Figbird: A probabilistic method for filling gaps in genome assemblies

Motivation: Advances in sequencing technologies have led to sequencing of genomes of a multitude of organisms. However, draft genomes of many of these organisms contain a large number of gaps due to repeats in genomes, low sequencing coverage and limitations in sequencing technologies. Although there exist several tools for filling gaps, many of these do not utilize all information relevant to gap filling. Results: Here, we present a probabilistic method for filling gaps in draft genome assemblies using second generation reads based on a generative model for sequencing that takes into account information on insert sizes and sequencing errors. Our method is based on the expectation-maximization(EM) algorithm unlike the graph based methods adopted in the literature. Experiments on real biological datasets show that this novel approach can fill up large portions of gaps with small number of errors and misassemblies compared to other state of the art gap filling tools. Availability and Implementation:The method is implemented using C++ in a software named "Filling Gaps by Iterative Read Distribution (Figbird)", which is available at: https://github.com/SumitTarafder/Figbird.

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Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab034 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Jean-Marc Aury ◽

Benjamin Istace

Keyword(s):

Single Molecule ◽

Direct Consequence ◽

High Quality ◽

Sequencing Errors ◽

Coding Regions ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Genome Assemblies

Abstract Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from high-quality reads (short or long-reads) to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.

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Factorial estimating assembly base errors using k-mer abundance difference (KAD) between short reads and genome assembled sequences

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa075 ◽

2020 ◽

Vol 2 (3) ◽

Author(s):

Cheng He ◽

Guifang Lin ◽

Hairong Wei ◽

Haibao Tang ◽

Frank F White ◽

...

Keyword(s):

Copy Number ◽

Error Rates ◽

Genome Sequences ◽

Short Reads ◽

Sequencing Technologies ◽

Insertion And Deletion ◽

Novel Approach ◽

Long Reads ◽

Long Read ◽

Genome Assemblies

Abstract Genome sequences provide genomic maps with a single-base resolution for exploring genetic contents. Sequencing technologies, particularly long reads, have revolutionized genome assemblies for producing highly continuous genome sequences. However, current long-read sequencing technologies generate inaccurate reads that contain many errors. Some errors are retained in assembled sequences, which are typically not completely corrected by using either long reads or more accurate short reads. The issue commonly exists, but few tools are dedicated for computing error rates or determining error locations. In this study, we developed a novel approach, referred to as k-mer abundance difference (KAD), to compare the inferred copy number of each k-mer indicated by short reads and the observed copy number in the assembly. Simple KAD metrics enable to classify k-mers into categories that reflect the quality of the assembly. Specifically, the KAD method can be used to identify base errors and estimate the overall error rate. In addition, sequence insertion and deletion as well as sequence redundancy can also be detected. Collectively, KAD is valuable for quality evaluation of genome assemblies and, potentially, provides a diagnostic tool to aid in precise error correction. KAD software has been developed to facilitate public uses.

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Estimating Assembly Base Errors Using K-mer Abundance Difference (KAD) Between Short Reads and Genome Assembled Sequences

10.1101/2020.03.17.994566 ◽

2020 ◽

Cited By ~ 1

Author(s):

Cheng He ◽

Guifang Lin ◽

Hairong Wei ◽

Haibao Tang ◽

Frank F White ◽

...

Keyword(s):

Copy Number ◽

Error Rates ◽

Genome Sequences ◽

Short Reads ◽

Sequencing Technologies ◽

Insertion And Deletion ◽

Novel Approach ◽

Long Reads ◽

Long Read ◽

Genome Assemblies

ABSTRACTGenome sequences provide genomic maps with a single-base resolution for exploring genetic contents. Sequencing technologies, particularly long reads, have revolutionized genome assemblies for producing highly continuous genome sequences. However, current long-read sequencing technologies generate inaccurate reads that contain many errors. Some errors are retained in assembled sequences, which are typically not completely corrected by using either long reads or more accurate short reads. The issue commonly exists but few tools are dedicated for computing error rates or determining error locations. In this study, we developed a novel approach, referred to as K-mer Abundance Difference (KAD), to compare the inferred copy number of each k-mer indicated by short reads and the observed copy number in the assembly. Simple KAD metrics enable to classify k-mers into categories that reflect the quality of the assembly. Specifically, the KAD method can be used to identify base errors and estimate the overall error rate. In addition, sequence insertion and deletion as well as sequence redundancy can also be detected. Therefore, KAD is valuable for quality evaluation of genome assemblies and, potentially, provides a diagnostic tool to aid in precise error correction. KAD software has been developed to facilitate public uses.

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Hapo-G, Haplotype-Aware Polishing Of Genome Assemblies

10.1101/2020.12.14.422624 ◽

2020 ◽

Author(s):

Jean-Marc Aury ◽

Benjamin Istace

Keyword(s):

Single Molecule ◽

Direct Consequence ◽

Short Reads ◽

Sequencing Errors ◽

Coding Regions ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read ◽

Genome Assemblies

Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from short reads to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.

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Detection of plasmid contigs in draft genome assemblies using customized Kraken databases

Microbial Genomics ◽

10.1099/mgen.0.000550 ◽

2021 ◽

Vol 7 (4) ◽

Author(s):

Ryota Gomi ◽

Kelly L. Wyres ◽

Kathryn E. Holt

Keyword(s):

Sensitivity And Specificity ◽

Large Scale ◽

Draft Genome ◽

Ground Truth ◽

Classification Performance ◽

Content Type ◽

Contig Length ◽

Sequencing Technologies ◽

Antimicrobial Resistance Genes ◽

Genome Assemblies

Plasmids play an important role in bacterial evolution and mediate horizontal transfer of genes including virulence and antimicrobial resistance genes. Although short-read sequencing technologies have enabled large-scale bacterial genomics, the resulting draft genome assemblies are often fragmented into hundreds of discrete contigs. Several tools and approaches have been developed to identify plasmid sequences in such assemblies, but require trade-off between sensitivity and specificity. Here we propose using the Kraken classifier, together with a custom Kraken database comprising known chromosomal and plasmid sequences of Klebsiella pneumoniae species complex (KpSC), to identify plasmid-derived contigs in draft assemblies. We assessed performance using Illumina-based draft genome assemblies for 82 KpSC isolates, for which complete genomes were available to supply ground truth. When benchmarked against five other classifiers (Centrifuge, RFPlasmid, mlplasmids, PlaScope and Platon), Kraken showed balanced performance in terms of overall sensitivity and specificity (90.8 and 99.4 %, respectively, for contig count; 96.5 and >99.9 %, respectively, for cumulative contig length), and the highest accuracy (96.8% vs 91.8-96.6% for contig count; 99.8% vs 99.0-99.7 % for cumulative contig length), and F1-score (94.5 % vs 84.5-94.1 %, for contig count; 98.0 % vs 88.9-96.7 % for cumulative contig length). Kraken also achieved consistent performance across our genome collection. Furthermore, we demonstrate that expanding the Kraken database with additional known chromosomal and plasmid sequences can further improve classification performance. Although we have focused here on the KpSC, this methodology could easily be applied to other species with a sufficient number of completed genomes.

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Bridging the Gap between Vertebrate Cytogenetics and Genomics with Single-Chromosome Sequencing (ChromSeq)

Genes ◽

10.3390/genes12010124 ◽

2021 ◽

Vol 12 (1) ◽

pp. 124

Author(s):

Alessio Iannucci ◽

Alexey I. Makunin ◽

Artem P. Lisachov ◽

Claudio Ciofi ◽

Roscoe Stanyon ◽

...

Keyword(s):

Genome Evolution ◽

Karyotype Evolution ◽

Genomic Data ◽

Anolis Carolinensis ◽

Vertebrate Genome ◽

Single Chromosome ◽

Sequencing Technologies ◽

Novel Approaches ◽

Genome Assemblies ◽

Generation Sequencing

The study of vertebrate genome evolution is currently facing a revolution, brought about by next generation sequencing technologies that allow researchers to produce nearly complete and error-free genome assemblies. Novel approaches however do not always provide a direct link with information on vertebrate genome evolution gained from cytogenetic approaches. It is useful to preserve and link cytogenetic data with novel genomic discoveries. Sequencing of DNA from single isolated chromosomes (ChromSeq) is an elegant approach to determine the chromosome content and assign genome assemblies to chromosomes, thus bridging the gap between cytogenetics and genomics. The aim of this paper is to describe how ChromSeq can support the study of vertebrate genome evolution and how it can help link cytogenetic and genomic data. We show key examples of ChromSeq application in the refinement of vertebrate genome assemblies and in the study of vertebrate chromosome and karyotype evolution. We also provide a general overview of the approach and a concrete example of genome refinement using this method in the species Anolis carolinensis.

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Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird-of-paradise

10.1101/2019.12.19.882399 ◽

2019 ◽

Cited By ~ 5

Author(s):

Valentina Peona ◽

Mozes P.K. Blom ◽

Luohao Xu ◽

Reto Burri ◽

Shawn Sullivan ◽

...

Keyword(s):

Dark Matter ◽

Genome Assembly ◽

Sex Chromosome ◽

De Novo ◽

Model Organism ◽

Technology Choice ◽

High Quality ◽

Sequencing Technologies ◽

Downstream Analysis ◽

Genome Assemblies

AbstractGenome assemblies are currently being produced at an impressive rate by consortia and individual laboratories. The low costs and increasing efficiency of sequencing technologies have opened up a whole new world of genomic biodiversity. Although these technologies generate high-quality genome assemblies, there are still genomic regions difficult to assemble, like repetitive elements and GC-rich regions (genomic “dark matter”). In this study, we compare the efficiency of currently used sequencing technologies (short/linked/long reads and proximity ligation maps) and combinations thereof in assembling genomic dark matter starting from the same sample. By adopting different de-novo assembly strategies, we were able to compare each individual draft assembly to a curated multiplatform one and identify the nature of the previously missing dark matter with a particular focus on transposable elements, multi-copy MHC genes, and GC-rich regions. Thanks to this multiplatform approach, we demonstrate the feasibility of producing a high-quality chromosome-level assembly for a non-model organism (paradise crow) for which only suboptimal samples are available. Our approach was able to reconstruct complex chromosomes like the repeat-rich W sex chromosome and several GC-rich microchromosomes. Telomere-to-telomere assemblies are not a reality yet for most organisms, but by leveraging technology choice it is possible to minimize genome assembly gaps for downstream analysis. We provide a roadmap to tailor sequencing projects around the completeness of both the coding and non-coding parts of the genomes.

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A Deep Dive into Genome Assemblies of Non-vertebrate Animals

10.20944/preprints202111.0170.v1 ◽

2021 ◽

Author(s):

Nadège Guiglielmoni ◽

Ramón Rivera-Vicéns ◽

Romain Koszul ◽

Jean-François Flot

Keyword(s):

Genome Assembly ◽

Current Knowledge ◽

Genome Structure ◽

Deep Dive ◽

Sequencing Technologies ◽

Current State ◽

Animal Diversity ◽

And Function ◽

Genome Assemblies ◽

Genome Projects

Non-vertebrate species represent about ~95% of known metazoan (animal) diversity. They remain to this day relatively unexplored genetically, but understanding their genome structure and function is pivotal for expanding our current knowledge of evolution, ecology and biodiversity. Following the continuous improvements and decreasing costs of sequencing technologies, many genome assembly tools have been released, leading to a significant amount of genome projects being completed in recent years. In this review, we examine the current state of genome projects of non-vertebrate animal species. We present an overview of available sequencing technologies, assembly approaches, as well as pre and post-processing steps, genome assembly evaluation methods, and their application to non-vertebrate animal genomes.

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SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

10.1101/2020.10.27.356907 ◽

2020 ◽

Author(s):

David Heller ◽

Martin Vingron

Keyword(s):

Genetic Information ◽

Source Code ◽

Supplementary Information ◽

Supplementary Data ◽

Diploid Genome ◽

Insertions And Deletions ◽

Structural Variant ◽

Sequencing Technologies ◽

Variant Detection ◽

Genome Assemblies

AbstractMotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/[email protected] informationSupplementary data are available online.

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Accurate Filtering of Privacy-Sensitive Information in Raw Genomic Data

10.1101/292185 ◽

2018 ◽

Author(s):

Jérémie Decouchant ◽

Maria Fernandes ◽

Marcus Völp ◽

Francisco M Couto ◽

Paulo Esteves-Veríssimo

Keyword(s):

High Performance ◽

Genomic Data ◽

Sensitive Information ◽

Sensitive Data ◽

Variable Regions ◽

Fine Grained ◽

Sequencing Errors ◽

Sequencing Technologies ◽

Human Genomes ◽

Long Reads

AbstractSequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data entails enormous risks if not protected to the highest standards. In this article, we follow the position and argue that post-alignment privacy is not enough and that data should be automatically protected as early as possible in the genomics workflow, ideally immediately after the data is produced. We show that a previous approach for filtering short reads cannot extend to long reads and present a novel filtering approach that classifies raw genomic data (i.e., whose location and content is not yet determined) into privacy-sensitive (i.e., more affected by a successful privacy attack) and non-privacy-sensitive information. Such a classification allows the fine-grained and automated adjustment of protective measures to mitigate the possible consequences of exposure, in particular when relying on public clouds. We present the first filter that can be indistinctly applied to reads of any length, i.e., making it usable with any recent or future sequencing technologies. The filter is accurate, in the sense that it detects all known sensitive nucleotides except those located in highly variable regions (less than 10 nucleotides remain undetected per genome instead of 100,000 in previous works). It has far less false positives than previously known methods (10% instead of 60%) and can detect sensitive nucleotides despite sequencing errors (86% detected instead of 56% with 2% of mutations). Finally, practical experiments demonstrate high performance, both in terms of throughput and memory consumption.

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