Biology subjects

Andrade-Navarro, M. A.

Publications and source records attributed to Andrade-Navarro, M. A..

16 recordsLinked to original sources

bioRxiv2026

A map of human protein-protein interaction embeddings for functional discovery

A proteins function depends not just on its own structure and localization, but also on the interactions with its partners. Many proteins are therefore better described by a set of partner-dependent roles than by a single annotation. Yet most approaches to the functional interpretation of protein-protein interactions (PPIs) remain protein or set-centric. They rely on pre-existing annotations, and perform worst where knowledge is sparse. Here, we present MAPPIE (Map of Protein-Protein Interaction Embeddings), a method that treats each PPI, rather than each protein, as a unit of representation. From 199,137 human interactions spanning 15,503 proteins, we build a two-dimensional map of the human PPI landscape for functional discovery. Protein language model embeddings for two protein interaction partners are combined and compressed into a latent space, with model selection guided by domain-domain interactions used as a structural proxy for interaction similarity. The resulting geometry separates domain defined interaction classes, organizes disorder associated interactions spatially, and splits interactions involving the same protein by partner. A query PPIs latent neighbourhood recovers its own annotated functions across molecular, complex, pathway, and biological processes. MAPPIE contributes most where existing functional evidence is weakest, outperforming interactome and sequence identity baselines for sparsely connected interactions. MAPPIE neighbours of query PPIs are enriched for partners in independent protein networks, recovering curated complex-level function even when subunits are spread across the map. Applied to a human dark interactome, MAPPIE assigns specific, experimentally supported functions to dark hub proteins.

bioinformatics↗

bioRxiv2026

GenesetDiseaseDrugNetwork (GDDN): a web server for disease enrichment and drug prioritization

SummaryOmics technologies profile thousands of genetic and molecular features to provide a comprehensive and quantitative measure of the cellular state. Transcriptomics and proteomics have, especially, guided discoveries of the most important biomarkers and therapeutic targets. By virtue of ongoing developments in single-cell and spatial technologies, fields of targeted therapeutics and personalized medicine are rapidly advancing. However, downstream functional analysis and disease association still remain daunting tasks in bioinformatics. We address these challenges with the GenesetDiseaseDrugNetwork (GDDN) web server. GDDN facilitates functional discovery by connecting gene-sets to enriched diseases and their corresponding therapeutics in a single step. Using a ranking system that incorporates regulatory impact, specificity, and potency, GDDN effectively prioritizes drugs with the highest clinical relevance. Our platform facilitates the interpretation of omics outputs into disease associations and personalized drug identification. Availability and ImplementationThe GDDN web server is implemented in R Shiny and is freely accessible at https://cbdm-01.zdv.uni-mainz.de/shiny/piyusmor/GDDN/, supporting all major web browsers. Contactpiyusmor@uni-mainz.de Supplementary informationSupplementary data is available at Bioinformatics online.

bioinformatics↗

bioRxiv2025

Integrating the ENCODE blocklist for machine learning quality control of ChIP-seq with seqQscorer

MotivationQuality assessment of next-generation sequencing data is a complex but important task to ensure correct conclusions from experiments in molecular biology, biomedicine, and biotechnology. We previously introduced seqQscorer, a quality assessment tool using machine learning to support this process. To improve seqQscorer in terms of accuracy and processing time, we integrated the ENCODE blocklist* to derive a new type of quality-related features, supposed to be more informative and faster in generation than those conventionally used by seqQscorer. ResultsThe novel seqQscorer extension, called seqBLQ, allows us to improve the quality assessment for ChIP-seq data derived from human tissues and cell lines. Furthermore, seqBLQ enhances the usability of the tool by simplifying the installation procedure and reducing the computational resources required for feature generation. Availability and implementationhttps://github.com/salbrec/seqQscorer

bioinformatics↗

bioRxiv2023

Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

The transcription factor interferon regulatory factor 4 (IRF4) is crucial for the differentiation and fate determination of pro-inflammatory T helper (Th)17 and the functionally opposing group of immunomodulatory regulatory T (Treg) cells. However, molecular mechanisms of how IRF4 steers diverse transcriptional programs in Th17 and Treg cells are far from being definitive. To unveil IRF4-driven lineage determination in Th17 and Treg cells, we integrated data derived from affinity-purification and full mass spectrometry-based proteome analysis with chromatin immune precipitation sequencing (ChIP-Seq). This allowed the characterization of subtype-specific molecular programs and the identification of novel, previously unknown IRF4 interactors in the Th17/Treg context, such as ROR{gamma}t, AHR, IRF8, BACH2, SATB1, and FLI1. Moreover, our data reveal that most of these transcription factors are recruited to IRF composite elements for the regulation of cell type-specific transcriptional programs providing a valuable resource for studying IRF4-mediated gene regulatory programs in pro- and anti-inflammatory immune responses.

immunology↗

bioRxiv2022

An outstanding bacterial membranome requires CRISPR-Cas systems to avoid the intervention of phages

Antimicrobial resistance is widely recognized as a serious global public health problem. To combat this threat, a thorough understanding of bacterial genomes is necessary. The current wide availability of bacterial genomes provides us with an in-depth understanding of the great variability of dispensable genes and their relationship with antimicrobials. Some of these accessory genes are those involved in CRISPR-Cas systems, which are acquired immunity systems that are present in part of bacterial genomes. They prevent viral infections through small DNA fragments called spacers. But the vast majority of these spacers have not yet been associated with the virus they recognize, and this has been named CRISPR dark matter. By analyzing the spacers of tens of thousands of genomes from six bacterial species highly resistant to antibiotics, we have been able to reduce the CRISPR dark matter from 80-90% to as low as 15% in some of the species. In addition, we have observed that, when a genome presents CRISPR-Cas systems, this is accompanied by particular collections of membrane proteins. Our results suggest that when a bacterium presents membrane proteins that make it compete better in its environment, and these proteins are in turn receptors for specific phages, it would be forced to acquire CRISPR-Cas immunity systems to avoid infection by these phages.

bioinformatics↗

bioRxiv2022

Role of Yap1 in adult neural stem cell activation

Most adult hippocampal neural stem cells (NSCs) remain quiescent with only a minor portion undergoing active proliferation and neurogenesis. The molecular mechanisms that trigger eventually the transition from quiescence to activation are still poorly understood. Here, we found the activity of the transcriptional activator Yap1 to be enriched in active NSCs. Genetic deletion of Yap1 led to a significant reduction in the relative proportion of active NSCs supporting a physiological role of Yap1 in regulating the transition from quiescence to activation. Overexpression of wild type Yap1 in adult NSCs did not induce NSC activation suggesting tight upstream control mechanisms, but overexpression of a gain-of-function mutant (Yap1-5SA) elicited cell cycle entry in NSCs and hilar astrocytes. Consistent with a role of Yap1 in NSC activation, single cell RNA sequencing revealed the partial induction of an activated NSC gene expression program. Yet, Yap1-5SA expression also induced Taz and other key components of the Yap/Taz regulon previously identified in glioblastoma stem cell-like cells. Consequently, dysregulated Yap1 activity led to repression of hippocampal neurogenesis, promoting aberrant differentiation instead.

neuroscience↗

bioRxiv2021

Identification of functional residues using machine learning provides insights into the evolution of odorant receptor gene families in solitary and social insects

The gene family of insect odorant receptors (ORs) has greatly expanded in the course of evolution. ORs allow insects to detect volatile chemicals and therefore play an important role in social interactions, the detection of enemies and preys, and during foraging. The sequences of several thousand ORs are known, but their specific function or ligands have been identified only for very few of them. To advance the functional characterization of ORs, we compiled, curated and aligned the sequences of 3,902 ORs from 21 insect species. We identified the amino acid positions that best predict the response to ligands using machine learning on sets of functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator. We studied the conservation of these predicted relevant residues across all OR subfamilies and show that the subfamilies that expanded strongly in social insects exhibit high levels of conservation in their binding sites. This indicates that ORs of social insect families are typically finely tuned and exhibit a sensitivity to very similar odorants. Our novel approach provides a powerful tool to use functional information from a limited number of genes to investigate the functional evolution of large gene families.

bioinformatics↗

bioRxiv2019

SPARTA imputes sparse single-cell ChIP-seq signals leveraging epigenomic bulk ENCODE data

MotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors. ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real data. Results on simulated data show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways. An imputation method that allows the interpretation of the underlying models facilitates users to gain an even deeper understanding of individual cells and, consequently, of sparse scChIP-seq datasets. Availability and implementationOur interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA

bioinformatics↗

bioRxiv2019

Machine Learning in Quality Assessment of Early Stage Next-Generation Sequencing Data

Controlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following URL: https://github.com/salbrec/seqQscorer.

bioinformatics↗

bioRxiv2019

Assessment of computational methods for the analysis of single-cell ATAC-seq data

BackgroundRecent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans) lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (20-50% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level.\n\nResultsWe present a benchmarking framework that was applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were evaluated by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed.\n\nConclusionsThis reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC was the only method able to analyze a large dataset (> 80,000 cells).

bioinformatics↗

bioRxiv2019

The Developmental Transcriptome for Lytechinus variegatus Exhibits Temporally Punctuated Gene Expression Changes

Embryonic development is arguably the most complex process an organism undergoes during its lifetime, and understanding this complexity is best approached with a systems-level perspective. The sea urchin has become a highly valuable model organism for understanding developmental specification, morphogenesis, and evolution. As a non-chordate deuterostome, the sea urchin occupies an important evolutionary niche between protostomes and vertebrates. Lytechinus variegatus (Lv) is an Atlantic species that has been well studied, and which has provided important insights into signal transduction, patterning, and morphogenetic changes during embryonic and larval development. The Pacific species, Strongylocentrotus purpuratus (Sp), is another well-studied sea urchin, particularly for gene regulatory networks (GRNs) and cis-regulatory analyses. A well-annotated genome and transcriptome for Sp are available, but similar resources have not been developed for Lv. Here, we provide an analysis of the Lv transcriptome at 11 timepoints during embryonic and larval development. The data indicate that the gene regulatory networks that underlie specification are well-conserved among sea urchin species. We show that the major transitions in variation of embryonic transcription divide the developmental time series into four distinct, temporally sequential phases. Our work shows that sea urchin development occurs via sequential intervals of relatively stable gene expression states that are punctuated by abrupt transitions.

developmental biology↗

bioRxiv2018

Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs

BackgroundKnowledge of the three-dimensional structure of the genome is necessary to understand how gene expression is regulated. Recent experimental techniques such as Hi-C or ChIA-PET measure long-range interactions genome-wide but are experimentally elaborate and have limited resolution. Here, we present Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs (7C).\n\nResultsWhile ChIP-seq was not designed to detect contacts, the formaldehyde treatment in the ChIP-seq protocol cross-links proteins with each other and with DNA. Consequently, also regions that are not directly bound by the targeted TF but interact with the binding site via chromatin looping are co-immunoprecipitated and sequenced. This produces minor ChIP-seq signals at loop anchor regions close to the directly bound site. We use the position and shape of ChIP-seq signals around CTCF motif pairs to predict whether they interact or not.\n\nWe applied 7C to all CTCF motif pairs within 1 MB in the human genome and validated predicted interactions with high-resolution Hi-C and ChIA-PET. A single ChIP-seq experiment from known architectural proteins (CTCF, Rad21, Znf143) but also from other TFs (like TRIM22 or RUNX3) predicts loops accurately. Importantly, 7C predicts loops in cell types and for TF ChIP-seq datasets not used in training.\n\nConclusion7C predicts chromatin loops with base-pair resolution and can be used to associate TF binding sites to regulated genes in a condition-specific manner. Furthermore, profiling of hundreds of ChIP-seq datasets results in novel candidate factors functionally involved in chromatin looping. Our method is available as an R package: https://ibn-salem.github.io/sevenC/

genomics↗

bioRxiv2018

Evaluating Cell Identity from Transcription Profiles

Induced pluripotent stem cells (iPS) and direct lineage programming offer promising autologous and patient-specific sources of cells for personalized drug-testing and cell-based therapy. Before these engineered cells can be widely used, it is important to evaluate how well the engineered cell types resemble their intended target cell types. We have developed a method to generate CellScore, a cell identity score that can be used to evaluate the success of an engineered cell type in relation to both its initial and desired target cell type, which are used as references. Of 20 cell transitions tested, the most successful transitions were the iPS cells (CellScore > 0.9), while other transitions (e.g. induced hepatocytes or motor neurons) indicated incomplete transitions (CellScore < 0.5). In principle, the method can be applied to any engineered cell undergoing a cell transition, where transcription profiles are available for the reference cell types and the engineered cell type.\n\nHighlightsO_LIA curated standard dataset of transcription profiles from normal cell types was created.\nC_LIO_LICellScore evaluates the cell identity of engineered cell types, using the curated dataset.\nC_LIO_LICellScore considers the initial and desired target cell type.\nC_LIO_LICellScore identifies the most successfully engineered clones for further functional testing.\nC_LI

bioinformatics↗

bioRxiv2017

Evolutionary stability of topologically associating domains is associated with conserved gene regulation

BackgroundThe human genome is highly organized in the three-dimensional nucleus. Chromosomes fold locally into topologically associating domains (TADs) defined by increased intra-domain chromatin contacts. TADs contribute to gene regulation by restricting chromatin interactions of regulatory sequences, such as enhancers, with their target genes. Disruption of TADs can result in altered gene expression and is associated to genetic diseases and cancers. However, it is not clear to which extent TAD regions are conserved in evolution and whether disruption of TADs by evolutionary rearrangements can alter gene expression.\n\nResultsHere, we hypothesize that TADs represent essential functional units of genomes, which are selected against rearrangements during evolution. We investigate this using whole-genome alignments to identify evolutionary rearrangement breakpoints of different vertebrate species. Rearrangement breakpoints are strongly enriched at TAD boundaries and depleted within TADs across species. Furthermore, using gene expression data across many tissues in mouse and human, we show that genes within TADs have more conserved expression patterns. Disruption of TADs by evolutionary rearrangements is associated with changes in gene expression profiles, consistent with a functional role of TADs in gene expression regulation.\n\nConclusionsTogether, these results indicate that TADs are conserved building blocks of genomes with regulatory functions that are often reshuffled as a whole instead of being disrupted by rearrangements.

genomics↗

bioRxiv2017

The latent geometry of the human protein interaction network

To mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

systems biology↗

bioRxiv2017

A reliable and unbiased human protein network with the disparity filter

The living cell operates thanks to an intricate network of protein interactions. Proteins activate, transport, degrade, stabilise and participate in the production of other proteins. As a result, a reliable and systematically generated protein wiring diagram is crucial for a deeper understanding of cellular functions. Unfortunately, current human protein networks are noisy and incomplete. Also, they suffer from both study and technical biases: heavily studied proteins (e.g. those of pharmaceutical interest) are known to be involved in more interactions than proteins described in only a few publications. Here, we use the experimental evidence supporting the interaction between proteins, in conjunction with the so-called disparity filter, to construct a reliable and unbiased proteome-scale human interactome. The application of a global filter, i.e. only considering interactions with multiple pieces of evidence, would result in an excessively pruned network. In contrast, the disparity filter preserves interactions supported by a statistically significant number of studies and does not overlook small-scale protein associations. The resulting disparity-filtered protein network covers 67% of the human proteome and retains most of the networks weight and connectivity properties.

bioinformatics↗