scholarly journals CompStor Novos: a low cost yet fast assembly-based variant calling for personal genomes

2018 ◽  
Author(s):  
Travis Oenning ◽  
Taejeong Bae ◽  
Aravind Iyengar ◽  
Barrett Brickner ◽  
Madushanka Soysa ◽  
...  
Keyword(s):  
De Novo ◽  
Low Cost ◽  
Variant Calling ◽  
Computation Time ◽  
Next Generation Sequencing Data ◽  
Personal Genome ◽  
Sequencing Data ◽  
Commercial Off The Shelf ◽  
Genome Analyses ◽  
Assembly Algorithms

Application of assembly methods for personal genome analysis from next generation sequencing data has been limited by the requirement for an expensive supercomputer hardware or long computation times when using ordinary resources. We describe CompStor Novos, achieving supercomputer-class performance in de novo assembly computation time on standard server hardware, based on a tiered-memory algorithm. Run on commercial off-the-shelf servers, Novos assembly is more precise and 10-20 times faster than that of existing assembly algorithms. Furthermore, we integrated Novos into a variant calling pipeline and demonstrate that both compute times and precision of calling point variants and indels compare well with standard alignment-based pipelines. Additionally, assembly eliminates bias in the estimation of allele frequency for indels and naturally enables discovery of breakpoints for structural variants with base pair resolution. Thus, Novos bridges the gap between alignment-based and assembly-based genome analyses. Extension and adaption of its underlying algorithm will help quickly and fully harvest information in sequencing reads for personal genome reconstruction.

scholarly journals Aquila_stLFR: assembly based variant calling package for stLFR and hybrid assembly for linked-reads

2019 ◽  
Author(s):  
Xin Zhou ◽  
Lu Zhang ◽  
Xiaodong Fang ◽  
Yichen Liu ◽  
David L. Dill ◽  
...  
Keyword(s):  
De Novo ◽  
Low Cost ◽  
Variant Calling ◽  
Hybrid Assembly ◽  
Structural Variants ◽  
Sequencing Data ◽  
Single Tube ◽  
Large Numbers ◽  
Key Characteristics ◽  
Hybrid Assemblies

AbstractHuman diploid genome assembly enables identifying maternal and paternal genetic variations. Algorithms based on 10x linked-read sequencing have been developed for de novo assembly, variant calling and haplotyping. Another linked-read technology, single tube long fragment read (stLFR), has recently provided a low-cost single tube solution that can enable long fragment data. However, no existing software is available for human diploid assembly and variant calls. We develop Aquila stLFR to adapt to the key characteristics of stLFR. Aquila stLFR assembles near perfect diploid assembled contigs, and the assembly-based variant calling shows that Aquila stLFR detects large numbers of structural variants which were not easily spanned by Illumina short-reads. Furthermore, the hybrid assembly mode Aquila hybrid allows a hybrid assembly based on both stLFR and 10x linked-reads libraries, demonstrating that these two technologies can always be complementary to each other for assembly to improve contiguity and the variants detection, regardless of assembly quality of the library itself from single sequencing technology. The overlapped structural variants (SVs) from two independent sequencing data of the same individual, and the SVs from hybrid assemblies provide us a high-confidence profile to study them.AvailabilitySource code and documentation are available on https://github.com/maiziex/Aquila_stLFR.

scholarly journals DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data

2021 ◽  
Author(s):  
Gelana Khazeeva ◽  
Karolis Sablauskas ◽  
Bart van der Sanden ◽  
Wouter Steyaert ◽  
Michael Kwint ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
De Novo ◽  
Genetic Disorders ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Accurate Identification ◽  
Whole Exome ◽  
De Novo Variant ◽  
Generation Sequencing

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes alignment of sequence reads for a trio as 160×164 resolution images. DeNovoCNN was trained on DNMs of whole exome sequencing (WES) of 2003 trios achieving on average 99.2% recall and 93.8% precision. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing de novo calling approaches (GATK, DeNovoGear, Samtools) based on the Genome in a Bottle reference dataset. Sanger validations of DNMs called in both exome and genome datasets confirm that DeNovoCNN outperforms existing methods. Most importantly, we show that DeNovoCNN is robust against different exome sequencing and analyses approaches, thereby allowing it to be applied on other datasets. DeNovoCNN is freely available and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

scholarly journals Extensive sequencing of seven human genomes to characterize benchmark reference materials

2015 ◽  
Author(s):  
Justin M Zook ◽  
David Catoe ◽  
Jennifer McDaniel ◽  
Lindsay Vang ◽  
Noah Spies ◽  
...  
Keyword(s):  
Human Genome ◽  
Reference Materials ◽  
De Novo ◽  
Variant Calling ◽  
Genome Project ◽  
Genome Comparison ◽  
Personal Genome ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Human Genomes

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCodeTM WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.

scholarly journals Joint variant andde novomutation identification on pedigrees from high-throughput sequencing data

2014 ◽  
Author(s):  
John G Cleary ◽  
Ross Braithwaite ◽  
Kurt Gaastra ◽  
Brian S Hilbush ◽  
Stuart Inglis ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Cost Optimization ◽  
Snp Array ◽  
Variant Calling ◽  
Ground Truth ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Mendelian Segregation ◽  
High Throughput Sequencing Data

The analysis of whole-genome or exome sequencing data from trios and pedigrees has being successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyses data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detectde novomutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of WGS data from a 17 individual, 3-generation CEPH pedigree sequenced to 50X average depth. Compared to singleton calling, our family caller produced more high quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance. We developed a ground truth dataset to further evaluate our calls by identifying recombination cross-overs in the pedigree and testing variants for consistency with the inferred phasing, and we show that our method significantly outperforms singleton and population variant calling in pedigrees. We identify all previously validatedde novomutations in NA12878, concurrent with a 7X precision improvement. Our results show that our method is scalable to large genomics and human disease studies and allows cost optimization by rational sequencing capacity distribution.

scholarly journals De Novo Assembly of Two Swedish Genomes Reveals Missing Segments from the Human GRCh38 Reference and Improves Variant Calling of Population-Scale Sequencing Data

Genes ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 486 ◽  
Author(s):  
Adam Ameur ◽  
Huiwen Che ◽  
Marcel Martin ◽  
Ignas Bunikis ◽  
Johan Dahlberg ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
De Novo Assembly ◽  
De Novo ◽  
Variant Calling ◽  
Whole Genome Sequencing Data ◽  
Personal Genome ◽  
Whole Genome ◽  
Sequencing Data ◽  
Short Read ◽  
Population Scale

The current human reference sequence (GRCh38) is a foundation for large-scale sequencing projects. However, recent studies have suggested that GRCh38 may be incomplete and give a suboptimal representation of specific population groups. Here, we performed a de novo assembly of two Swedish genomes that revealed over 10 Mb of sequences absent from the human GRCh38 reference in each individual. Around 6 Mb of these novel sequences (NS) are shared with a Chinese personal genome. The NS are highly repetitive, have an elevated GC-content, and are primarily located in centromeric or telomeric regions. Up to 1 Mb of NS can be assigned to chromosome Y, and large segments are also missing from GRCh38 at chromosomes 14, 17, and 21. Inclusion of NS into the GRCh38 reference radically improves the alignment and variant calling from short-read whole-genome sequencing data at several genomic loci. A re-analysis of a Swedish population-scale sequencing project yields > 75,000 putative novel single nucleotide variants (SNVs) and removes > 10,000 false positive SNV calls per individual, some of which are located in protein coding regions. Our results highlight that the GRCh38 reference is not yet complete and demonstrate that personal genome assemblies from local populations can improve the analysis of short-read whole-genome sequencing data.

scholarly journals De novo assembly of two Swedish genomes reveals missing segments from the human GRCh38 reference and improves variant calling of population-scale sequencing data

2018 ◽  
Author(s):  
Adam Ameur ◽  
Huiwen Che ◽  
Marcel Martin ◽  
Ignas Bunikis ◽  
Johan Dahlberg ◽  
...  
Keyword(s):  
De Novo ◽  
Gc Content ◽  
Variant Calling ◽  
Whole Genome Sequencing Data ◽  
Personal Genome ◽  
Sequencing Data ◽  
Swedish Population ◽  
Protein Coding ◽  
Sequencing Project ◽  
Population Scale

AbstractWe have performed de novo assembly of two Swedish genomes using long-read sequencing and optical mapping, resulting in total assembly sizes of nearly 3 Gb and hybrid scaffold N50 values of over 45 Mb. A further analysis revealed over 10 Mb of sequences absent from the human GRCh38 reference in each individual. Around 6 Mb of these novel sequences (NS) are shared with a Chinese personal genome. The NS are highly repetitive, have elevated GC-content and are primarily located in centromeric or telomeric regions. A BLAST search showed that 31% of the NS are different from any sequences deposited in nucleotide databases. The remaining NS correspond to human (62%) or primate (6%) nucleotide entries, while 1% of hits show the highest similarity to other species, including mouse and a few different classes of parasitic worms. Up to 1 Mb of NS can be assigned to chromosome Y, and large segments are missing from GRCh38 also at chromosomes 14, 17 and 21. Inclusion of these novel sequences into the GRCh38 reference radically improves the alignment and variant calling of whole-genome sequencing data at several genomic loci. Through a re-analysis of 200 samples from a Swedish population-scale sequencing project, we obtained over 75,000 putative novel SNVs per individual when using a custom version of GRCh38 extended with 17.3 Mb of NS. In addition, about 10,000 false positive SNV calls per individual were removed from the GRCh38 autosomes and sex chromosomes in the re-analysis, with some of them located in protein coding regions.

scholarly journals METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

2021 ◽  
Vol 22 (S10) ◽  
Author(s):  
Zhenmiao Zhang ◽  
Lu Zhang
Keyword(s):  
De Novo ◽  
Label Propagation ◽  
Next Generation Sequencing Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Fecal Samples ◽  
Microbial Genomes ◽  
Metagenome Assembly ◽  
High Chance ◽  
Mock Communities

Abstract Background Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Conclusions Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.

scholarly journals SLR-superscaffolder: a de novo scaffolding tool for synthetic long reads using a top-to-bottom scheme

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lidong Guo ◽  
Mengyang Xu ◽  
Wenchao Wang ◽  
Shengqiang Gu ◽  
Xia Zhao ◽  
...  
Keyword(s):  
High Efficiency ◽  
De Novo ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Draft Assembly ◽  
Screening Algorithm ◽  
Long Reads ◽  
Hybrid Genome ◽  
Genomics Research ◽  
Negative Effect

Abstract Background Synthetic long reads (SLR) with long-range co-barcoding information are now widely applied in genomics research. Although several tools have been developed for each specific SLR technique, a robust standalone scaffolder with high efficiency is warranted for hybrid genome assembly. Results In this work, we developed a standalone scaffolding tool, SLR-superscaffolder, to link together contigs in draft assemblies using co-barcoding and paired-end read information. Our top-to-bottom scheme first builds a global scaffold graph based on Jaccard Similarity to determine the order and orientation of contigs, and then locally improves the scaffolds with the aid of paired-end information. We also exploited a screening algorithm to reduce the negative effect of misassembled contigs in the input assembly. We applied SLR-superscaffolder to a human single tube long fragment read sequencing dataset and increased the scaffold NG50 of its corresponding draft assembly 1349 fold. Moreover, benchmarking on different input contigs showed that this approach overall outperformed existing SLR scaffolders, providing longer contiguity and fewer misassemblies, especially for short contigs assembled by next-generation sequencing data. The open-source code of SLR-superscaffolder is available at https://github.com/BGI-Qingdao/SLR-superscaffolder. Conclusions SLR-superscaffolder can dramatically improve the contiguity of a draft assembly by integrating a hybrid assembly strategy.

scholarly journals Inherited variants in CHD3 demonstrate variable expressivity in Snijders Blok-Campeau syndrome

2021 ◽  
Author(s):  
Jet van der Spek ◽  
Joery den Hoed ◽  
Lot Snijders Blok ◽  
Alexander J. M. Dingemans ◽  
Dick Schijven ◽  
...  
Keyword(s):  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Underlying Mechanism ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Human Phenotype ◽  
Variable Expressivity ◽  
Pathogenic Variants ◽  
Reduced Penetrance ◽  
Coding Variants

Interpretation of next-generation sequencing data of individuals with an apparent sporadic neurodevelopmental disorder (NDD) often focusses on pathogenic variants in genes associated with NDD, assuming full clinical penetrance with limited variable expressivity. Consequently, inherited variants in genes associated with dominant disorders may be overlooked when the transmitting parent is clinically unaffected. While de novo variants explain a substantial proportion of cases with NDDs, a significant number remains undiagnosed possibly explained by coding variants associated with reduced penetrance and variable expressivity. We characterized twenty families with inherited heterozygous missense or protein-truncating variants (PTVs) in CHD3, a gene in which de novo variants cause Snijders Blok-Campeau syndrome, characterized by intellectual disability, speech delay and recognizable facial features (SNIBCPS). Notably, the majority of the inherited CHD3 variants were maternally transmitted. Computational facial and human phenotype ontology-based comparisons demonstrated that the phenotypic features of probands with inherited CHD3 variants overlap with the phenotype previously associated with de novo variants in the gene, while carrier parents are mildly or not affected, suggesting variable expressivity. Additionally, similarly reduced expression levels of CHD3 protein in cells of an affected proband and of related healthy carriers with a CHD3 PTV, suggested that compensation of expression from the wildtype allele is unlikely to be an underlying mechanism. Our results point to a significant role of inherited variation in SNIBCPS, a finding that is critical for correct variant interpretation and genetic counseling and warrants further investigation towards understanding the broader contributions of such variation to the landscape of human disease.

scholarly journals A unified haplotype-based method for accurate and comprehensive variant calling

2018 ◽  
Author(s):  
Daniel P Cooke ◽  
David C Wedge ◽  
Gerton Lunter
Keyword(s):  
De Novo ◽  
Variant Calling ◽  
Normal Sample ◽  
Sequencing Data ◽  
Somatic Variation ◽  
Data Set ◽  
Small Complex ◽  
Physical Linkage ◽  
Germline Variation ◽  
Almost All

Haplotype-based variant callers, which consider physical linkage between variant sites, are currently among the best tools for germline variation discovery and genotyping from short-read sequencing data. However, almost all such tools were designed specifically for detecting common germline variation in diploid populations, and give sub-optimal results in other scenarios. Here we present Octopus, a versatile haplotype-based variant caller that uses a polymorphic Bayesian genotyping model capable of modeling sequencing data from a range of experimental designs within a unified haplotype-aware framework. We show that Octopus accurately calls de novo mutations in parent-offspring trios and germline variants in individuals, including SNVs, indels, and small complex replacements such as microinversions. In addition, using a carefully designed synthetic-tumour data set derived from clean sequencing data from a sample with known germline haplotypes, and observed mutations in large cohort of tumour samples, we show that Octopus accurately characterizes germline and somatic variation in tumours, both with and without a paired normal sample. Sequencing reads and prior information are combined to phase called genotypes of arbitrary ploidy, including those with somatic mutations. Octopus also outputs realigned evidence BAMs to aid validation and interpretation.

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