Biology subjects

Koo, B.

Publications and source records attributed to Koo, B..

15 recordsLinked to original sources

bioRxiv2026

Manta and devil ray genome assemblies uncover signatures of pelagic adaptation

Manta and devil rays (Mobulidae) are iconic marine megafauna highly adapted to life in the open ocean, with forward-facing mouths, enlarged pectoral fins and specialised filter-feeding apparatus. These innovations have enabled mobulids to occupy vast pelagic habitats, yet the genetic changes underlying them remain poorly understood. Here we use a combination of chromatin conformation capture, PacBio HiFi and Nanopore long-reads to generate highly contiguous annotated chromosome-level reference genomes for two mobulid species: the Atlantic pygmy devil ray (Mobula hypostoma) and the Atlantic manta ray (Mobula yarae). The assemblies span 3.5 and 4.0 Gb, with contig N50s of 23.3 and 7.3 Mb, respectively. We combine these into a Progressive Cactus whole genome alignment with benthic batoid outgroups to characterise the landscape of structural variation and identify candidate molecular regions associated with pelagic adaptation. This approach revealed that over 1.5 million structural rearrangements occurred along the ancestral mobulid lineage, with deletions encompassing three times more sequence compared to the benthic sister lineage. Genes affected by mobulid specific deletions were significantly enriched for neurogenesis pathways linked to craniofacial patterning, with several deletions overlapping the putative promoter sequences of craniofacial and roof of mouth development genes. Our findings highlight candidate developmental pathways through which ancestral deletions may have driven mobulid adaptations, while the high-quality genome resources provide a foundation for comparative and functional studies in Chondrichthyes.

evolutionary biology↗

bioRxiv2025

The complete genome of a songbird

Bird genomes are the smallest among amniotes, but remain challenging to assemble due to their structural complexity. This study presents the first fully phased, diploid, telomere-to-telomere (T2T) reference genome for the zebra finch (Taeniopygia guttata), a model organism for neuroscience and evolutionary genomics. Combining multiple sequencing strategies resulted in closing nearly all gaps, adding [~]90 Mbp of previously missing sequence (7.8%). This includes T2T assemblies for all microchromosomes, including dot chromosomes, and the previously almost entirely missing chr16. The T2T genome is comprehensively annotated for genes, repeats, structural variants, and long-read methylation calls. Complete centromeric structures were assembled and annotated along with kinetochore binding sites. Relative to the previous high-quality reference of the Vertebrate Genomes Project, 2,778 (8.51%) previously unassembled or unannotated genes were identified, of which 9% overlap with segmental duplications. This first complete genome of a songbird, now the new public reference, illuminates avian genome architecture and function.

genomics↗

bioRxiv2025

Evaluation of sequencing reads at scale using rdeval

MotivationLarge sequencing data sets are produced and deposited into public archives at unprecedented rates. The availability of tools that can reliably and efficiently generate and store sequencing read summary statistics has become critical. ResultsAs part of the effort by the Vertebrate Genomes Project (VGP) to generate high-quality reference genomes at scale, we sought to address the community need for efficient sequencing data evaluation by developing rdeval, a standalone tool to quickly compute and dynamically display sequencing read metrics. Rdeval can either run on the fly or store key sequence data metrics in read sketches, with dramatic compression gains. Statistics can then be efficiently recalled from sketches for additional processing. Rdeval can convert fa*[.gz] files to and from other popular formats including BAM and CRAM for better compression. Overall, while CRAM achieves the best compression, the gain is marginal, and BAM achieves the best compromise between data compression and accessing speed. Rdeval also generates a detailed visual report with multiple data analytics that can be exported in various formats. We showcase rdevals functionalities using human and VGP read data from different sequencing platforms and species. For PacBio long-read sequencing, our analysis shows dramatic improvements both in read length and quality over time, and a benefit of additional coverage for genome assembly. Availability and implementationRdeval is implemented in C++ for data processivity and in R for data visualization. Precompiled releases (Linux, MacOS, Windows) and commented source code for rdeval are available under MIT license at https://github.com/vgl-hub/rdeval. Documentation is available using ReadTheDocs (https://rdeval-documentation.readthedocs.io). Rdeval is also available in Bioconda and in Galaxy (https://usegalaxy.org). An automated test workflow ensures the consistency of software updates. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

mTORC1 and STAT3 signalings are indispensable for in vitro TGFβ1-dependent Three-Dimensional (3D) tendon construct.

The Transforming Growth Factor-{beta} 1 (TGF{beta}1) is a well-known growth factor involved in tenocytes differentiation, extracellular matrix production, and cell fate regulation. We previously demonstrated that TGF{beta}1 has a critical role in the formation of in vitro 3D tendon constructs using mouse primary tendon cells. In this study, we investigated the function of Mammalian target of rapamycin complex 1 (mTORC1) and Signal transducer and activator of transcription 3 (STAT3) signaling in the formation of TGF{beta}1-induced in vitro 3D tendon constructs using specific inhibitors, rapamycin (mTORC1 inhibitor) and stattic (stat3 inhibitor). TGF{beta}1 treatment activated both mTORC1 and STAT3 in 3D tendon constructs. The treatment of rapamycin or stattic partly attenuated TGF{beta}1-dependent cellular, molecular, and matrix changes in the 3D tendon constructs. Overall, this study demonstrates that mTORC1-STAT3 signaling axis is a downstream mediator of TGF{beta}1 signaling in the formation of 3D tendon constructs.

bioengineering↗

bioRxiv2023

Deep learning-based survival prediction using DNA methylation-derived 3D genomic information

Three-dimensional (3D) genome states are closely related to cancer development. Nonetheless, the 3D genome information has not been clinically utilized to the best of our knowledge, due to the costly production of Hi-C data which is a manifest source of 3D genome information. Therefore, there is a need for a novel metric computable from a 3D genome-related data which is more easily accessible for the clinical utilization of 3D genome information. We here propose a method to extract 3D genome-aware epigenetic features from DNA methylation data and use these features for a deep learning-based survival prediction. These features are derived from the 3D genome structures which are rebuilt from the DNA methylation data in an individual level. The results showed that usage of 3D genome-aware features contributed to more accurate risk prediction across seven cancer types, suggesting the effectiveness of the knowledge about 3D genome structure embedded in these features. The deeper biological investigation revealed that altered DNA methylation level in risk-high group could be related to the anomalously activated genes involved in cancer-related pathways. Altogether, the risks predicted from 3D genome-aware epigenetic features showed its significance as a survival predictor in seven cancer types, along with its biological importance.

bioinformatics↗

bioRxiv2023

AMLs harboring DNMT3A-destabilizing variants show increased intratumor DNA methylation heterogeneity at bivalent chromatin domains

The mechanistic link between the complex mutational landscape of de novo methyltransferase DNMT3A and the pathology of acute myeloid leukemia (AML) has not been clearly elucidated so far. A recent discovery on the catalogue of DNMT3A-destabilizing mutations throughout the DNMT3A gene as well as the oligomerization-dependent catalytic property of DNMT3A prompted us to investigate the common effect of DNMT3A-destabilizing mutations (DNMT3AINS) on the genomewide methylation patterns of AML cells. In this study, we describe the characteristics of DNMT3AINS AML methylomes through the comprehensive computational analyses on three independent AML cohorts. As a result, we show that methylomes of DNMT3AINS AMLs are considerably different from those of DNMT3AR882 AMLs in that they exhibit both locally disordered DNA methylation states and increased across-cell DNA methylation heterogeneity in bivalent chromatin domains. This increased epigenetic heterogeneity was functionally associated with heterogeneous expression of membrane-associated factors shaping stem cell niche, implying the diversification of the modes of leukemic stem cell-niche interactions. We also present that the level of methylation disorder at bivalent domains predicts the response of AML cells to hypomethylating agents through cell line- and patient-level analyses, which supports that the survival of AML cells depends on stochastic DNA methylations at bivalent domains. Altogether, our work provides a novel mechanistic model suggesting the genomic origin of the aberrant epigenomic heterogeneity in disease conditions.

cancer biology↗

bioRxiv2022

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments

MotivationPhased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often overlooked by computing average methylation levels at CpG sites. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. ResultsWe benchmark the performance of Metheor against existing code implementation for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing the same results with the original implementation. AvailabilitySource code for Metheor is at https://github.com/dohlee/metheor and is freely available for non-commercial users. Contactsunkim.bioinfo@snu.ac.kr

bioinformatics↗

bioRxiv2021

Stress granules are shock absorbers that prevent excessive innate immune responses to dsRNA

Proper defense against microbial infection depends on the controlled activation of the immune system. This is particularly important for the RIG-I-like receptors (RLRs), which recognize viral dsRNA and initiate antiviral innate immune responses with the potential of triggering systemic inflammation and immunopathology. Here we show that stress granules (SGs), molecular condensates that form in response to various stresses including viral dsRNA, play key roles in controlled activation of RLR signaling. Without the SG nucleators G3BP1/2 and UBAP2L, dsRNA triggers excessive inflammation and immune-mediated apoptosis. In addition to exogenous dsRNA, we find that host-derived dsRNA generated in response to ADAR1 deficiency is also controlled by SG biology. Intriguingly, SGs can function beyond immune control by suppressing viral replication independent of the RLR pathway. These observations thus highlight the multi-functional nature of SGs as cellular "shock absorbers" that converge on protecting cell homeostasis-by dampening both toxic immune response and viral replication.

immunology↗

bioRxiv2019

Associations between Processing Speed and Psychopathology in a Transdiagnostic, Pediatric Sample

ObjectiveThe present study sought to examine the relationships between processing speed (PS), mental health disorders, and learning disorders. Prior work has tended to explore relationships between PS deficits and individual diagnoses (i.e., anxiety, autism, ADHD, depressive) in isolation of one another, often relying on relatively modest sample sizes. In contrast, the present work simultaneously investigated associations between PS deficits and these diagnoses, along with specific learning disabilities (i.e., reading, math), in a large-scale, transdiagnostic, community self-referred sample.\n\nMethodA total of 843 children, ages 8-16 were included from the Healthy Brain Network (HBN) Biobank. Given the presence of four PS tasks in HBN, principal component analysis (PCA) was employed to create a composite measure that represented the shared variance of the four PS tasks, referred to as PC1. Intraclass correlation coefficient (ICC) between the four PS measures, as well as PC1, were calculated to assess reliability. We then used multiple linear regression models to assess specific relationships between PS deficits and psychiatric diagnoses.\n\nResultsICCs were moderate between WISC-V tasks (0.663), and relatively modest between NIH Toolbox Pattern Comparison and other PS scales (0.14-0.27). Regression analyses revealed specific significant relationships between PS and reading and math disabilities, ADHD-inattentive type (ADHD-I), and ADHD-combined type (ADHD-C). Secondary analyses accounting for inattention dimensionally diminished associations with ADHD-C, but not ADHD-I or specific learning disability subtypes. The present study did not find a significant relationship with Autism Spectrum Disorder after accounting for inattentive symptoms. Consistent with prior work, demographic variables, including sex, socioeconomic status, and motor control exhibited independent relationships with PC1 as well.\n\nDiscussionThis study provided a comprehensive examination of PS, mental health disorders, and learning disabilities through a transdiagnostic approach. Implications for understanding how PS interacts with a highly heterogeneous childhood sample, as well as the need for increased focus on detection of affected populations are discussed.

developmental biology↗

bioRxiv2018

Passive Audio Vocal Capture and Measurement in the Evaluation of Selective Mutism

Selective Mutism (SM) is an anxiety disorder often diagnosed in early childhood and characterized by persistent failure to speak in certain social situations but not others. Diagnosing SM and monitoring treatment response can be quite complex, due in part to changing definitions of and scarcity of research about the disorder. Subjective self-reports and parent/teacher interviews can complicate SM diagnosis and therapy, given that similar speech problems of etiologically heterogeneous origin can be attributed to SM. The present perspective discusses the potential for passive audio capture to help overcome psychiatrys current lack of objective and quantifiable assessments in the context of SM. We present evidence from two pilot studies indicating the feasibility of using a digital wearable device to quantify child vocalization features affected by SM. We also highlight limitations in the design and implementation of this preliminary work that can help guide future efforts.

neuroscience↗

bioRxiv2017

An open resource for nonhuman primate imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience.\n\nUnfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys). We also outline the unique pitfalls and challenges that should be considered in the analysis of the non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

neuroscience↗

bioRxiv2017

Assessment of the impact of shared data on the scientific literature

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used pay to play models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally.

bioinformatics↗

bioRxiv2017

Assessing actimeters for inclusion in the Healthy Brain Network

BackgroundThe Healthy Brain Network is an openly shared pediatric psychiatric biobank with a target of 10,000 participants between the ages of 5 and 21, inclusively In adding ecological actimetry to the Healthy Brain Network, we intend to use appropriate, accurate, reliable tools. Currently a wide range of personal activity trackers are commercially available, providing a wide variety of sensor configurations. For many of these devices, accelerometry provides the basis of measuring both physical activity and sleep with comparable derivative measures.\n\nResultsIn order to include an ecological biotracker in the Healthy Brain Network protocol, we first evaluated the specifications of a variety of actimeters available for purchase. We then acquired physical instances of 5 of these devices (ActiGraph wGT3X-BT, Empatica Embrace, Empatica E4, GENEActiv Original, and Wavelet Wristband) and wore each of them in our daily lives, annotating our activities and evaluating the reasonableness of the data from each device and the logistical affordances of each device.\n\nConclusionsWe decided that the ActiGraph wGT3X-BT is the most appropriate device for inclusion in the Healthy Brain Network. However, none of the devices we evaluated was clearly superior or inferior to the rest; rather, each device seems to have use cases in which that device excels beyond the others.

bioinformatics↗

bioRxiv2017

The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

neuroscience↗

bioRxiv2017

The Healthy Brain Network Biobank: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics, and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n = 664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).

neuroscience↗