Biology subjects

Zook, J.

Publications and source records attributed to Zook, J..

7 recordsLinked to original sources

bioRxiv2023

StratoMod: Predicting sequencing and variant calling errors with interpretable machine learning

Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their application. Currently, assessing such tradeoffs relies on intuition about how a certain pipeline will perform in a given genomic context. We present Stratomod, which addresses this problem using an interpretable machine-learning classifier to predict variant calling errors in a data-driven manner. We showed Stratomod can precisely quantify the likelihood of missing variants using Hifi or Illumina, and leveraged Stratomods interpretability to measure contributions from difficult-to-map and homopolymer regions for each respective outcome. Furthermore, we used Statomod to assess the likelihood of missing variants due to mismapping using linear vs. graph-based references, and identified the hard-to-map regions where graph-based methods excelled and by how much. For these we utilized our new benchmark based on the Q100 HG002 assembly, which contains previously-inaccessible difficult regions. Furthermore, Stratomod presents a new method of finding likely false negatives, which is an improvement over current pipelines which only filter false positives. We anticipate this being useful for performing precise risk-reward analyses when designing variant calling pipelines.

genomics↗

bioRxiv2021

Characterization and computational simulation of human Syx, a RhoGEF implicated in glioblastoma

Structural discovery of guanine nucleotide exchange factor (GEF) protein complexes is likely to become increasingly relevant with the development of new therapeutics targeting small GTPases and development of new classes of small molecules that inhibit protein-protein interactions. Syx (also known as PLEKHG5 in humans) is a RhoA GEF implicated in the pathology of glioblastoma (GBM). Here we investigated protein expression and purification of ten different human Syx constructs and performed biophysical characterizations and computational studies that provide insights into why expression of this protein was previously intractable. We show that human Syx can be expressed and isolated and Syx is folded as observed by circular dichroism (CD) spectroscopy and actively binds to RhoA as determined by co-elution during size exclusion chromatography (SEC). This characterization may provide critical insights into the expression and purification of other recalcitrant members of the large class of oncogenic -- Diffuse B-cell lymphoma (Dbl) homology GEF proteins. In addition, we performed detailed homology modeling and molecular dynamics simulations on the surface of a physiologically realistic membrane. These simulations reveal novel insights into GEF activity and allosteric modulation by the plekstrin homology (PH) domain. These newly revealed interactions between the GEF PH domain and the membrane embedded region of RhoA support previously unexplained experimental findings regarding the allosteric effects of the PH domain from numerous activity studies of Dbl homology GEF proteins. This work establishes new hypotheses for structural interactivity and allosteric signal modulation in Dbl homology RhoGEFs.

biochemistry↗

bioRxiv2019

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗

bioRxiv2018

A Rigorous Interlaboratory Examination of the Need to Confirm NGS-Detected Variants by an Orthogonal Method in Clinical Genetic Testing

Orthogonal confirmation of NGS-detected germline variants has been standard practice, although published studies have suggested that confirmation of the highest quality calls may not always be necessary. The key question is how laboratories can establish criteria that consistently identify those NGS calls that require confirmation. Most prior studies addressing this question have limitations: These studies are generally small, omit statistical justification, and explore limited aspects of the underlying data. The rigorous definition of criteria that separate high-accuracy NGS calls from those that may or may not be true remains a critical issue.\n\nWe analyzed five reference samples and over 80,000 patient specimens from two laboratories. We examined quality metrics for approximately 200,000 NGS calls with orthogonal data, including 1662 false positives. A classification algorithm used these data to identify a battery of criteria that flag 100% of false positives as requiring confirmation (CI lower bound: 98.5-99.8% depending on variant type) while minimizing the number of flagged true positives. These criteria identify false positives that the previously published criteria miss. Sampling analysis showed that smaller datasets resulted in less effective criteria.\n\nOur methodology for determining test and laboratory-specific criteria can be generalized into a practical approach that can be used by many laboratories to help reduce the cost and time burden of confirmation without impacting clinical accuracy.

genetics↗

bioRxiv2018

Reproducible integration of multiple sequencing datasets to form high-confidence SNP, indel, and reference calls for five human genome reference materials

Benchmark small variant calls from the Genome in a Bottle Consortium (GIAB) for the CEPH/HapMap genome NA12878 (HG001) have been used extensively for developing, optimizing, and demonstrating performance of sequencing and bioinformatics methods. Here, we develop a reproducible, cloud-based pipeline to integrate multiple sequencing datasets and form benchmark calls, enabling application to arbitrary human genomes. We use these reproducible methods to form high-confidence calls with respect to GRCh37 and GRCh38 for HG001 and 4 additional broadly-consented genomes from the Personal Genome Project that are available as NIST Reference Materials. These new genomes broad, open consent with few restrictions on availability of samples and data is enabling a uniquely diverse array of applications. Our new methods produce 17% more high-confidence SNPs, 176% more indels, and 12% larger regions than our previously published calls. To demonstrate that these calls can be used for accurate benchmarking, we compare other high-quality callsets to ours (e.g., Illumina Platinum Genomes), and we demonstrate that the majority of discordant calls are errors in the other callsets, We also highlight challenges in interpreting performance metrics when benchmarking against imperfect high-confidence calls. We show that benchmarking tools from the Global Alliance for Genomics and Health can be used with our calls to stratify performance metrics by variant type and genome context and elucidate strengths and weaknesses of a method.

genomics↗

bioRxiv2017

Authentic Enzyme Intermediates Captured "on-the-fly" by Mix-and-Inject Serial Crystallography

Ever since the first atomic structure of an enzyme was solved, the discovery of the mechanism and dynamics of reactions catalyzed by biomolecules has been the key goal for the understanding of the molecular processes that drive life on earth. Despite a large number of successful methods for trapping reaction intermediates, the direct observation of an ongoing reaction has been possible only in rare and exceptional cases. Here, we demonstrate a general method for capturing enzyme catalysis in-action by mix-and-inject serial crystallography. Specifically, we follow the catalytic reaction of the Mycobacterium tuberculosis -lactamase with the 3rd generation antibiotic ceftriaxone by time-resolved serial femtosecond crystallography. The results reveal, in near atomic detail, antibiotic cleavage and inactivation on the millisecond to second time scales including the crossover from transition state kinetics to steady-state kinetics.\n\nSynopsisAn enzymatically catalyzed reaction is initiated by diffusion based mixing of substrate and followed at runtime by time-resolved serial crystallography using a free electron laser.

biophysics↗

bioRxiv2016

CrowdVariant: a crowdsourcing approach to classify copy number variants

Copy number variants (CNVs) are an important type of genetic variation and play a causal role in many diseases. However, they are also notoriously difficult to identify accurately from next-generation sequencing (NGS) data. For larger CNVs, genotyping arrays provide reasonable benchmark data, but NGS allows us to assay a far larger number of small (< 10kbp) CNVs that are poorly captured by array-based methods. The lack of high quality benchmark callsets of small-scale CNVs has limited our ability to assess and improve CNV calling algorithms for NGS data. To address this issue we developed a crowdsourcing framework, called CrowdVariant, that leverages Googles high-throughput crowdsourcing platform to create a high confidence set of copy number variants for NA24385 (NIST HG002/RM 8391), an Ashkenazim reference sample developed in partnership with the Genome In A Bottle Consortium. In a pilot study we show that crowdsourced classifications, even from non-experts, can be used to accurately assign copy number status to putative CNV calls and thereby identify a high-quality subset of these calls. We then scale our framework genome-wide to identify 1,781 high confidence CNVs, which multiple lines of evidence suggest are a substantial improvement over existing CNV callsets, and are likely to prove useful in benchmarking and improving CNV calling algorithms. Our crowdsourcing methodology may be a useful guide for other genomics applications.

genomics↗