Fast characterization of segmental duplications in genome assemblies

Numanagić, Ibrahim; Gökkaya, Alim Şükrücan; Zhang, Lillian; Berger, Bonnie; Alkan, Can; Hach, Faraz

doi:10.1093/bioinformatics/bty586

Cited by 65 publications

(58 citation statements)

References 47 publications

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“…We identified segmental duplications in the Zoey and CanFam3.1 assemblies based on assembly self-alignment (43) and read-depth (44) approaches (see Supplementary Information, Section 3).…”

Section: Annotation Of Genome Featuresmentioning

confidence: 99%

Long-read assembly of a Great Dane genome highlights the contribution of GC-rich sequence and mobile elements to canine genomes

Halo

Pendleton

Shen

et al. 2020

Preprint

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Technological advances have allowed improvements in genome reference sequence assemblies. Here, we combined long- and short-read sequence resources to assemble the genome of a female Great Dane dog. This assembly has improved continuity compared to the existing Boxer-derived (CanFam3.1) reference genome. Annotation of the Great Dane assembly identified 22,182 protein-coding gene models and 7,049 long non-coding RNAs, including 49 protein-coding genes not present in the CanFam3.1 reference. The Great Dane assembly spans the majority of sequence gaps in the CanFam3.1 reference and illustrates that 2,151 gaps overlap the transcription start site of a predicted protein-coding gene. Moreover, a subset of the resolved gaps, which have an 80.95% median GC content, localize to transcription start sites and recombination hotspots more often than expected by chance, suggesting the stable canine recombinational landscape has shaped genome architecture. Alignment of the Great Dane and CanFam3.1 assemblies identified 16,834 deletions and 15,621 insertions, as well as 2,665 deletions and 3,493 insertions located on secondary contigs. These structural variants are dominated by retrotransposon insertion/deletion polymorphisms and include 16,221 dimorphic canine short interspersed elements (SINECs) and 1,121 dimorphic long interspersed element-1 sequences (LINE-1_Cfs). Analysis of sequences flanking the 3’ end of LINE-1_Cfs (i.e., LINE-1_Cf 3’-transductions) suggests multiple retrotransposition-competent LINE-1_Cfs segregate among dog populations. Consistent with this conclusion, we demonstrate that a canine LINE-1_Cf element with intact open reading frames can retrotranspose its own RNA and that of a SINEC_Cf consensus sequence in cultured human cells, implicating ongoing retrotransposon activity as a driver of canine genetic variation.SignificanceAdvancements in long-read DNA sequencing technologies provide more comprehensive views of genomes. We used long-read sequences to assemble a Great Dane dog genome that provides several improvements over the existing reference derived from a Boxer dog. Assembly comparisons revealed that gaps in the Boxer assembly often occur at the beginning of protein-coding genes and have a high-GC content, which likely reflects limitations of previous technologies in resolving GC-rich sequences. Dimorphic LINE-1 and SINEC retrotransposon sequences represent the predominant differences between the Great Dane and Boxer assemblies. Proof-of-principle experiments demonstrated that expression of a canine LINE-1 could promote the retrotransposition of itself and a SINEC_Cf consensus sequence in cultured human cells. Thus, ongoing retrotransposon activity may contribute to canine genetic diversity.

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“…We identified segmental duplications in the Zoey and CanFam3.1 assemblies based on assembly self-alignment (43) and read-depth (44) approaches (see Supplementary Information, Section 3).…”

Section: Annotation Of Genome Featuresmentioning

confidence: 99%

Long-read assembly of a Great Dane genome highlights the contribution of GC-rich sequence and mobile elements to canine genomes

Halo

Pendleton

Shen

et al. 2020

Preprint

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“…SegDups are two or more large genomic duplications (≥1-Kb long with ≥90% identity), but their numbers have not been determined so far (Fromer et al, 2012;Numanagic et al, 2018). In short-read sequencing data analysis, SegDups are known to be one of the main sources of false variant calls (Gong et al, 2020).…”

Section: Segdup Overlapping Ratio For Autosomal Cnvsmentioning

confidence: 99%

Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses

Uchiyama

Yamaguchi²,

Iwama

et al. 2020

Human Mutation

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Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.

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“…The Jaccard coefficient is generally used to measure the similarity of two discrete objects. Numanagic et al proposed the SEDEF framework based on the Jaccard coefficient, which can accurately predict segmental duplications (SDs) [40]. Wallace et al introduced the Jaccard coefficient into the prediction of disease-disease relationship and deduced the information of the interaction network [41].…”

Section: Jaccard Similarity Indexmentioning

confidence: 99%

A Novel Essential Protein Identification Method Based on PPI Networks and Gene Expression Data

Zhong

Tang

Peng

et al. 2020

Preprint

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Background: Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network based on gene expression and the PPI network data. Our experiments show that our method can improve the accuracy in predicting essential proteins. Results: In this paper, we propose a new measure, named JDC, based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We respectively perform experiments on Yeast data and E.coli data and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins from active PPI networks constructed with dynamic gene expression. Conclusions: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network . (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

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Fast characterization of segmental duplications in genome assemblies

Cited by 65 publications

References 47 publications

Long-read assembly of a Great Dane genome highlights the contribution of GC-rich sequence and mobile elements to canine genomes

Long-read assembly of a Great Dane genome highlights the contribution of GC-rich sequence and mobile elements to canine genomes

Efficient detection of copy‐number variations using exome data: Batch‐ and sex‐based analyses

A Novel Essential Protein Identification Method Based on PPI Networks and Gene Expression Data

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