Biology subjects

Friedberg, I.

Publications and source records attributed to Friedberg, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Advances in protein function prediction from the fifth CAFA challenge

The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements. 1

bioinformatics↗

bioRxiv2026

How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning

MotivationPredicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. ResultsHere we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AvailabilityEnzPlacer is available from https://github.com/drxiangma/EnzPlacer under a GPL3 license. Contactqli@iastate.edu

bioinformatics↗

bioRxiv2026

A PLUM Job: Peptide modeLs for Understanding and engineering antiMicrobial therapeutics

Motivation: Antibiotic-resistant infections in humans and animals are rising, creating an urgent need for new antimicrobial strategies. This challenge extends from human health to food production, where foodborne pathogens cause substantial animal and human illness annually. Antimicrobial peptides (AMPs) are promising alternatives because of their broad activity and potentially lower resistance risk. However, rational AMP design remains challenging, particularly when generating targeted peptides with desired function and length for downstream experimental testing. Results: We introduce Peptide modeLs for Understanding and engineering antiMicrobial therapeutics (PLUM), a structured conditional variational autoencoder for controlled AMP generation. PLUM organizes the latent space into components associated with function, length, and residual sequence context, enabling de novo and prototype-guided generation across the 5-35 amino acid range. In de novo generation, PLUM achieved the highest classifier-confirmed yields for both AMP and non-AMP targets. PLUM-generated AMPs were also substantially distinct from the training set and exhibited broad internal sequence diversity. They further showed favorable predicted safety, stability, and potency profiles, including low toxicity, the lowest median instability index among the evaluated models, and a large number of candidates predicted to have high antibacterial potency. In prototype-guided generation, PLUM yielded more AMP-classified variants than HydrAMP while also supporting non-AMP-targeted generation from existing prototypes. Together, these results support PLUM as a scalable framework for function- and length-controlled AMP design. Availability: https://github.com/priyamayur/PLUM

bioinformatics↗

bioRxiv2024

Lineage labeling with zebrafish hand2 Cre and CreERT2 recombinase CRISPR knock-ins

BackgroundThe ability to generate endogenous Cre recombinase drivers using CRISPR- Cas9 knock-in technology allows lineage tracing, cell type specific gene studies, and in vivo validation of inferred developmental trajectories from phenotypic and gene expression analyses. This report describes endogenous zebrafish hand2 Cre and CreERT2 drivers generated with GeneWeld CRISPR-Cas9 precision targeted integration. Resultshand2-2A-cre and hand2-2A-creERT2 knock-ins crossed with ubiquitous loxP-based Switch reporters led to broad labeling in expected mesodermal and neural crest-derived lineages in cardiac, pectoral fins, pharyngeal arch, liver, intestine, and mesothelial tissues, as well as enteric neurons. Novel patterns of hand2 lineage tracing appeared in venous blood vessels. CreERT2 induction at 24 hours reveals late emerging hand2 progenitors in the 24 - 48 hour embryo contribute to the venous and intestinal vasculature. Induction in 3 dpf larva restricts hand2 lineage labeling to mesoderm-derived components of the branchial arches, heart, liver and enteric neurons. Conclusionshand2 progenitors from the lateral plate mesoderm and ectoderm contribute to numerous lineages in the developing embryo. Later emerging hand2 progenitors become restricted to a subset of lineages in the larva. The hand2 Cre and CreERT2 drivers establish critical new tools to investigate hand2 lineages in zebrafish embryogenesis and larval organogenesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/626907v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@12f58cborg.highwire.dtl.DTLVardef@16e95c8org.highwire.dtl.DTLVardef@12b8e9dorg.highwire.dtl.DTLVardef@a01848_HPS_FORMAT_FIGEXP M_FIG C_FIG Zebrafish hand2 Cre and CreERT2 drivers generated with GeneWeld CRISPR/Cas9 precision targeted integration label numerous lineages from the mesoderm and ectoderm. Temporal CreERT2 Tamoxifen regulated switching re{-} veals late emerging hand2 progenitors contribute to the embryonic venous vasculature and larval organs.

developmental biology↗

bioRxiv2024

A Longitudinal Analysis of Function Annotations of the Human Proteome Reveals Consistently High Biases

The resources required to study gene function are limited, especially when considering the number of genes in the human genome and the complexity of their function. Therefore, genes are prioritized for experimental studies based on many different considerations, including, but not limited to, perceived biomedical importance, such as disease-associated genes, or the understanding of biological processes, such as cell signaling pathways. At the same time, most genes are not studied or are under-characterized, which hampers our understanding of their function and potential effects on human health and wellness. Understanding function annotation disparity is a necessary first step toward understanding how much functional knowledge is gained from the human genome, and toward guidelines for better targeting future studies of the genes in the human genome effectively. Here, we present a comprehensive longitudinal analysis of the human proteome utilizing data analysis tools from economics and information theory. Specifically, we view the human proteome as a population of proteins within a knowledge economy: we treat the quantified knowledge of the proteins function as the analog of wealth and examine the distribution of information in a population of proteins in the proteome in the same manner distribution of wealth is studied in societies. Our results show a highly skewed distribution of information about human proteins over the last decade, in which the inequality in the annotations given to the proteins remains high. Additionally, we examine the correlation between the knowledge about protein function as captured in databases and the interest in proteins as reflected by mentions in the scientific literature. We show a large gap between knowledge and interest and dissect the factors leading to this gap. In conclusion, our study shows that research efforts should be redirected to less studied proteins to mitigate the disparity among human proteins both in databases and literature.

bioinformatics↗

bioRxiv2024

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknome". This large knowledge shortfall is one of the final frontiers of biology. Machine-Learning (ML) approaches are enticing, with early successes demonstrating the ability to propagate functional knowledge from experimentally characterized proteins. An open question is the ability of machine-learning approaches to predict enzymatic functions unseen in the training sets. Using a set of Escherichia coli unknowns, we evaluated the current state-of-the-art machine-learning approaches and found that these methods currently lack the ability to integrate scientific reasoning into their prediction algorithms. While human annotators can leverage the plethora of genomic data in making plausible predictions into the unknown, current ML methods not only fail to make novel predictions but also make basic logic errors in their predictions. This underscores the need to include assessments of prediction uncertainty in model output and to test for hallucinations (logic failures) as a part of model evaluation. Explainable AI (XAI) analysis can be used to identify indicators of prediction errors, potentially identifying the most relevant data to include in the next generation of computational models. Article SummaryMany proteins in any genome, ranging from 30% to 70% of the genome, lack an assigned function. This knowledge gap limits the full use of the vast available genomic data. Machine learning has shown promise in transferring functional knowledge within isofunctional families, but it largely fails to predict novel functions not seen in its training data. Understanding these failures can guide the development of better machine-learning methods to help experts make accurate functional predictions for uncharacterized proteins.

bioinformatics↗

bioRxiv2024

Assembling bacterial puzzles: piecing together functions into microbial pathways

Functional metagenomics enables the study of unexplored bacterial diversity, gene families, and pathways essential to microbial communities. However, discovering biological insights with these data is impeded by the scarcity of quality annotations. Here, we use a co-occurrence-based analysis of predicted microbial protein functions to uncover pathways in genomic and metagenomic biological systems. Our approach, based on phylogenetic profiles, improves the identification of functional relationships, or participation in the same biochemical pathway, between enzymes over a comparable homology-based approach. We optimized the design of our profiles to identify potential pathways using minimal data, clustered functionally related enzyme pairs into multi-enzymatic pathways, and evaluated our predictions against reference pathways in KEGG. We then demonstrated a novel extension of this approach to predict inter-bacterial protein interactions amongst members of a marine microbiome. Most significantly, we show our method predicts emergent biochemical pathways between known and unknown functions. Thus, our work establishes a basis for identifying the potential functional capacities of the entire metagenome, capturing previously unknown and abstract functions into discrete putative pathways.

bioinformatics↗

bioRxiv2024

EpicTope: narrating protein sequence features to identify non-disruptive epitope tagging sites

Epitope tagging is a valuable technique enabling the in vivo identification, tracking, and purification of proteins. We developed a tool, EpicTope, to facilitate this method by identifying amino acid positions most suitable for epitope insertion. Our method uses a scoring function that considers protein sequence secondary and tertiary structural features, solvent accessibility, and disordered binding regions to determine locations least disruptive to the proteins function. We validated our approach on the zebrafish Smad5 and Hdac1 proteins. We show that multiple predicted internally tagged Smad5 proteins rescue zebrafish smad5 mutant embryos, while the N- and C-terminal tagged variants do not, as predicted. Similarly, we found that optimally-predicted internal and C-terminal Hdac1 tags rescued hdac1 mutant embryos, while a less-optimal N-terminal tag did not. We further show that these functionally tagged Smad5 and Hdac1 proteins are accessible to antibodies in wholemount zebrafish embryo immunohistochemistry, by western blot, and by immunoprecipitation from embryo extracts. Our work demonstrates that EpicTope is an accessible and effective tool for designing epitope tag insertion sites.

developmental biology↗

bioRxiv2023

An accurate and interpretable model to predict antimicrobial resistance in One Health settings

Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.

bioinformatics↗

bioRxiv2022

GOThresher: a program to remove annotation biases from protein function annotation datasets

MotivationAdvances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets. ResultsWe introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases. Implementation and AvailabilityGOThresher is written in Python and released via PyPI https://pypi.org/project/gothresher/ and on the Bioconda Anaconda channel https://anaconda.org/bioconda/gothresher. The source code is hosted on GitHub https://github.com/FriedbergLab/GOThresher and distributed under the GPL 3.0 license. Contact{idoerg@iastate.edu | parnal@iastate.edu}

bioinformatics↗

bioRxiv2022

Rethinking bacterial relationships in light of their molecular abilities

Determining the repertoire of a microbes molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.

bioinformatics↗

bioRxiv2022

Discovering genomic islands in unannotated bacterialgenomes using sequence embedding

MotivationGenomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. Genomic islands play a crucial role in the evolution of bacteria by helping them adapt quickly to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is therefore an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs, but most of the studies rely either on detecting differences between closely related genomes, or on annotated nucleotide sequences with predictions based on a fixed set of known features. ResultsHere we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland performs competitively when compared with other GEI predictors, enabling the identification of genomic islands in unannotated and taxonomically isolated bacterial genomes. AvailabilityThe source code and the datasets used in this study are available at: https://github.com/priyamayur/GenomicIslandPrediction Contactidoerg@iastate.edu Supplementary informationSupplementary Material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

The field of protein function prediction as viewed by different domain scientists

Experimental biologists, biocurators, and computational biologists all play a role in characterizing a proteins function. The discovery of protein function in the laboratory by experimental scientists is the foundation of our knowledge about proteins. Experimental findings are compiled in knowledge-bases by biocurators to provide standardized, readily accessible, and computationally amenable information. Computational biologists train their methods using these data to predict protein function and guide subsequent experiments. To understand the state of affairs in this ecosystem, centered here around protein function prediction, we surveyed scientists from these three constituent communities. Our objective was to understand their views on this research area, including the importance of the problem, the usefulness of the methods, the bottlenecks in the field, and the level of interaction between the communities. We show that the three core communities have common but also idiosyncratic perspectives on the field. Most strikingly, experimentalists rarely use modern prediction software, but when presented with predictions, report many to be surprising and useful. Ontologies appear to be highly valued by biocurators, less so by experimentalists and computational biologists, yet controlled vocabularies bridge the communities and simplify the prediction task. Additionally, many software tools are not readily accessible and the predictions presented to the users can be broad and uninformative. To meet both the social and technical challenges in the field, a more productive and meaningful interaction between members of the core communities is necessary.

bioinformatics↗

bioRxiv2021

Crowdsourcing biocuration: the Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about known gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a ten-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills. Significance StatementThe primary scientific literature catalogs the results from publicly funded scientific research about gene function in human-readable format. Information captured from those studies in a widely adopted, machine-readable standard format comes in the form of Gene Ontology annotations about gene functions from all domains of life. Manual annotations based on inferences directly from the scientific literature, including the evidence used to make such inferences, represents the best return on investment by improving data accessibility across the biological sciences. To supplement professional curation, our CACAO project enabled annotation of the scientific literature by community annotators, in this case undergraduates, which resulted in contribution of thousands of validated entries to public resources. These annotations are now being used by scientists worldwide.

bioinformatics↗

bioRxiv2020

Transfer learning improves antibiotic resistance class prediction

MotivationAntibiotic resistance is a growing public health problem, which affects millions of people worldwide, and if left unchecked is expected to upend many aspects of healthcare as it is practiced today. Identifying the type of antibiotic resistant genes in genome and metagenomic sample is of utmost importance in the prevention, diagnosis, and treatment of infections. Today there are multiple tools available that predict antibiotic resistance class from DNA and protein sequences, yet there is a lack of benchmarks on the performances of these tools. ResultsWe have developed a dataset that is curated from 15 available databases, and annotated with their antibiotic class labels. We also developed a transfer learning approach with neural networks, TRAC, that outperforms existing antiobiotic resistance prediction tools. While TRAC provides the current state-of-the-art performance, we hope our newly developed dataset will also provide the community with a much needed standardized dataset to develop novel methods that can predict antibiotic resistance class with superior prediction performance. AvailabilityTRAC is available at github (https://github.com/nafizh/TRAC) and the datasets are available at figshare (https://doi.org/10.6084/m9.figshare.11413302). Contactmhamid@mgh.harvard.edu, idoerg@iastate.edu

bioinformatics↗

bioRxiv2019

Identifying Core Operons in Metagenomic Data

An operon is a functional unit of DNA whose genes are co-transcribed on polycistronic mRNA, in a co-regulated fashion. Operons are a powerful mechanism of introducing functional complexity in bacteria, and are therefore of interest in microbial genetics, physiology, biochemistry, and evolution. Here we present a Pipeline for Operon Exploration in Metagenomes or POEM. At the heart of POEM lies the concept of a core operon, a functional unit enabled by a predicted operon in a metagenome. Using a series of benchmarks, we show the high accuracy of POEM, and demonstrate its use on a human gut metagenome sample. We conclude that POEM is a useful tool for analyzing metagenomes beyond the genomic level, and for identifying multi-gene functionalities and possible neofunctionalization in metagenomes. Availability: https://github.com/Rinoahu/POEM_py3k

bioinformatics↗

bioRxiv2019

Hierarchical Markov Random Field model captures spatial dependency in gene expression, demonstrating regulation via the 3D genome

AO_SCPLOWBSTRACTC_SCPLOWHiC technology has revealed many details about the eukaryotic genomes complex 3D architecture. It has been shown that the genome is separated into organizational structures which are associated with gene expression. However, to the best of our knowledge, no studies have quantitatively measured the level of gene expression in the context of the 3D genome. Here we present a novel model that integrates data from RNA-seq and HiC experiments, and determines how much of the variation in gene expression can be accounted for by the genes spatial locations. We used Poisson hierarchical Markov Random Field (PhiMRF), to estimate the level of spatial dependency among protein-coding genes in two different human cell lines. The inference of PhiMRF follows a Bayesian framework, and we introduce the Spatial Interaction Estimate (SIE) to measure the strength of spatial dependency in gene expression. We find that the quantitative expression of genes in some chromosomes show meaningful positive intra-chromosomal spatial dependency. Interestingly, the spatial dependency is much stronger than the dependency based on linear gene neighborhoods, suggesting that 3D chromosome structures such as chromatin loops and Topologically Associating Domains (TADs) are strongly associated with gene expression levels. In some chromosomes the spatial dependency in gene expression is only detectable when the spatial neighborhoods are confined within TADs, suggesting TAD boundaries serve as insulating barriers for spatial gene regulation in the genome. We also report high inter-chromosomal spatial correlations in the majority of chromosome pairs, as well as the whole genome. Some functional groups of genes show strong spatial dependency in gene expression as well, providing new insights into the regulation mechanisms of these molecular functions. This study both confirms and quantifies widespread spatial correlation in gene expression. We propose that, with the growing influx of HiC data complementing gene expression data, the use of spatial dependence should be an integral part of the toolkit in the computational analysis of the relationship between chromosome structure and gene expression.

bioinformatics↗

bioRxiv2019

Unraveling a tangled skein: Evolutionary analysis of the bacterial gibberellin biosynthetic operon

Gibberellin (GA) phytohormones are ubiquitous regulators of growth and developmental processes in vascular plants. The convergent evolution of GA production by plant-associated bacteria, including both symbiotic, nitrogen-fixing rhizobia and phytopathogens, suggests that manipulation of GA signaling is a powerful mechanism for microbes to gain an advantage in these interactions. Although homologous operons encode GA biosynthetic enzymes in both rhizobia and phytopathogens, notable genetic heterogeneity and scattered operon distribution in these lineages suggests distinct functions for GA in varied plant-microbe interactions. Therefore, deciphering GA operon evolutionary history could provide crucial evidence for understanding the distinct biological roles for bacterial GA production. To further establish the genetic composition of the GA operon, two operon-associated genes that exhibit limited distribution among rhizobia were biochemically characterized, verifying their roles in GA biosynthesis. Additionally, a maximum-parsimony ancestral gene block reconstruction algorithm was employed to characterize loss, gain, and horizontal gene transfer (HGT) of GA operon genes within alphaproteobacteria rhizobia, which exhibit the most heterogeneity among GA operon-containing bacteria. Collectively, this evolutionary analysis reveals a complex history for HGT of both individual genes and the entire GA operon, and ultimately provides a basis for linking genetic content to bacterial GA functions in diverse plant-microbe interactions.

microbiology↗