Increasing the coverage of a metapopulation consensus genome by iterative read mapping and assembly

Motivation: Most microbial species can not be cultured in the laboratory. Metagenomic sequencing may still yield a complete genome if the sequenced community is enriched and the sequencing coverage is high. However, the complexity in a natural population may cause the enrichment culture to contain multiple related strains. This diversity can confound existing strict assembly programs and lead to a fragmented assembly, which is unnecessary if we have a related reference genome available that can function as a scaffold

Increasing the coverage of a metapopulation consensus genome by iterative read mapping and assembly

Motivation: Most microbial species can not be cultured in the laboratory. Metagenomic sequencing may still yield a complete genome if the sequenced community is enriched and the sequencing coverage is high. However, the complexity in a natural population may cause the enrichment culture to contain multiple related strains. This diversity can confound existing strict assembly programs and lead to a fragmented assembly, which is unnecessary if we have a related reference genome available that can function as a scaffold. Results: Here, we map short metagenomic sequencing reads from a population of strains to a related reference genome, and compose a genome that captures the consensus of the population's sequences. We show that by iteration of the mapping and assembly procedure, the coverage increases while the similarity with the reference genome decreases. This indicates that the assembly becomes less dependent on the reference genome and approaches the consensus genome of the multi-strain population

Dynamic read mapping and online consensus calling for better variant detection

Variant detection from high-throughput sequencing data is an essential step in identification of alleles involved in complex diseases and cancer. To deal with these massive data, elaborated sequence analysis pipelines are employed. A core component of such pipelines is a read mapping module whose accuracy strongly affects the quality of resulting variant calls.We propose a dynamic read mapping approach that significantly improves read alignment accuracy. The general idea of dynamic mapping is to continuously update the reference sequence on the basis of previously computed read alignments. Even though this concept already appeared in the literature, we believe that our work provides the first comprehensive analysis of this approach.To evaluate the benefit of dynamic mapping, we developed a software pipeline (http://github.com/karel-brinda/dymas) that mimics different dynamic mapping scenarios. The pipeline was applied to compare dynamic mapping with the conventional static mapping and, on the other hand, with the so-called iterative referencing – a computationally expensive procedure computing an optimal modification of the reference that maximizes the overall quality of all alignments. We conclude that in all alternatives, dynamic mapping results in a much better accuracy than static mapping, approaching the accuracy of iterative referencing.To correct the reference sequence in the course of dynamic mapping, we developed an online consensus caller named Ococo (http://github.com/karel-brinda/ococo). Ococo is the first consensus caller capable to process input reads in the online fashion.Finally, we provide conclusions about the feasibility of dynamic mapping and discuss main obstacles that have to be overcome to implement it. We also review a wide range of possible applications of dynamic mapping with a special emphasis on variant detection

A Genomic Investigation of Divergence Between Tuna Species

Effective management and conservation of marine pelagic fishes is heavily dependent on a robust understanding of their population structure, their evolutionary history, and the delineation of appropriate management units. The Yellowfin tuna (Thunnus albacares) and the Blackfin tuna (Thunnus atlanticus) are two exploited epipelagic marine species with overlapping ranges in the tropical and sub-tropical Atlantic Ocean. This work analyzed genome-wide genetic variation of both species in the Atlantic basin to investigate the occurrence of population subdivision and adaptive variation. A de novo assembly of the Blackfin tuna genome was generated using Illumina paired-end sequencing data and applied as a reference for population genomic analysis of specimens from 9 localities spanning most of the Blackfin tuna range. Analysis suggested the presence of four weakly differentiated units corresponding to the northwestern Atlantic Ocean, Gulf of Mexico, Caribbean Sea, and southwestern Atlantic Ocean, respectively. Significant spatial autocorrelation of genotypes was observed for specimens collected within 800 km of each other. A high-quality genome assembly generated for the Yellowfin tuna using PacBio and Illumina sequences was scaffolded by a linkage map developed through analysis of the segregation of genome wide Single Nucleotide Polymorphisms in 164 larvae offspring from a single pair produced by controlled breeding. The genome assembly was used as a reference for population genomic analysis of juvenile specimens from the 4 main nursery areas hypothesized in the Atlantic Ocean basin. Analyses corroborated previously reported population subdivision between the east and west Atlantic Ocean, but also suggested subdivision associated with individual nursery areas within the east and west regions. Draft reference assemblies were generated for Albacore, Bigeye and Longtail tunas and used in combination with the Yellowfin and Blackfin tuna genomes obtained in this work and existing assemblies for bluefin tunas in preliminary analyses of genome wide variation between species of the Thunnus genus. Whole-genome derived SNP-based phylogenetic analysis of the Thunnus genus suggests phylogenetic relationships may be more complex than suggested in earlier work based on Restriction-site Associated DNA sequencing or muscle transcriptome sequencing and prompt for further analysis of the genus using a more comprehensive sampling of taxa in each oceanic basin

EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data

Recovery of ribosomal small subunit genes by assembly of short read community DNA sequence data generally fails, making taxonomic characterization difficult. Here, we solve this problem with a novel iterative method, based on the expectation maximization algorithm, that reconstructs full-length small subunit gene sequences and provides estimates of relative taxon abundances. We apply the method to natural and simulated microbial communities, and correctly recover community structure from known and previously unreported rRNA gene sequences. An implementation of the method is freely available at https://github.com/csmiller/EMIRGE

Genomic sequencing and analysis of a Chinese hamster ovary cell line using Illumina sequencing technology

<p>Abstract</p> <p>Background</p> <p>Chinese hamster ovary (CHO) cells are among the most widely used hosts for therapeutic protein production. Yet few genomic resources are available to aid in engineering high-producing cell lines.</p> <p>Results</p> <p>High-throughput Illumina sequencing was used to generate a 1x genomic coverage of an engineered CHO cell line expressing secreted alkaline phosphatase (SEAP). Reference-guided alignment and assembly produced 3.57 million contigs and CHO-specific sequence information for ~ 18,000 mouse and ~ 19,000 rat orthologous genes. The majority of these genes are involved in metabolic processes, cellular signaling, and transport and represent attractive targets for cell line engineering.</p> <p>Conclusions</p> <p>This demonstrates the applicability of next-generation sequencing technology and comparative genomic analysis in the development of CHO genomic resources.</p

CAPRG: Sequence Assembling Pipeline for Next Generation Sequencing of Non-Model Organisms

Our goal is to introduce and describe the utility of a new pipeline “Contigs Assembly Pipeline using Reference Genome” (CAPRG), which has been developed to assemble “long sequence reads” for non-model organisms by leveraging a reference genome of a closely related phylogenetic relative. To facilitate this effort, we utilized two avian transcriptomic datasets generated using ROCHE/454 technology as test cases for CAPRG assembly. We compared the results of CAPRG assembly using a reference genome with the results of existing methods that utilize de novo strategies such as VELVET, PAVE, and MIRA by employing parameter space comparisons (intra-assembling comparison). CAPRG performed as well or better than the existing assembly methods based on various benchmarks for “gene-hunting.” Further, CAPRG completed the assemblies in a fraction of the time required by the existing assembly algorithms. Additional advantages of CAPRG included reduced contig inflation resulting in lower computational resources for annotation, and functional identification for contigs that may be categorized as “unknowns” by de novo methods. In addition to providing evaluation of CAPRG performance, we observed that the different assembly (inter-assembly) results could be integrated to enhance the putative gene coverage for any transcriptomics study

Computational pan-genomics: status, promises and challenges

International 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 genomic origins of the world’s first farmers

The precise genetic origins of the first Neolithic farming populations in Europe and Southwest Asia, as well as the processes and the timing of their differentiation, remain largely unknown. Demogenomic modeling of high-quality ancient genomes reveals that the early farmers of Anatolia and Europe emerged from a multiphase mixing of a Southwest Asian population with a strongly bottlenecked western hunter-gatherer population after the last glacial maximum. Moreover, the ancestors of the first farmers of Europe and Anatolia went through a period of extreme genetic drift during their westward range expansion, contributing highly to their genetic distinctiveness. This modeling elucidates the demographic processes at the root of the Neolithic transition and leads to a spatial interpretation of the population history of Southwest Asia and Europe during the late Pleistocene and early Holocene.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]