229 research outputs found
Genome assembly forensics: Finding the elusive mis-assembly
No full textWe present the first collection of tools aimed at automated genome assembly validation. This work formalizes several mechanisms for detecting mis-assemblies, and describes their implementation in our automated validation pipeline, called amosvalidate. We demonstrate the application of our pipeline in both bacterial and eukaryotic genome assemblies, and highlight several assembly errors in both draft and finished genomes. The software described is compatible with common assembly formats and is released, open-source, at http://amos.sourceforge.net. © 2008 Phillippy et al.; licensee BioMed Central Ltd
The rise of a digital immune system
No full textAbstract Driven by million-fold improvements in biotechnology, biology is increasingly shifting towards high-resolution, quantitative approaches to study the molecular dynamics of entire populations. One exciting application enabled by this new era of biology is the “digital immune system”. It would work in much the same way as an adaptive, biological immune system: by observing the microbial landscape, detecting potential threats, and neutralizing them before they spread beyond control. With the potential to have an enormous impact on public health, it is time to integrate the necessary biotechnology, computational, and organizational systems to seed the development of a global, sequencing-based pathogen surveillance system.</p
One chromosome, one contig: complete microbial genomes from long-read sequencing and assembly
No full textLike a jigsaw puzzle with large pieces, a genome sequenced with long reads is easier to assemble. However, recent sequencing technologies have favored lowering per-base cost at the expense of read length. This has dramatically reduced sequencing cost, but resulted in fragmented assemblies, which negatively affect downstream analyses and hinder the creation of finished (gapless, high-quality) genomes. In contrast, emerging long-read sequencing technologies can now produce reads tens of kilobases in length, enabling the automated finishing of microbial genomes for under $1000. This promises to improve the quality of reference databases and facilitate new studies of chromosomal structure and variation. We present an overview of these new technologies and the methods used to assemble long reads into complete genomes
Comprehensive DNA signature discovery and validation.
No full textDNA signatures are nucleotide sequences that can be used to detect the presence of an organism and to distinguish that organism from all other species. Here we describe Insignia, a new, comprehensive system for the rapid identification of signatures in the genomes of bacteria and viruses. With the availability of hundreds of complete bacterial and viral genome sequences, it is now possible to use computational methods to identify signature sequences in all of these species, and to use these signatures as the basis for diagnostic assays to detect and genotype microbes in both environmental and clinical samples. The success of such assays critically depends on the methods used to identify signatures that properly differentiate between the target genomes and the sample background. We have used Insignia to compute accurate signatures for most bacterial genomes and made them available through our Web site. A sample of these signatures has been successfully tested on a set of 46 Vibrio cholerae strains, and the results indicate that the signatures are highly sensitive for detection as well as specific for discrimination between these strains and their near relatives. Our approach, whereby the entire genomic complement of organisms are compared to identify probe targets, is a promising method for diagnostic assay development, and it provides assay designers with the flexibility to choose probes from the most relevant genes or genomic regions. The Insignia system is freely accessible via a Web interface and has been released as open source software at: http://insignia.cbcb.umd.edu
Recombination between heterologous human acrocentric chromosomes
No full textThe short arms of the human acrocentric chromosomes 13, 14, 15, 21 and 22 (SAACs) share large homologous regions, including ribosomal DNA repeats and extended segmental duplications1,2. Although the resolution of these regions in the first complete assembly of a human genome—the Telomere-to-Telomere Consortium’s CHM13 assembly (T2T-CHM13)—provided a model of their homology3, it remained unclear whether these patterns were ancestral or maintained by ongoing recombination exchange. Here we show that acrocentric chromosomes contain pseudo-homologous regions (PHRs) indicative of recombination between non-homologous sequences. Utilizing an all-to-all comparison of the human pangenome from the Human Pangenome Reference Consortium4 (HPRC), we find that contigs from all of the SAACs form a community. A variation graph5 constructed from centromere-spanning acrocentric contigs indicates the presence of regions in which most contigs appear nearly identical between heterologous acrocentric chromosomes in T2T-CHM13. Except on chromosome 15, we observe faster decay of linkage disequilibrium in the pseudo-homologous regions than in the corresponding short and long arms, indicating higher rates of recombination6,7. The pseudo-homologous regions include sequences that have previously been shown to lie at the breakpoint of Robertsonian translocations8, and their arrangement is compatible with crossover in inverted duplications on chromosomes 13, 14 and 21. The ubiquity of signals of recombination between heterologous acrocentric chromosomes seen in the HPRC draft pangenome suggests that these shared sequences form the basis for recurrent Robertsonian translocations, providing sequence and population-based confirmation of hypotheses first developed from cytogenetic studies 50 years ago9.Our work depends on the HPRC draft human pangenome resource established in the accompanying Article4, and we thank the production and assembly groups for their efforts in establishing this resource. This work used the computational resources of the UTHSC Octopus cluster and NIH HPC Biowulf cluster. We acknowledge support in maintaining these systems that was critical to our analyses. The authors thank M. Miller for the development of a graphical synopsis of our study (Fig. 5); and R. Williams and N. Soranzo for support and guidance in the design and discussion of our work. This work was supported, in part, by National Institutes of Health/NIDA U01DA047638 (E.G.), National Institutes of Health/NIGMS R01GM123489 (E.G.), NSF PPoSS Award no. 2118709 (E.G. and C.F.), the Tennessee Governor’s Chairs programme (C.F. and E.G.), National Institutes of Health/NCI R01CA266339 (T.P., L.G.d.L. and J.L.G.), and the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health (A.R., S.K. and A.M.P.). We acknowledge support from Human Technopole (A.G.), Consiglio Nazionale delle Ricerche, Italy (S.B. and V.C.), and Stowers Institute for Medical Research (T.P., L.G.d.L., B.R. and J.L.G.).Peer Reviewed"Article signat per 13 autors/es: Andrea Guarracino, Silvia Buonaiuto, Leonardo Gomes de Lima, Tamara Potapova, Arang Rhie, Sergey Koren, Boris Rubinstein, Christian Fischer, Human Pangenome Reference Consortium, Jennifer L. Gerton, Adam M. Phillippy, Vincenza Colonna & Erik Garrison "
Human Pangenome Reference Consortium: "Haley J. Abel, Lucinda L. Antonacci-Fulton, Mobin Asri, Gunjan Baid, Carl A. Baker, Anastasiya Belyaeva, Konstantinos Billis, Guillaume Bourque, Silvia Buonaiuto, Andrew Carroll, Mark J. P. Chaisson, Pi-Chuan Chang, Xian H. Chang, Haoyu Cheng, Justin Chu, Sarah Cody, Vincenza Colonna, Daniel E. Cook, Robert M. Cook-Deegan, Omar E. Cornejo, Mark Diekhans, Daniel Doerr, Peter Ebert, Jana Ebler, Evan E. Eichler, Jordan M. Eizenga, Susan Fairley, Olivier Fedrigo, Adam L. Felsenfeld, Xiaowen Feng, Christian Fischer, Paul Flicek, Giulio Formenti, Adam Frankish, Robert S. Fulton, Yan Gao, Shilpa Garg, Erik Garrison, Nanibaa’ A. Garrison, Carlos Garcia Giron, Richard E. Green, Cristian Groza, Andrea Guarracino, Leanne Haggerty, Ira Hall, William T. Harvey, Marina Haukness, David Haussler, Simon Heumos, Glenn Hickey, Kendra Hoekzema, Thibaut Hourlier, Kerstin Howe, Miten Jain, Erich D. Jarvis, Hanlee P. Ji, Eimear E. Kenny, Barbara A. Koenig, Alexey Kolesnikov, Jan O. Korbel, Jennifer Kordosky, Sergey Koren, HoJoon Lee, Alexandra P. Lewis, Heng Li, Wen-Wei Liao, Shuangjia Lu, Tsung-Yu Lu, Julian K. Lucas, Hugo Magalhães, Santiago Marco-Sola, Pierre Marijon, Charles Markello, Tobias Marschall, Fergal J. Martin, Ann McCartney, Jennifer McDaniel, Karen H. Miga, Matthew W. Mitchell, Jean Monlong, Jacquelyn Mountcastle, Katherine M. Munson, Moses Njagi Mwaniki, Maria Nattestad, Adam M. Novak, Sergey Nurk, Hugh E. Olsen, Nathan D. Olson, Benedict Paten, Trevor Pesout, Adam M. Phillippy, Alice B. Popejoy, David Porubsky, Pjotr Prins, Daniela Puiu, Mikko Rautiainen, Allison A. Regier, Arang Rhie, Samuel Sacco, Ashley D. Sanders, Valerie A. Schneider, Baergen I. Schultz, Kishwar Shafin, Jonas A. Sibbesen, Jouni Sirén, Michael W. Smith, Heidi J. Sofia, Ahmad N. Abou Tayoun, Françoise Thibaud-Nissen, Chad Tomlinson, Francesca Floriana Tricomi, Flavia Villani, Mitchell R. Vollger, Justin Wagner, Brian Walenz, Ting Wang, Jonathan M. D. Wood, Aleksey V. Zimin & Justin M. Zook"Postprint (published version
Interactive metagenomic visualization in a Web browser
No full textAbstract Background A critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables. Results Here we present Krona, a new visualization tool that allows intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications. Krona combines a variant of radial, space-filling displays with parametric coloring and interactive polar-coordinate zooming. The HTML5 and JavaScript implementation enables fully interactive charts that can be explored with any modern Web browser, without the need for installed software or plug-ins. This Web-based architecture also allows each chart to be an independent document, making them easy to share via e-mail or post to a standard Web server. To illustrate Krona's utility, we describe its application to various metagenomic data sets and its compatibility with popular metagenomic analysis tools. Conclusions Krona is both a powerful metagenomic visualization tool and a demonstration of the potential of HTML5 for highly accessible bioinformatic visualizations. Its rich and interactive displays facilitate more informed interpretations of metagenomic analyses, while its implementation as a browser-based application makes it extremely portable and easily adopted into existing analysis packages. Both the Krona rendering code and conversion tools are freely available under a BSD open-source license, and available from: http://krona.sourceforge.net.</p
Efficient oligonucleotide probe selection for pan-genomic tiling arrays
No full textBackground: Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.
Results: This paper presents a new probe selection algorithm (PanArray) that can tile multiple whole genomes using a minimal number of probes. Unlike arrays built on clustered gene families, PanArray uses an unbiased, probe-centric approach that does not rely on annotations, gene clustering, or multi-alignments. Instead, probes are evenly tiled across all sequences of the pangenome at a consistent level of coverage. To minimize the required number of probes, probes conserved across multiple strains in the pan-genome are selected first, and additional probes are used only where necessary to span polymorphic regions of the genome. The viability of the algorithm is demonstrated by array designs for seven different bacterial pan-genomes and, in particular, the design of a 385,000 probe array that fully tiles the genomes of 20 different Listeria monocytogenes strains with overlapping probes at greater than twofold coverage.
Conclusion: PanArray is an oligonucleotide probe selection algorithm for tiling multiple genome sequences using a minimal number of probes. It is capable of fully tiling all genomes of a species on a single microarray chip. These unique pan-genome tiling arrays provide maximum flexibility for the analysis of both known and uncharacterized strains.https://doi.org/10.1186/1471-2105-10-29
The variation and evolution of complete human centromeres
No full textHuman centromeres have been traditionally very difficult to sequence and assemble owing to their repetitive nature and large size1. As a result, patterns of human centromeric variation and models for their evolution and function remain incomplete, despite centromeres being among the most rapidly mutating regions2,3. Here, using long-read sequencing, we completely sequenced and assembled all centromeres from a second human genome and compared it to the finished reference genome4,5. We find that the two sets of centromeres show at least a 4.1-fold increase in single-nucleotide variation when compared with their unique flanks and vary up to 3-fold in size. Moreover, we find that 45.8% of centromeric sequence cannot be reliably aligned using standard methods owing to the emergence of new α-satellite higher-order repeats (HORs). DNA methylation and CENP-A chromatin immunoprecipitation experiments show that 26% of the centromeres differ in their kinetochore position by >500 kb. To understand evolutionary change, we selected six chromosomes and sequenced and assembled 31 orthologous centromeres from the common chimpanzee, orangutan and macaque genomes. Comparative analyses reveal a nearly complete turnover of α-satellite HORs, with characteristic idiosyncratic changes in α-satellite HORs for each species. Phylogenetic reconstruction of human haplotypes supports limited to no recombination between the short (p) and long (q) arms across centromeres and reveals that novel α-satellite HORs share a monophyletic origin, providing a strategy to estimate the rate of saltatory amplification and mutation of human centromeric DNA
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