579 research outputs found
Computational pan-genomics: Status, promises and challenges
No full textMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different Computational methods and paradigms are needed.We will witness the rapid extension of Computational pan-genomics, a new sub-area of research in Computational biology. In this article, we generalize existing definitions and understand a pangenome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a Computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common Computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to Computational pangenomics can help address many of the problems currently faced in various domains
Computational pan-genomics: status, promises and challenges
No full textMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge
of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will
approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be
sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational
methods and paradigms are needed.We will witness the rapid extension of computational pan-genomics, a new
sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pangenome
as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already
available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies
and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent
example for a computational paradigm shift, we particularly highlight the transition from the representation of reference
genomes as strings to representations as graphs. We outline how this and other challenges from different application domains
translate into common computational problems, point out relevant bioinformatics techniques and identify open
problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pangenomics
can help address many of the problems currently faced in various domains
Computational pan-genomics: status, promises and challenges
No full textMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains
Computational pan-genomics: status, promises and challenges
No full textInternational audienceMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains
The Amphibian Genomics Consortium: advancing genomic and genetic resources for amphibian research and conservation
No full textT.A.K. was supported by Australian Research Council grants (FT190100462 and LP200301370). M.T.-S. was supported by María Zambrano fellowship from Complutense University of Madrid and NextGenerationEU. The Xenopus laevis Research Resource for Immunobiology is supported by the National Institute of Health (R24-AI-05983). A.A.S. is supported by the NSF Postdoctoral Research Fellowships in Biology Program under Grant No. 2305939.Amphibians represent a diverse group of tetrapods, marked by deep divergence times between their three systematic orders and families. Studying amphibian biology through the genomics lens increases our understanding of the features of this animal class and that of other terrestrial vertebrates. The need for amphibian genomic resources is more urgent than ever due to the increasing threats to this group. Amphibians are one of the most imperiled taxonomic groups, with approximately 41% of species threatened with extinction due to habitat loss, changes in land use patterns, disease, climate change, and their synergistic effects. Amphibian genomic resources have provided a better understanding of ontogenetic diversity, tissue regeneration, diverse life history and reproductive modes, anti-predator strategies, and resilience and adaptive responses. They also serve as essential models for studying broad genomic traits, such as evolutionary genome expansions and contractions, as they exhibit the widest range of genome sizes among all animal taxa and possess multiple mechanisms of genetic sex determination. Despite these features, genome sequencing of amphibians has significantly lagged behind that of other vertebrates, primarily due to the challenges of assembling their large, repeat-rich genomes and the relative lack of societal support. The emergence of long-read sequencing technologies, combined with advanced molecular and computational techniques that improve scaffolding and reduce computational workloads, is now making it possible to address some of these challenges. To promote and accelerate the production and use of amphibian genomics research through international coordination and collaboration, we launched the Amphibian Genomics Consortium (AGC, https://mvs.unimelb.edu.au/amphibian-genomics-consortium) in early 2023. This burgeoning community already has more than 282 members from 41 countries. The AGC aims to leverage the diverse capabilities of its members to advance genomic resources for amphibians and bridge the implementation gap between biologists, bioinformaticians, and conservation practitioners. Here we evaluate the state of the field of amphibian genomics, highlight previous studies, present challenges to overcome, and call on the research and conservation communities to unite as part of the AGC to enable amphibian genomics research to “leap” to the next level.Australian Research CouncilUniversidad Complutense de MadridEuropean CommissionNational Institutes of Health (U.S.)National Science Foundation (U.S.)Depto. de Biodiversidad, Ecología y EvoluciónDepto. de Genética, Fisiología y MicrobiologíaFac. de Ciencias BiológicasTRUEpu
Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition (Nature Genetics, (2020), 52, 3, (306-319), 10.1038/s41588-019-0562-0)
No full textCorrection to: Nature Genetics, published online 05 February 2020. In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper. Additional affiliations have been added for author Kathleen H. Burns (McKusick-Nathans Institute of Genetic Medicine, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, USA) and author Guillaume Bourque (Canadian Center for Computational Genomics, McGill University, Montreal, Quebec, Canada).</p
Computational pan-genomics: Status, promises and challenges
No full textMany disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different Computational methods and paradigms are needed.We will witness the rapid extension of Computational pan-genomics, a new sub-area of research in Computational biology. In this article, we generalize existing definitions and understand a pangenome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a Computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common Computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to Computational pangenomics can help address many of the problems currently faced in various domains
Comparative BAC-based mapping in the white-throated sparrow, a novel behavioral genomics model, using interspecies overgo hybridization
No full textBACKGROUND
The genomics era has produced an arsenal of resources from sequenced organisms allowing researchers to target species that do not have comparable mapping and sequence information. These new "non-model" organisms offer unique opportunities to examine environmental effects on genomic patterns and processes. Here we use comparative mapping as a first step in characterizing the genome organization of a novel animal model, the white-throated sparrow (Zonotrichia albicollis), which occurs as white or tan morphs that exhibit alternative behaviors and physiology. Morph is determined by the presence or absence of a complex chromosomal rearrangement. This species is an ideal model for behavioral genomics because the association between genotype and phenotype is absolute, making it possible to identify the genomic bases of phenotypic variation.
FINDINGS
We initiated a genomic study in this species by characterizing the white-throated sparrow BAC library via filter hybridization with overgo probes designed for the chicken, turkey, and zebra finch. Cross-species hybridization resulted in 640 positive sparrow BACs assigned to 77 chicken loci across almost all macro-and microchromosomes, with a focus on the chromosomes associated with morph. Out of 216 overgos, 36% of the probes hybridized successfully, with an average number of 3.0 positive sparrow BACs per overgo.
CONCLUSIONS
These data will be utilized for determining chromosomal architecture and for fine-scale mapping of candidate genes associated with phenotypic differences. Our research confirms the utility of interspecies hybridization for developing comparative maps in other non-model organisms
High-throughput computational and experimental techniques in structural genomics
No full textStructural genomics has as its goal the provision of structural information for all possible ORF sequences through a combination of experimental and computational approaches. The access to genome sequences and cloning resources from an ever-widening array of organisms is driving high-throughput structural studies by the New York Structural Genomics Research Consortium. In this report, we outline the progress of the Consortium in establishing its pipeline for structural genomics, and some of the experimental and bioinformatics efforts leading to structural annotation of proteins. The Consortium has established a pipeline for structural biology studies, automated modeling of ORF sequences using solved (template) structures, and a novel high-throughput approach (metallomics) to examining the metal binding to purified protein targets. The Consortium has so far produced 493 purified proteins from>1077 expression vectors. A total of 95 have resulted in crystal structures, and 81 are deposited in the Protein Data Bank (PDB). Comparative modeling of these structures has generated>40,000 structural models. We also initiated a high-throughput metal analysis of the purified proteins; this has determined that 10%–15 % of the targets contain a stoichiometric structural or catalytic transition metal atom. The progress of the structural genomics centers in the U.S. and around the world suggests that the goal of providing useful structural information on most all ORF domains will be realized. This projected resource will provide structural biology information important to understanding the function of most proteins of the cell
Towards comparative pan-genomics
No full textComparative genomics investigates the genomic makeup of species to unravel their unique variations and evolutionary relationships. High-throughput sequencing technologies have enabled reading the DNA content of a wide variety of species at an unprecedented rate. With the ongoing advances in these technologies, many species are or will soon be represented by a large number of genomes. Such genomes can be highly similar, but their differences in sequence and structure are of interest in many applications as they usually underlie specific traits. Having a wealth of genomes for a species, the current practice of basing comparative studies on a single reference genome is neither efficient nor effective. Traditional reference-based approaches make use of only a single reference genome, ignoring the potentially novel genomic content found in other individuals. As a result, over the last decade there has been a growing interest in developing pan-genome structures capable of capturing a wide genomic landscape of species. In this thesis, we develop a pan-genomic platform based on a novel representation of genomes with some functionalities for sequence retrieval, structural annotation, homology detection and read mapping.Chapter 1 briefly introduces molecular biology and the revolution in genome sequencing. Then we introduce evolution and some basic concepts in genomics and comparative genomics which are necessary for the readers to be able to follow the chapters of this thesis. We emphasize the shortcomings of traditional reference-based approaches in comparative genomics and introduce pan-genomics as a solution which recently has received much attention. We introduce the essentials of a pan-genomic platform from the perspective of the Computational Pan-genomics Consortium, and classify existing pan-genomic data structures into two general categories of variation-aware and multi-genome data structures. Finally, we discuss the de Bruijn graph including the stranded version we introduce in chapter 2. Chapter 2 highlights the necessity of a transition from reference-centric to pan-genomic approaches. As a comprehensive representation of large number of genomes, we introduce a generalized de Bruijn graph. We present a novel algorithm to construct such a DBG and take advantage of the Neo4j graph database for consistent and scalable storage of the graph. We develop a toolset, called PanTools, which provides some useful functionalities e.g. for annotation, graph update and sequence retrieval. We demonstrate the performance of PanTools on large datasets of bacterial, fungal and plant genomes. We illustrate how sequence variation creates specific sub-structures in the pan-genome including an example of the variability of a famous gene, called FRIGIDA, among 19 A. thaliana accessions.Chapter 3 emphasizes the need for highly efficient tools to detect homology in the ever-increasing genomic data. We present an efficient method for detecting homology across a large number of individuals at various evolutionary distances. The presented k-mer based approach considerably reduces the number of alignments between pairs of peptide sequences without sacrificing sensitivity. We demonstrate accuracy, scalability, efficiency and applicability of the presented method in large proteomes of bacteria, fungi, plants and Metazoa. The detected homology groups are stored in the pan-genome graph database, and can be queried, for example, for their size, copy number and conservation rate.Chapter 4 focuses on correcting errors in next-generation sequencing reads which can improve the performance of assembly and increase the accuracy and sensitivity of quantitative analyses such as differential expression analyses and variant calling. We develop a tool, called ACE, based on a k-mer trie data structure to correct for substitution errors in short read data. We show that ACE yields higher gains in terms of coverage depth, outperforming state-of-the-art competitors in the majority of cases, on both MiSeq and HiSeq Illumina data.Chapter 5 presents a multi-genome read mapping approach which utilizes the index and pan-genome structure, introduced in Chapter 2, to map short reads to large number of genomes, simultaneously. One advantage is the efficiency as the joint index enables anchoring the reads to all the genomes at once avoiding repetitive alignments when the genomes are highly similar. Another advantage is that we can resolve the reference bias by including regions that are entirely missing in the reference but present in some other accessions. Moreover, such a multi-genome read mapper can be utilized in binning and abundance estimation of meta-genomic samples. In this chapter, we successfully apply this approach to map genomic and metagenomic reads to large collections of viral, archaeal, bacterial, fungal and plant genomes.Chapter 6 puts forward some ideas on the future challenges and opportunities in the field of pan-genomics. We discuss the emerging shift from reference-centric to pan-genomic approaches and the necessity of substantial adjustments and redevelopments of traditional methods and applications such as genome annotation, structural variation detection and real-time pan-genome visualization. We conclude that the design and engineering introduced in this thesis contributes to the field and the growing number of similar efforts indicates a bright future ahead for comparative pan-genomics.  
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