DACCOR–Detection, characterization, and reconstruction of repetitive regions in bacterial genomes

DACCOR - Detection, charACterization, and reconstruction of Repetitive regions in bacterial genomes

10.7287/peerj.preprints.3480v1 ◽

2017 ◽

Author(s):

Alexander Seitz ◽

Friederike Hanssen ◽

Kay Nieselt

Keyword(s):

Base Pair ◽

Repetitive Sequence ◽

Reference Genome ◽

De Novo ◽

Treponema Pallidum ◽

Sequencing Data ◽

Bacterial Genomes ◽

Short Read ◽

Short Reads ◽

Short Read Sequencing

The reconstruction of genomes using mapping based approaches with short reads experiences difficulties when resolving repetitive regions. These repetitive regions in genomes result in low mapping qualities of the respective reads, which in turn lead to many unresolved bases of the genotypers. Currently, the reconstruction of these regions is often based on modified references in which the repetitive regions are masked. However, for many references such masked genomes are not available or are based on repetitive regions of other genomes. Our idea is to identify repetitive regions in the reference genome de novo. These regions can then be used to reconstruct them separately using short read sequencing data. Afterwards the reconstructed repetitive sequence can be inserted into the reconstructed genome. We present the program DACCOR, which performs these steps automatically. Our results show an increased base pair resolution of the repetitive regions in the reconstruction of Treponema pallidum samples, resulting in fewer unresolved bases.

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DACCOR - Detection, charACterization, and reconstruction of Repetitive regions in bacterial genomes

10.7287/peerj.preprints.3480 ◽

2017 ◽

Author(s):

Alexander Seitz ◽

Friederike Hanssen ◽

Kay Nieselt

Keyword(s):

Base Pair ◽

Repetitive Sequence ◽

Reference Genome ◽

De Novo ◽

Treponema Pallidum ◽

Sequencing Data ◽

Bacterial Genomes ◽

Short Read ◽

Short Reads ◽

Short Read Sequencing

The reconstruction of genomes using mapping based approaches with short reads experiences difficulties when resolving repetitive regions. These repetitive regions in genomes result in low mapping qualities of the respective reads, which in turn lead to many unresolved bases of the genotypers. Currently, the reconstruction of these regions is often based on modified references in which the repetitive regions are masked. However, for many references such masked genomes are not available or are based on repetitive regions of other genomes. Our idea is to identify repetitive regions in the reference genome de novo. These regions can then be used to reconstruct them separately using short read sequencing data. Afterwards the reconstructed repetitive sequence can be inserted into the reconstructed genome. We present the program DACCOR, which performs these steps automatically. Our results show an increased base pair resolution of the repetitive regions in the reconstruction of Treponema pallidum samples, resulting in fewer unresolved bases.

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ClusTrast: a short read de novo transcript isoform assembler guided by clustered contigs

10.1101/2022.01.02.473666 ◽

2022 ◽

Author(s):

Karl Johan Westrin ◽

Warren W Kretzschmar ◽

Olof Emanuelsson

Keyword(s):

Alternative Splicing ◽

Reference Genome ◽

De Novo ◽

Transcriptome Assembly ◽

Sequencing Data ◽

Short Read ◽

Transcript Isoforms ◽

Short Reads ◽

Moderate Reduction ◽

Eukaryotic Species

Motivation: Transcriptome assembly from RNA sequencing data in species without a reliable reference genome has to be performed de novo, but studies have shown that de novo methods often have inadequate reconstruction ability of transcript isoforms. This impedes the study of alternative splicing, in particular for lowly expressed isoforms. Result: We present the de novo transcript isoform assembler ClusTrast, which clusters a set of guiding contigs by similarity, aligns short reads to the guiding contigs, and assembles each clustered set of short reads individually. We tested ClusTrast on datasets from six eukaryotic species, and showed that ClusTrast reconstructed more expressed known isoforms than any of the other tested de novo assemblers, at a moderate reduction in precision. An appreciable fraction were reconstructed to at least 95% of their length. We suggest that ClusTrast will be useful for studying alternative splicing in the absence of a reference genome. Availability and implementation: The code and usage instructions are available at https://github.com/karljohanw/clustrast.

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An Improved Genome Assembly of Azadirachta indica A. Juss.

10.1101/033290 ◽

2015 ◽

Author(s):

Neeraja M Krishnan ◽

Prachi Jain ◽

Saurabh Gupta ◽

Arun K Hariharan ◽

Binay Panda

Keyword(s):

Azadirachta Indica ◽

Genome Assembly ◽

Draft Genome ◽

Fold Increase ◽

Sequencing Data ◽

Short Read ◽

Short Reads ◽

Short Read Sequencing ◽

Long Reads ◽

Ncbi Short Read Archive

Neem (Azadirachta indica A. Juss.), an evergreen tree of the Meliaceae family, is known for its medicinal, cosmetic, pesticidal and insecticidal properties. We had previously sequenced and published the draft genome of the plant, using mainly short read sequencing data. In this report, we present an improved genome assembly generated using additional short reads from Illumina and long reads from Pacific Biosciences SMRT sequencer. We assembled short reads and error corrected long reads using Platanus, an assembler designed to perform well for heterozygous genomes. The updated genome assembly (v2.0) yielded 3- and 3.5-fold increase in N50 and N75, respectively; 2.6-fold decrease in the total number of scaffolds; 1.25-fold increase in the number of valid transcriptome alignments; 13.4-fold less mis-assembly and 1.85-fold increase in the percentage repeat, over the earlier assembly (v1.0). The current assembly also maps better to the genes known to be involved in the terpenoid biosynthesis pathway. Together, the data represents an improved assembly of the A. indica genome. The raw data described in this manuscript are submitted to the NCBI Short Read Archive under the accession numbers SRX1074131, SRX1074132, SRX1074133, and SRX1074134 (SRP013453).

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A Family-Based Probabilistic Method for Capturing De Novo Mutations from High-Throughput Short-Read Sequencing Data

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1713 ◽

2012 ◽

Vol 11 (2) ◽

Cited By ~ 11

Author(s):

Reed A. Cartwright ◽

Julie Hussin ◽

Jonathan E. M. Keebler ◽

Eric A. Stone ◽

Philip Awadalla

Keyword(s):

High Throughput ◽

De Novo ◽

Probabilistic Method ◽

Sequencing Data ◽

De Novo Mutations ◽

Short Read ◽

Short Read Sequencing ◽

Family Based

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Extensive gene duplication in Arabidopsis revealed by pseudo-heterozygosity

10.1101/2021.11.15.468652 ◽

2021 ◽

Author(s):

Benjamin Jaegle ◽

Luz Mayela Soto-Jimenez ◽

Robin Burns ◽

Fernando A. Rabanal ◽

Magnus Nordborg

Keyword(s):

Copy Number ◽

Structural Variation ◽

De Novo ◽

Sequencing Data ◽

Heterozygous Snps ◽

Mendelian Segregation ◽

Short Read ◽

Short Read Sequencing ◽

Snp Data ◽

Number Variation

Background: It is becoming apparent that genomes harbor massive amounts of structural variation, and that this variation has largely gone undetected for technical reasons. In addition to being inherently interesting, structural variation can cause artifacts when short-read sequencing data are mapped to a reference genome. In particular, spurious SNPs (that do not show Mendelian segregation) may result from mapping of reads to duplicated regions. Recalling SNP using the raw reads of the 1001 Arabidopsis Genomes Project we identified 3.3 million heterozygous SNPs (44% of total). Given that Arabidopsis thaliana (A. thaliana) is highly selfing, we hypothesized that these SNPs reflected cryptic copy number variation, and investigated them further. Results: While genuine heterozygosity should occur in tracts within individuals, heterozygosity at a particular locus is instead shared across individuals in a manner that strongly suggests it reflects segregating duplications rather than actual heterozygosity. Focusing on pseudo-heterozygosity in annotated genes, we used GWAS to map the position of the duplicates, identifying 2500 putatively duplicated genes. The results were validated using de novo genome assemblies from six lines. Specific examples included an annotated gene and nearby transposon that, in fact, transpose together. Conclusions: Our study confirms that most heterozygous SNPs calls in A. thaliana are artifacts, and suggest that great caution is needed when analysing SNP data from short-read sequencing. The finding that 10% of annotated genes are copy-number variables, and the realization that neither gene- nor transposon-annotation necessarily tells us what is actually mobile in the genome suggest that future analyses based on independently assembled genomes will be very informative.

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Pangenome-based genome inference

10.1101/2020.11.11.378133 ◽

2020 ◽

Author(s):

Jana Ebler ◽

Wayne E. Clarke ◽

Tobias Rausch ◽

Peter A. Audano ◽

Torsten Houwaart ◽

...

Keyword(s):

Reference Genome ◽

Wide Spectrum ◽

Sequencing Data ◽

Short Read ◽

Short Read Sequencing ◽

Linkage Information ◽

Almost All ◽

Genome Assemblies ◽

The Given ◽

Genotype A

AbstractTypical analysis workflows map reads to a reference genome in order to detect genetic variants. Generating such alignments introduces references biases, in particular against insertion alleles absent in the reference and comes with substantial computational burden. In contrast, recent k-mer-based genotyping methods are fast, but struggle in repetitive or duplicated regions of the genome. We propose a novel algorithm, called PanGenie, that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation. The given haplotypes enable our method to take advantage of linkage information to aid genotyping in regions poorly covered by unique k-mers and provides access to regions otherwise inaccessible by short reads. Compared to classic mapping-based approaches, our approach is more than 4× faster at 30× coverage and at the same time, reached significantly better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (> 50bp), where we are able to genotype > 99.9% of all tested variants with over 90% accuracy at 30× short-read coverage, where the best competing tools either typed less than 60% of variants or reached accuracies below 70%. PanGenie now enables the inclusion of this commonly neglected variant type in downstream analyses.

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Drastic mutations can be hidden in short-read mapping: thousands of mutation clusters in human genome are explained by short-range template switching

10.1101/2021.11.26.470150 ◽

2021 ◽

Author(s):

Ari Löytynoja

Keyword(s):

Human Genome ◽

De Novo ◽

Human Populations ◽

Template Switching ◽

Sequencing Data ◽

De Novo Mutations ◽

Sequence Comparisons ◽

Short Read ◽

Short Read Sequencing ◽

Template Switch

Variation within human genomes is distributed unevenly and variants show spatial clustering. DNA-replication related template switching is a poorly known mutational mechanism capable of causing major chromosomal rearrangements as well as creating short inverted sequence copies that appear as local mutation clusters in sequence comparisons. We reanalyzed haplotype-resolved genome assemblies representing 25 human populations and multinucleotide variants aggregated from 140,000 human sequencing experiments. We found local template switching to explain thousands of complex mutation clusters across the human genome, the loci segregating within and between populations with a small number appearing as de novo mutations. We developed computational tools for genotyping candidate template switch loci using short-read sequencing data and for identification of template switch events using both short-read data and genotype data. These tools will enable building a catalogue of affected loci and studying the cellular mechanisms behind template switching both in healthy organisms and in disease. Strikingly, we noticed that widely-used analysis pipelines for short-read sequencing data - capable of identifying single nucleotide changes - may miss TSM-origin inversions of tens of base pairs, potentially invalidating medical genetic studies searching for causative alleles behind genetic diseases.

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