Biology subjects

Garimella, K. V.

Publications and source records attributed to Garimella, K. V..

10 recordsLinked to original sources

bioRxiv2026

Genetic and Epigenetic Landscape of Self-Identified Hispanics/Latino in All Of Us

Human genetic studies still underrepresent many populations, limiting discovery and interpretation of medically relevant variation. Extensive admixture in self-identified Hispanic/Latino populations increases the need for representative reference data and ancestry-aware interpretation of medically relevant variation. We used long-read sequencing to analyze genetic variation, ancestry, and DNA methylation in 1,490 self-identified Hispanic/Latino participants from the All of Us Research Program. We identified 10 million novel variants and more than 100 candidate phenotype associations, with fine-mapping prioritizing structural variants over nearby single-nucleotide variants in more than half. We linked 3,329 tandem repeats to DNA methylation, resolved widespread allele-specific methylation, and identified ancestry-linked regulation across paralogous genes. These results reveal previously inaccessible genetic and regulatory variation and show the value of long-read sequencing for diverse populations.

genetics↗

bioRxiv2025

Himito: a Graph-based Toolkit for Mitochondrial Genome Analysis using Long Reads

Understanding the genetic and epigenetic regulation of mitochondrial DNA (mtDNA) is essential for elucidating mechanisms of aging and disease. Long-read sequencing can span the entire mitochondrial genome and directly capture base-modification signals, yet analytical tools for such data remain limited. We developed Himito, a graph-based toolkit for analyzing mitochondrial genome using long reads. Himito filters reads originating from nuclear mitochondrial insertions (NUMTs), constructs a sequence graph to represent mtDNA diversity, assembles primary haplotypes, calls variants, and analyzes 5-methylcytosine (5mC) modifications within a unified framework. Benchmarking on high-quality reference datasets shows Himito achieves superior performance in assembly and variant calling compared with existing tools. Applied to the All of Us (AoU) v8 dataset, Himito identified pathogenic mtDNA variants, revealed population-scale haplogroup diversity, and uncovered age-related genetic and epigenetic patterns. These results demonstrate that long-read sequencing, combined with graph-based analysis, enables integrated characterization of mitochondrial genomic and epigenomic variation. Himito is available at https://github.com/broadinstitute/Himito.

bioinformatics↗

bioRxiv2024

Fast and exact gap-affine partial order alignment with POASTA

MotivationPartial order alignment is a widely used method for computing multiple sequence alignments, with applications in genome assembly and pangenomics, among many others. Current algorithms to compute the optimal, gap-affine partial order alignment do not scale well to larger graphs and sequences. While heuristic approaches exist, they do not guarantee optimal alignment and sacrifice alignment accuracy. ResultsWe present POASTA, a new optimal algorithm for partial order alignment that exploits long stretches of matching sequence between the graph and a query. We benchmarked POASTA against the state-of-the-art on several diverse bacterial gene datasets and demonstrated an average speed-up of 4.1x and up to 9.8x, using less memory. POASTAs memory scaling characteristics enabled the construction of much larger POA graphs than previously possible, as demonstrated by megabase-length alignments of 342 Mycobacterium tuberculosis sequences. Availability and implementationPOASTA is available on Github at https://github.com/broadinstitute/poasta.

bioinformatics↗

bioRxiv2023

Resolution of ring chromosomes, Robertsonian translocations, and complex structural variants from long-read sequencing and telomere-to-telomere assembly

The capacity to resolve structural variants (SVs) at sequence resolution in highly repetitive genomic regions has long been intractable. Consequently, the properties, origins, and functional effects of multiple classes of complex rearrangement are unknown. To resolve these challenges, we leveraged recent technical milestones: 1) Oxford-Nanopore (ONT) sequencing; 2) the gapless Telomere-to-Telomere (T2T) genome assembly; and 3) a novel tool to discover large-scale rearrangements from long-reads. We applied these technologies across 13 patients with ring chromosomes, Robertsonian translocations, and complex balanced SVs that were unresolved by short-read sequencing. We resolved 10 of 13 events, including ring chromosomes, the complex SVs, and a Robertsonian translocation. Multiple breakpoints were localized to highly repetitive regions inaccessible to short-read alignment, such as acrocentric p-arms, ribosomal DNA arrays, and telomeric repeats, and involved complex structures such as a deletion-inversion and interchromosomal dispersed duplications. We also leveraged ONT native methylation detection to discover phased differential methylation in a gene promoter proximal to a ring fusion site, suggesting a long-range positional effect with heterochromatin spreading. Breakpoint sequences were consistent with common mechanisms of SV formation, including microhomology-mediated mechanisms, non-homologous end-joining, and non-allelic homologous recombination. These methods provide some of the first glimpses into the sequence resolution of ring chromosomes and Robertsonian translocations and illuminate the structural diversity of chromosomal rearrangements with implications for molecular diagnosis and genome biology. HighlightsO_LICryptic rearrangements revealed in repetitive and previously inaccessible regions C_LIO_LIAligning long-reads to telomere-to-telomere assembly enabled breakpoint discovery C_LIO_LIFirst sequence resolution of a Robertsonian translocation breakpoint C_LIO_LIHaplotype-specific methylation changes associated with ring formation C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/555775v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@188cef8org.highwire.dtl.DTLVardef@cc2ed5org.highwire.dtl.DTLVardef@116b7e6org.highwire.dtl.DTLVardef@837f73_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Whole-genome long-read sequencing downsampling and its effect on variant calling precision and recall

Advances in long-read sequencing (LRS) technology continue to make whole-genome sequencing more complete, affordable, and accurate. LRS provides significant advantages over short-read sequencing approaches, including phased de novo genome assembly, access to previously excluded genomic regions, and discovery of more complex structural variants (SVs) associated with disease. Limitations remain with respect to cost, scalability, and platform-dependent read accuracy and the tradeoffs between sequence coverage and sensitivity of variant discovery are important experimental considerations for the application of LRS. We compare the genetic variant calling precision and recall of Oxford Nanopore Technologies (ONT) and PacBio HiFi platforms over a range of sequence coverages. For read-based applications, LRS sensitivity begins to plateau around 12-fold coverage with a majority of variants called with reasonable accuracy (F1 score above 0.5), and both platforms perform well for SV detection. Genome assembly increases variant calling precision and recall of SVs and indels in HiFi datasets with HiFi outperforming ONT in quality as measured by the F1 score of assembly-based variant callsets. While both technologies continue to evolve, our work offers guidance to design cost-effective experimental strategies that do not compromise on discovering novel biology.

genomics↗

bioRxiv2022

A beary good genome: Haplotype-resolved, chromosome-level assembly of the brown bear (Ursus arctos)

The brown bear (Ursus arctos) is the second largest and most widespread extant terrestrial carnivore on Earth and has recently emerged as a medical model for human metabolic diseases. Here, we report a fully-phased chromosome-level assembly of a male North American brown bear built by combining Pacific Biosciences (PacBio) HiFi data and publicly available Hi-C data. The final genome size is 2.47 Gigabases (Gb) with a scaffold and contig N50 length of 70.08 and 43.94 Mb, respectively. BUSCO analysis revealed that 94.5% of single-copy orthologs from mammalia were present in the genome (the highest of any ursid genome to date). Repetitive elements accounted for 44.48% of the genome and a total of 20,480 protein coding genes were identified. Based on whole genome alignment, the brown bear is highly syntenic with the polar bear, and our phylogenetic analysis of 7,246 single-copy BUSCOs supports the currently proposed species tree for Ursidae. This highly contiguous genome assembly will support future research on both the evolutionary history of the bear family and the physiological mechanisms behind hibernation, the latter of which has broad medical implications. SignificanceBrown bears (Ursus arctos) are the most widespread, large terrestrial carnivore on the planet and represent an interesting example of speciation through hybridization, as well as a medical model for sedentary lifestyle-related disease. Although a previous genome for a brown bear has been published, the reported contig N50 was low (only [~]530 kb), despite being scaffolded into putative chromosomes. Genomes of this quality limit the accuracy of analyses which rely on long contiguous stretches of the genome to be assembled (such as with many demographic analyses) as well as attempts at connecting genotype to phenotype (such as in association analyses). In order to support studies on both the complex hybridization history of the brown bear and investigations into medically-relevant phenotypes, we generated a fully-phased, chromosome-level assembly from a male grizzly bear. The genome has a total size of 2.47 Gb and 90% of the genome is contained in 36 scaffolds, roughly corresponding to one autosome per scaffold. This high-quality genome will enable studies across a variety of disciplines, including conservation, evolution, and medicine.

genomics↗

bioRxiv2022

A deep learning framework for structural variant discovery and genotyping

Structural variants (SV) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine and our understanding of human genetics. Existing SV callers rely on hand-engineered features and heuristics to model SVs, which cannot easily scale to the vast diversity of SV types nor fully harness all the information available in sequencing datasets. Since deep neural networks can learn complex abstractions directly from the data, they offer a promising approach for general SV discovery. Here we propose an extensible deep learning framework, Cue, to call and genotype SVs. At a high level, Cue converts sequence alignments to multi-channel images that capture multiple SV-informative signals and uses a stacked hourglass convolutional neural network to predict the type, genotype, and genomic locus of the SVs captured in each image. We show that Cue outperforms the state of the art in the detection of five classes of SVs (including two types of complex SVs and subclonal SVs) on synthetic and real short-read data and that it can be easily extended to other sequencing platforms, such as long and linked read sequencing technologies, while achieving competitive performance. By design, Cue can also be automatically extended to support new SV classes: this versatility is crucial as novel SV types are discovered in ongoing population-scale sequencing initiatives.

bioinformatics↗

bioRxiv2021

High-throughput RNA isoform sequencing using programmable cDNA concatenation

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.

genetics↗

bioRxiv2019

Detection of simple and complex de novo mutations without, with, or with multiple reference sequences

The characterization of de novo mutations in regions of high sequence and structural diversity from whole genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, where short-reads do not capture the long-range context required for resolution, and mapping approaches, where improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of genetic variant. We construct multi-sample, coloured de Bruijn graphs from shortread data for all samples, align long-read-derived haplotypes and multiple reference data sources to restore graph connectivity information, and call variants using graph path-finding algorithms and a model for simultaneous alignment and recombination. We validate and evaluate the approach using extensive simulations and use it to characterize the rate and spectrum of de novo mutation events in 119 progeny from four Plasmodium falciparum experimental crosses, using long-read data on the parents to inform reconstructions of the progeny and to detect several known and novel non-allelic homologous recombination events.

bioinformatics↗

bioRxiv2017

Integrating long-range connectivity information into de Bruijn graphs

MotivationThe de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input.\n\nResultsWe present a novel assembly graph data structure: the Linked de Bruijn Graph (LdBG). Constructed by adding annotations on top of a de Bruijn graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn graph. With assembly simulations we demonstrate that the LdBG data structure outperforms both the de Bruijn graph and the String Graph Assembler (SGA). Finally we apply the LdBG to Klebsiella pneumoniae short read data to make large (12 kbp) variant calls, which we validate using PacBio sequencing data, and to characterise the genomic context of drug-resistance genes.\n\nAvailabilityLinked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, available under the MIT license at https://github.com/mcvean/mccortex.\n\nContactturner.isaac@gmail.com.

bioinformatics↗