The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.

/pdf/assembly-of-long-error-prone-reads-using-de-bruijn-graphs-23ljyaagts.pdf

Assembly of long error-prone reads using de Bruijn graphs.

Many 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.

/pdf/computational-pan-genomics-status-promises-and-challenges-muc40emicg.pdf

Computational pan-genomics: status, promises and challenges.

Summary: Bacterial genomes are simpler than mammalian ones, and yet assembling the former from the data currently generated by high-throughput short-read sequencing machines still results in hundreds of contigs. To improve assembly quality, recent studies have utilized longer Pacific Biosciences (PacBio) reads or jumping libraries to connect contigs into larger scaffolds or help assemblers resolve ambiguities in repetitive regions of the genome. However, their popularity in contemporary genomic research is still limited by high cost and error rates. In this work, we explore the possibility of improving assemblies by using complete genomes from closely related species/strains. We present Ragout, a genome rearrangement approach, to address this problem. In contrast with most reference-guided algorithms, where only one reference genome is used, Ragout uses multiple references along with the evolutionary relationship among these references in order to determine the correct order of the contigs. Additionally, Ragout uses the assembly graph and multi-scale synteny blocks to reduce assembly gaps caused by small contigs from the input assembly. In simulations as well as real datasets, we believe that for common bacterial species, where many complete genome sequences from related strains have been available, the current high-throughput short-read sequencing paradigm is sufficient to obtain a single high-quality scaffold for each chromosome.

Availability: The Ragout software is freely available at: https://github.com/fenderglass/Ragout.

Contact: ude.klas@mahps

/pdf/ragout-a-reference-assisted-assembly-tool-for-bacterial-3cbqldp51y.pdf

Ragout—a reference-assisted assembly tool for bacterial genomes

Background The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infected over 3300 healthcare workers in early 2020 in China. Little information is known about nosocomial infections of healthcare workers in the initial period. We analysed data from healthcare workers with nosocomial infections in Wuhan Union Hospital (Wuhan, China) and their family members. Methods We collected and analysed data on exposure history, illness timelines and epidemiological characteristics from 25 healthcare workers with laboratory-confirmed coronavirus disease 2019 (COVID-19) and two healthcare workers in whom COVID-19 was highly suspected, as well as 10 of their family members with COVID-19, between 5 January and 12 February 2020. The demographics and clinical features of the 35 laboratory-confirmed cases were investigated and viral RNA of 12 cases was sequenced and analysed. Results Nine clusters were found among the patients. All patients showed mild to moderate clinical manifestation and recovered without deterioration. The mean period of incubation was 4.5 days, the mean±sd clinical onset serial interval (COSI) was 5.2±3.2 days, and the median virus shedding time was 18.5 days. Complete genomic sequences of 12 different coronavirus strains demonstrated that the viral structure, with small irrelevant mutations, was stable in the transmission chains and showed remarkable traits of infectious traceability. Conclusions SARS-CoV-2 can be rapidly transmitted from person to person, regardless of whether they have symptoms, in both hospital settings and social activities, based on the short period of incubation and COSI. The public health service should take practical measures to curb the spread, including isolation of cases, tracing close contacts, and containment of severe epidemic areas. Besides this, healthcare workers should be alert during the epidemic and self-quarantine if self-suspected of infection.

https://erj.ersjournals.com/content/erj/55/6/2000544.full.pdf

Nosocomial outbreak of COVID-19 pneumonia in Wuhan, China.

Accurate identification of orthologous genomic regions (OGRs) between two closely related genomes is crucial for the reliable detection of genomic changes, which range from small-scale changes (e.g., single nucleotide or small nucleotides) to large-scale structural changes. Although diverse OGRs inferred at different levels have been successfully applied to address various biological questions, a limited number of studies have simultaneously integrated OGRs from different levels. Here, we report on a new approach to construct a hierarchical map of OGRs. Using different types of genomic markers, this approach was applied to two very closely related cucumber genomes [ L. var. and L. var. (Royle) Alef.]. We identified two different levels of OGRs using Mugsy (denoted as dnaOGRs) and i-ADHoRe (denoted as proOGRs). Using information regarding the anchored chromosomes of the two genomes, a third level of OGRs (denoted chrOGRs) could be built at the chromosomal level. Together, these OGRs could be organized into a hierarchical map that represented the parent-child relationships (chrOGR:proOGRs:dnaOGRs) between the two genomes. For this case study, the map consisted of seven chrOGRs, 540 proOGRs, and 22,321 dnaOGRs. Based on this map, we designed different methods to detect both small-scale and large-scale genomic changes. Surprisingly, many genomic changes were detected at each OGR level despite the very short divergence time between the two subspecies. Together, our results show that a hierarchical map of OGRs and their related genomic changes are useful resources for elucidating the diversity and evolution of cucumber genomes and phenotypes.

https://acsess.onlinelibrary.wiley.com/doi/pdf/10.3835/plantgenome2015.10.0099

Hierarchical Map of Orthologous Genomic Regions Reconstructed from Two Closely Related Genomes: Cucumber Case Study

Comparing strains within the same microbial species has proven effective in the identification of genes and genomic regions responsible for virulence, as well as in the diagnosis and treatment of infectious diseases. In this paper, we present Sibelia, a tool for finding synteny blocks in multiple closely related microbial genomes using iterative de Bruijn graphs. Unlike most other tools, Sibelia can find synteny blocks that are repeated within genomes as well as blocks shared by multiple genomes. It represents synteny blocks in a hierarchy structure with multiple layers, each of which representing a different granularity level. Sibelia has been designed to work efficiently with a large number of microbial genomes; it finds synteny blocks in 31 S. aureus genomes within 31 minutes and in 59 E.coli genomes within 107 minutes on a standard desktop. Sibelia software is distributed under the GNU GPL v2 license and is available at: https://github.com/bioinf/Sibelia. Sibelia’s web-server is available at: http://etool.me/software/sibelia.

Sibelia: A Scalable and Comprehensive Synteny Block Generation Tool for Closely Related Microbial Genomes

We present C-Sibelia, a highly accurate and easy-to-use software tool for comparing two closely related bacterial genomes, which can be presented as either finished sequences or fragmented assemblies. C-Sibelia takes as input two FASTA files and produces: (1) a VCF file containing all identified single nucleotide variations and indels; (2) an XMFA file containing alignment information. The software also produces Circos diagrams visualizing high level genomic architecture for rearrangement analyses. C-Sibelia is a part of the Sibelia comparative genomics suite, which is freely available under the GNU GPL v.2 license at http://sourceforge.net/projects/sibelia-bio. C-Sibelia is compatible with Unix-like operating systems. A web-based version of the software is available at http://etool.me/software/csibelia.

C-Sibelia: an easy-to-use and highly accurate tool for bacterial genome comparison

Comparing strains within the same microbial species has proven effective in the identification of genes and genomic regions responsible for virulence, as well as in the diagnosis and treatment of infectious diseases. In this paper, we present Sibelia, a tool for finding synteny blocks in multiple closely related microbial genomes using iterative de Bruijn graphs. Unlike most other tools, Sibelia can find synteny blocks that are repeated within genomes as well as blocks shared by multiple genomes. It represents synteny blocks in a hierarchy structure with multiple layers, each of which representing a different granularity level. Sibelia has been designed to work efficiently with a large number of microbial genomes; it finds synteny blocks in 31 S. aureus genomes within 31 minutes and in 59 E.coli genomes within 107 minutes on a standard desktop. Sibelia software is distributed under the GNU GPL v2 license and is available at: this https URL Sibelia's web-server is available at: this http URL

Ilya Minkin

Papers

Sibelia: A Scalable and Comprehensive Synteny Block Generation Tool for Closely Related Microbial Genomes

C-Sibelia: an easy-to-use and highly accurate tool for bacterial genome comparison

Sibelia: A scalable and comprehensive synteny block generation tool for closely related microbial genomes