of S. lemnae and O. trifallax, we found divergent, coexpressed variants corresponding to four of the five core nucleosomal proteins (H1.2, H2A.6, H2B.4, and H3.7) suggesting that these ciliates may possess specialized nucleosomes involved in genome processing during nuclear differentiation. The assembly of the S. lemnae macronuclear genome demonstrates that largely complete, well-assembled highly fragmented genomes of similar size and complexity may be produced from one library and lane of Illumina HiSeq 2000 shotgun sequencing. The provision of the S. lemnae macronuclear genome sets the stage for future detailed experimental studies of chromatin-mediated, RNA-guided developmental genome rearrangements.
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BACKGROUND The free-living amoeba Naegleria fowleri is the causative agent of the rapidly progressing and typically fatal primary amoebic meningoencephalitis (PAM) in humans. Despite the devastating nature of this disease, which results in > 97% mortality, knowledge of the pathogenic mechanisms of the amoeba is incomplete. This work presents a comparative proteomic approach based on an experimental model in which the pathogenic potential of N. fowleri trophozoites is influenced by the compositions of different media. RESULTS As a scaffold for proteomic analysis, we sequenced the genome and transcriptome of N. fowleri. Since the sequence similarity of the recently published genome of Naegleria gruberi was far lower than the close taxonomic relationship of these species would suggest, a de novo sequencing approach was chosen. After excluding cell regulatory mechanisms originating from different media compositions, we identified 22 proteins with a potential role in the pathogenesis of PAM. Functional annotation of these proteins revealed, that the membrane is the major location where the amoeba exerts its pathogenic potential, possibly involving actin-dependent processes such as intracellular trafficking via vesicles. CONCLUSION This study describes for the first time the 30 Mb-genome and the transcriptome sequence of N. fowleri and provides the basis for the further definition of effective intervention strategies against the rare but highly fatal form of amoebic meningoencephalitis.
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To identify novel quantitative trait loci (QTL) within horses, we performed genome-wide association studies (GWAS) based on sequence-level genotypes for conformation and performance traits in the Franches-Montagnes (FM) horse breed. Sequence-level genotypes of FM horses were derived by re-sequencing 30 key founders and imputing 50K data of genotyped horses. In total, we included 1077 FM horses genotyped for ~4 million SNPs and their respective de-regressed breeding values of the traits in the analysis. Based on this dataset, we identified a total of 14 QTL associated with 18 conformation traits and one performance trait. Therefore, our results suggest that the application of sequence-derived genotypes increases the power to identify novel QTL which were not identified previously based on 50K SNP chip data.
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A complete reference genome of the Apis mellifera Filamentous virus (AmFV) was determined using Illumina Hiseq sequencing. The AmFV genome is a double stranded DNA molecule of approximately 498,500 nucleotides with a GC content of 50.8%. It encompasses 247 non-overlapping open reading frames (ORFs), equally distributed on both strands, which cover 65% of the genome. While most of the ORFs lacked threshold sequence alignments to reference protein databases, twenty-eight were found to display significant homologies with proteins present in other large double stranded DNA viruses. Remarkably, 13 ORFs had strong similarity with typical baculovirus domains such as PIFs (per os infectivity factor genes: pif-1, pif-2, pif-3 and p74) and BRO (Baculovirus Repeated Open Reading Frame). The putative AmFV DNA polymerase is of type B, but is only distantly related to those of the baculoviruses. The ORFs encoding proteins involved in nucleotide metabolism had the highest percent identity to viral proteins in GenBank. Other notable features include the presence of several collagen-like, chitin-binding, kinesin and pacifastin domains. Due to the large size of the AmFV genome and the inconsistent affiliation with other large double stranded DNA virus families infecting invertebrates, AmFV may belong to a new virus family.
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Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^
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Embryonic stem cells (ESCs) possess two unique characteristics: infinite self-renewal and the potential to differentiate into almost every cell type (pluripotency). Recently, global expression analyses of metastatic breast and lung cancers revealed an ESC-like expression program or signature, specifically for cancers that are mutant for p53 function. Surprisingly, although p53 is widely recognized as the guardian of the genome, due to its roles in cell cycle checkpoints, programmed cell death or senescence, relatively little is known about p53 functions in normal cells, especially in ESCs. My hypothesis is that p53 has specific transcription regulatory functions in human ESCs (hESCs) that a) oppose pluripotency and b) protect the stem cell genome in response to DNA damage and stress signaling. In mouse ESCs, these roles are believed to coincide, as p53 promotes differentiation in response to DNA damage, but this is unexplored in hESCs. To determine the biological roles of p53, specifically in hESCs, we mapped genome-wide chromatin interactions of p53 by chromatin immunoprecipitation and massively parallel tag sequencing (ChIP-Seq), and did so under three VIdifferent conditions of hESC status: pluripotency, differentiation-initiated and DNA-damage-induced. ChIP-Seq showed that p53 is enriched at distinct, induction-specific gene loci during each of these different conditions. Microarray gene expression analysis and functional annotation of the distinct p53-target genes revealed that p53 regulates specific genes encoding developmental regulators, which are expressed in differentiation-initiated but not DNA- damaged hESCs. We further discovered that, in response to differentiation signaling, p53 binds regions of chromatin that are repressed but also poised for rapid activation by core pluripotency factors OCT4 and NANOG in pluripotent hESCs. In response to DNA damage, genes associated with migration and motility are targeted by p53; whereas, the prime targets of p53 in control of cell death are conserved for p53 regulation in both differentiation and DNA damage. Our genome-wide profiling and bioinformatics analyses show that p53 occupies a special set of developmental regulatory genes during early differentiation of hESCs and functions in an induction-specific manner. In conclusion, our research unveiled previously unknown functions of p53 in ESC biology, which augments our understanding of one of the most deregulated proteins in human cancers.
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The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^
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Background: Zooplankton play an important role in our oceans, in biogeochemical cycling and providing a food source for commercially important fish larvae. However, difficulties in correctly identifying zooplankton hinder our understanding of their roles in marine ecosystem functioning, and can prevent detection of long term changes in their community structure. The advent of massively parallel Next Generation Sequencing technology allows DNA sequence data to be recovered directly from whole community samples. Here we assess the ability of such sequencing to quantify the richness and diversity of a mixed zooplankton assemblage from a productive monitoring site in the Western English Channel. Methodology/Principle Findings: Plankton WP2 replicate net hauls (200 µm) were taken at the Western Channel Observatory long-term monitoring station L4 in September 2010 and January 2011. These samples were analysed by microscopy and metagenetic analysis of the 18S nuclear small subunit ribosomal RNA gene using the 454 pyrosequencing platform. Following quality control a total of 419,042 sequences were obtained for all samples. The sequences clustered in to 205 operational taxonomic units using a 97% similarity cut-off. Allocation of taxonomy by comparison with the National Centre for Biotechnology Information database identified 138 OTUs to species level, 11 to genus level and 1 to order, <2.5% of sequences were classified as unknowns. By comparison a skilled microscopic analyst was able to routinely enumerate only 75 taxonomic groups. Conclusions: The percentage of OTUs assigned to major eukaryotic taxonomic groups broadly aligns between the metagenetic and morphological analysis and are dominated by Copepoda. However, the metagenetics reveals a previously hidden taxonomic richness, especially for Copepoda and meroplankton such as Bivalvia, Gastropoda and Polychaeta. It also reveals rare species and parasites. We conclude that Next Generation Sequencing of 18S amplicons is a powerful tool for estimating diversity and species richness of zooplankton communities.
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The pufferfish Fugu rubripes has a genome ≈7.5 times smaller than that of mammals but with a similar number of genes. Although conserved synteny has been demonstrated between pufferfish and mammals across some regions of the genome, there is some controversy as to what extent Fugu will be a useful model for the human genome, e.g., [Gilley, J., Armes, N. & Fried, M. (1997) Nature (London) 385, 305–306]. We report extensive conservation of synteny between a 1.5-Mb region of human chromosome 11 and <100 kb of the Fugu genome in three overlapping cosmids. Our findings support the idea that the majority of DNA in the region of human chromosome 11p13 is intergenic. Comparative analysis of three unrelated genes with quite different roles, WT1, RCN1, and PAX6, has revealed differences in their structural evolution. Whereas the human WT1 gene can generate 16 protein isoforms via a combination of alternative splicing, RNA editing, and alternative start site usage, our data predict that Fugu WT1 is capable of generating only two isoforms. This raises the question of the extent to which the evolution of WT1 isoforms is related to the evolution of the mammalian genitourinary system. In addition, this region of the Fugu genome shows a much greater overall compaction than usual but with significant noncoding homology observed at the PAX6 locus, implying that comparative genomics has identified regulatory elements associated with this gene.
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A loxP-transposon retrofitting strategy for generating large nested deletions from one end of the insert DNA in bacterial artificial chromosomes and P1 artificial chromosomes was described recently [Chatterjee, P. K. & Coren, J. S. (1997) Nucleic Acids Res. 25, 2205–2212]. In this report, we combine this procedure with direct sequencing of nested-deletion templates by using primers located in the transposon end to illustrate its value for position-specific single-nucleotide polymorphism (SNP) discovery from chosen regions of large insert clones. A simple ampicillin sensitivity screen was developed to facilitate identification and recovery of deletion clones free of transduced transposon plasmid. This directed approach requires minimal DNA sequencing, and no in vitro subclone library generation; positionally oriented SNPs are a consequence of the method. The procedure is used to discover new SNPs as well as physically map those identified from random subcloned libraries or sequence databases. The deletion templates, positioned SNPs, and markers are also used to orient large insert clones into a contig. The deletion clone can serve as a ready resource for future functional genomic studies because each carries a mammalian cell-specific antibiotic resistance gene from the transposon. Furthermore, the technique should be especially applicable to the analysis of genomes for which a full genome sequence or radiation hybrid cell lines are unavailable.
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Multiple-complete-digest mapping is a DNA mapping technique based on complete-restriction-digest fingerprints of a set of clones that provides highly redundant coverage of the mapping target. The maps assembled from these fingerprints order both the clones and the restriction fragments. Maps are coordinated across three enzymes in the examples presented. Starting with yeast artificial chromosome contigs from the 7q31.3 and 7p14 regions of the human genome, we have produced cosmid-based maps spanning more than one million base pairs. Each yeast artificial chromosome is first subcloned into cosmids at a redundancy of ×15–30. Complete-digest fragments are electrophoresed on agarose gels, poststained, and imaged on a fluorescent scanner. Aberrant clones that are not representative of the underlying genome are rejected in the map construction process. Almost every restriction fragment is ordered, allowing selection of minimal tiling paths with clone-to-clone overlaps of only a few thousand base pairs. These maps demonstrate the practicality of applying the experimental and software-based steps in multiple-complete-digest mapping to a target of significant size and complexity. We present evidence that the maps are sufficiently accurate to validate both the clones selected for sequencing and the sequence assemblies obtained once these clones have been sequenced by a “shotgun” method.
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The function of many of the uncharacterized open reading frames discovered by genomic sequencing can be determined at the level of expressed gene products, the proteome. However, identifying the cognate gene from minute amounts of protein has been one of the major problems in molecular biology. Using yeast as an example, we demonstrate here that mass spectrometric protein identification is a general solution to this problem given a completely sequenced genome. As a first screen, our strategy uses automated laser desorption ionization mass spectrometry of the peptide mixtures produced by in-gel tryptic digestion of a protein. Up to 90% of proteins are identified by searching sequence data bases by lists of peptide masses obtained with high accuracy. The remaining proteins are identified by partially sequencing several peptides of the unseparated mixture by nanoelectrospray tandem mass spectrometry followed by data base searching with multiple peptide sequence tags. In blind trials, the method led to unambiguous identification in all cases. In the largest individual protein identification project to date, a total of 150 gel spots—many of them at subpicomole amounts—were successfully analyzed, greatly enlarging a yeast two-dimensional gel data base. More than 32 proteins were novel and matched to previously uncharacterized open reading frames in the yeast genome. This study establishes that mass spectrometry provides the required throughput, the certainty of identification, and the general applicability to serve as the method of choice to connect genome and proteome.
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The Mouse Genome Database (MGD) is the community database resource for the laboratory mouse, a key model organism for interpreting the human genome and for understanding human biology and disease (http://www.informatics.jax.org). MGD provides standard nomenclature and consensus map positions for mouse genes and genetic markers; it provides a curated set of mammalian homology records, user-defined chromosomal maps, experimental data sets and the definitive mouse ‘gene to sequence’ reference set for the research community. The integration and standardization of these data sets facilitates the transition between mouse DNA sequence, gene and phenotype annotations. A recent focus on allele and phenotype representations enhances the ability of MGD to organize and present data for exploring the relationship between genotype and phenotype. This link between the genome and the biology of the mouse is especially important as phenotype information grows from large mutagenesis projects and genotype information grows from large-scale sequencing projects.
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The SWISS-PROT group at EBI has developed the Proteome Analysis Database utilising existing resources and providing comparative analysis of the predicted protein coding sequences of the complete genomes of bacteria, archaea and eukaryotes (http://www.ebi.ac.uk/proteome/). The two main projects used, InterPro and CluSTr, give a new perspective on families, domains and sites and cover 31–67% (InterPro statistics) of the proteins from each of the complete genomes. CluSTr covers the three complete eukaryotic genomes and the incomplete human genome data. The Proteome Analysis Database is accompanied by a program that has been designed to carry out InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database.
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The Medicago Genome Initiative (MGI) is a database of EST sequences of the model legume Medicago truncatula. The database is available to the public and has resulted from a collaborative research effort between the Samuel Roberts Noble Foundation and the National Center for Genome Resources to investigate the genome of M.truncatula. MGI is part of the greater integrated Medicago functional genomics program at the Noble Foundation (http://www.noble .org), which is taking a global approach in studying the genetic and biochemical events associated with the growth, development and environmental interactions of this model legume. Our approach will include: large-scale EST sequencing, gene expression profiling, the generation of M.truncatula activation-tagged and promoter trap insertion mutants, high-throughput metabolic profiling, and proteome studies. These multidisciplinary information pools will be interfaced with one another to provide scientists with an integrated, holistic set of tools to address fundamental questions pertaining to legume biology. The public interface to the MGI database can be accessed at http://www.ncgr.org/research/mgi.