DNA Sequencing Projects Research Articles

Genome sequencing projects reveal new insights into the mammalian Gonadotropin-releasing Hormone II system.

The type II gonadotropin-releasing hormone (GnRH-II) was first discovered in chicken (Gallus gallus) brain and then shown to be present in many vertebrates. Indeed, its structure is conserved unchanged throughout vertebrate evolution from teleost fish through to mammals suggesting a crucial function. Yet the functional significance has been largely unexplored. Studies in comparative endocrinology show that the GnRH-II system is differentially functional in mammalian species. Intact GnRH-II neuropeptide and receptor genes (GnRH2 and GnRH receptor 2 GnRHR2) occur in marmoset monkeys (Callithrix jacchus), musk shrews (Suncus murinus) and pigs (Sus scrofa). However, one or other or both of these genes are inactivated in other species, where mutations or remnants affecting GnRH2 neuropeptide and/or type II GnRHR exons are retained in conserved genomic loci. New data from DNA sequencing projects facilitate extensive analysis of species-specific variation in these genes. Here, we describe GnRH2 and GnRHR2 genes spanning a collection of 21 taxonomic orders, encompassing around 140 species from Primates, Scandentia, Eulipotyphla, Rodentia, Lagomorpha, Artiodactyla, Carnivora, Perissodactyls, Pholidota, Chiroptera, Afrotheria, Xenarthra and Marsupialia. Intact coding exons for both GnRH2 and GnRHR2 occur in monkeys, tree shrews, shrews, moles, hedgehogs, several rodents (degu, kangaroo-rat, pocket mouse), pig, pecarry and warthog, camels and alpaca, bears, Weddell seal, hyena, elephant, aardvark and marsupials. Inactivating mutations affecting GnRH2 and GnRHR2, some located at conserved sites within exons, occur in species of primates, most rodents, lagomorphs, bovidae, cetaceans, felidae, canidae and other carnivora, pangolins, most bats, armadillo, brushtail and echidna. A functional GnRH-II system appears retained within several taxonomic families of mammals, but intact retention does not extend to whole taxonomic orders. Defining how endogenous GnRH-II neuropeptide operates in different mammals may afford functional insight into its actions in the brain, especially as, unlike the type I GnRH system, it is expressed in the mid brain and not the hypothalamus.

1990s dinomania: Public and popular cultures of palaeontology from Jurassic Park to Friends

In the 1990s, the English-speaking world was swept up in a deluge of popular cultural content on palaeontology. Dinosaurs were featured in programming ranging from B-movie horrors to children's cartoon franchises to popular novels. In some ways, this widespread public interest in palaeontology was mirroring broader public and scientific conflicts such as the Alverez hypothesis, the ‘birds are dinosaurs’ debate, ownership rights over dinosaur fossils, intelligent design and creationism in schools, and major DNA sequencing projects. But in other ways, this popular culture material existed beyond these specific debates, and speaks to the ways that depictions of palaeontology fostered public understanding of science in this period. Perhaps no two depictions of palaeontology frame different, and in some ways conflicting, aspects of the field better than two of the biggest popular culture phenomena of the 1990s: Jurassic Park and Friends. Jurassic Park, for which we are approaching the 30th anniversary of its initial release, spawned two sequels within a decade, and three more since then. The heroes of the original movie were two palaeontologists, Alan Grant (Sam Neill) and Ellie Sattler (Laura Dern). Friends, which aired 1994-2004, was certainly the most successful sitcom of the period and one of the most popular television shows of all time. One of the six main characters, Ross Geller (David Schwimmer), is a palaeontologist, who at the beginning of the series works for the American Museum of Natural History and by the show's conclusion, has received tenure at New York University. The depiction of palaeontology in these two programs could not be more different. Drs. Grant and Sattler are competent action heroes, whose expertise regarding dinosaur morphology and behaviour are central to both their survival and their identity. Notably, the filmmakers for Jurassic Park centred the science of palaeontology in its story, working close with palaeontologist Jack Horner. They were also helped by the fact that their source material, Michael Crichton's 1990 novel of the same name, closely engages with scientific ideas and ethics. For Ross Geller on the other hand, his career as a palaeontologist is only on the outskirts of the TV series. Viewers occasionally saw Ross in professional settings (the classroom, the museum, a conference, a fellowship interview), but are not provided with enough information to even determine what sort of research he even does. The focus is always on his personal life; his interest in dinosaurs is just one aspect of his personality, one that does not even get mentioned in many episodes. It thus offers an unusual representation of scientists and science as fully integrated into society and the lived experiences of non-scientists. The palaeoscience occurs off-screen as a facet of the character rather than their defining characteristic. In this article, the authors will situate Friends and Jurassic Park within the dinosaur-mania of the 1990s, while analysing the different versions of palaeontological study that they projected to the public. It will draw connections between how palaeontology was depicted on-screen to the major real-life debates and discoveries in the field at the same time.

MutFusVAE: Mutational Fusion Variational Autoencoder for Predicting Primary Sites of Cancer

The metastatic propensity of malignant primary tumors is a recurring theme when it comes to the cause of mortality in cancer. Establishing the primary site of a metastatic cancer is a significant but challenging task. There are ∼3% of metastatic cancer cases diagnosed as cancer of unknown primary (CUP), and the conventional diagnostic process fails to detect the primary site for 80% of CUP patients. Benefiting from the explosion of the information available from large-scale tumor DNA sequencing projects, it became favorable to predict the cancer primary sites from genomic perspective. The existing methods on the task intensively studied the mutational and oncogenic features with assists of machine learning (ML) and deep learning (DL) techniques, yet lack of development of a model architecture tailored for the mutational features. To address the gap, in this research, we aimed to develop a DL methodology specialized for the mutational data. A mutational fusion variational autoencoder (MutFusVAE) deep architecture is proposed to actualize the idea1. We downloaded mutational profiles meeting our criteria of 2,603 tumor samples, which get split into 2,082 training samples and 521 (20%) held-out testing samples, from the International Cancer Genome Consortium (ICGC) database. The proposed methods achieved 94% overall classification accuracy for differentiating among seven primary sites on the held-out testing set. The results show the discriminative power brought by a specialized design of deep models for the mutational data and gain insights to facilitate DL-based genomic diagnostics for cancer from a modeling view.

A Population-Specific Major Allele Reference Genome From The United Arab Emirates Population.

The ethnic composition of the population of a country contributes to the uniqueness of each national DNA sequencing project and, ideally, individual reference genomes are required to reduce the confounding nature of ethnic bias. This work represents a representative Whole Genome Sequencing effort of an understudied population. Specifically, high coverage consensus sequences from 120 whole genomes and 33 whole exomes were used to construct the first ever population specific major allele reference genome for the United Arab Emirates (UAE). When this was applied and compared to the archetype hg19 reference, assembly of local Emirati genomes was reduced by ∼19% (i.e., some 1 million fewer calls). In compiling the United Arab Emirates Reference Genome (UAERG), sets of annotated 23,038,090 short (novel: 1,790,171) and 137,713 structural (novel: 8,462) variants; their allele frequencies (AFs) and distribution across the genome were identified. Population-specific genetic characteristics including loss-of-function variants, admixture, and ancestral haplogroup distribution were identified and reported here. We also detect a strong correlation between FST and admixture components in the UAE. This baseline study was conceived to establish a high-quality reference genome and a genetic variations resource to enable the development of regional population specific initiatives and thus inform the application of population studies and precision medicine in the UAE.

Short reads from honey bee (Apis sp.) sequencing projects reflect microbial associate diversity.

High throughput (or ‘next generation’) sequencing has transformed most areas of biological research and is now a standard method that underpins empirical study of organismal biology, and (through comparison of genomes), reveals patterns of evolution. For projects focused on animals, these sequencing methods do not discriminate between the primary target of sequencing (the animal genome) and ‘contaminating’ material, such as associated microbes. A common first step is to filter out these contaminants to allow better assembly of the animal genome or transcriptome. Here, we aimed to assess if these ‘contaminations’ provide information with regard to biologically important microorganisms associated with the individual. To achieve this, we examined whether the short read data from Apis retrieved elements of its well established microbiome. To this end, we screened almost 1,000 short read libraries of honey bee (Apis sp.) DNA sequencing project for the presence of microbial sequences, and find sequences from known honey bee microbial associates in at least 11% of them. Further to this, we screened ∼500 Apis RNA sequencing libraries for evidence of viral infections, which were found to be present in about half of them. We then used the data to reconstruct draft genomes of three Apis associated bacteria, as well as several viral strains de novo. We conclude that ‘contamination’ in short read sequencing libraries can provide useful genomic information on microbial taxa known to be associated with the target organisms, and may even lead to the discovery of novel associations. Finally, we demonstrate that RNAseq samples from experiments commonly carry uneven viral loads across libraries. We note variation in viral presence and load may be a confounding feature of differential gene expression analyses, and as such it should be incorporated as a random factor in analyses.

Assessment of Whole-Exome Sequence Data in Attempted Suicide within a Bipolar Disorder Cohort

Suicidal behavior is a complex and devastating phenotype with a heritable component that has not been fully explained by existing common genetic variant analyses. This study represents the first large-scale DNA sequencing project designed to assess the role of rare functional genetic variation in suicidal behavior risk. To accomplish this, whole-exome sequencing data for ∼19,000 genes were generated for 387 bipolar disorder subjects with a history of suicide attempt and 631 bipolar disorder subjects with no prior suicide attempts. Rare functional variants were assessed in all exome genes as well as pathways hypothesized to contribute to suicidal behavior risk. No result survived conservative Bonferroni correction, though many suggestive findings have arisen that merit additional attention. In addition, nominal support for past associations in genes, such as BDNF, and pathways, such as the hypothalamic-pituitary-adrenal axis, was also observed. Finally, a novel pathway was identified that is driven by aldehyde dehydrogenase genes. Ultimately, this investigation explores variation left largely untouched by existing efforts in suicidal behavior, providing a wealth of novel information to add to future investigations, such as meta-analyses.

A history of DNA sequence assembly

Abstract DNA sequence assembly is a rich combinatorial problem that arose with the first DNA sequencing projects in the early 80's. Here we give a short history of the progression of algorithmic ideas used to solve the de novo problem of inferring a genome given a large sampling of substrings covering it. This classic inverse problem is compounded by a variety of experimental features and artifacts that must be considered in any realistic solution. While current methods produce very good results, the perfect assembler has yet to be built.

Application of denaturing high-performance liquid chromatography for rice variety identification and seed purity assessment

Recent DNA sequencing projects and the establishment of high-throughput assays have provided an abundance of sequence information and data on nucleotide polymorphisms in rice. Based on previously identified single-nucleotide polymorphisms (SNPs) and insertions/deletions, we employed denaturing high-performance liquid chromatography (DHPLC) to genotype rice varieties using a chromatographic pattern-based strategy. In this study, 12 amplicons harboring multiple and informative SNPs were screened. PCR products of the 12 amplicons from 47 rice varieties were analyzed by DHPLC and DNA sequencing. Each homozygous sample with a single peak pattern in the initial DHPLC analysis was mixed with zhenshan97 for a second DHPLC analysis. The 12 amplicons were found to be polymorphic across the hybrids, and mixed homozygous samples with 43 distinct DHPLC elution profiles detected. Sequence analysis confirmed that the distinct DHPLC patterns corresponded to different DNA sequences. A set of distinct characteristic profiles in six amplicons differentiated between all of the hybrids, inbred lines, and restorer lines and produced unique a fingerprint for these lines. In addition, we found that the DHPLC pattern of the hybrid was in accordance with the results obtained by DHPLC analysis of a mixed sample of the two parents. These results demonstrate that DHPLC can be efficiently applied for the rapid and automated identification of diverse rice varieties and could possibly be utilized for seed genetic purity testing on a high-throughput scale.

DNA-seq analysis of Garcinia mangostana.

Mangosteen (Garcinia mangostana Linn.) is a tropical tree mainly found in South East Asia and considered as “the queen of fruits”. The asexually produced fruit is dark purple or reddish in color, with white flesh which is slightly acidic with sweet flavor and a pleasant aroma. The purple pericarp tissue is rich in xanthones which are useful for medical purposes. We performed the first genome sequencing of this commercially important fruit tree to study its genome composition and attempted draft genome assembly. Raw reads of the DNA sequencing project have been deposited to SRA database with the accession number SRX1426419.

Abstract 2767: Identification of kinase fusion genes in bladder cancer through kinome-centered RNA sequencing

Abstract Urothelial carcinoma (UC) of the bladder is one of the most common cancers worldwide. The treatment of patients with metastatic or locally advanced UC consists of platinum-based chemotherapy. Unfortunately, a large number of patients will ultimately develop resistance. Therapeutic options at that point are very limited: the last FDA drug approval for the treatment of bladder cancer dates back more than 2 decades. Identification of activated signaling pathways in UC can provide new targets for treatment. Recent DNA and RNA sequencing projects in invasive UC have revealed somatic mutations in several cancer genes. Some of these mutated genes, such as FGFR3 and PIK3CA, could potentially guide therapy. However, our knowledge of gene rearrangements in bladder cancer remains limited. These fusion genes often involve multiple fusion partners, as was reported for FGFR3 fusions in bladder cancer, which represents a significant challenge for discovery and for subsequent diagnostic screening. Therefore, a global detection method is needed to fully understand the diversity of alterations driving this disease. We used a high-throughput platform to systematically profile kinase fusions through specific enrichment of kinase transcripts. Using this approach, we screened 80 muscle invasive UC specimens and identified a number of activating mutations and novel fusion transcripts. The fusion genes identified in this discovery set will be validated in a second cohort of UC specimens to determine their frequency. Functional validation will be presented for some of these fusion genes. These genetic alterations may provide new avenues for individualized molecular treatment of UC patients. Citation Format: Floris Groenendijk, Iris de Rink, Laura Mertens, Annegien Broeks, Yann Neuzillet, Jeroen de Jong, Bas van Rhijn, Rene Bernards, Michiel van der Heijden. Identification of kinase fusion genes in bladder cancer through kinome-centered RNA sequencing. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2767. doi:10.1158/1538-7445.AM2014-2767

Pseudo DNA Sequence Generation of Non-Coding Distributions Using Variant Maps on Cellular Automata

In a recent decade, many DNA sequencing projects are developed on cells, plants and animals over the world into huge DNA databases. Researchers notice that mammalian genomes encoding thousands of large noncoding RNAs (lncRNAs), interact with chromatin regulatory complexes, and are thought to play a role in localizing these complexes to target loci across the genome. It is a challenge target using higher dimensional tools to organize various complex interactive properties as visual maps. In this paper, a Pseudo DNA Variant MapPDVM is proposed following Cellular Automata to represent multiple maps that use four Meta symbols as well as DNA or RNA representations. The system architecture of key components and the core mechanism on the PDVM are described. Key modules, equations and their I/O parameters are discussed. Applying the PDVM, two sets of real DNA sequences from both the sample human (noncoding DNA) and corn (coding DNA) genomes are collected in comparison with two sets of pseudo DNA sequences generated by a stream cipher HC-256 under different modes to show their intrinsic properties in higher levels of similar relationships among relevant DNA sequences on 2D maps. Sample 2D maps are listed and their characteristics are illustrated under a controllable environment. Various distributions can be observed on both noncoding and coding conditions from their symmetric properties on 2D maps.

Staircase Method: A Novel Method for Parallelizing S-W Algorithm

Sequence comparison is a basic operation in DNA sequencing projects, and most of sequence comparison methods are based on heuristics, which are fast but not sensitive. The Dynamic Programming Algorithm, Smith-Waterman, obtains the best alignment, but at the expense of computational time. Unfortunately, the inefficiency in the performance of the Smith-Waterman algorithm limits its applications in the real world. A possible way out of this is to use parallelization methods for decreasing the time taken to execute the algorithm. In this paper, we present a two master method and a novel parallel technique called staircase method to improve the performance of the Smith-Waterman algorithm.

Variant Map System to Simulate Complex Properties of DNA Interactions Using Binary Sequences

Stream cipher, DNA cryptography and DNA analysis are the most important R&D fields in both Cryptography and Bioinformatics. HC-256 is an emerged scheme as the new generation of stream ciphers for advanced network security. From a random sequencing viewpoint, both sequences of HC-256 and real DNA data may have intrinsic pseudo-random properties respectively. In a recent decade, many DNA sequencing projects are developed on cells, plants and animals over the world into huge DNA databases. Researchers notice that mammalian genomes encode thousands of large noncoding RNAs (lncRNAs), interact with chromatin regulatory complexes, and are thought to play a role in localizing these complexes to target loci across the genome. It is a challenge target using higher dimensional visualization tools to organize various complex interactive properties as visual maps. The Variant Map System (VMS) as an emerging scheme is systematically proposed in this paper to apply multiple maps that used four Meta symbols as same as DNA or RNA representations. System architecture of key components and core mechanism on the VMS are described. Key modules, equations and their I/O parameters are discussed. Applying the VM System, two sets of real DNA sequences from both sample human (noncoding DNA) and corn (coding DNA) genomes are collected in comparison with pseudo DNA sequences generated by HC-256 to show their intrinsic properties in higher levels of similar relationships among relevant DNA sequences on 2D maps. Sample 2D maps are listed and their characteristics are illustrated under controllable environment. Visual results are briefly analyzed to explore their intrinsic properties on selected genome sequences.

A Simple DNA Characterization Method Using Fiber-Fluorescence in situ Hybridization Performed without DNA Fragmentation

We performed high-resolution fluorescence imaging of lambda phage DNA molecules hybridized with fluorescent-labeled DNA and peptide nucleic acid probes. In this method, the target DNA and probe were mixed, rapidly denatured and then subjected to liquid hybridization conditions. The hybridized DNA sample was then spotted onto a nontreated glass substrate and subjected to molecular combing. The resultant continuous fluorescence signal of intact lambda DNA shows that the fluorescent-labeled probes bound to the predicted sites but in a pattern that was clearly different to the beads-on-a-string pattern typical for fiber-fluorescence in situ hybridization. The key changes to the conventional method are hybridization of the free target DNA in liquid and lowering the denaturation temperature. The method described here allows the rapid and direct visualization of the specific binding sites of intact DNA molecules without damaging the DNA fibers and causing fragmentation of the fluorescence signal. This technique should be a useful tool in studies of genetics and also large-scale DNA sequencing projects.

GAMES identifies and annotates mutations in next-generation sequencing projects

Next-generation sequencing (NGS) methods have the potential for changing the landscape of biomedical science, but at the same time pose several problems in analysis and interpretation. Currently, there are many commercial and public software packages that analyze NGS data. However, the limitations of these applications include output which is insufficiently annotated and of difficult functional comprehension to end users. We developed GAMES (Genomic Analysis of Mutations Extracted by Sequencing), a pipeline aiming to serve as an efficient middleman between data deluge and investigators. GAMES attains multiple levels of filtering and annotation, such as aligning the reads to a reference genome, performing quality control and mutational analysis, integrating results with genome annotations and sorting each mismatch/deletion according to a range of parameters. Variations are matched to known polymorphisms. The prediction of functional mutations is achieved by using different approaches. Overall GAMES enables an effective complexity reduction in large-scale DNA-sequencing projects. GAMES is available free of charge to academic users and may be obtained from http://aqua.unife.it/GAMES.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

Listeria monocytogenes: Knowledge Gained Through DNA Sequence-Based Subtyping, Implications, and Future Considerations

The purpose of subtyping is to differentiate bacterial isolates beyond the classification of species or subspecies. Subtyping methods can be grouped into two broad categories based on the cellular components targeted: (1) phenotypic subtyping methods that differentiate isolates by the enzymes, proteins, or other metabolites expressed by the cell, and (2) molecular subtyping methods that discriminate isolates based on interrogation of nucleic acid sequences. The two major types of molecular subtyping methods include band-based methods based on fragment pattern data or DNA fingerprints, and methods that generate DNA sequence data. Molecular subtyping methods have shown that Listeria monocytogenes isolates can be classified into four genetic lineages or divisions. Although band-based molecular subtyping methods continue to serve as the gold standard for routine molecular subtyping of most clinically important foodborne pathogens, including L. monocytogenes, the explosion of recently completed and ongoing DNA sequencing projects, and thus available DNA sequence data, have stimulated efforts to develop highly discriminatory and high-throughput DNA sequence-based subtyping methods for L. monocytogenes. L. monocytogenes represents one of the most highly sequenced human pathogens; more than 20 genome sequences are currently available for this organism. This review provides an overview of the concepts behind subtyping and discusses the application of molecular subtyping methods, with an emphasis on DNA sequence-based subtyping methods to characterize L. monocytogenes.

Deleterious amino acid polymorphisms in Arabidopsis thaliana and rice

Plant genetic diversity has been mainly investigated with neutral markers, but large-scale DNA sequencing projects now enable the identification and analysis of different classes of genetic polymorphisms, such as non-synonymous single nucleotide polymorphisms (nsSNPs) in protein coding sequences. Using the SIFT and MAPP programs to predict whether nsSNPs are tolerated (i.e., effectively neutral) or deleterious for protein function, genome-wide nsSNP data from Arabidopsis thaliana and rice were analyzed. In both species, about 20% of polymorphic sites with nsSNPs were classified as deleterious; they segregate at lower allele frequencies than tolerated nsSNPs due to purifying selection. Furthermore, A. thaliana accessions from marginal populations show a higher relative proportion of deleterious nsSNPs, which likely reflects differential selection or demographic effects in subpopulations. To evaluate the sensitivity of predictions, genes from model and crop plants with known functional effects of nsSNPs were inferred with the algorithms. The programs predicted about 70% of nsSNPs correctly as tolerated or deleterious, i.e., as having a functional effect. Forward-in-time simulations of bottleneck and domestication models indicated a high power to detect demographic effects on nsSNP frequencies in sufficiently large datasets. The results indicate that nsSNPs are useful markers for analyzing genetic diversity in plant genetic resources and breeding populations to infer natural/artificial selection and genetic drift.

Innovative high-throughput protein purification in 96-array format

High-throughput biology has been pioneered by genomics through the application of robotics to expedite DNA-sequencing projects. Advances in high-throughput protein methods are needed to drive the protein production line for high-throughput structural and functional analysis of newly discovered genes. This will require the development and application of a variety of recombinant-protein expression systems to produce the diversity of proteins from both humans and model organisms.

Spruce proteome DB: a resource for conifer proteomics research

Proteomics research is hampered in many organisms due to a lack of an appropriate reference genome sequence that can be used in the interpretation of tandem mass spectrometry data for the identification of proteins. Public DNA sequence repositories have grown to considerable size and can, in most cases, serve to provide at least partial interpretation of a large-scale proteomics dataset. However, when species-specific sequences or sequences from a closely related species are available, a boutique sequence database can provide considerable increases in specificity, confidence, and completeness of protein identification. Here, we describe the development of a protein database from a large-scale expressed sequence tag and full-length complementary DNA sequencing project in the economically and ecologically important spruce (Picea) genus.