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DOE OSTI2024

Exabiome: Advancing Microbial Science through Exascale Computing

The Exabiome project seeks to improve the understanding of microbiomes through the development of methods for accelerating metagenomic science using exascale computing. This article gives an overview of scientific impact of the three components of the project: metagenome assembly, protein family detection, and comparative analysis of metagenomes. Exabiome developed MetaHipMer, the only metagenome assembler capable of scaling to full exascale systems. MetaHipMer has enabled ground-breaking assemblies on the Frontier supercomputer, with many scientific benefits, such as the discovery of rare species and viral genomes. To investigate protein families, Exabiome developed two exascale tools, PASTIS and HipMCL. Together, these can utilize exascale resources to understand the functional diversity of billions of dark matter proteins and novel protein families. For comparative analysis, Exabiome developed kmerprof, a tool that can be used to compare huge metagenomes for many different scientific purposes, for example, grouping human microbiomes according to body location.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Transcriptome-Wide Cleavage Site Mapping on Cellular mRNAs Reveals Features Underlying Sequence-Specific Cleavage by the Viral Ribonuclease SOX

Many viruses express factors that reduce host gene expression through widespread degradation of cellular mRNA. An example of this class of proteins is the mRNA-targeting endoribonuclease SOX from the gamma-herpesvirus Kaposi’s sarcoma-associated herpesvirus (KSHV). Previous studies indicated that cleavage of messenger RNAs (mRNA) by SOX occurs at specific locations defined by the sequence of the target RNA, which is at odds with the down-regulation of a large portion of cellular transcripts. In this study, we address this paradox by using high-throughput sequencing of cleavage intermediates combined with a custom bioinformatics-based analysis pipeline to identify SOX cleavage sites across the mRNA transcriptome. These data, coupled with targeted mutagenesis, reveal that while cleavage sites are specific and reproducible, they are defined by a degenerate sequence motif containing a small number of conserved residues rather than a strong consensus sequence. This degenerate element is well represented in both human and KSHV mRNA, and its presence correlates with RNA destabilization by SOX. This represents a new endonuclease targeting strategy, in which use of a degenerate targeting element enables RNA cleavage at specific locations without restricting the range of targets. Furthermore, it shows that strong target selectivity can be achieved without a high degree of sequence specificity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Advances in molecular serotyping and subtyping of Escherichia coli

Escherichia coli plays an important role as a member of the gut microbiota; however, pathogenic strains also exist, including various diarrheagenic E. coli pathotypes and extraintestinal pathogenic E. coli that cause illness outside of the GI-tract. E. coli have traditionally been serotyped using antisera against the ca. 186 O-antigens and 53 H-flagellar antigens. Phenotypic methods, including bacteriophage typing and O- and H- serotyping for differentiating and characterizing E. coli have been used for many years; however, these methods are generally time consuming and not always accurate. Advances in next generation sequencing technologies have made it possible to develop genetic-based subtyping and molecular serotyping methods for E. coli , which are more discriminatory compared to phenotypic typing methods. Furthermore, whole genome sequencing (WGS) of E. coli is replacing established subtyping methods such as pulsedfield gel electrophoresis, providing a major advancement in the ability to investigate food-borne disease outbreaks and for trace-back to sources. Furthermore, a variety of sequence analysis tools and bioinformatic pipelines are being developed to analyze the vast amount of data generated by WGS and to obtain specific information such as O- and H-group determination and the presence of virulence genes and other genetic markers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Single-molecule Real-time (SMRT) Isoform Sequencing (Iso-Seq) in Plants: The Status of the Bioinformatics Tools to Unravel the Transcriptome Complexity

Background: The advent of the Single-Molecule Real-time (SMRT) Isoform Sequencing(Iso-Seq) has paved the way to obtain longer full-length transcripts. This method was found tobe much superior in identifying full-length splice variants and other post-transcriptional events ascompared to the Next Generation Sequencing (NGS)-based short read sequencing (RNA-Seq).Several different bioinformatics tools to analyze the Iso-Seq data have been developed and someof them are still being refined to address different aspects of transcriptome complexity. However, acomprehensive summary of the available tools and their utility is still lacking. Objective: Here, we summarized the existing Iso-Seq analysis tools and presented an integratedbioinformatics pipeline for Iso-Seq analysis, which overcomes the limitations of NGS and generateslong contiguous Full-Length Non-Chimeric (FLNC) reads for the analysis of posttranscriptionalevents. Results: In this review, we summarized recent applications of Iso-Seq in plants, which include improvedgenome annotations, identification of novel genes and lncRNAs, identification of fulllengthsplice isoforms, detection of novel Alternative Splicing (AS) and Alternative Polyadenylation(APA) events. In addition, we also discussed the bioinformatics pipeline for comprehensiveIso-Seq data analysis, including how to reduce the error rate in the reads and how to identify andquantify post-transcriptional events. Furthermore, the visualization approach of Iso-Seq was discussedas well. Finally, we discussed methods to combine Iso-Seq data with RNA-Seq for transcriptomequantification. Conclusion: Overall, this review demonstrates that the Iso-Seq is pivotal for analyzing transcriptomecomplexity and this new method offers unprecedented opportunities to comprehensively understandtranscripts diversity.

Biochemistry & Molecular Biology↗

DOE OSTI2025

Demographic drivers of gut microbiome diversity

Abstract The gut microbiome plays a central role in orchestrating metabolic, immune, and neurological functions essential for human health. While extensive research has explored the effects of diseases and pathological conditions on gut microbiome composition, the influence of demographic factors remains underexplored, limiting our understanding of microbiome variations in disease states. This study addresses this gap by investigating the impact of demographic variables, including age, sex, and geography, on gut microbiome diversity in healthy individuals. Using the American Gut Project’s extensive dataset and the QIIME2 bioinformatics pipeline, we conducted a comprehensive analysis of microbial profiles across diverse demographic groups. Our results revealed significant age-related shifts in microbial richness and composition, and geographic location strongly influenced phylogenetic diversity. In contrast, sex exhibited limited impact on microbial diversity within healthy BMI ranges. These findings highlight the critical role of demographic factors in shaping gut microbiome diversity, providing a foundational framework to better contextualize disease-related microbiome variations and advance personalized healthcare approaches.

Biotechnology & Applied Microbiology↗

DOE OSTI2024

Results from a multi-laboratory ocean metaproteomic intercomparison: effects of LC-MS acquisition and data analysis procedures

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R 2 values averaged 0.62±0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research, including integration into global-scale ocean surveys and ocean biogeochemical models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Diazotroph Community Characterization via a High-Throughput nifH Amplicon Sequencing and Analysis Pipeline

The dinitrogenase reductase gene ( nifH ) is the most widely established molecular marker for the study of nitrogen-fixing prokaryotes in nature. A large number of PCR primer sets have been developed for nifH amplification, and the effective deployment of these approaches should be guided by a rapid, easy-to-use analysis protocol. Bioinformatic analysis of marker gene sequences also requires considerable expertise. In this study, we advance the state of the art for nifH analysis by evaluating nifH primer set performance, developing an improved amplicon sequencing workflow, and implementing a user-friendly bioinformatics pipeline. Furthermore, the developed amplicon sequencing workflow is a three-stage PCR-based approach that uses established technologies for incorporating sample-specific barcode sequences and sequencing adapters. Based on our primer evaluation, we recommend the Ando primer set be used with a modified annealing temperature of 58°C, as this approach captured the largest diversity of nifH templates, including paralog cluster IV/V sequences. To improve nifH sequence analysis, we developed a computational pipeline which infers taxonomy and optionally filters out paralog sequences. In addition, we employed an empirical model to derive optimal operational taxonomic unit (OTU) cutoffs for the nifH gene at the species, genus, and family levels. A comprehensive workflow script named TaxADivA (TAXonomy Assignment and DIVersity Assessment) is provided to ease processing and analysis of nifH amplicons. Our approach is then validated through characterization of diazotroph communities across environmental gradients in beach sands impacted by the Deepwater Horizon oil spill in the Gulf of Mexico, in a peat moss-dominated wetland, and in various plant compartments of a sugarcane field. IMPORTANCE: Nitrogen availability often limits ecosystem productivity, and nitrogen fixation, exclusive to prokaryotes, comprises a major source of nitrogen input that sustains food webs. The nifH gene, which codes for the iron protein of the nitrogenase enzyme, is the most widely established molecular marker for the study of nitrogen-fixing microorganisms (diazotrophs) in nature. In this study, a flexible sequencing/analysis pipeline, named TaxADivA, was developed for nifH amplicons produced by Illumina paired-end sequencing, and it enables an inference of taxonomy, performs clustering, and produces output in formats that may be used by programs that facilitate data exploration and analysis. Diazotroph diversity and community composition are linked to ecosystem functioning, and our results advance the phylogenetic characterization of diazotroph communities by providing empirically derived nifH similarity cutoffs for species, genus, and family levels. Thus, the utility of our pipeline is validated for diazotroph communities in a variety of ecosystems, including contaminated beach sands, peatland ecosystems, living plant tissues, and rhizosphere soil.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

ChloroSeq, an optimized chloroplast RNA-Seq bioinformatic pipeline, reveals remodeling of the organellar transcriptome under heat stress

Although RNA-Seq has revolutionized transcript analysis, organellar transcriptomes are rarely assessed even when present in published datasets. Here, we describe the development and application of a rapid and convenient method, ChloroSeq, to delineate qualitative and quantitative features of chloroplast RNA metabolism from strand-specific RNA-Seq datasets, including processing, editing, splicing, and relative transcript abundance. The use of a single experiment to analyze systematically chloroplast transcript maturation and abundance is of particular interest due to frequent pleiotropic effects observed in mutants that affect chloroplast gene expression and/or photosynthesis. To illustrate its utility, ChloroSeq was applied to published RNA-Seq datasets derived from Arabidopsis thaliana grown under control and abiotic stress conditions, where the organellar transcriptome had not been examined. The most appreciable effects were found for heat stress, which induces a global reduction in splicing and editing efficiency, and leads to increased abundance of chloroplast transcripts, including genic, intergenic, and antisense transcripts. Moreover, by concomitantly analyzing nuclear transcripts that encode chloroplast gene expression regulators from the same libraries, we demonstrate the possibility of achieving a holistic understanding of the nucleus-organelle system. In conclusion, ChloroSeq thus represents a unique method for streamlining RNA-Seq data interpretation of the chloroplast transcriptome and its regulators.

RNA splicing↗

DOE OSTI2020

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to be a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. As a result, this bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Edge Bioinformatics

Edge Bioinformatics is a developmental bioinformatics and data management platform which seeks to supply laboratories with bioinformatics pipelines for analyzing data associated with common samples case goals. Edge Bioinformatics enables sequencing as a solution and forward-deployed situations where human-resources, space, bandwidth, and time are limited. The Edge bioinformatics pipeline was designed based on following USE CASES and specific to illumina sequencing reads. 1. Assay performance adjudication (PCR): Analysis of an existing PCR assay in a genomic context, and automated design of a new assay to resolve conflicting results; 2. Clinical presentation with extreme symptoms: Characterization of a known pathogen or co-infection with a. Novel emerging disease outbreak or b. Environmental surveillance

Lo, Chien-Chi↗

DOE OSTI2025

Creation of an Acyltransferase Toolbox for Plant Biomass Engineering (Final Report)

The major goal of this project was to expand our understanding of acyl‐CoA ligases and BAHD acyltransferases and their utility in plant engineering. We combined bioinformatic analysis of genes and transcripts with functional fingerprinting of synthesized genes produced by JGI. Best candidates from this experimental pipeline were transferred into bioenergy plants to study their effects on lignin composition. We found combinations of ligase and transferase genes encoding enzymes with interesting catalytic specificities. Our work demonstrated the feasibility of use of acyl-CoA ligases and BAHD acyltransferases to alter the composition of plant cell walls without deleterious effects on the modified plant.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Edge Bioinformatics

Lo, Chien-Chi↗

DOE OSTI2014

Enabling a systems biology knowledgebase with gaggle and firegoose

The overall goal of this project was to extend the existing Gaggle and Firegoose systems to develop an open-source technology that runs over the web and links desktop applications with many databases and software applications. This technology would enable researchers to incorporate workflows for data analysis that can be executed from this interface to other online applications. The four specific aims were to (1) provide one-click mapping of genes, proteins, and complexes across databases and species; (2) enable multiple simultaneous workflows; (3) expand sophisticated data analysis for online resources; and enhance open-source development of the Gaggle-Firegoose infrastructure. Gaggle is an open-source Java software system that integrates existing bioinformatics programs and data sources into a user-friendly, extensible environment to allow interactive exploration, visualization, and analysis of systems biology data. Firegoose is an extension to the Mozilla Firefox web browser that enables data transfer between websites and desktop tools including Gaggle. In the last phase of this funding period, we have made substantial progress on development and application of the Gaggle integration framework. We implemented the workspace to the Network Portal. Users can capture data from Firegoose and save them to the workspace. Users can create workflows to start multiple software components programmatically and pass data between them. Results of analysis can be saved to the cloud so that they can be easily restored on any machine. We also developed the Gaggle Chrome Goose, a plugin for the Google Chrome browser in tandem with an opencpu server in the Amazon EC2 cloud. This allows users to interactively perform data analysis on a single web page using the R packages deployed on the opencpu server. The cloud-based framework facilitates collaboration between researchers from multiple organizations. We have made a number of enhancements to the cmonkey2 application to enable and improve the integration within different environments, and we have created a new tools pipeline for generating EGRIN2 models in a largely automated way.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Discovering the desirable alleles contributing to the lignocellulosic biomass traits in Saccharum germplasm collections for energy cane improvement

1) The success in crop improvement programs depends largely on the extent of genetic variability available. Germplasm collections assembles all the available genetic resources and are critical for long-term crop improvement. This world sugarcane germplasm collection contains enormous genetic variability for various morphological traits, biomass yield components, adaptation and many quality traits, prospectively imbeds a large number of valuable alleles for biofuel traits such as high biomass yield, quantity and quality of lignocelluloses, stress tolerance, and nutrient use efficiency. The germplasm collection is of little value unless it is characterized and utilized for crop improvement. In this project, we phenotypically and genotypically characterized the sugarcane world germplasm collection (The results were published in two papers already and another two papers are to be published). This data will be made available for public to refer to for germplasm unitization specifically in the sugarcane and energy cane breeding programs. In addition, we are identifying the alleles contributing to the biomass traits in sugarcane germplasm. This part of project is very challenging due to the large genome and highly polyploid level of this crop. We firstly established a high throughput sugarcane genotyping pipeline in the genome and bioinformatics era (a paper is published in 2016). We identified and modified a software for genome-wide association analysis of polyploid species. The results of the alleles associated to the biomass traits will be published soon, which will help the scientific community understand the genetic makeup of the biomass components of sugarcane. Molecular breeders can develop markers for marker assisted selection of biomass traits improvement. Further, the development and release of new energy cane cultivars through this project not only improved genetic diversity but also improved dry biomass yields and resistance to diseases. These new cultivars were tested on marginal soils in Florida and showed very promising yield potential that is important for the successful use of energy cane as a dedicated feedstock for lignocellulosic ethanol production. 2) Multiple techniques at different project progress stages were utilized. For example, for the whole world germplasm accession genotyping, a cheap widely used SSR marker genotyping platform was utilized due to the large number of samples (over thousand). But the throughput of this technique is low in generating data points. However, the purpose the genotyping is to form a core collection for further high throughput genotyping. Thus the results from the SSR genotyping was quite good enough to generated the core collection. To genotype the few hundred core collection accessions, an target enrichment sequencing technology was used, which is not only high throughput in generating large number of genotyping data, but also has the candidate genes targeted to genotyping. The data generated would be sufficient in identifying the alleles contributing to the traits of interests. All the techniques used in this project are effective though extensive time was invested specifically for establish the pipeline in the experimental design, data analysis, and different approach comparison. 3) the research can benefit to the public in polyploid genotyping and new and cost efficient genotyping platform development

59 BASIC BIOLOGICAL SCIENCES↗