scholarly journals Using de novo assembly to identify structural variation of complex immune system gene regions

2021 ◽  
Author(s):  
Jia-Yuan Zhang ◽  
Hannah Roberts ◽  
David S. C. Flores ◽  
Antony J. Cutler ◽  
Andrew C. Brown ◽  
...  
Keyword(s):  
Immune System ◽  
T Cell ◽  
Structural Variation ◽  
De Novo ◽  
Optical Mapping ◽  
Association Studies ◽  
Killer Cell ◽  
Error Rates ◽  
Single Individual ◽  
Long Read

AbstractDriven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14+ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data; application of these methods to larger samples would provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies.

scholarly journals Using de novo assembly to identify structural variation of eight complex immune system gene regions

2021 ◽  
Vol 17 (8) ◽  
pp. e1009254
Author(s):  
Jia-Yuan Zhang ◽  
Hannah Roberts ◽  
David S. C. Flores ◽  
Antony J. Cutler ◽  
Andrew C. Brown ◽  
...  
Keyword(s):  
Immune System ◽  
T Cell ◽  
Structural Variation ◽  
De Novo ◽  
Optical Mapping ◽  
Association Studies ◽  
Killer Cell ◽  
Error Rates ◽  
Single Individual ◽  
Long Read

Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14+ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data. Continued reductions in the cost of these technologies will enable application of these methods to larger samples and provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies.

scholarly journals MiniScrub: de novo long read scrubbing using approximate alignment and deep learning

2018 ◽  
Author(s):  
Nathan LaPierre ◽  
Rob Egan ◽  
Wei Wang ◽  
Zhong Wang
Keyword(s):  
Open Source Software ◽  
Genome Assembly ◽  
Large Scale ◽  
Structural Variation ◽  
De Novo ◽  
Error Rates ◽  
Sequencing Technologies ◽  
Oxford Nanopore ◽  
Long Read ◽  
Reference Genomes

AbstractLong read sequencing technologies such as Oxford Nanopore can greatly de-crease the complexity of de novo genome assembly and large structural variation iden-tification. Currently Nanopore reads have high error rates, and the errors often cluster into low-quality segments within the reads. Many methods for resolving these errors require access to reference genomes, high-fidelity short reads, or reference genomes, which are often not available. De novo error correction modules are available, often as part of assembly tools, but large-scale errors still remain in resulting assemblies, motivating further innovation in this area. We developed a novel Convolutional Neu-ral Network (CNN) based method, called MiniScrub, for de novo identification and subsequent “scrubbing” (removal) of low-quality Nanopore read segments. MiniScrub first generates read-to-read alignments by MiniMap, then encodes the alignments into images, and finally builds CNN models to predict low-quality segments that could be scrubbed based on a customized quality cutoff. Applying MiniScrub to real world con-trol datasets under several different parameters, we show that it robustly improves read quality. Compared to raw reads, de novo genome assembly with scrubbed reads pro-duces many fewer mis-assemblies and large indel errors. We propose MiniScrub as a tool for preprocessing Nanopore reads for downstream analyses. MiniScrub is open-source software and is available at https://bitbucket.org/berkeleylab/jgi-miniscrub

scholarly journals Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads

2020 ◽  
Author(s):  
David Porubsky ◽  
◽  
Peter Ebert ◽  
Peter A. Audano ◽  
Mitchell R. Vollger ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Assembly ◽  
De Novo ◽  
Error Rates ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
De Novo Genome Assembly ◽  
Parental Data ◽  
Human Genome Assembly ◽  
Long Read

AbstractHuman genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing1,2 with continuous long-read or high-fidelity3 sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.

scholarly journals High contiguity long read assembly of Brassica nigra allows localization of active centromeres and provides insights into the ancestral Brassica genome

2020 ◽  
Author(s):  
Sampath Perumal ◽  
Chu Shin Koh ◽  
Lingling Jin ◽  
Miles Buchwaldt ◽  
Erin Higgins ◽  
...  
Keyword(s):  
De Novo ◽  
Low Complexity ◽  
Error Rates ◽  
Brassica Nigra ◽  
Genome Integrity ◽  
Ancestral Genome ◽  
Genomic Distance ◽  
Long Read ◽  
Genome Assemblies ◽  
Technology Comparison

AbstractHigh-quality nanopore genome assemblies were generated for two Brassica nigra genotypes (Ni100 and CN115125); a member of the agronomically important Brassica species. The N50 contig length for the two assemblies were 17.1 Mb (58 contigs) and 0.29 Mb (963 contigs), respectively, reflecting recent improvements in the technology. Comparison with a de novo short read assembly for Ni100 corroborated genome integrity and quantified sequence related error rates (0.002%). The contiguity and coverage allowed unprecedented access to low complexity regions of the genome. Pericentromeric regions and coincidence of hypo-methylation enabled localization of active centromeres and identified a novel centromere-associated ALE class I element which appears to have proliferated through relatively recent nested transposition events (<1 million years ago). Computational abstraction was used to define a post-triplication Brassica specific ancestral genome and to calculate the extensive rearrangements that define the genomic distance separating B. nigra from its diploid relatives.

scholarly journals The population genomics of structural variation in a songbird genus

2019 ◽  
Author(s):  
Matthias H. Weissensteiner ◽  
Ignas Bunikis ◽  
Ana Catalán ◽  
Kees-Jan Francoijs ◽  
Ulrich Knief ◽  
...  
Keyword(s):  
Structural Variation ◽  
Population Genomics ◽  
De Novo ◽  
Natural Populations ◽  
Evolutionary Significance ◽  
Substantial Part ◽  
Sequencing Data ◽  
Premating Isolation ◽  
Population Genetic Analysis ◽  
Long Read

AbstractStructural variation (SV) accounts for a substantial part of genetic mutations segregating across eukaryotic genomes with important medical and evolutionary implications. Here, we characterized SV across evolutionary time scales in the songbird genus Corvus using de novo assembly and read mapping approaches. Combining information from short-read (N = 127) and long-read re-sequencing data (N = 31) as well as from optical maps (N = 16) revealed a total of 201,738 insertions, deletions and inversions. Population genetic analysis of SV in the Eurasian crow speciation model revealed an evolutionary young (~530,000 years) cis-acting 2.25-kb retrotransposon insertion reducing expression of the NDP gene with consequences for premating isolation. Our results attest to the wealth of SV segregating in natural populations and demonstrate its evolutionary significance.

scholarly journals BELLA: Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper

2018 ◽  
Author(s):  
Giulia Guidi ◽  
Marquita Ellis ◽  
Daniel Rokhsar ◽  
Katherine Yelick ◽  
Aydın Buluç
Keyword(s):  
Genome Assembly ◽  
Probabilistic Model ◽  
De Novo ◽  
Sparse Matrix ◽  
Genome Structure ◽  
Matrix Multiplication ◽  
Synthetic Data ◽  
Error Rates ◽  
Species Variation ◽  
Long Read

AbstractRecent advances in long-read sequencing enable the characterization of genome structure and its intra- and inter-species variation at a resolution that was previously impossible. Detecting overlaps between reads is integral to many long-read genomics pipelines, such as de novo genome assembly. While longer reads simplify genome assembly and improve the contiguity of the reconstruction, current long-read technologies come with high error rates. We present Berkeley Long-Read to Long-Read Aligner and Overlapper (BELLA), a novel algorithm for computing overlaps and alignments via sparse matrix-matrix multiplication that balances the goals of recall and precision, performing well on both.We present a probabilistic model that demonstrates the feasibility of using short k-mers for detecting candidate overlaps. We then introduce a notion of reliable k-mers based on our probabilistic model. Combining reliable k-mers with our binning mechanism eliminates both the k-mer set explosion that would otherwise occur with highly erroneous reads and the spurious overlaps from k-mers originating in repetitive regions. Finally, we present a new method based on Chernoff bounds for separating true overlaps from false positives using a combination of alignment techniques and probabilistic modeling. Our methodologies aim at maximizing the balance between precision and recall. On both real and synthetic data, BELLA performs amongst the best in terms of F1 score, showing performance stability which is often missing for competitor software. BELLA’s F1 score is consistently within 1.7% of the top entry. Notably, we show improved de novo assembly results on synthetic data when coupling BELLA with the Miniasm assembler.

scholarly journals Two Synthetic 18-Way Outcrossed Populations of Diploid Budding Yeast with Utility for Complex Trait Dissection

Genetics ◽  
2020 ◽  
Vol 215 (2) ◽  
pp. 323-342 ◽  
Author(s):  
Robert A. Linder ◽  
Arundhati Majumder ◽  
Mahul Chakraborty ◽  
Anthony Long
Keyword(s):  
Complex Traits ◽  
De Novo ◽  
Budding Yeast ◽  
Association Studies ◽  
Haplotype Frequency ◽  
Error Rates ◽  
Genome Wide Association Studies ◽  
Link Type ◽  
Standing Variation ◽  
Median Error

Advanced-generation multiparent populations (MPPs) are a valuable tool for dissecting complex traits, having more power than genome-wide association studies to detect rare variants and higher resolution than F2 linkage mapping. To extend the advantages of MPPs in budding yeast, we describe the creation and characterization of two outbred MPPs derived from 18 genetically diverse founding strains. We carried out de novo assemblies of the genomes of the 18 founder strains, such that virtually all variation segregating between these strains is known, and represented those assemblies as Santa Cruz Genome Browser tracks. We discovered complex patterns of structural variation segregating among the founders, including a large deletion within the vacuolar ATPase VMA1, several different deletions within the osmosensor MSB2, a series of deletions and insertions at PRM7 and the adjacent BSC1, as well as copy number variation at the dehydrogenase ALD2. Resequenced haploid recombinant clones from the two MPPs have a median unrecombined block size of 66 kb, demonstrating that the population is highly recombined. We pool-sequenced the two MPPs to 3270× and 2226× coverage and demonstrated that we can accurately estimate local haplotype frequencies using pooled data. We further downsampled the pool-sequenced data to ∼20–40× and showed that local haplotype frequency estimates remained accurate, with median error rates 0.8 and 0.6% at 20× and 40×, respectively. Haplotypes frequencies are estimated much more accurately than SNP frequencies obtained directly from the same data. Deep sequencing of the two populations revealed that 10 or more founders are present at a detectable frequency for > 98% of the genome, validating the utility of this resource for the exploration of the role of standing variation in the architecture of complex traits.

scholarly journals De novo assembly of Dekkera bruxellensis: a multi technology approach using short and long-read sequencing and optical mapping

GigaScience ◽  
2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Remi-Andre Olsen ◽  
Ignas Bunikis ◽  
Ievgeniia Tiukova ◽  
Kicki Holmberg ◽  
Britta Lötstedt ◽  
...  
Keyword(s):  
De Novo Assembly ◽  
De Novo ◽  
Optical Mapping ◽  
Dekkera Bruxellensis ◽  
Long Read

scholarly journals Carbohydrate intake during endurance exercise increases natural killer cell responsiveness to IL-2

2004 ◽  
Vol 96 (1) ◽  
pp. 271-275 ◽  
Author(s):  
Brian K. McFarlin ◽  
Michael G. Flynn ◽  
Laura K. Stewart ◽  
Kyle L. Timmerman
Keyword(s):  
Immune System ◽  
T Cell ◽  
Natural Killer ◽  
Endurance Exercise ◽  
Nk Cell ◽  
Venous Blood ◽  
Killer Cell ◽  
Cell Activity ◽  
Cytotoxic T Cell

The purpose of this study was to evaluate the effect of high-intensity endurance exercise and carbohydrate consumption on in vitro responsiveness of natural killer (NK) to IL-2 (2.5 U/ml for 24 h). Thirteen male subjects (18-26 yr old; peak O2consumption = 59.79 ± 5.13 ml·kg-1·ml-1) were recruited to complete two 1-h (75-80% peak O2consumption) cycling trials in a random counterbalanced order: carbohydrate (CHO) and placebo (Pla). Venous blood samples were collected before (Pre), immediately (Post), 2 h (2H), and 4 h (4H) after exercise. All resting samples were taken after 15 min of seated rest. NK (CD3-/56+), activated NK (CD3-/56+/69+), helper T cell (Th; CD3+/4+), and cytotoxic T cell (Tc; CD3+/8+) number were measured by using flow cytometry. NK cell activity (NKCA) was determined by using both a51Cr release assay (NKCA-51) and activated NK cell number (NKCA-69). Immune system variables were not different between CHO and Pla, with the exception of NK cell responsiveness to IL-2, where Post (116.2%) and 4H (48.4%) was significantly greater in CHO ( P < 0.05). NK, Th, and Tc were significantly higher Post (40.7, 102.7, and 82.0%, respectively) and lower at 2H (-51.9, -53.3, and -53.2%, respectively) than Pre (time effect). 4H was not different from Pre for NK, Th, and Tc. NKCA was significantly lower 2H (NKCA-51, NKCA-69) and 4H (NKCA-69) than Pre. CHO consumption during exercise did not prevent disruptions in unstimulated immune system function, but it did enhance NK responsiveness to IL-2.

scholarly journals De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm

2018 ◽  
Author(s):  
Kristoffer Sahlin ◽  
Paul Medvedev
Keyword(s):  
Clustering Algorithm ◽  
De Novo ◽  
Substantial Improvement ◽  
Error Rates ◽  
Reconstruction Algorithms ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Transcript Reconstruction ◽  
Oxford Nanopore Technologies

AbstractLong-read sequencing of transcripts with PacBio Iso-Seq and Oxford Nanopore Technologies has proven to be central to the study of complex isoform landscapes in many organisms. However, current de novo transcript reconstruction algorithms from long-read data are limited, leaving the potential of these technologies unfulfilled. A common bottleneck is the dearth of scalable and accurate algorithms for clustering long reads according to their gene family of origin. To address this challenge, we develop isONclust, a clustering algorithm that is greedy (in order to scale) and makes use of quality values (in order to handle variable error rates). We test isONclust on three simulated and five biological datasets, across a breadth of organisms, technologies, and read depths. Our results demonstrate that isONclust is a substantial improvement over previous approaches, both in terms of overall accuracy and/or scalability to large datasets. Our tool is available at https://github.com/ksahlin/isONclust.

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