Biology subjects

Zhi, D.

Publications and source records attributed to Zhi, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Local Haplotype Classifiers enable Efficient, Flexible, and Secure Genotype Imputation and Downstream Analyses

The decreasing cost of genotyping technologies led to abundant availability and usage of genetic data. Although it offers many potentials for improving health and curing diseases, genetic data is highly intrusive in many aspects of individual privacy. Secure genotype analysis methods have been developed to perform numerous tasks such as genome-wide association studies, meta-analysis, kinship inference, and genotype imputation outsourcing. Here we present a new approach for using lightweight haplotype classifier models to use predicted haplotype information in a flexible privacy-preserving framework to perform genotype imputation and downstream tasks. Compared to the previous secure methods that rely main on linear models, our approach utilizes efficient models that rely on utilizing haplotypic information, which improves accuracy and increases the throughput of imputation by performing multiple imputations per model evaluation.

bioinformatics↗

bioRxiv2024

Protein-ligand binding affinity prediction: Is 3D binding pose needed?

Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction.

bioinformatics↗

bioRxiv2023

Fast and accurate local ancestry inference with Recomb-Mix

The availability of large genotyped cohorts brings new opportunities for revealing the high-resolution genetic structure of admixed populations via local ancestry inference (LAI), the process of identifying the ancestry of each segment of an individual haplotype. Though current methods achieve high accuracy in standard cases, LAI is still challenging when reference populations are more similar (e.g., intra-continental), when the number of reference populations is too numerous, or when the admixture events are deep in time, all of which are increasingly unavoidable in large biobanks. Here, we present a new LAI method, Recomb-Mix. Recomb-Mix integrates the elements of existing methods of the site-based Li and Stephens model and introduces a new graph collapsing trick to simplify counting paths with the same ancestry label readout. Through comprehensive benchmarking on various simulated datasets, we show that Recomb-Mix is more accurate than existing methods in diverse sets of scenarios while being competitive in terms of resource efficiency. We expect that Recomb-Mix will be a useful method for advancing genetics studies of admixed populations.

bioinformatics↗

bioRxiv2023

ProxyTyper: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

One of the major challenges in genomic data sharing is protecting the privacy of participants. Numerous studies demonstrated that genetic data and related summary statistics can be used for identifying individuals. These studies led to a strong chilling effect on researchers that hindered collaborative data sharing. Consequently, population-level genetic databases are often siloed in central repositories with complex and burdensome data usage agreements. While cryptographic methods that are provably secure have been developed, these methods require high-level expertise in security and depend on large computational resources. To fill the methodological gap in this domain, we present ProxyTyper, a set of data protection mechanisms to generate "proxy-panels" from sensitive haplotype-level genetic datasets. ProxyTyper uses haplotype sampling, allele hashing, and anonymization to protect the genetic variant coordinates, genetic maps, and chromosome-wide haplotypes. These mechanisms can provide strong deterrence against honest-but-curious entities and well-known re-identification and linking attacks. The proxy panels can be used as input to existing tools without any modifications to the underlying algorithms. We focus on building proxy-panels for genotype imputation to protect typed and untyped variants. We demonstrate that proxy-based imputation provides protection against well-known attacks with a minor decrease of imputation accuracy for variants in wide range of allele frequencies.

bioinformatics↗

bioRxiv2023

A hierarchical atlas of the human cerebellum for functional precision mapping

The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from 7 large-scale datasets, outperforming existing group atlasses. The new atlas has three further advantages: First, the atlas allows for precision mapping in individuals: The integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Second, we provide both asymmetric and symmetric versions of the atlas. The symmetric version, which is obtained by constraining the boundaries to be the same across hemispheres, is especially useful in studying functional lateralization. Finally, the regions are hierarchically organized across 3 levels, allowing analyses at the appropriate level of granularity. Overall, the new atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.

neuroscience↗

bioRxiv2023

A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets

One important barrier in the development of complex models of human brain organization is the lack of a large and comprehensive task-based neuro-imaging dataset. Therefore, current atlases of functional brain organization are mainly based on single and homogeneous resting-state datasets. Here, we propose a hierarchical Bayesian framework that can learn a probabilistically defined brain parcellation across numerous task-based and resting-state datasets, exploiting their combined strengths. The framework is partitioned into a spatial arrangement model that defines the probability of a specific individual brain parcellation, and a set of dataset-specific emission models that defines the probability of the observed data given the individual brain organization. We show that the framework optimally combines information from different datasets to achieve a new population-based atlas of the human cerebellum. Furthermore, we demonstrate that, using only 10 min of individual data, the framework is able to generate individual brain parcellations that outperform group atlases.

neuroscience↗

bioRxiv2023

FastRecomb: Fast inference of genetic recombination rates in biobank scale data

While rates of recombination events across the genome (genetic maps) are fundamental to genetic research, the majority of current studies only use one standard map. There is evidence suggesting population differences in genetic maps, and thus estimating population-specific maps are of interest. While the recent availability of biobank-scale data offers such opportunities, current methods are not efficient at leveraging very large sample sizes. The most accurate methods are still linkage-disequilibrium (LD)-based methods that are only tractable for a few hundred samples. In this work, we propose a fast and memory-efficient method for estimating genetic maps from population genotyping data. Our method, FastRecomb, leverages the efficient positional Burrows-Wheeler transform (PBWT) data structure for counting IBD segment boundaries as potential recombination events. We used PBWT blocks to avoid redundant counting of pairwise matches. Moreover, we used a panel smoothing technique to reduce the noise from errors and recent mutations. Using simulation, we found that FastRecomb achieves state-of-the-art performance at 10k resolution, in terms of correlation coefficients between the estimated map and the ground truth. This is mainly due to the fact that FastRecomb can effectively take advantage of large panels comprising more than hundreds of thousands of haplotypes. At the same time, other methods lack the efficiency to handle such data. We believe further refinement of FastRecomb would deliver more accurate genetic maps for the genetics community.

bioinformatics↗

bioRxiv2023

Minimal Positional Substring Cover: A Haplotype Threading Alternative to Li & Stephens Model

The Li & Stephens (LS) hidden Markov model (HMM) models the process of reconstructing a haplotype as a mosaic copy of haplotypes in a reference panel (haplotype threading). For small panels the probabilistic parameterization of LS enables modeling the uncertainties of such mosaics, and has been the foundational model for haplotype phasing and imputation. However, LS becomes inefficient when sample size is large (tens of thousands to millions), because of its linear time complexity (O(MN), where M is the number of haplotypes and N is the number of sites in the panel). Recently the PBWT, an efficient data structure capturing the local haplotype matching among haplotypes, was proposed to offer fast methods for giving some optimal solution (Viterbi) to the LS HMM. But the solution space of the LS for large panels is still elusive. Previously we introduced the Minimal Positional Substring Cover (MPSC) problem as an alternative formulation of LS whose objective is to cover a query haplotype by a minimum number of segments from haplotypes in a reference panel. The MPSC formulation allows the generation of a haplotype threading in time constant to sample size (O(N)). This allows haplotype threading on very large biobank scale panels on which the LS model is infeasible. Here we present new results on the solution space of the MPSC by first identifying a property that any MPSC will have a set of required regions, and then proposing a MPSC graph. In addition, we derived a number of optimal algorithms for MPSC, including solution enumerations, the Length Maximal MPSC, and h-MPSC solutions. In doing so, our algorithms reveal the solution space of LS for large panels. Even though we only solved an extreme case of LS where the emission probability is 0, our algorithms can be made more robust by PBWT smoothing. We show that our method is informative in terms of revealing the characteristics of biobank-scale data sets and can improve genotype imputation.

bioinformatics↗

bioRxiv2022

Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets

Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental and cognitive states in the healthy and disordered brain. However, the lack of accurate and precise estimations of comparable functional patterns across datasets, individuals, and ever-changing brain states in a way that captures both individual variation and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. We posit that using reliable network templates and advanced group-informed network estimation approaches to accurately and precisely obtain individualized (dynamic) networks that retain cross-subject correspondence while maintaining subject-specific information is one potential solution to overcome the aforementioned barrier when considering cross-study comparability, independence of subject-level estimates, the limited data available in single studies, and the low signal-to-noise ratio (SNR) of rsfMRI. Toward this goal, we first obtained a reliable and replicable network template. We combined rsfMRI data of over 100k individuals across private and public datasets and selected around 58k that meet quality control (QC) criteria. We then applied multi-model-order independent component analysis (ICA) and subsampling to obtain reliable canonical intrinsic connectivity networks (ICNs) across multiple spatial scales. The selected ICNs (i.e., network templates) were also successfully replicated by independently analyzing the data that did not pass the QC criteria, highlighting the robustness of our adaptive template to data quality. We next studied the feasibility of estimating the corresponding subject-specific ICNs using a multivariate-spatially constrained ICA as an example of group-informed network estimation approaches. The results highlight that several factors, including ICNs themselves, data length, and spatial resolution, play key roles in successfully estimating the ICNs at the subject level. Large-scale ICNs, in general, require less data to achieve a specific level of spatial similarity with their templates (as well as within- and between-subject spatial similarity). Moreover, increasing data length can reduce an ICNs subject-level specificity, suggesting longer scans might not always be desirable. We also show spatial smoothing can alter results, and the positive linear relationship we observed between data length and spatial smoothness (we posit that it is at least partially due to averaging over intrinsic dynamics or individual variation) indicates the importance of considering this factor in studies such as those focused on optimizing data length. Finally, the consistency in the spatial similarity between ICNs estimated using the full-length of data and subset of it across different data lengths may suggest that the lower within-subject spatial similarity in shorter data lengths is not necessarily only defined by lower reliability in ICN estimates; rather, it can also be an indication of brain dynamics (i.e., different subsets of data may reflect different ICN dynamics), and as we increase the data length, the result approaches the average (also known as static) ICN pattern, and therefore loses its distinctiveness.

neuroscience↗

bioRxiv2022

mcPBWT: Space-efficient Multi-column PBWT Scanning Algorithm for Composite Haplotype Matching

Positional Burrows-Wheeler Transform (PBWT) is a data structure that supports efficient algorithms for finding matching segments in a panel of haplotypes. It is of interest to study the composite patterns of multiple matching segments or blocks arranged contiguously along a same haplotype as they can indicate recombination crossover events, gene-conversion tracts, or, some-times, errors of phasing algorithms. However, current PBWT algorithms do not support search of such composite patterns efficiently. Here, we present our algorithm, mcPBWT (multi-column PBWT), that uses multiple synchronized runs of PBWT at different variant sites providing a "look-ahead" information of matches at those variant sites. Such "look-ahead" information allows us to analyze multiple contiguous matching pairs in a single pass. We present two specific cases of mcPBWT, namely double-PBWT and triple-PBWT which utilize two and three columns of PBWT respectively. double-PBWT finds two matching pairs combinations representative of crossover event or phasing error while triple-PBWT finds three matching pairs combinations representative of gene-conversion tract.

bioinformatics↗

bioRxiv2022

RaPID-Query for Fast Identity by Descent Search and Genealogical Analysis

The size of genetic databases has grown large enough such that, genetic genealogical search, a process of inferring familial relatedness by identifying DNA matches, has become a viable approach to help individuals finding missing family members or law enforcement agencies locating suspects. However, a fast and accurate method is needed to search an out-of-database individual against millions of individuals in such databases. Most existing approaches only offer all-vs-all within panel match. Some prototype algorithms offer 1-vs-all query from out-of-panel individual, but they do not tolerate errors. A new method, random projection-based identical-by-descent (IBD) detection (RaPID) query, referred as RaPID-Query, is introduced to make fast genealogical search possible. RaPID-Query method identifies IBD segments between a query haplotype and a panel of haplotypes. By integrating matches over multiple PBWT indexes, RaPID-Query method is able to locate IBD segments quickly with a given cutoff length while allowing mismatched sites in IBD segments. A single query against all UK biobank autosomal chromosomes can be completed within 2.76 seconds CPU time on average, with the minimum 7 cM IBD segment length and minimum 700 markers. Using the same criteria, RaPID-Query can achieve 0.099 false negative rate and 0.017 false positive rate at the same time on a chromosome 20 sequencing panel having 92,296 sites, which is comparable to the state-of-the-art IBD detection method Hap-IBD. For the relatedness degree separation experiments, RaPID-Query is able to distinguish up to fourth degree of the familial relatedness for a given individual pair, and the area under the receiver operating characteristic curve values are at least 97.28%. It is anticipated that RaPID-Query will make genealogical search convenient and effective, potentially with the integration of complex inference models.

bioinformatics↗

bioRxiv2022

Syllable-PBWT for space-efficient haplotype long-match query

The positional Burrows-Wheeler transform (PBWT) has led to tremendous strides in haplotype matching on biobank-scale data. For genetic genealogical search, PBWT-based methods have optimized the asymptotic runtime of finding long matches between a query haplotype and a predefined panel of haplotypes. However, to enable fast query searches, the full-sized panel and PBWT data structures must be kept in memory, preventing existing algorithms from scaling up to modern biobank panels consisting of millions of haplotypes. In this work, we propose a space-efficient variation of PBWT named Syllable-PBWT, which divides every haplotype into syllables, builds the PBWT positional prefix arrays on the compressed syllabic panel, and leverages the polynomial rolling hash function for positional substring comparison. With the Syllable-PBWT data structures, we then present a long match query algorithm named Syllable-Query. Compared to Algorithm 3 of Sanaullah et al. (2021), the most time- and space-efficient previously published solution to the long match query problem, Syllable-Query reduced the memory use by a factor of over 100 on both the UK Biobank genotype data and the 1000 Genomes Project sequence data. Surprisingly, the smaller size of our syllabic data structures allows for more efficient iteration and CPU cache usage, granting Syllable-Query even faster runtime than existing solutions. The implementation of our algorithm is available at https://github.com/ZhiGroup/Syllable-PBWT.

bioinformatics↗

bioRxiv2021

Evaluating brain parcellations using the distance controlled boundary coefficient

One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural or functional Magnetic Resonance Imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within-parcel to between-parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the Distance Controlled Boundary Coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting-state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task-based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting-state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi-modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct.

neuroscience↗

bioRxiv2021

MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning

With advances in high-throughput DNA sequencing, numerous genetic variants have been discovered in the human genome. One challenge we face is interpreting these variants to help in disease screening, diagnosis, and treatment. While multiple computational approaches have been proposed to improve our understanding of genetic variants, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare non-synonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). A recurrent neural network incorporating a +/- 1 codon window around the affected codon was combined with 28 high-level annotation scores and allele frequency features to develop the two proposed models. We use independent test datasets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from the nsSNV-based and the nfINDEL-based models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. In addition, we provide pre-computed MetaRNN scores for all possible human nsSNVs and a Linux executable file for a fast one-stop annotation of nsSNVs and nfINDELs. All the resources are available at http://www.liulab.science/MetaRNN.

bioinformatics↗

bioRxiv2020

Mutating PINK1 gene by paired truncated sgRNA/Cas9-D10A in Cynomolgus Monkey

Mutations of PINK1 cause early-onset Parkinsons disease (PD) with selective neurodegeneration in humans. However,current PINK1 knockout mouse and pig models are unable to recapitulate the typical neurodegenerative phenotypes observed in PD patients, indicating that it is essential to generate PINK1 disease models in non-human primates (NHPs) that are highly close to humans, to investigate the unique function of PINK1 in primate brains. Paired single guide RNA (sgRNA)/Cas9-D10A nickases and truncated sgRNA/Cas9, both enabling the reduction of the off-target effect without apparently compromising the on-target editing, are two optimized strategies in applying the CRISPR/Cas9 system to establish disease animal models. Here, we combined the two strategies and injected Cas9-D10A mRNA and two truncated sgRNAs into one-cell-stage cynomolgus zygotes to target PINK1 gene. We show precise and efficient gene editing of the target site in the three new born cynomolgus monkeys. The frame shift mutations of PINK1 in mutant fibroblasts leaded to the reduction of the mRNA, However, western-blot and immunofluorescencestaining confirmed the PINK1 proteinlevels werecomparable to that in the wild-type fibroblasts. We further reprogramed mutant fibroblast into induced pluripotent stem cells (iPSCs) and they show similar ability of differentiation into dopamine (DA) neurons. Taken together, our results showed that coinjection of Cas9-D10A nickase mRNA and sgRNA into one-cell-stage cynomolgus embryos enable the generation of human disease models in NHPs and the target editing by paired truncated sgRNA/Cas9-D10A in PINK1 gene exon 2 did not impact the protein expression.

genetics↗

bioRxiv2020

Meta-Align: A Novel HMM-based Algorithm for Pairwise Alignment of Error-Prone Sequencing Reads

BackgroundInsertion and deletion sequencing errors are relatively common in next-generation sequencing data and produce long stretches of mistranslated sequence. These frameshifting errors can cause very serious damages to downstream data analysis of reads. However, it is possible to obtain more precise alignment of DNA sequences by taking into account both coding frame and sequencing errors estimated by quality scores. ResultsHere we designed and proposed a novel hidden Markov model (HMM)-based pairwise alignment algorithm, Meta-Align, that aligns DNA sequences in the protein space, incorporating quality scores from the DNA sequences and allowing frameshifts caused by insertions and deletions. Our model is based on both an HMM transducer of a pair HMM and profile HMMs for all possible amino acid pairs. A Viterbi algorithm over our model produces the optimal alignment of a pair of metagenomic reads taking into account all possible translating frames and gap penalties in both the protein space and the DNA space. To reduce the sheer number of states of this model, we also derived and implemented a computationally feasible model, leveraging the degeneracy of the genetic code. In a benchmark test on a diverse set of simulated reads based on BAliBASE we show that Meta-Align outperforms TBLASTX which compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database using the BLAST algorithm. We also demonstrate the effects of incorporating quality scores on Meta-Align. ConclusionsMeta-Align will be particularly effective when applied to error-prone DNA sequences. The package of our software can be downloaded at https://github.com/shravan-repos/Metaalign.

bioinformatics↗

bioRxiv2020

d-PBWT: dynamic positional Burrows-Wheeler transform

Durbins PBWT, a scalable data structure for haplotype matching, has been successfully applied to identical by descent (IBD) segment identification and genotype imputation. Once the PBWT of a haplotype panel is constructed, it supports efficient retrieval of all shared long segments among all individuals (long matches) and efficient query between an external haplotype and the panel. However, the standard PBWT is an array-based static data structure and does not support dynamic updates of the panel. Here, we generalize the static PBWT to a dynamic data structure, d-PBWT, where the reverse prefix sorting at each position is represented by linked lists. We developed efficient algorithms for insertion and deletion of individual haplotypes. In addition, we verified that d-PBWT can support all algorithms of PBWT. In doing so, we systematically investigated variations of set maximal match and long match query algorithms: while they all have average case time complexity independent of database size, they have different worst case complexities, linear time complexity with the size of the genome, and dependency on additional data structures.

bioinformatics↗

bioRxiv2019

Personalized genealogical history inferred from biobank-scale IBD segments

When modern biobanks collect genotype information for a significant fraction of a population, dense genetic connections of a person can be traced using identity by descent (IBD) segments. These connections offer opportunities to characterize individuals in the context of the underlying populations. Here, we conducted an individual-centric analysis of IBDs among the UK Biobank participants that represent 0.7% of the UK population. On average, one UK individual shares IBDs over 5 cM with 14,000 UK Biobank participants, which we refer to as "cousins". Using these segments, approximately 80% of a persons genome can be reconstructed. Also, using changes of cousin counts sharing IBDs at different lengths, we identified a group, potentially British Jews, who has a distinct pattern of familial expansion history. Finally, using the enrichment of cousins in ones neighborhood, we identified regional variations of personal preference favoring living closer to ones extended families. In summary, our analysis revealed genetic makeup, personal genealogical history, and social behaviors at population scale, opening possibilities for further studies of individuals genetic connections in biobank data.

genetics↗