Biology subjects

Jha, A.

Publications and source records attributed to Jha, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Multi-agent AI System for High Quality Metadata Curation at Scale

High-quality metadata is essential for downstream AI applications, yet metadata curation remains a persistent bottleneck in biomedical research. The core challenge lies in balancing quality and scalability. Manual curation delivers high quality, reliable annotations but is time-intensive and non-scalable; automated approaches, including those based on natural language processing, can scale but often fall short in accuracy and completeness. This tradeoff is particularly severe in public datasets, where metadata is frequently distributed across multiple sources for a single dataset. A notable example includes multi-omics datasets available on GEO and similar public repositories, which are often accompanied by associated publications. These supplementary sources can be leveraged to substantially enhance metadata quality, thereby supporting downstream applications such as classifier model development or the identification of biologically relevant cohorts, etc. We present a first-in-class multi-agent AI system that bridges the gap, achieving both high quality and scalability in metadata curation. Built on large-language models (LLMs), our system orchestrates a set of specialized agents that collaboratively extract, normalize, and infer critical metadata fields such as tissue, disease, cell line, sampling site, demographics, and experimental context from GEO entries, associated publications. A central orchestrator agent delegates tasks such as data retrieval, document parsing, ontology mapping, and context inference to expert sub-agents, enabling scalable, end-to-end automation. Applied to GEO data sets, a notoriously difficult metadata domain, our system achieves a 93% recall on average across 23 key fields (all original terms, normalized terms, and corresponding ontology ids) that include information about disease, tissue, treatment, donor-related information, outperforming existing automated baselines and approaches expert level quality. We also present a system that can easily scale to curate hundreds of metadata fields of interest with similar precision. This work demonstrates that an LLM-based multi-agent architecture can overcome traditional trade-offs in metadata curation, enabling both precision and scale, and offers a promising path forward for curating large public biomedical repositories for downstream AI applications.

scientific communication and education↗

bioRxiv2025

Canonical nuclear envelope protein emerin regulates structure and integrity of the erythrocyte plasma membrane

Red blood cells (RBC) are essential for the survival of aerobic organisms. Mutations in RBC plasma membrane proteins can give rise to a spectrum of diseases characterized by hemolysis, inefficient gas exchange, and anemia. We tested whether emerin (EMD), a nuclear envelope (NE) protein, is also expressed in anuclear cell types to regulate membrane structure and function. We find that emerin is localized to the RBC plasma membrane and regulates the localization of other membrane proteins involved in actin branching and cytoskeletal dynamics. Additionally, we find that loss of Emd in mice results in decreased RBC corpuscular volume and hemoglobin content in both sexes, as well as a male-specific increase in RBC number and hematocrit, consistent with other male-dominated EMD phenotypes. Along with decreased RBC size, we observed a reduction in the rate of osmotic lysis in Emd KO animals, particularly males. We found that human EMD mutations also have a statistically significant association with RBC phenotypes, namely MCV. Individuals with emerin-related Emery-Dreifuss muscular dystrophy (EDMD) also demonstrate a mild yet consistent RBC phenotype according to patient CBC data. Together, these data suggest that emerin can have cell-specific localization and functions independent of its canonical repertoire in nucleated cells.

cell biology↗

bioRxiv2025

Benchmarking overlapping community detection methods for applications in human connectomics

Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multi-functional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here we overcome this limitation by introducing a datadriven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human structural connectome, we find that the Order Statistics Local Optimization Method (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble and yields a seven-network decomposition of the human cortex. These modules are bridged by fifteen overlapping regions that generally sit at the apex of the putative cortical hierarchy--suggesting integrative, higher-order function-- with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.

neuroscience↗

bioRxiv2025

Generative modeling for RNA splicing predictions and design

Alternative splicing (AS) of pre-mRNA plays a crucial role in tissue-specific gene regulation, with disease implications due to splicing defects. Predicting and manipulating AS can therefore uncover new regulatory mechanisms and aid in therapeutics design. We introduce TrASPr+BOS, a generative AI model with Bayesian Optimization for predicting and designing RNA for tissue-specific splicing outcomes. TrASPr is a multi-transformer model that can handle different types of AS events and generalize to unseen cellular conditions. It then serves as an oracle, generating labeled data to train a Bayesian Optimization for Splicing (BOS) algorithm to design RNA for condition-specific splicing outcomes. We show TrASPr+BOS outperforms existing methods, enhancing tissue-specific AUPRC by up to 1.8 fold and capturing tissue-specific regulatory elements. We validate hundreds of predicted novel tissue-specific splicing variations and confirm new regulatory elements using dCas13. We envision TrASPr+BOS as a light yet accurate method researchers can probe or adopt for specific tasks.

bioinformatics↗

bioRxiv2025

CD44 and Ezrin restrict EGF receptor mobility to generate a novel spatial arrangement of cytoskeletal signaling modules driving bleb-based migration

Cells under high confinement form highly polarized hydrostatic pressure-driven, stable leader blebs that enable efficient migration in low adhesion, environments. Here we investigated the basis of the polarized bleb morphology of metastatic melanoma cells migrating in non-adhesive confinement. Using high-resolution time-lapse imaging and specific molecular perturbations, we found that EGF signaling via PI3K stabilizes and maintains a polarized leader bleb. Protein activity biosensors revealed a unique EGFR/PI3K activity gradient decreasing from rear-to-front, promoting PIP3 and Rac1-GTP accumulation at the bleb rear, with its antagonists PIP2 and RhoA-GTP concentrated at the bleb tip, opposite to the front-to-rear organization of these signaling modules in integrin-mediated mesenchymal migration. Optogenetic experiments showed that disrupting this gradient caused bleb retraction, underscoring the role of this signaling gradient in bleb stability. Mathematical modeling and experiments identified a mechanism where, as the bleb initiates, CD44 and ERM proteins restrict EGFR mobility in a membrane-apposed cortical actin meshwork in the bleb rear, establishing a rear-to-front EGFR-PI3K-Rac activity gradient. Thus, our study reveals the biophysical and molecular underpinnings of cell polarity in bleb-based migration of metastatic cells in non-adhesive confinement, and underscores how alternative spatial arrangements of migration signaling modules can mediate different migration modes according to the local microenvironment.

cell biology↗

bioRxiv2024

The role of inflammation in a humanized mouse model of transthyretin cardiac amyloidosis

BackgroundSystemic amyloidosis represents a group of protein-misfolding diseases that confer significant morbidity and mortality for millions of patients worldwide. Transthyretin cardiac amyloidosis (ATTR) is a particularly devastating amyloid disease that affects middle-aged and elderly individuals and leads to cardiomyopathy (ATTR-CM), which has a median survival of 2.5 to 3.5 years [1, 2]. ATTR-CM can be hereditary, leading to a more aggressive disease course in younger patients. The most prevalent TTR variant in the United States is V122I, which is found in 3-4 % of African Americans [3]. Despite the significant healthcare burden, ATTR-CM remains underdiagnosed due to a lack of disease awareness and limited diagnostic techniques [4]. Informative in vivo models have proven elusive during the past decade [5]. Moreover, there is no available treatment to reverse cardiac dysfunction due to amyloid fibril deposition [1, 6, 7]. Therefore, a better understanding of the molecular mechanisms of ATTR-CM is imperative to developing novel, effective therapies. Method and ResultsTo explore the pathogenesis of ATTR, we created a murine TTR knockout (TTR-KO) model expressing the human V122I TTR variant. To study the gender differences, both male and female TTR-KO mice were utilized in this study. Significant elevations of human TTR were observed in both male and female ATTR murine plasma post-injection 3 months (human TTR level (ng/ml) Male ATTR: 109.9 {+/-} 5.568; Male control: 28.17 {+/-} 7.010; p=0.0008, N=3 mice/group; Female ATTR: 127.5 {+/-} 32.43; Female control: 20.08 {+/-} 8.351; p=0.0327, N=3 mice/group) with preserved cardiac function (FS% Male ATTR: 26.07 {+/-} 3.667; Male control: 22.69 {+/-} 1.585; p=0.3712, N=6-8 mice/group; Female ATTR: 26.62 {+/-} 1.980; Female control: 31.25 {+/-} 4.482;p=0.3397, N=5-6 mice/group). Notably, the mouse model exhibited cardiac amyloid deposits confirmed by amyloidotic-specific Congo Red staining and Thioflavin T Staining. Transmission electron microscopy revealed both immature and mature amyloid fibrils in the extracellular matrix. RNA-sequencing of the ATTR mouse heart identified distinct transcriptomic patterns and conserved inflammation pathways similar to those seen in a cohort of human ATTR heart samples, including leukocyte transendothelial migration, T-cell receptor signaling, and apoptosis, along with upregulation of inflammatory markers CXCL-1/2/3 and CCL20, were observed in ATTR murine hearts. At the posttranslational level, we confirmed an increased level of CCL5 (MFI ATTR: 801 {+/-} 105; Control: 426{+/-} 64; p=0.0061, N=3 mice/group) in murine plasma post-injection 3 months by a luminance-based immunoassay. The CXCL- and CCL-chemokines family are critical for directing leukocytes to inflammation sites. ConclusionIn this study, we developed a humanized V122I ATTR mouse model with elevated circulating human TTR level and Congophilic amyloid deposits in the murine heart and kidneys. Our transcriptomic study suggested that inflammation may contribute to the ATTR-CM pathogenesis. Further studies are needed to decipher the precise interactions between inflammation and ATTR-CM. Highlights/Whats new/Clinical relevanceO_LIWe developed a humanized mouse model to replicate the multisystem complexity and clinical diversity associated with V122I ATTR-CM. C_LIO_LIOur study unveiled the pathogenic molecular mechanisms of amyloid deposition in ATTR-CM via a novel mouse model. C_LIO_LIWe identified signature inflammatory pathways that uncover potential therapeutic targets for ATTR-CM. C_LIO_LIOur ATTR mouse model allows for preclinical pharmacogenomic assessments of novel therapeutics, which will undoubtedly improve outcomes for ATTR-CM patients. C_LI

pathology↗

bioRxiv2024

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species

Nuclear DNA is organized into a compact three-dimensional (3D) structure that impacts critical cellular processes. High-throughput chromosome conformation capture (Hi-C) is the most widely used method for measuring 3D genome architecture, while linear epigenomic assays, such as ATAC-seq, DNase-seq, and ChIP-seq, are extensively employed to characterize epigenomic regulation. However, the integrative analysis of chromatin interactions and associated epigenomic regulation remains challenging due to the pairwise nature of Hi-C data, mismatched resolution between Hi-C and epigenomic assays, and inconsistencies among analysis tools. Here we propose HiCFoundation, a Hi-C-based foundation model for integrative analysis linking chromatin structure to downstream regulatory function. HiCFoundation is trained from hundreds of Hi-C assays encompassing 118 million contact matrix submatrices. The model achieves state-of-the-art performance on multiple types of 3D genome analysis, including reproducibility analysis, resolution enhancement, and loop detection. We further demonstrate the models generalizability through genome architecture analysis of 316 species. Notably, by enhancing low-coverage experimental Hi-C data, HiCFoundation reveals genome-wide loop loss during differentiation of hematopoietic stem and progenitor cells (HSPCs) to neutrophils. Additionally, HiCFoundation is able to predict multiple types of epigenomic activity from Hi-C input and further interprets the link between Hi-C input and epigenomic output to reveal the relationship between chromatin conformation and genome function. Finally, HiCFoundation can analyze single-cell Hi-C data, shedding light on genome structure at single-cell resolution. HiCFoundation thus provides a unified, e"cient, generalizable, and interpretable foundation for genome architecture, single-cell and multi-omics analysis across species, paving the path for systematically studying genome 3D architecture and its regulatory mechanisms.

bioinformatics↗

bioRxiv2024

Modeling Complex Animal Behavior with Latent State Inverse Reinforcement Learning

Understanding complex animal behavior is crucial for linking brain computation to observed actions. While recent research has shifted towards modeling behavior as a dynamic process, few approaches exist for modeling long-term, naturalistic behaviors such as navigation. We introduce discrete Dynamical Inverse Reinforcement Learning (dDIRL), a latent state-dependent paradigm for modeling complex animal behavior over extended periods. dDIRL models animal behavior as being driven by internal state-specific rewards, with Markovian transitions between the distinct internal states. Using expectation-maximization, we infer reward functions corresponding to each internal states and the transition probabilities between them, from observed behavior. We applied dDIRL to water-starved mice navigating a labyrinth, analyzing each animal individually. Our results reveal three distinct internal states sufficient to describe behavior, including a consistent water-seeking state occupied for less than half the time. We also identified two clusters of animals with different exploration patterns in the labyrinth. dDIRL offers a nuanced understanding of how internal states and their associated rewards shape observed behavior in complex environments, paving the way for deeper insights into the neural basis of naturalistic behavior.

neuroscience↗

bioRxiv2024

Metastatic organotropism in small cell lung cancer

Metastasis is the leading cause of cancer-related deaths, yet its regulatory mechanisms are not fully understood. Small-cell lung cancer (SCLC) is the most metastatic form of lung cancer, with most patients presenting with widespread disease, making it an ideal model for studying metastasis. However, the lack of suitable preclinical models has limited such studies. We utilized rapid autopsy-derived tumors to develop xenograft models that mimic key features of SCLC, including histopathology, rapid and widespread development of metastasis to the liver, brain, adrenal, bone marrow, and kidneys within weeks, and response to chemotherapy. By integrating in vivo lineage selection with comprehensive bulk and single cell multiomic profiling of transcriptomes and chromatin accessibility, we identified critical cellular programs driving metastatic organotropism to the liver and brain, the most common sites of SCLC metastasis. Our findings reveal the key role of nuclear-cytoskeletal interactions in SCLC liver metastasis. Specifically, the loss of the nuclear envelope protein lamin A/C, encoded by the LMNA gene, increased nuclear deformability and significantly increased the incidence of liver metastasis. Human liver metastases exhibited reduced LMNA expression compared to other metastatic sites, correlating with poorer patient outcomes and increased mortality. This study introduces novel preclinical models for SCLC metastasis and highlights pathways critical for organ-specific metastasis, offering new avenues for the development of targeted therapies to prevent or treat metastatic disease.

cancer biology↗

bioRxiv2024

Active reconfiguration of neural task states

The ability to switch between tasks is a core component of human intelligence, yet a mechanistic understanding of this capacity has remained elusive. Long-standing debates over how task switching is influenced by preparation for upcoming tasks or interference from previous tasks have been difficult to resolve without quantitative neural predictions. We advance this debate by using state-space modeling to directly compare the latent task dynamics in task-optimized recurrent neural networks and human electroencephalographic recordings. Over the inter-trial interval, both networks and brains converged into a neutral task state, a novel control strategy that reconciles the role of preparation and interference in task switching. These findings provide a quantitative account of cognitive flexibility and a promising paradigm for bridging artificial and biological neural networks.

neuroscience↗

bioRxiv2024

Machine learning-optimized targeted detection of alternative splicing

RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

genomics↗

bioRxiv2024

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC

Three-dimensional nuclear DNA architecture comprises well-studied intra-chromosomal (cis) folding and less characterized inter-chromosomal (trans) interfaces. Current predictive models of 3D genome folding can effectively infer pairwise cis-chromatin interactions from the primary DNA sequence but generally ignore trans contacts. There is an unmet need for robust models of trans-genome organization that provide insights into their underlying principles and functional relevance. We present TwinC, an interpretable convolutional neural network model that reliably predicts trans contacts measurable through proximity ligation-dependent (in situ and intact Hi-C) and independent (DNA SPRITE) genome-wide chromatin conformation assays.. TwinC uses a paired sequence design from replicate Hi-C experiments to learn single base pair relevance in trans interactions across two stretches of DNA. The method achieves high predictive accuracy (AUROC=0.80) on a cross-chromosomal test set from in situ and intact Hi-C experiments in heart tissue. Furthermore, we train TwinC using in situ Hi-C data from the widely used GM12878 cell line and validate its performance with orthogonal DNA SPRITE assay in the same cell type. Mechanistically, the neural network learns the importance of compartments, chromatin accessibility, clustered transcription factor binding and G-quadruplexes in forming trans contacts. In summary, TwinC models and interprets trans genome architecture, shedding light on this poorly understood aspect of gene regulation.

bioinformatics↗

bioRxiv2024

State of India's Birds 2023: A framework to leverage semi-structured citizen science for bird conservation

Birds and their habitats are threatened with extinction around the world. Regional assessments of the State of Birds are a vital means to prioritize data-driven conservation action by informing national and global policy. Such evaluations have traditionally relied on data derived from extensive, long-term, systematic surveys that require significant resources, limiting their feasibility to a few regions in the world. In the absence of such structured long-term datasets, semi-structured datasets have recently emerged as a promising alternative in other regions around the world. Semi-structured data are generated and uploaded by birdwatchers to citizen science platforms like eBird. Such data contain inherent biases because birdwatchers are not required to adhere to a fixed protocol. An evaluation of the status of birds from semi-structured data is therefore a difficult task that requires careful curation of data and the use of robust statistical methods to reduce errors and biases. In this paper, we present a methodology that was developed for this purpose, and was applied to produce the comprehensive State of Indias Birds (SoIB) 2023 report. SoIB 2023 assessed the status of 942 bird species in India by evaluating each species based on three metrics: 1) long-term change: change in abundance between the year 2022 and the year-interval pre-2000; 2) current annual trend: mean annual change in abundance from 2015 to 2022; and 3) distribution range size. We found evidence that 204 species have declined in the long term, and 142 species are currently declining. We present and discuss important insights about Indias birds that can guide research and conservation action in the region. We hope that the detailed methodology described here can act as a blueprint to produce State of Birds assessments from semi-structured citizen science datasets and springboard conservation action in many other regions where structured data is lacking but strong communities of birders exist. Open Research StatementThe primary data are already publicly available on eBird (Sullivan et al. 2014), and other data are already published in a GitHub repository (stateofindiasbirds 2024).

ecology↗

bioRxiv2024

Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk

Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.

genomics↗

bioRxiv2024

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and to predict the effects of cell-specific perturbations on neural activity or behavior. To overcome these limitations, we introduce the "cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for both cell types, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that the animals choice can be decoded from both E and I latents and thus is not restricted to a single cell-class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the experimentally observed effects of different perturbations on behavior, whereas a standard LDS model--which does not differentiate between cell types--did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.

neuroscience↗

bioRxiv2023

Enhancing Hi-C contact matrices for loop detection with Capricorn, a multi-view diffusion model

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. On the other hand, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops, and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17.8% in mean squared error and 22.9% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 15.7% relative to existing methods. We further show that our multi-view idea can also be used to improve several existing methods, Hi-CARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

bioinformatics↗

bioRxiv2023

Characterisation of an Omnitrap-Orbitrap platform equipped with IRMPD, UVPD and ExD for the analysis of peptides and proteins

We describe an instrument configuration based on the Orbitrap Exploris 480 mass spectrometer that has been coupled to an Omnitrap platform. The Omnitrap possesses three distinct ion-activation regions, that can be used to perform resonant based collision induced dissociation, several forms of electron associated fragmentation, and ultraviolet photodissociation. Each section can also be combined with infrared multiphoton dissociation. In this work, we demonstrate all these modes of operation on a range of peptides and proteins. The results show that this instrument configuration produces similar data to previous implementations of each activation technique and at similar efficiency levels. We demonstrate that this unique instrument configuration is extremely versatile for the interrogation of polypeptides.

systems biology↗

bioRxiv2023

Fibertools: fast and accurate DNA-m6A calling using single-molecule long-read sequencing

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation as well as the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as co-processing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semi-supervised convolutional neural network for fast and accurate identification of m6A-marked bases using PacBio single-molecule long-read sequencing, as well as the co-processing of long-read genetic and epigenetic data produced using either PacBio or Oxford Nanopore sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kilobase long DNA molecules with a [~]1,000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

bioinformatics↗