Recent bcoe_cse_oapolicydeposits items

Towards optimal selection of ultra-deep sequencing reads for de novo genome assembly

Thu, 12 Mar 2026 00:00:00 +0000

When sequencing a new genome, it is common practice to expect that 30-50× sequencing depth will be sufficient for a complete and highly contiguous assembly. With the rapid decrease in the cost of sequencing DNA, on small genomes it is not uncommon to have excessive sequencing data, sometimes exceeding 1000× sequencing depth (which we call ultra-deep). Because ultra-deep sequencing data significantly degrades the quality of the final assembly (for reasons not entirely clear to us), one faces the problem of how to select a subsample of the data for optimal assembly. The optimal read selection problem for genome assembly is largely unexplored. Here we first show that this problem is related to the minimum tiling path (MTP) problem which is known to be NP-hard. Then, we propose a heuristic (called AWinK) based on single-copy k-mer to select a subset of ultra-deep sequencing reads that maximizes the genomic coverage. Our experiments on both synthetic and real ultra-deep sequencing...

Prediction of DNA Methylation With Long-Range State-Space Models

Thu, 12 Mar 2026 00:00:00 +0000

The prediction of DNA methylation from the primary DNA sequence allows one to impute the methylation status of cytosines with insufficient sequencing coverage. Various deep learning models have been proposed in the literature, including transformer-based models and convolutional neural networks. In this study, we investigate the performance of long-range state-space models based on the Hyena architecture on the task of DNA methylation prediction on six plant species. First, we train the HyenaDNA framework to obtain a genome-wide foundation model for each species. Then, we fine-tune these foundation models using the sequence data surrounding the methylated or unmethylated cytosines. Extensive experimental results show that our model predicts DNA methylation with higher accuracy than state-of-the-art methods in the literature.

Babesia hegotelforum sp. nov., a zoonotic Babesia species previously referred to as Babesia sp. MO1.

Thu, 12 Mar 2026 00:00:00 +0000

A zoonotic Babesia species previously referred to as Babesia sp. MO1 is formally described and named here as Babesia hegotelforum sp. nov. This taxon is distinct from Babesia divergens based on genome-wide sequence divergence, phylogenetic placement, host associations, and clinical presentation. The parasite infects erythrocytes of humans, and eastern cottontail rabbits (Sylvilagus floridanus), and is transmitted by Ixodes dentatus. The holotype consists of a Giemsa-stained thin blood smear and cryopreserved infected erythrocytes from the cloned isolate BML-Bh-B12 at ≤10 passages in continuous in vitro culture. Paratype material includes five additional clones (BML-Bh-H1, BML-Bh-F12, BML-Bh-H6, BML-Bh-A3, and BML-Bh-F1) derived from BEI Resources strain NR-50441, along with the original mixed isolate NR-50441. This species description meets the requirements of the International Code of Zoological...

Recent Advances in Securing Networked Systems

Thu, 4 Dec 2025 00:00:00 +0000

Recent Advances in Securing Networked Systems

Fine-tuned protein language model identifies antigen-specific B cell receptors from immune repertoires

Thu, 4 Dec 2025 00:00:00 +0000

Abstract Scalable identification of antigen-specific antibodies from whole immune repertoire V(D)J sequences is a central challenge in biomedical engineering. We show that protein language models (PLMs) fine-tuned on antibody heavy-chain sequences can directly predict antigen specificity from unselected immune repertoires. We assessed our model, Antigen Specificity Predictor (ASPred), against SARS-CoV-2, influenza, and HIV-AIDS antigens, observing comparable predictive performance. In the whole immune repertoire V(D)J sequences of mice immunized with the SARS-CoV-2 spike protein’s receptor-binding domain (RBD), ASPred identified antibody sequences specific to RBD. Several candidate sequences were validated, including one as a heavy chain-only nanobody with 20.7 nM dissociation constant. Molecular dynamics simulations supported the predicted interactions at coarse-grained and atomic levels. Benchmarking against Barcode-Enabled Antigen Mapping (BEAM) of B cell receptor sequence...

SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing

Thu, 4 Dec 2025 00:00:00 +0000

MOTIVATION: Single-cell Hi-C (scHi-C) technologies have significantly advanced our understanding of the 3D genome organization. However, scHi-C data are often sparse and noisy, leading to substantial computational challenges in downstream analyses. RESULTS: In this study, we introduce SHICEDO, a novel deep-learning model specifically designed to enhance scHi-C contact matrices by imputing missing or sparsely captured chromatin contacts through a generative adversarial framework. SHICEDO leverages the unique structural characteristics of scHi-C matrices to derive customized features that enable effective data enhancement. Additionally, the model incorporates a channel-wise attention mechanism to mitigate the over-smoothing issue commonly associated with scHi-C enhancement methods. Through simulations and real-data applications, we demonstrate that SHICEDO outperforms the state-of-the-art methods, achieving superior quantitative and qualitative results. Moreover, SHICEDO enhances...

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Thu, 4 Dec 2025 00:00:00 +0000

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods...

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Thu, 4 Dec 2025 00:00:00 +0000

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods...

Author Correction: A universal language for finding mass spectrometry data patterns

Fri, 21 Nov 2025 00:00:00 +0000

Correction to: Nature Methodshttps://doi.org/10.1038/s41592-025-02660-z, published online 12 May 2025. This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.

Koina: Democratizing machine learning for proteomics research

Wed, 19 Nov 2025 00:00:00 +0000

Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which the community adopts these models is slow. This is in part due to a lack of findability and accessibility of these models as well as the technical challenges involved in incorporating these models into data analysis pipelines and demonstrating their reusability for end-users. Here we show Koina, an open-source decentralized and online-accessible model repository to facilitate publication of ML models. Koina enables ML model usage via an easy-to-use online interface, facilitating the integration of ML models in data analysis pipelines. Using the widely used FragPipe computational platform as an example, we demonstrate how Koina can be integrated with existing proteomics software tools...

A drug repurposing approach reveals targetable epigenetic pathways in Plasmodium vivax hypnozoites

Thu, 23 Oct 2025 00:00:00 +0000

Radical cure of Plasmodium vivax malaria must include elimination of quiescent 'hypnozoite' forms in the liver; however, the only FDA-approved treatments are contraindicated in many vulnerable populations. To identify new drugs and drug targets for hypnozoites, we screened the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library and a collection of epigenetic inhibitors against P. vivax liver stages. From both libraries, we identified inhibitors targeting epigenetics pathways as selectively active against P. vivax and P. cynomolgi hypnozoites. These include DNA methyltransferase inhibitors as well as several inhibitors targeting histone post-translational modifications. Immunofluorescence staining of Plasmodium liver forms showed strong nuclear 5-methylcystosine signal, indicating liver stage parasite DNA is methylated. Using bisulfite sequencing, we mapped genomic DNA methylation in sporozoites, revealing DNA methylation signals...

Kingdom-wide CRISPR guide design with ALLEGRO

Thu, 28 Aug 2025 00:00:00 +0000

Designing CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) single guide RNA (sgRNA) libraries targeting entire kingdoms of life will significantly advance genetic research in diverse and underexplored taxa. Current sgRNA design tools are often species-specific and fail to scale to large, phylogenetically diverse datasets, limiting their applicability to comparative genomics, evolutionary studies, and biotechnology. Here, we introduce ALLEGRO, a combinatorial optimization algorithm designed to compose minimal, yet highly effective sgRNA libraries targeting thousands of species at the same time. Leveraging integer linear programming, ALLEGRO identified compact sgRNA sets simultaneously targeting multiple genes of interest for over 2000 species across the fungal kingdom. We experimentally validated sgRNAs designed by ALLEGRO in Kluyveromyces marxianus, Komagataella phaffii, Yarrowia lipolytica, and Saccharomyces cerevisiae, confirming successful genome...

Air-powered logic circuits for error detection in pneumatic systems

Thu, 28 Aug 2025 00:00:00 +0000

Air-powered logic circuits for error detection in pneumatic systems

Rapid development and optimization of paper microfluidic designs using software automation

Thu, 28 Aug 2025 00:00:00 +0000

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used...

Multi-Objective Design Automation for Microfluidic Capture Chips

Thu, 28 Aug 2025 00:00:00 +0000

Microfluidic capture chips are useful for preparing or analyzing a wide range of different chemical, biological, and medical samples. A typical microfluidic capture chip contains features that capture certain targets (i.e. molecules, particles, cells) as they flow through the chip. However, creating optimal capture chip designs is difficult because of the inherent relationship between capture efficiency and flow resistance: as more capture features are added to the chip, the capture efficiency increases, but the additional features slow the flow of fluid through the chip. This paper introduces the use of multi-objective optimization to generate capture chip designs that balance the trade-off between maximizing target capture efficiency and minimizing resistance to fluid flow. Design automation for this important class of microfluidic chips has not been attempted previously. Our approach automatically produces a Pareto front of non-dominated chip designs in a reasonable amount...

Controlling Biomedical Devices Using Pneumatic Logic

Thu, 28 Aug 2025 00:00:00 +0000

Many biomedical devices are powered and controlled by electrical components. These electronics add to the cost of a device (possibly making the device too expensive for use in resource-limited or point-of-care settings) and can also render the device unsuitable for use in some environments (for example, high-humidity areas such as incubators where condensation could cause electrical short circuits, ovens where electronic components may overheat, or explosive or flammable environments where electric sparks could cause serious accidents). In this work, we show that pneumatic logic can be used to power and control biomedical devices without the need for electricity or electric components. Originally developed for controlling microfluidic “lab-on-a-chip” devices, these circuits use microfluidic valves like transistors in air-powered logic “circuits.” We show that a modification to the basic valve design—adding additional air channels in parallel through the valve—creates a “high-flow”...

FPGA-based Acceleration of Time Series Similarity Prediction: From Cloud to Edge

Thu, 28 Aug 2025 00:00:00 +0000

With the proliferation of low-cost sensors and the Internet of Things, the rate of producing data far exceeds the compute and storage capabilities of today’s infrastructure. Much of this data takes the form of time series, and in response, there has been increasing interest in the creation of time series archives in the past decade, along with the development and deployment of novel analysis methods to process the data. The general strategy has been to apply a plurality of similarity search mechanisms to various subsets and subsequences of time series data to identify repeated patterns and anomalies; however, the computational demands of these approaches renders them incompatible with today’s power-constrained embedded CPUs. To address this challenge, we present FA-LAMP, an FPGA-accelerated implementation of the Learned Approximate Matrix Profile (LAMP) algorithm, which predicts the correlation between streaming data sampled in real-time and a representative time series...

Predicting Antibody–Antigen Interactions with Structure-Aware LLMs: Insights from SARS-CoV‑2 Variants

Thu, 28 Aug 2025 00:00:00 +0000

Predicting antibody-antigen interactions is a critical step in developing new therapeutics to defend against viral infections. However, measuring the extent of these interactions in vitro is costly and time-consuming. With the increased availability of experimental data, predictive methods using machine learning, particularly large language models (LLMs), have emerged as a powerful alternative to wet lab experiments. Here we focus on antibodies targeting SARS-CoV-2 variants, given the abundance of data on this highly contagious virus and the impact of COVID-19 on human life. The objective of this work is to predict the binding and the neutralizing properties of SARS-CoV-2 antibodies. While there are many studies on predicting binding, to the best of our knowledge, we are the first to address the problem of predicting the neutralizing properties of SARS-CoV-2 antibodies. Here we propose a new classifier that combines LLMs with structural information. Extensive experimental...

Characterization of the critical lift-off of a single flat-plate microchip particle in straight rectangular microchannel flows

Thu, 14 Aug 2025 00:00:00 +0000

Characterization of the critical lift-off of a single flat-plate microchip particle in straight rectangular microchannel flows

Matrix Profile Index Approximation for Streaming Time Series

Thu, 14 Aug 2025 00:00:00 +0000

Discovery of motifs (repeated patterns) in time series is a key factor across numerous industries and scientific fields. These and related problems have effectively been solved for offline analysis of time series; however, these approaches are computationally intensive and do not lend themselves to streaming time series, where the sampling rate imposes real-time constraints on computation and there is strong desire to locate computation as close as possible to the sensor. One promising solution is to use low-cost machine learning models to provide approximate answers to these problems. For example, prior work has trained models to predict the similarity of the most recently sampled window of data points to a representative time series used for training. This work addresses a more challenging problem: to predict not only the "strength" of the match, but also the relative location in the representative time series where the match occurs. We evaluate our approach on two different...

BioScript

Thu, 14 Aug 2025 00:00:00 +0000

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry that executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contribution of the paper is the BioScript type system. The type system ensures that certain types of errors, specific to biochemistry, do not occur, such as the interaction of chemicals that may be unsafe. Results are obtained using a custom-built compiler that implements the BioScript language and type system.

Acoustic Side Channel Attack Against DNA Synthesis Machines: Poster Abstract

Thu, 14 Aug 2025 00:00:00 +0000

Synthetic DNA molecules play an essential role in genomics research and are a promising, high-capacity data storage medium. Currently, researchers use automated DNA synthesizers to custom-build sequences of oligonucleotides (short DNA strands) using the nucleobases: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T). Research laboratories invest large amounts of capital to engineer unique oligonucleotide sequences. In our work, we demonstrate the vulnerability of commonly used DNA synthesizers to acoustic side-channel attacks, where confidentiality can be breached to steal precious DNA sequences. We introduce a novel methodology to reverse engineer the acoustic noise generated by the DNA synthesizer and extract the type and order of the nucleobases delivered to the output. To the best of our knowledge, this is the first work which highlights the possibility of physical-to-cyber attacks in DNA synthesis technologies.

Compiling Functions onto Digital Microfluidics

Thu, 14 Aug 2025 00:00:00 +0000

Compiling Functions onto Digital Microfluidics

Specification, Integration, and Benchmarking of Continuous Flow Microfluidic Devices: Invited Paper

Thu, 14 Aug 2025 00:00:00 +0000

The lack of standardization in the specification and representation of microfluidic designs and their corresponding architectures is one of the largest hurdles faced by the developers of Microfluidic Design Automation (MDA) tools. In this paper, we introduce MINT, a Microfluidic Hardware Description Language (MHDL) for defining components and devices in a human readable manner, and ParchMint, an MDA interchange format and associated benchmark suite that can be used to compare the performance of different physical design algorithms. We further demonstrate how the introduction of MINT and ParchMint into the engineering workflow can bridge the gaps from the specification to the fabrication of microfluidic devices. While recent efforts to democratize microfluidics have been recognized by the community, there is an unfortunate lack of open source tools, design languages, and standards. Consequently, microfluidic designs shared on open platforms such as Metafluidics[15] leave conceptual...

Time- and resource-constrained scheduling for digital microfluidic biochips

Thu, 14 Aug 2025 00:00:00 +0000

Time- and resource-constrained scheduling for digital microfluidic biochips

Feature Extraction Accelerator for Streaming Time Series

Thu, 14 Aug 2025 00:00:00 +0000

Feature Extraction Accelerator for Streaming Time Series

TRFill: synergistic use of HiFi and Hi-C sequencing enables accurate assembly of tandem repeats for population-level analysis.

Sat, 2 Aug 2025 00:00:00 +0000

The highly repetitive content of eukaryotic genomes, including long tandem repeats, segmental duplications, and centromeres, makes haplotype-resolved genome assembly hard. Repeat sequences introduce gaps or mis-joins in the assemblies. We introduce TRFill, a novel algorithm that can close the gaps in a draft chromosome-level assembly using exclusively PacBio HiFi and Hi-C data. Experimental results on human centromeres and tomato subtelomeres show that TRFill can improve the completeness and correctness of about two-thirds of the tandem repeats. We also show that the improved completeness of subtelomeric tandem repeats in the tomato pangenome enables a population-level analysis of these complex repeats.

TRFill: synergistic use of HiFi and Hi-C sequencing enables accurate assembly of tandem repeats for population-level analysis

Thu, 31 Jul 2025 00:00:00 +0000

A universal language for finding mass spectrometry data patterns

Fri, 25 Jul 2025 00:00:00 +0000

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the...

An evaluation methodology for machine learning-based tandem mass spectra similarity prediction

Fri, 18 Jul 2025 00:00:00 +0000

BackgroundUntargeted tandem mass spectrometry serves as a scalable solution for the organization of small molecules. One of the most prevalent techniques for analyzing the acquired tandem mass spectrometry data (MS/MS) - called molecular networking - organizes and visualizes putatively structurally related compounds. However, a key bottleneck of this approach is the comparison of MS/MS spectra used to identify nearby structural neighbors. Machine learning (ML) approaches have emerged as a promising technique to predict structural similarity from MS/MS that may surpass the current state-of-the-art algorithmic methods. However, the comparison between these different ML methods remains a challenge because there is a lack of standardization to benchmark, evaluate, and compare MS/MS similarity methods, and there are no methods that address data leakage between training and test data in order to analyze model generalizability.ResultIn this work, we present the creation of a new evaluation...

Combining Neural and Behavioral Measures Enhances Adaptive Training

Thu, 17 Jul 2025 00:00:00 +0000

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4-13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD...

Enabling pan-repository reanalysis for big data science of public metabolomics data

Tue, 15 Jul 2025 00:00:00 +0000

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

MSTmap Online: enhanced usability, visualization, and accessibility

Thu, 8 May 2025 00:00:00 +0000

Genetic linkage maps are an essential tool in population genetics and plant breeding research, yet user-friendly online tools for constructing and visualizing them remain scarce. MSTmap Online addresses this gap by providing a modern, accessible platform for generating high-quality genetic linkage maps from genotypic data. The web server quickly computes linkage groups using the MSTmap algorithm and generates detailed output files, including publication-ready PDF visualizations of linkage groups. The server supports bookmarking and asynchronous processing, allowing users to revisit their results at a later time. A companion Python library for MSTmap Online enables seamless integration into custom analysis pipelines. MSTmap Online is free and open to all users with no login requirement at https://mstmap.org. The companion Python library is available at https://pypi.org/project/mstmap/.

RAmbler resolves complex repeats in human Chromosomes 8, 19, and X

Thu, 27 Mar 2025 00:00:00 +0000

Repetitive regions in eukaryotic genomes often contain important functional or regulatory elements. Despite significant algorithmic and technological advancements in genome sequencing and assembly over the past three decades, modern de novo assemblers still struggle to accurately reconstruct highly repetitive regions. In this work, we introduce RAmbler (Repeat Assembler), a reference-guided assembler specialized for the assembly of complex repetitive regions exclusively from Pacific Biosciences (PacBio) HiFi reads. RAmbler (1) identifies repetitive regions by detecting unusually high coverage regions after mapping HiFi reads to the draft genome assembly, (2) finds single-copy k-mers from the HiFi reads, (i.e., k-mers that are expected to occur only once in the genome), (3) uses the relative location of single-copy k-mers to barcode each HiFi read, (4) clusters HiFi reads based on their shared barcodes, (5) generates contigs by assembling the reads in each...

Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model

Thu, 27 Mar 2025 00:00:00 +0000

DNA methylation is an epigenetic marker that directly or indirectly regulates several critical cellular processes. While cytosines in mammalian genomes generally maintain stable methylation patterns over time, other cytosines that belong to specific regulatory regions, such as promoters and enhancers, can exhibit dynamic changes. These changes in methylation are driven by a complex cellular machinery, in which the enzymes DNMT3 and TET play key roles. The objective of this study is to design a machine learning model capable of accurately predicting which cytosines have a fluctuating methylation level [hereafter called differentially methylated cytosines (DMCs)] from the surrounding DNA sequence. Here, we introduce L-MAP, a transformer-based large language model that is trained on DNMT3-knockout and TET-knockout data in human and mouse embryonic stem cells. Our extensive experimental results demonstrate the high accuracy of L-MAP in predicting DMCs. Our experiments also explore...

Network Topology Evaluation and Transitive Alignments for Molecular Networking

Mon, 24 Mar 2025 00:00:00 +0000

Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence...

Subscribing to big data at scale

Fri, 28 Feb 2025 00:00:00 +0000

Today, data is being actively generated by a variety of devices, services, and applications. Such data is important not only for the information that it contains, but also for its relationships to other data and to interested users. Most existing Big Data systems focus on passively answering queries from users, rather than actively collecting data, processing it, and serving it to users. To satisfy both passive and active requests at scale, application developers need either to heavily customize an existing passive Big Data system or to glue one together with systems like Streaming Engines and Pub-sub services. Either choice requires significant effort and incurs additional overhead. In this paper, we present the BAD (Big Active Data) system as an end-to-end, out-of-the-box solution for this challenge. It is designed to preserve the merits of passive Big Data systems and introduces new features for actively serving Big Data to users at scale. We show the design and implementation...

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Mon, 17 Feb 2025 00:00:00 +0000

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification....

A mini-review of single-cell Hi-C embedding methods

Sat, 14 Dec 2024 00:00:00 +0000

Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization...

MLSNet: A Policy Complying Multilevel Security Framework for Software Defined Networking