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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples

Rapid and comprehensive analysis of metagenomic data from samples associated with foodborne outbreaks is of critical importance in food safety. Equally important is the need for automated analysis pipelines that allow the rapid and effective construction of metagenomic assembled genomes (MAGs) to enable bacterial source-tracking from metagenomic data. Here, we present centriflaken, an automated precision metagenomics pipeline for detecting and characterizing Shiga toxin-producing Escherichia coli (STEC) from metagenomic data. centriflaken streamlines the process of generating metagenome-assembled genomes (MAGs) and conducting in silico analyses of STECs, significantly reducing the time and manual effort required for comprehensive pathogen profiling. centriflaken was validated using Oxford Nanopore long-read sequencing data from agricultural water enrichments, successfully reproducing results from our previous study that involved multiple manual bioinformatics steps (Maguire et al., 2021). The tools efficacy was further demonstrated through its application to ZymoBIOMICS microbial community standards and 21 additional irrigation water samples, completing STEC precision metagenomics analyses in less than 7 hours per sample. centriflakens versatility allows for the analysis of user-defined taxa beyond STEC, including other foodborne pathogens like Listeria monocytogenes or Salmonella. The pipeline generates comprehensive summary plots and tables, accessible through a MultiQC HTML report. Designed for portability, centriflaken packages all software dependencies within containers and virtual environments. This open-source tool is available on GitHub under the MIT license (https://github.com/CFSAN-Biostatistics/centriflaken), offering a powerful resource for rapid, automated pathogen detection and characterization in food safety applications. Author summaryMetagenomic sequencing, particularly using nanopore technology, generates vast amounts of data that are challenging to analyze, especially when searching for specific pathogens like Shiga toxin-producing Escherichia coli (STEC). This challenge is compounded when processing multiple samples simultaneously. The pipeline "centriflaken" was developed to streamline this complex process, automating the extraction of E. coli reads from metagenomic data and performing in-silico characterization of potential STEC present in the samples. centriflaken builds on the concept of precision metagenomics, employing a suite of automated data analysis workflows powered by Nextflow. The pipeline processes metagenomic data, generates metagenome-assembled genomes (MAGs), and conducts in-silico analyses as described in Maguire et al. (2021). By running steps in parallel, centriflaken enables users without extensive bioinformatics skills or STEC genomic knowledge to efficiently analyze complex metagenomic data. Key features of centriflaken include: 1) Automated workflow for STEC detection and characterization, 2) Parallel processing for improved efficiency, 3) User-friendly interface for non-specialists, 4) Scalability for analyzing multiple samples simultaneously, and 5) Compatibility with high-performance computing (HPC) clusters and cloud environments. This freely available software democratizes complex metagenomic analysis, making it accessible to a broader range of researchers and food safety professionals.

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bioRxiv2025

Rethinking scRNA-seq Trajectories in Phylogenetic Paradigms: Overcoming Challenges of Missing Ancestral Information

In recent decades, many bioinformatics tools have been developed to reconstruct trajectories of biological processes, e.g., cell differentiation, using single-cell RNA-sequencing (scRNA-seq) data. Most tools tacitly assume that a cells ancestral transcriptomic profile can be approximated by means of its neighboring cells in an embedded gene expression space. However, many scRNA-seq datasets lack ancestral information due to missing early or transient states at the time of sequencing. We introduce CellREST, a bioinformatics tool that reformulates trajectory reconstruction as a phylogenetic inference problem. It infers trees linking cells that are assumed to share a common ancestral expression state. Using maximum likelihood tree inference, CellREST uncovers multiple different aspects of the transcriptomic landscape underlying a single scRNA-seq dataset, which can be visualized and combined into a single-cell network. We showcase CellRESTs performance on simulated and experimental scRNA-seq data and recover circular processes as well as cell type converging differentiation scenarios. By introducing and adapting phylogenetic concepts, CellREST provides a framework for interpreting transcriptomic relationships between cells within scRNA-seq data.

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bioRxiv2025

Protein Embeddings and Local Alignments

BackgroundThe advent of protein embeddings has revolutionized bioinformatics by providing contextual representations that capture functional and evolutionary patterns. They have become, alongside sequence alignments, the cornerstone of bioinformatics. While embeddings cannot replace alignments, they can greatly help improving their quality. Our goal is replacing the BLOSUM matrices, the decades-old standard scoring system for protein alignment, with an embedding-based scoring method. ResultsWe introduce a new scoring function and algorithm for local alignment of protein sequences, we offer a new comprehensive framework for evaluating local alignments. The score between two residues is given by the cosine similarity of their Ankh-embedding vectors and the algorithm uses dynamic programming with affine penalty. For the evaluation, we built multiple datasets, using both natural and inserted sequences, from the Conserved Domain Database, BAL-iBASE, and GPCRdb, designed a new algorithm for local alignment extraction, localization and quality evaluation, and employed five distance metrics to evaluate the similarity with the true alignment. We performed nearly one and a half million tests to compare the new algorithm with the best BLOSUM matrices, specialized GPCRtm matrices, and top programs, such as PEbA, ProtT5-score, DEDAL, vcMSA and pLM-BLAST. Regarding the protein embedding models, Ankh not only surpasses the best combination of ProtT5 and ESM2, but appears to better understand the "language" of proteins, as it behaves much better on natural sequences compared to artificial ones obtained by inserting domains in random protein sequences. Also, while ProtT5 and ESM2 combine to produce better results, Ankh does not combine well with other embeddings. ConclusionsThe new Ankh-score-based program is vastly superior to the BLOSUM matrices and clearly superior to all existing methods. New light shed on the protein embeddings can guide future improvements. In order to facilitate the use of the new method and protocol, they are freely available as a web server at e-score.csd.uwo.ca and as source code at github.com/lucian-ilie/E-score.

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bioRxiv2025

SALVE: prediction of interorgan communication with transcriptome latent space representation

Massive transcriptomics data allow gene relationships to be discovered from their correlated expression. We describe SALVE, a method to infer the associations between secretome-encoding transcripts and gene modules in a distal organ from RNA sequencing data. This method builds upon similar bioinformatics approaches by introducing transcriptome latent space representations and transfer learning to simultaneously increase discovery power and predict downstream functional associations. Applied to GTEx v8 data, we show the method readily recapitulates canonical endocrine relationships, including insulin and adiponectin signaling, while inferring new candidate organokines and their signaling modality. We also explore its utility for generating new hypotheses on cardiokine candidates and finding distal factors that may affect cardiac protein synthesis and metabolism. The predictions suggest a potential role of circulating galectin-3 (LGALS3) in regulating cardiac protein synthesis and homeostasis, which can be recapitulated in part in human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes. This method may aid in ongoing efforts to delineate interorgan communications and endocrine networks in various areas of study. New & NoteworthyWe describe a bioinformatics strategy to find associations between the secretome-coding genes and functional pathways of two organs. This approach may be used to find crosstalk signals between the heart, adipose, liver, and other tissues. Applied to GTEx data, it suggests a potential role of circulating galectin-3 in regulating cardiac protein synthesis and homeostasis pathways.

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bioRxiv2025

ProtSEC: Ultrafast Protein Sequence Embedding in Complex Space Using Fast Fourier Transform

Among the various approaches for representing protein sequences as vectors, embeddings derived from protein language models (PLMs) have been empirically shown to enhance accuracy in downstream bioinformatics tasks. However, the substantial computational demands of PLMs, both during training and inference, pose significant challenges. We introduce ProtSEC (Protein Sequence Embedding in Complex Space), a novel approach that encodes each amino acid as a unique complex number derived from the BLOSUM62 substitution matrix. By modeling protein sequences as complex signals and applying the Fast Fourier Transform (FFT), ProtSEC generates embeddings in the complex space. Unlike PLMs, ProtSEC requires no pre-training on large protein sequence datasets and operates independently of any pre-trained models. Our benchmarking demonstrate that ProtSEC achieves a 20,000-fold reduction in runtime and an 85-fold improvement in memory efficiency compared to popular PLMs (e.g., esm2_3B, esm2_35M, prot_t5, prot_bert). Depending on the task, ProtSEC demonstrates either superior or comparable accuracy to PLMs in sequence similarity search, sequence classification and phylogenetic tree reconstruction. ProtSEC provides fast and accurate protein sequence embeddings in complex numbers, facilitating efficient integration into diverse downstream bioinformatics workflows. ProtSEC is available at https://github.com/omics-lab/ProtSEC.

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bioRxiv2025

OmicsFUSION: A Pretrained Hyena-Based Framework for Encoding, Reconstruction, and Representation of the Omics and DNA Data

Recent advances in Natural Language Processing (NLP) have spurred the application of Large Language Models (LLMs) to bioinformatics, enabling innovative approaches to DNA sequence encoding. However, genomic function is not solely determined by the primary DNA sequence--it also depends on complex, multi-layered omics data. This auxiliary data is inherently sparse, structured across multiple tracks, and poses a challenge for traditional unimodal approaches. Simultaneously, many bioinformatics tasks demand a unified signal source that encapsulates this information, rather than requiring researchers to input each omics feature individually. To overcome these limitations, we introduce a novel framework for the integrated representation of DNA sequences and their associated omics data. Central to our approach is a large-scale, batch-structured omics dataset optimized for deep learning at scale. Our framework is built around four key novelties: (1) a new model architecture that jointly processes DNA and omics signals; (2) an extension of Masked Language Modeling (MLM) to omics tracks for effective data reconstruction; (3) single-nucleotide embeddings that fuse all input modalities; and (4) interval-level embeddings that summarize broader genomic regions. We release a collection of pretrained models capable of reconstructing, embedding, and generating unified representations of DNA enriched with functional omics context. Codehttps://github.com/aaai-2025-submission/anomymous_submission_aaai2025 Model and Datasetshttps://drive.google.com/drive/folders/10hcuq4rTCr8bKQFqkpz7R91MGtKvR0Va?usp=sharing

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bioRxiv2025

Evaluating Multiomics Integration Architectures for Training With Structured Missingness

Multimodal bioinformatics datasets are increasingly common in biomedical research, for tasks such as cancer subtyping and outcome prediction. It is feasible that data from a given patient, or even all data from a given institution, does not have coverage across all modalities; the available data is contingent on both the assay choice at the institution alongside technical aspects and associated drop-out. Consequently, algorithms for machine learning models must be tolerant to structured data missingness (or occurrence) for entire modalities when training. In this paper, we compare general strategies for training multimodal models in the context of structured modality missingness, employing suitable strategies for the stage of modality integration: early by concatenation of features, intermediate by max pooling of latent features, and late by aggregating model predictions probabilistically. We evaluate our strategies on a real-world bioinformatics dataset for the task of breast cancer subtyping, constructing a range of structured missingness scenarios. We highlight that, despite their inability to learn cross-modality interactions, late integration models outperform against early and intermediate integration strategies across a range of scenarios according to the level and nature of missingness. Logistic regression models, although simple, also outperform neural networks within the same settings. Fundamentally, we show that understanding the structure of missingness within a dataset is necessary when selecting a method of integration, and that simple models and approaches should not be dismissed when working with structured missingness.

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bioRxiv2025

Multi-modal data integration for machine learning applications

The integration of multi-modal genomic data, encompassing sequences, annotations, and coverage tracks, remains a major bottleneck in bioinformatics, both for exploratory data analysis and machine learning applications. Current approaches rely on several specialized tools for different data modalities, leading to inefficient workflows and computational overhead. Here, we present momics, a unified framework to consolidate multi-omics data in a single repository and interrogate it with a high-performance query engine. Compared to existing tools, momics ingests genomic sequences, feature annotations, and unlimited coverage tracks into TileDB-backed repositories, and provides a scalable query engine for concurrent multi-modal queries across millions of genomic loci. Our benchmarks demonstrate up to 20-fold better data compression and up to 100-fold speed improvements over standard tools like pyBigWig, with a sublinear time complexity ideal for large-scale queries. Momics provides a python library optimized for exploratory data analysis and machine learning workflows, natively supporting current state-of-the-art bioinformatic ecosystems and cloud storage systems. We demonstrate momics utility through two real-world applications: (1) multi-modal data integration of hundreds of ChIP-seq datasets together with genomic sequence, and (2) multi-modal deep learning for chromatin accessibility prediction. By eliminating the need for multiple data parsing tools and providing a unified interface for all genomic data types, momics represents a paradigm shift in how large-scale multi-omics data can be managed and analyzed. Key pointsO_LImomics is a unified framework to consolidate sequences, annotations, and coverage tracks into a single queryable repository, addressing the critical bottleneck in genomic data analysis where researchers must juggle multiple specialized tools for different data modalities. C_LIO_LIWe show that momics can achieve up to 20-fold better data compression and 100-fold speed improvements over standard tools, with sublinear time complexity when querying millions of genomic positions simultaneously. C_LIO_LIWe use momics to formally demonstrate that multi-modal deep learning models can outperform single-modality approaches in predicting chromatin accessibility, achieving correlation of 0.84 when training with a combination of genomic sequence and MNase data. C_LIO_LIOur results establish a new paradigm for reproducible multi-omics modeling, where entire multi-omics analysis workflows from data storage to machine learning model training can be replicated. C_LI

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bioRxiv2025

Computer Vision Methods for Spatial Transcriptomics: A Survey

Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation in ST. However, within these frameworks, "spatial" information is often reduced to locations and relationships between molecular profiles, without fully leveraging the wealth of submicron morphological detail and histological knowledge available. Advances in computer vision-based artificial intelligence (AI) are opening exciting new avenues beyond conventional bioinformatics approaches by modeling complex histological patterns and linking morphology to molecular states. More excitingly, they bring fresh perspectives to potentially address key limitations of ST, including its high cost, limited clinical applicability, and reliance on twodimensional (2D) analysis of inherently three-dimensional (3D) tissues. For instance, models that predict ST directly from histology images enable "virtual sequencing," drastically reducing costs while integrating morphological insights from pathology with molecular biomarkers, thus accelerating clinical translation. Moreover, computer vision techniques can reconstruct pixel-aligned 3D tissue models, overcoming the technical barriers of 2D acquisition and advancing 3D spatial omics analytics. In this paper, we present the first systematic survey of computer vision AI models for ST analytics, categorizing approaches across architectures, learning paradigms, tasks, and datasets, and tracing their technological evolution. We highlight key challenges and future directions, offering a panoramic perspective on vision-driven ST and its potential to transform both basic research and clinical practice. The curated collection of vision-driven spatial transcriptomics papers is available at https://github.com/hrlblab/computer_vision_spatial_omics.

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bioRxiv2025

HCVTyper: A Nextflow pipeline for hepatitis C virus genome assembly, genotyping and antiviral resistance detection

MotivationHigh-throughput sequencing is increasingly used for the characterization of hepatitis C virus (HCV), enabling genotyping, detection of mixed infections, and identification of resistance-associated variants (RAVs). However, the bioinformatics analysis of capture-based or metagenomic HCV datasets remains challenging due to high genome diversity, co-infections, and the need for reproducible workflows. ResultsWe present HCVTyper, a reproducible bioinformatics pipeline for HCV genome analysis implemented in Nextflow. HCVTyper integrates quality control, taxonomic classification, two-step reference-based mapping, consensus generation, de novo assembly, and antiviral resistance annotation. Benchmarking with simulated mixtures and real-world datasets demonstrated robust identification of both major and minor genotypes across a wide abundance range, with user-adjustable thresholds for co-infection detection. Integration with HCV-GLUE enables sensitive detection of RAVs from mapping files, including low-frequency variants. The pipeline is fully containerized (Docker) and produces comprehensive reports to facilitate interpretation. Availability and implementationHCVTyper is open-source and available at: https://github.com/folkehelseinstituttet/hcvtyper Contactjon.brate@fhi.no

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bioRxiv2025

alpseq: an open-source workflow to turbocharge nanobody discovery with high-throughput sequencing

Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability, and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns, however the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed alpseq, an optimised, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. alpseq is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The alpseq software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. alpseq also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. alpseq thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. alspeq is available at https://github.com/kzeglinski/alpseq.

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bioRxiv2025

Uncovering biological patterns across studies through automated large-scale reanalyses of public transcriptomic data

Large amounts of transcriptomic data have been made available in public repositories. Systematic reanalyses of these data offer the potential to identifying conserved biological patterns or context-specific signatures. However, this is a labour intensive process requiring bioinformatic expertise and a long chain of manual decision making. Use of LLMs and agentic systems holds promise for automating these otherwise time-consuming tasks. Here, we present UORCA (Unified -Omics Reference Corpus of Analyses), a tool to systematically identify and analyse public transcriptomic datasets. UORCA uses an LLM-assisted framework to search for datasets relevant to a research question. These datasets are analysed through a multi-agent system that performs a standardised bioinformatic analyses to identify differentially expressed genes. Results of each analysis are then displayed in an interactive visual interface. We found that UORCA recapitulated findings reported from a manual comparison of datasets, but also found biological signatures that were not initially described. We find that UORCA generates targeted hypotheses relevant for drug design, and facilitates evaluation of experimental results where they differ from past literature. Together, these findings demonstrate how UORCA accelerates biomedical discovery by enabling scientists to extract actionable findings from diverse public datasets.

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bioRxiv2025

Fine-Tuning Protein Language Models Enhances the Identification and Interpretation of the Transcription Factors

Transcription factors (TFs) are pivotal regulators of gene expression and play essential roles in diverse cellular activities. The three-dimensional organization of the genome and transcriptional regulation are predominantly orchestrated by TFs. By recruiting the transcriptional machinery to gene enhancers or promoters, TFs can either activate or repress transcription, thereby controlling gene activity and various biological pathways. Accurate identification of TFs is vital for elucidating gene regulatory mechanisms within cells. However, experimental identification remains labor-intensive and time-consuming, highlighting the necessity for efficient computational approaches. In this study, we present a two-layer predictive framework utilizing protein language models (pLMs) via full fine-tuning and parameter-efficient fine-tuning. The initial layer robustly classifies and identifies transcription factors, while the subsequent layer predicts TFs with a binding preference for methylated DNA (TFPMs). Our approach further incorporates attention weights and protein sequence motifs to enhance interpretability and predictive capability. By leveraging attention mechanisms, we highlight biologically relevant regions of the protein sequences that contribute most strongly to the predictions. Additionally, motif analysis facilitates the identification of conserved sequence patterns that are critical for TF recognition and function. Across both TF and TFPM classification tasks, the inclusion of these features allowed our methods to consistently surpass contemporary models, as demonstrated by independent test results. KeypointsO_LIDeveloped a two-layer predictive framework using protein language models (pLMs) with both full fine-tuning and parameter-efficient fine-tuning methods. C_LIO_LIThe first layer accurately identifies transcription factors (TFs), and the second layer predicts TFs with binding preference for methylated DNA (TFPMs). C_LIO_LIIntegrated attention weights and protein sequence motifs to enhance model interpretability by highlighting biologically relevant sequence regions and conserved patterns. C_LIO_LIAchieved superior performance compared to state-of-the-art methods, validated by independent testing. C_LI Mir Tanveerul Hassan obtained his M.Tech. in Computer Science from the University of Kashmir, India, in 2020, and later earned his Ph.D. in Electronics and Information Engineering from Jeonbuk National University, Jeonju, South Korea. He is currently serving as a postdoctoral fellow at the Jeonbuk RICE Intelligence Innovation Research Center. His research interests encompass computational biology, bioinformatics, and pattern recognition. Saima Gaffar received her B.Tech. and M.Tech. degrees in Computer Science from the University of Kashmir, Srinagar, India, and her Ph.D. in Electronics and Information Engineering from Jeonbuk National University, South Korea. Her research focuses on bioinformatics, computational biology, deep learning, and image processing. Hamza Zahid received his B.S. degree in Mechatronics Engineering from the University of Engineering and Technology, Peshawar, Pakistan. He is currently pursuing the integrated M.S. and Ph.D. degrees in Electronics and Information Engineering at Jeonbuk National University, South Korea. His primary research interests include the applications of artificial intelligence in computational drug discovery. Sang Jun Lee received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from POSTECH, South Korea. Following his doctoral studies, he worked as a senior researcher at the Samsung Advanced Institute of Technology (SAIT). He is currently an Associate Professor in the Division of Electronics and Information Engineering at Jeonbuk National University, South Korea. His research interests include image analysis, deep learning, and medical image processing.

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bioRxiv2017

Prediction of potential disease-associated microRNAs using structural perturbation method

MotivationThe identification of disease-related microRNAs(miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.\n\nResultsThe link predictability of bilayer network is higher than that of miRNA-disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA-disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.\n\nAvailability and Implementationhttps://github.com/lecea/SPM-code.git\n\nContactlinyuan.lv@gmail.com, zouquan@nclab.net.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2017

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC

The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. We have developed a user-friendly R/Bioconductor package, named GDCRNATools, to facilitate downloading, organizing, and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs (ceRNAs) regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis.\n\nAvailabilityGDCRNATools is an R/Bioconductor package that is freely available at https://github.com/Jialab-UCR/GDCRNATools

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bioRxiv2017

Prometheus: omics portals for interkingdom comparative genomic analyses

Functional analyses of genes are crucial for unveiling biological responses, for genetic engineering, and for developing new medicines. However, functional analyses have largely been restricted to model organisms, representing a major hurdle for functional studies and industrial applications. To resolve this, comparative genome analyses can be used to provide clues to gene functions as well as their evolutionary history. To this end, we present Prometheus (http://prometheus.kobic.re.kr),web-based omics portal that contains more than 17,215 sequences from prokaryotic and eukaryotic genomes. This portal supports interkingdom comparative analyses via a domain architecture-based gene identification system, Gene Search, and users can easily and rapidly identify single or entire gene sets in specific pathways. Bioinformatics tools for further analyses are provided in Prometheus or through BioExpress, a cloud-based bioinformatics analysis platform. Prometheus suggests a new paradigm for comparative analyses with large amounts of genomic information.

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bioRxiv2017

Comparative Qualitative Phosphoproteomics Analysis Identifies Shared Phosphorylation Motifs and Associated Biological Processes in Flowering Plants

Phosphorylation is regarded as one of the most prevalent post-translational modifications and plays a key role in regulating cellular processes. In this work we carried out a comparative bioinformatics analysis of phosphoproteomics data, to profile two model species representing the largest subclasses in flowering plants the dicot Arabidopsis thaliana and the monocot Oryza sativa, to understand the extent to which phosphorylation signaling and function is conserved across evolutionary divergent plants. Using pre-existing mass spectrometry phosphoproteomics datasets and bioinformatic tools and resources, we identified 6,537 phosphopeptides from 3,189 phosphoproteins in Arabidopsis and 2,307 phosphopeptides from 1,613 phosphoproteins in rice. The relative abundance ratio of serine, threonine, and tyrosine phosphorylation sites in rice and Arabidopsis were highly similar: 88.3: 11.4: 0.4 and 86.7: 12.8: 0.5, respectively. Tyrosine phosphorylation shows features different from serine and threonine phosphorylation and was found to be more frequent in doubly-phosphorylated peptides in Arabidopsis. We identified phosphorylation sequence motifs in the two species to explore the similarities, finding nineteen pS motifs and two pT motifs that are shared in rice and Arabidopsis; among them are five novel motifs that have not previously been described in both species. The majority of shared motif-containing proteins were mapped to the same biological processes with similar patterns of fold enrichment, indicating high functional conservation. We also identified shared patterns of crosstalk between phosphoserines with motifs pSXpS, pSXXpS and pSXXXpS, where X is any amino acid, in both species indicating this is an evolutionary conserved signaling mechanism in flowering plants. However, our results are suggestive that there is greater co-occurrence of crosstalk between phosphorylation sites in Arabidopsis, and we were able to identify several pairs of motifs that are statistically significantly enriched to co-occur in Arabidopsis proteins, but not in rice.

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bioRxiv2018

Asymptotically optimal minimizers schemes

MotivationThe minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density, and thereby making existing and future bioinformatics tools even more efficient.\n\nResultsFrom the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare k-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the 3 type of schemes.\n\nContactgmarcais@cs.cmu.edu ckingsf@cs.cmu.edu

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