Biology subjects

Zook, J. M.

Publications and source records attributed to Zook, J. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Long-read genome sequencing reveals complex variability in lentiviral provirus insertions in deeply characterized Clonal CD19 CAR-T vector copy number reference cell lines

Chimeric antigen receptor (CAR)-T cell therapy is an important therapy involving provirus insertions in the genome. Characterizing these insertions is important for understanding the safety and efficacy of cell products, but the sequence of these insertions is not fully characterized. We generate clonal CD19 CAR-T cell lines with one to five copies of the lentiviral provirus insertions. Vector copy number (VCN) was determined by droplet digital PCR (ddPCR), which revealed that most of the elements (LTR, Psi, RRE, CD19, and WPRE) were 1 to 5 or 6 copies per cell. DdPCR data also revealed that there was an additional copy of eGFP gene in VCN4 and VCN5 cell lines. To fully characterize the sequences and locations of these insertions, we use short- and long-read whole genome sequencing as well as digital PCR and flow cytometry. Long-reads enable full resolution of each insertion, and we find that of 10 insertion events, 3 have the expected insertion sequence, 2 differ from the expected only in small variants, 3 have structural abnormalities, and 2 are small partial insertions missed by most other approaches. One particularly important structural abnormality resolved only by long-reads is a 724 bp deletion of the EF1 promoter disrupting expression of the CD19 CAR. Standard short-read and ddPCR approaches miss this deletion due to this commonly used promoter being in the unengineered human genome. These results demonstrated that these cell lines are suitable VCN reference standards for 1 to 5 or 6 copies and highlight the utility of long-read sequencing in characterizing both quantity and quality of insertions in lentiviral-engineered cells.

genomics↗

bioRxiv2025

Aardvark: Sifting through differences in a mound of variants

Variant benchmarking is critical in assessing the accuracy of genomic secondary pipelines. However, traditional benchmarking tools that require exact genotype matches inject biases from variant representation and are ill-suited for tandem repeat or structural variation. We describe Aardvark, a variant benchmarking tool that introduces the basepair score to directly compare haplotype sequences, reducing representation biases while allowing for partial credit scoring. The tool also includes a traditional genotype score and supports separate or joint benchmarking of small variants, tandem repeats, and structural variants (<10 kb). Aardvark accepts standard inputs, runs {approx}16x faster than hap.py, and is freely available and open source (https://github.com/PacificBiosciences/aardvark).

bioinformatics↗

bioRxiv2024

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

Somatic mosaicism is an important cause of disease, but mosaic and somatic variants are often challenging to detect because they exist in only a fraction of cells. To address the need for benchmarking subclonal variants in normal cell populations, we developed a benchmark containing mosaic variants in the Genome in a Bottle Consortium (GIAB) HG002 reference material DNA from a large batch of a normal lymphoblastoid cell line. First, we used a somatic variant caller with high coverage (300x) Illumina whole genome sequencing data from the Ashkenazi Jewish trio to detect variants in HG002 not detected in at least 5% of cells from the combined parental data. These candidate mosaic variants were subsequently evaluated using >100x BGI, Element, and PacBio HiFi data. High confidence candidate SNVs with variant allele fractions above 5% were included in the HG002 draft mosaic variant benchmark, with 13/85 occurring in medically relevant gene regions. We also delineated a 2.45 Gbp subset of the previously defined germline autosomal benchmark regions for HG002 in which no additional mosaic variants >2% exist, enabling robust assessment of false positives. The variant allele fraction of some mosaic variants is different between batches of cells, so using data from the homogeneous batch of reference material DNA is critical for benchmarking these variants. External validation of this mosaic benchmark showed it can be used to reliably identify both false negatives and false positives for a variety of technologies and detection algorithms, demonstrating its utility for optimization and validation. By adding our characterization of mosaic variants in this widely-used cell line, we support extensive benchmarking efforts using it in simulation, spike-in, and mixture studies.

bioinformatics↗

bioRxiv2024

The Platinum Pedigree: A long-read benchmark for genetic variants

Recent advances in genome sequencing have improved variant calling in complex regions of the human genome. However, it is difficult to quantify variant calling performance since existing standards often focus on specificity, neglecting completeness in difficult to analyze regions. To create a more comprehensive truth set, we used Mendelian inheritance in a large pedigree (CEPH-1463) to filter variants across Illumina, PacBio high-fidelity (HiFi), and Oxford Nanopore Technologies platforms. This generated a variant map with over 4.7 million single-nucleotide variants, 767,795 indels, 537,486 tandem repeats, and 24,315 structural variants, covering 2.77 Gb of the GRCh38 genome. This work adds [~]200 Mb of high-confidence regions, including 8% more small variants, and introduces the first tandem repeat and structural variant truth sets for NA12878. As an example of the value of this improved benchmark, we retrained DeepVariant using this data to reduce genotyping errors by [~]34%.

genomics↗

bioRxiv2024

Jointly benchmarking small and structural variant calls with vcfdist

In this work, we extend vcfdist to be the first variant call benchmarking tool to jointly evaluate phased single-nucleotide polymorphisms (SNPs), small insertions/deletions (INDELs), and structural variants (SVs) for the whole genome. First, we find that a joint evaluation of small and structural variants uniformly reduces measured errors for SNPs (-28.9%), INDELs (-19.3%), and SVs (-52.4%) across three datasets. Next, we correct a common flaw in phasing evaluations, reducing measured flip errors by over 50%. Lastly, we show that vcfdist is more accurate than previously published works and on par with the newest approaches, but with improved result interpretability.

bioinformatics↗

bioRxiv2024

Genome-wide profiling of genetic variation at tandem repeat from long reads

Tandem repeats are frequent across the human genome, and variation in repeat length has been linked to a variety of traits. Recent improvements in long read sequencing technologies have the potential to greatly improve TR analysis, especially for long or complex repeats. Here we introduce LongTR, which accurately genotypes tandem repeats from high fidelity long reads available from both PacBio and Oxford Nanopore Technologies. LongTR is freely available at https://github.com/gymrek-lab/longtr.

bioinformatics↗

bioRxiv2023

Benchmarking of small and large variants across tandem repeats

Tandem repeats (TRs) are highly polymorphic in the human genome, have thousands of associated molecular traits, and are linked to over 60 disease phenotypes. However, their complexity often excludes them from at-scale studies due to challenges with variant calling, representation, and lack of a genome-wide standard. To promote TR methods development, we create a comprehensive catalog of TR regions and explore its properties across 86 samples. We then curate variants from the GIAB HG002 individual to create a tandem repeat benchmark. We also present a variant comparison method that handles small and large alleles and varying allelic representation. The 8.1% of the genome covered by the TR catalog holds [~]24.9% of variants per individual, including 124,728 small and 17,988 large variants for the GIAB HG002 TR benchmark. We work with the GIAB community to demonstrate the utility of this benchmark across short and long read technologies.

bioinformatics↗

bioRxiv2023

Small variant benchmark from a complete assembly of X and Y chromosomes

The sex chromosomes contain complex, important genes impacting medical phenotypes, but differ from the autosomes in their ploidy and large repetitive regions. To evaluate variant detection on chromosomes X and Y, we created an 111,725 variant benchmark for the Genome in a Bottle HG002 reference material. We show how complete assemblies can expand benchmarks to difficult regions, but highlight remaining challenges benchmarking complex gene conversions, copy number variable gene arrays, and human satellites.

genomics↗

bioRxiv2023

The GIAB genomic stratifications resource for human reference genomes

Stratification of the genome into different genomic contexts is useful when developing bioinformatics software like variant callers, to assess performance in difficult regions in the human genome. Here we describe a set of genomic stratifications for the human reference genomes GRCh37, GRCh38, and T2T-CHM13v2.0. Generating stratifications for the new complete CHM13 reference genome is critical to understanding improvements in variant caller performance when using this new complete reference. The GIAB stratifications can be used when benchmarking variant calls to analyze difficult regions of the human genome in a standardized way. Here we present stratifications in the CHM13 genome in comparison to GRCh37 and GRCh38, highlighting expansions in hard-to-map and GC-rich stratifications which provide useful insight for accuracy of variants in these newly-added regions. To evaluate the reliability and utility of the new stratifications, we used the stratifications of the three references to assess accuracy of variant calls in diverse, challenging genomic regions. The means to generate these stratifications are available as a snakemake pipeline at https://github.com/ndwarshuis/giab-stratifications.

genomics↗

bioRxiv2022

GrapHiC: An integrative graph based approach for imputing missing Hi-C reads

Hi-C experiments allow researchers to study and understand the 3D genome organization and its regulatory function. Unfortunately, sequencing costs and technical constraints severely restrict access to high-quality Hi-C data for many cell types. Existing frameworks rely on a sparse Hi-C dataset or cheaper-to-acquire ChIP-seq data to predict Hi-C contact maps with high read coverage. However, these methods fail to generalize to sparse or cross-cell-type inputs because they do not account for the contributions of epigenomic features or the impact of the structural neighborhood in predicting Hi-C reads. We propose GrapHiC, which combines Hi-C and ChIP-seq in a graph representation, allowing more accurate embedding of structural and epigenomic features. Each node represents a binned genomic region, and we assign edge weights using the observed Hi-C reads. Additionally, we embed ChIP-seq and relative positional information as node attributes, allowing our representation to capture structural neighborhoods and the contributions of proteins and their modifications for predicting Hi-C reads. Our evaluations show that GrapHiC generalizes better than the current state-of-the-art on cross-cell-type settings and sparse Hi-C inputs. Moreover, we can utilize our framework to impute Hi-C reads even when no Hi-C contact map is available, thus making high-quality Hi-C data more accessible for many cell types. Availabilityhttps://github.com/rsinghlab/GrapHiC ACM Reference FormatGhulam Murtaza, Justin Wagner, Justin M. Zook, and Ritambhara Singh. 2018. GrapHiC: An integrative graph based approach for imputing missing Hi-C reads. In Proceedings of 22nd International Workshop on Data Mining in Bioinformatics (BioKDD 23). ACM, New York, NY, USA, 16 pages. https://doi.org/XXXXXXX.XXXXXXX

genomics↗

bioRxiv2022

Multiscale Analysis of Pangenome Enables Improved Representation of Genomic Diversity For Repetitive And Clinical Relevant Genes

The advancements in sequencing technologies and assembly methods enable the regular production of high-quality genome assemblies characterizing complex regions. However, challenges remain in efficiently interpreting variations at various scales, from smaller tandem repeats to megabase re-arrangements, across many human genomes. We present a pangenome research toolkit enabling analyses of complex pangenome variations at multiple scales. A graph decomposition method is developed for interpreting such variations. Surveying a set of 395 challenging and medically important genes in pangenome provides quantitative insights into repetitiveness and diversity that could impact the accuracy of variant calls. We apply the graph decomposition methods to the Y-chromosome gene, DAZ1/DAZ2/DAZ3/DAZ4, of which structural variants have been linked to male infertility, and X-chromosome genes OPN1LW and OPN1MW linked to eye disorders, highlighting the power of PGR-TK and pangenomics to resolve complex variation in regions of the genome that were previously too complex to analyze across many haplotypes.

bioinformatics↗

bioRxiv2022

Fixing reference errors efficiently improves sequencing results

The GRCh38 reference is the current standard in human genomics research and clinical applications, but includes errors across 33 protein-coding genes, including 12 with medical relevance. Current studies rely on the correctness of this reference genome and require an accurate and cost-effective way to improve variant calling and expression analysis across these erroneous loci. We identified likely artifacts in GTEx, gnomAD, 1000 Genomes Project, and other important genomic resources leading to wrong interpretations for these genes. Here, we present FixItFelix together with a modified GRCh38 version that improves the subsequent analysis across these genes within minutes for an existing BAM/CRAM file. We showcase these improvements over multi-ethnic control samples across short and long-read DNA-, and RNA-sequencing. Furthermore, applying our approach across thousands of genomes demonstrates improvements for population variant calling as well as eQTL studies. Still, some genes e.g., DUSP22 indicate mixed results due to their complexity.

genomics↗

bioRxiv2021

A complete reference genome improves analysis of human genetic variation

Compared to its predecessors, the Telomere-to-Telomere CHM13 genome adds nearly 200 Mbp of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome to clinical and functional study. Here we demonstrate how the new reference universally improves read mapping and variant calling for 3,202 and 17 globally diverse samples sequenced with short and long reads, respectively. We identify hundreds of thousands of novel variants per sample--a new frontier for evolutionary and biomedical discovery. Simultaneously, the new reference eliminates tens of thousands of spurious variants per sample, including up to 12-fold reduction of false positives in 269 medically relevant genes. The vast improvement in variant discovery coupled with population and functional genomic resources position T2T-CHM13 to replace GRCh38 as the prevailing reference for human genetics. One Sentence SummaryThe T2T-CHM13 reference genome universally improves the analysis of human genetic variation.

genomics↗

bioRxiv2021

Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies

Advances in long-read sequencing technologies and genome assembly methods have enabled the recent completion of the first Telomere-to-Telomere (T2T) human genome assembly, which resolves complex segmental duplications and large tandem repeats, including centromeric satellite arrays in a complete hydatidiform mole (CHM13). Though derived from highly accurate sequencing, evaluation revealed that the initial T2T draft assembly had evidence of small errors and structural misassemblies. To correct these errors, we designed a novel repeat-aware polishing strategy that made accurate assembly corrections in large repeats without overcorrection, ultimately fixing 51% of the existing errors and improving the assembly QV to 73.9. By comparing our results to standard automated polishing tools, we outline common polishing errors and offer practical suggestions for genome projects with limited resources. We also show how sequencing biases in both PacBio HiFi and Oxford Nanopore Technologies reads cause signature assembly errors that can be corrected with a diverse panel of sequencing technologies

genomics↗

bioRxiv2021

Towards a Comprehensive Variation Benchmark for Challenging Medically-Relevant Autosomal Genes

The repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.

genomics↗

bioRxiv2020

Benchmarking challenging small variants with linked and long reads

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here, we use accurate linked reads and long reads to expand the prior benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). For HG002, we include 92% of the autosomal GRCh38 assembly, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and reference errors) that should not have been in the previous version, which included 85% of GRCh38. By including difficult-to-map regions, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark.We have demonstrated the utility of this benchmark to reliably identify false positives and false negatives across technologies in more challenging regions, which enables continued technology and bioinformatics development.

genomics↗

bioRxiv2020

Multi-Platform Assessment of DNA Sequencing Performance using Human and Bacterial Reference Genomes in the ABRF Next-Generation Sequencing Study

Massively parallel DNA sequencing is a critical tool for genomics research and clinical diagnostics. Here, we describe the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Phase II Study to measure quality and reproducibility of DNA sequencing. Replicates of human and bacterial reference DNA samples were generated across multiple sequencing platforms, including well-established technologies such as Illumina, ThermoFisher Ion Torrent, and Pacific Biosciences, as well as emerging technologies such as BGI, Genapsys, and Oxford Nanopore. A total of 202 datasets were generated to investigate the performance of a total of 16 sequencing platforms, including mappability of reads, coverage and error rates in difficult genomic regions, and detection of small-scale polymorphisms and large-scale structural variants. This study provides a comprehensive baseline resource for continual benchmarking as chemistries, methods, and platforms evolve for DNA sequencing.

genomics↗

bioRxiv2020

Assembly and Annotation of an Ashkenazi Human Reference Genome

Here we describe the assembly and annotation of the genome of an Ashkenazi individual and the creation of a new, population-specific human reference genome. This genome is more contiguous and more complete than GRCh38, the latest version of the human reference genome, and is annotated with highly similar gene content. The Ashkenazi reference genome, Ash1, contains 2,973,118,650 nucleotides as compared to 2,937,639,212 in GRCh38. Annotation identified 20,157 protein-coding genes, of which 19,563 are >99% identical to their counterparts on GRCh38. Most of the remaining genes have small differences. 40 of the protein-coding genes in GRCh38 are missing from Ash1; however, all of these genes are members of multi-gene families for which Ash1 contains other copies. 11 genes appear on different chromosomes from their homologs in GRCh38. Alignment of DNA sequences from an unrelated Ashkenazi individual to Ash1 identified ~1 million fewer homozygous SNPs than alignment of those same sequences to the more-distant GRCh38 genome, illustrating one of the benefits of population-specific reference genomes.

genomics↗