Data Structure Research Articles

Nanopore tweezers show fractional-nucleotide translocation in sequence-dependent pausing by RNA polymerase

RNA polymerases (RNAPs) carry out the first step in the central dogma of molecular biology by transcribing DNA into RNA. Despite their importance, much about how RNAPs work remains unclear, in part because the small (3.4 Angstrom) and fast (~40 ms/nt) steps during transcription were difficult to resolve. Here, we used high-resolution nanopore tweezers to observe the motion of single Escherichia coli RNAP molecules as it transcribes DNA ~1,000 times improved temporal resolution, resolving single-nucleotide and fractional-nucleotide steps of individual RNAPs at saturating nucleoside triphosphate concentrations. We analyzed RNAP during processive transcription elongation and sequence-dependent pausing at the yrbL elemental pause sequence. Each time RNAP encounters the yrbL elemental pause sequence, it rapidly interconverts between five translocational states, residing predominantly in a half-translocated state. The kinetics and force-dependence of this half-translocated state indicate it is a functional intermediate between pre- and post-translocated states. Using structural and kinetics data, we show that, in the half-translocated and post-translocated states, sequence-specific protein-DNA interaction occurs between RNAP and a guanine base at the downstream end of the transcription bubble (core recognition element). Kinetic data show that this interaction stabilizes the half-translocated and post-translocated states relative to the pre-translocated state. We develop a kinetic model for RNAP at the yrbL pause and discuss this in the context of key structural features.

Recent Advances on Principles of Concurrent Data Structures

Recent Advances on Principles of Concurrent Data Structures

GraphSlimmer: Preserving Read Mappability with the Minimum Number of Variants.

Modern genomic datasets, like those generated under the 1000 Genome Project, contain millions of variants belonging to known haplotypes. Although these datasets are more representative than a single reference sequence and can alleviate issues like reference bias, they are significantly more computationally burdensome to work with, often involving large-indexed genome graph data structures for tasks such as read mapping. The construction, preprocessing, and mapping algorithms can require substantial computational resources depending on the size of these variant sets. Moreover, the accuracy of mapping algorithms has been shown to decrease when working with complete variant sets. Therefore, a drastically reduced set of variants that preserves important properties of the original set is desirable. This work provides a technique for finding a minimal subset of variants such that for given parameters α and δ, all substrings up to length α in the haplotypes are guaranteed to be still alignable to the appropriate locations with either Hamming or edit distance at most δ, using only . Our contributions include showing the NP-hardness and inapproximability of these optimization problems and providing Integer Linear Programming (ILP) formulations. Our edit distance ILP formulation carefully decomposes the problem according to variant locations, which allows it to scale to support all of chromosome 22's variants from the 1000 Genome Project. Our experiments also demonstrate a significant reduction in the number of variants. For example, for moderately long reads, e.g., α = 1000, over 75% of the variants can be removed while preserving read mappability with edit distance at most one.

STAG2-RAD21 complex: A unidirectional DNA ratchet mechanism in loop extrusion

DNA loop extrusion plays a key role in the regulation of gene expression and the structural arrangement of chromatin. Most existing mechanistic models of loop extrusion depend on some type of ratchet mechanism, which should permit the elongation of loops while preventing their collapse, by enabling DNA to move in only one direction. STAG2 is already known to exert a role as DNA anchor, but the available structural data suggest a possible role in unidirectional DNA motion. In this work, a computational simulation framework was constructed to evaluate whether STAG2 could enforce such unidirectional displacement of a DNA double helix. The results reveal that STAG2 V-shape allows DNA sliding in one direction, but blocks opposite DNA movement via a linear ratchet mechanism. Furthermore, these results suggest that RAD21 binding to STAG2 controls its flexibility by narrowing the opening of its V-shape, which otherwise remains widely open in absence of RAD21. Therefore, in the proposed model, in addition to its already described role as a DNA anchor, the STAG2-RAD21 complex would be part of a ratchet mechanism capable of exerting directional selectivity on DNA sliding during loop extrusion. The identification of the molecular basis of the ratchet mechanism of loop extrusion is a critical step in unraveling new insights into a broad spectrum of chromatin activities and their implications for the mechanisms of chromatin-related diseases.

Partitioning of an Enzyme-Polymer Surfactant Nanocomplex into Lipid-Rich Cellular Compartments Drives In Situ Hydrolysis of Organophosphates.

Most organophosphates (OPs) are hydrophobic, and after exposure, can sequester into lipophilic regions within the body, such as adipose tissue, resulting in long term chronic effects. Consequently, there is an urgent need for therapeutic agents that can decontaminate OPs in these hydrophobic regions. Accordingly, an enzyme-polymer surfactant nanocomplex is designed and tested comprising chemically supercharged phosphotriesterase (Agrobacterium radiobacter; arPTE) electrostatically conjugated to amphiphilic polymer surfactant chains ([cat.arPTE][S-]). Experimentally-derived structural data are combined with molecular dynamics (MD) simulations to provide atomic level detail on conformational ensembles of the nanocomplex using dielectric constants relevant to aqueous and lipidic microenvironments. These show the formation of a compact admicelle pseudophase surfactant corona under aqueous conditions, which reconfigures to yield an extended conformation at a low dielectric constant, providing insight into the mechanism underpinning cell membrane binding. Significantly, it demonstrated that [cat.arPTE][S-] spontaneously binds to human mesenchymal stem cell membranes (hMSCs), resulting in on-cell OP hydrolysis. Moreover, the nanoconstruct can endocytose and partition into the intracellular fatty vacuoles of adipocytes and hydrolyze sequestered OP.

Mitigating implicit and explicit bias in structured data without sacrificing accuracy in pattern classification

Mitigating implicit and explicit bias in structured data without sacrificing accuracy in pattern classification

Mitigating implicit and explicit bias in structured data without sacrificing accuracy in pattern classification

AbstractUsing biased data to train Artificial Intelligence (AI) algorithms will lead to biased decisions, discriminating against certain groups or individuals. Bias can be explicit (one or several protected features directly influence the decisions) or implicit (one or several protected features indirectly influence the decisions). Unsurprisingly, biased patterns are difficult to detect and mitigate. This paper investigates the extent to which explicit and implicit against one or more protected features in structured classification data sets can be mitigated simultaneously while retaining the data’s discriminatory power. The main contribution of this paper concerns an optimization-based bias mitigation method that reweights the training instances. The algorithm operates with numerical and nominal data and can mitigate implicit and explicit bias against several protected features simultaneously. The trade-off between bias mitigation and accuracy loss can be controlled using parameters in the objective function. The numerical simulations using real-world data sets show a reduction of up to 77% of implicit bias and a complete removal of explicit bias against protected features at no cost of accuracy of a wrapper classifier trained on the data. Overall, the proposed method outperforms the state-of-the-art bias mitigation methods for the selected data sets.

Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students

Across development, experience has a strong impact on the way we think and adapt. School experience affects academic and social-emotional outcomes, yet whether differences in pedagogical experience modulate underlying brain network development is still unknown. In this study, we compared the brain network dynamics of students with different pedagogical backgrounds. Specifically, we characterized the diversity and stability of brain activity at rest by combining both resting-state fMRI and diffusion-weighted structural imaging data of 87 4–18 years old students experiencing either the Montessori pedagogy (i.e., student-led, trial-and-error pedagogy) or the traditional pedagogy (i.e., teacher-led, test-based pedagogy). Our results revealed spatiotemporal brain dynamics differences between students as a function of schooling experience at the whole-brain level. Students from Montessori schools showed overall higher functional integration (higher system diversity) and neural stability (lower spatiotemporal diversity) compared to traditionally schooled students. Higher integration was explained mainly through the cerebellar (CBL) functional network. In contrast, higher temporal stability was observed in the ventral attention, dorsal attention, somatomotor, frontoparietal, and CBL functional networks. This study suggests a form of experience-dependent dynamic functional connectivity plasticity, in learning-related networks.

Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students

Across development, experience has a strong impact on the way we think and adapt. School experience affects academic and social-emotional outcomes, yet whether differences in pedagogical experience modulate underlying brain network development is still unknown. In this study, we compared the brain network dynamics of students with different pedagogical backgrounds. Specifically, we characterized the diversity and stability of brain activity at rest by combining both resting-state fMRI and diffusion-weighted structural imaging data of 87 4–18 years old students experiencing either the Montessori pedagogy (i.e., student-led, trial-and-error pedagogy) or the traditional pedagogy (i.e., teacher-led, test-based pedagogy). Our results revealed spatiotemporal brain dynamics differences between students as a function of schooling experience at the whole-brain level. Students from Montessori schools showed overall higher functional integration (higher system diversity) and neural stability (lower spatiotemporal diversity) compared to traditionally schooled students. Higher integration was explained mainly through the cerebellar (CBL) functional network. In contrast, higher temporal stability was observed in the ventral attention, dorsal attention, somatomotor, frontoparietal, and CBL functional networks. This study suggests a form of experience-dependent dynamic functional connectivity plasticity, in learning-related networks.

A large-scale assessment of eastern whip-poor-will (Antrostomus vociferus) occupancy across a gradient of forest management intensity using autonomous recording units

Conservationists spend considerable resources to create and enhance wildlife habitat. Monitoring how species respond to these efforts helps managers allocate limited resources. However, monitoring efforts often encounter logistical challenges that are exacerbated as geographic extent increases. We used autonomous recording units (ARUs) and automated acoustic classification to mitigate the challenges of assessing Eastern Whip-poor-will (Antrostomus vociferus) response to forest management across the eastern USA. We deployed 1263 ARUs in forests with varying degrees of management intensity. Recordings were processed using an automated classifier and the resulting detection data were used to assess occupancy. Whip-poor-wills were detected at 401 survey locations. Across our study region, whip-poor-will occupancy decreased with latitude and elevation. At the landscape scale, occupancy decreased with the amount of impervious cover, increased with herbaceous cover and oak and evergreen forests, and exhibited a quadratic relationship with the amount of shrub-scrub cover. At the site-level, occupancy was negatively associated with basal area and brambles (Rubus spp.) and exhibited a quadratic relationship with woody stem density. Implementation of practices that create and sustain a mosaic of forest age classes and a diverse range of canopy closure within oak (Quercus spp.) dominated landscapes will have the highest probability of hosting whip-poor-wills. The use of ARUs and a machine learning classifier helped overcome challenges associated with monitoring a nocturnal species with a short survey window across a large spatial extent. Future monitoring efforts that combine ARU-based protocols and mappable fine-resolution structural vegetation data would likely further advance our understanding of whip-poor-will response to forest management.

On suffix tree detection

A suffix tree is a fundamental data structure for string processing and information retrieval, however, its structure is still not well understood. The suffix trees reverse engineering problem, which its research aims at reducing this gap, is the following. Given an ordered rooted tree T with unlabeled edges, determine whether there exists a string w such that the unlabeled-edges suffix tree of w is isomorphic to T. Previous studies on this problem consider the relaxation of having the suffix links as well as assume a binary alphabet. This paper is the first to consider the suffix tree detection problem, in which the relaxation of having suffix links as input is removed. We study suffix tree detection on two scenarios that are interesting per se. We provide a suffix tree detection algorithm for general alphabet periodic strings. Given an ordered tree T with n leaves, our detection algorithm takes O(n+|Σ|p)-time, where p is the unknown in advance length of a period that repeats at least 3 times in a string S having a suffix tree structure identical to T, if such S exists. Therefore, it is a polynomial time algorithm if p is a constant and a linear time algorithm if, in addition, the alphabet has a sub-linear size. We also show some necessary (but insufficient) conditions for binary alphabet general strings suffix tree detection. By this we take another step towards understanding suffix trees structure.

A mechanism of customizing learning programs for older adults’ lifelong learning

AbstractJapan, like many countries, is facing problems with an aging society, and lifelong learning is becoming more and more important. To provide older adults with the opportunity to enroll in lifelong learning programs, it is essential to offer suitable programs. However, designing learning programs for older adults is not easy because they may have various physical and mental conditions. To approach the problem, the authors have proposed a mechanism for personalizing learning programs for older adults. The basic ideas of the mechanism are: (1) omitting unessential steps and (2) suggesting collaboration with others. In this paper, the algorithm and the data structures of the mechanism are explained, and results of experiments are shown.

Riemannian Geodesic Discriminant Analysis–Minimum Riemannian Mean Distance: A Robust and Effective Method Leveraging a Symmetric Positive Definite Manifold and Discriminant Algorithm for Image Set Classification

This study introduces a novel method for classifying sets of images, called Riemannian geodesic discriminant analysis–minimum Riemannian mean distance (RGDA-MRMD). This method first converts image data into symmetric positive definite (SPD) matrices, which capture important features related to the variability within the data. These SPD matrices are then mapped onto simpler, flat spaces (tangent spaces) using a mathematical tool called the logarithm operator, which helps to reduce their complexity and dimensionality. Subsequently, regularized local Fisher discriminant analysis (RLFDA) is employed to refine these simplified data points on the tangent plane, focusing on local data structures to optimize the distances between the points and prevent overfitting. The optimized points are then transformed back into a complex, curved space (SPD manifold) using the exponential operator to enhance robustness. Finally, classification is performed using the minimum Riemannian mean distance (MRMD) algorithm, which assigns each data point to the class with the closest mean in the Riemannian space. Through experiments on the ETH-80 (Eidgenössische Technische Hochschule Zürich-80 object category), AFEW (acted facial expressions in the wild), and FPHA (first-person hand action) datasets, the proposed method demonstrates superior performance, with accuracy scores of 97.50%, 37.27%, and 88.47%, respectively. It outperforms all the comparison methods, effectively preserving the unique topological structure of the SPD matrices and significantly boosting image set classification accuracy.

In silico studies of the open form of human tissue transglutaminase

Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.

Syntheses and Patterns of Changes in Structural Parameters of the New Quaternary Tellurides EuRECuTe3 (RE = Ho, Tm, and Sc): Experiment and Theory

The layered orthorhombic quaternary tellurides EuRECuTe3 (RE = Ho, Tm, Sc) with Cmcm symmetry were first synthesized. Single crystals of the compounds up to 500 μm in size were obtained by the halide-flux method at 1120 K from elements taken in a ratio of Eu/RE/Cu/Te = 1:1:1:3. In the series of compounds, the changes in lattice parameters were in the ranges a = 4.3129(3)–4.2341(3) Å, b = 14.3150(9)–14.1562(9) Å, c = 11.2312(7)–10.8698(7) Å, V = 693.40(8)–651.52(7) Å3. In the structures, the cations Eu2+, RE3+ (RE = Ho, Tm, Sc), and Cu+ occupied independent crystallographic positions. The structures were built with distorted copper tetrahedra forming infinite chains [CuTe4]7− and octahedra [RETe6]9− forming two-dimensional layers along the a-axis. These coordination polyhedra formed parallel two-dimensional layers CuRETe32−∞2. Between the layers, along the a-axis, chains of europium trigonal prisms [EuTe6]10− were located. Regularities in the variation of structural parameters and the degree of distortion of coordination polyhedra depending on the ionic radius of the rare-earth metal in the compounds EuRECuCh3 (RE = Ho, Er, Tm, Lu, Sc; Ch = S, Se, Te) were established. It is shown that with a decrease in the ionic radius ri(RE3+) in the compounds EuRECuTe3, the unit-cell volume, bond length d(RE–Te), distortion degree [CuTe4]7−, and crystallographic compression of layers [RECuTe3]2− decreased. The distortion degree of tetrahedral polyhedra [CuCh4]7−, as well as the structural parameters in europium rare-earth copper tellurides EuRECuTe3, were higher than in isostructural quaternary chalcogenides. Ab initio calculations of the crystalline structure, phonon spectrum, and elastic properties of compounds EuRECuTe3 (RE = Ho, Tm, and Sc) ere conducted. The types and wave numbers of fundamental modes were determined, and the involvement of ions in IR and Raman modes was assessed. The calculated data of the crystal structure correlated well with the experimental results.

Syntheses and Patterns of Changes in Structural Parameters of the New Quaternary Tellurides EuRECuTe3 (RE = Ho, Tm, and Sc): Experiment and Theory

The layered orthorhombic quaternary tellurides EuRECuTe3 (RE = Ho, Tm, Sc) with Cmcm symmetry were first synthesized. Single crystals of the compounds up to 500 μm in size were obtained by the halide-flux method at 1120 K from elements taken in a ratio of Eu/RE/Cu/Te = 1:1:1:3. In the series of compounds, the changes in lattice parameters were in the ranges a = 4.3129(3)–4.2341(3) Å, b = 14.3150(9)–14.1562(9) Å, c = 11.2312(7)–10.8698(7) Å, V = 693.40(8)–651.52(7) Å3. In the structures, the cations Eu2+, RE3+ (RE = Ho, Tm, Sc), and Cu+ occupied independent crystallographic positions. The structures were built with distorted copper tetrahedra forming infinite chains [CuTe4]7− and octahedra [RETe6]9− forming two-dimensional layers along the a-axis. These coordination polyhedra formed parallel two-dimensional layers CuRETe32−∞2. Between the layers, along the a-axis, chains of europium trigonal prisms [EuTe6]10− were located. Regularities in the variation of structural parameters and the degree of distortion of coordination polyhedra depending on the ionic radius of the rare-earth metal in the compounds EuRECuCh3 (RE = Ho, Er, Tm, Lu, Sc; Ch = S, Se, Te) were established. It is shown that with a decrease in the ionic radius ri(RE3+) in the compounds EuRECuTe3, the unit-cell volume, bond length d(RE–Te), distortion degree [CuTe4]7−, and crystallographic compression of layers [RECuTe3]2− decreased. The distortion degree of tetrahedral polyhedra [CuCh4]7−, as well as the structural parameters in europium rare-earth copper tellurides EuRECuTe3, were higher than in isostructural quaternary chalcogenides. Ab initio calculations of the crystalline structure, phonon spectrum, and elastic properties of compounds EuRECuTe3 (RE = Ho, Tm, and Sc) ere conducted. The types and wave numbers of fundamental modes were determined, and the involvement of ions in IR and Raman modes was assessed. The calculated data of the crystal structure correlated well with the experimental results.

Arsenic speciation in picromerite K2Mg(SO4)2•6H2O: Electron paramagnetic resonance and synchrotron X-ray absorption spectroscopic characterizations and implications for organic fertilizers

Picromerite K2Mg(SO4)2•6H2O (also known as schoenite, schönite, or sulfate of potash) formed from alkaline lakes by evaporation is an increasingly important chlorine-free fertilizer and has been used to produce other organic fertilizers such as arcanite K2SO4 and boussingaultite (NH4)2Mg(SO4)2•6H2O. Picromerite and boussingaultite, two Tutton’s salts, are also common secondary solid phases in diverse types of mine tailings and play important roles in controlling the mobility and bioavailability of various heavy metal(loid)s, including arsenic. In this study, picromerite crystals containing 64 ppm As have been synthesized at ambient conditions from aqueous solutions by slow evaporation. Arsenic K-edge X-ray absorption near-edge-structure (XANES) and extended X-ray absorption fine structure (EXAFS) data show that the dominant oxidation state is +5 and that As5+ occupies the S site in picromerite. Single-crystal and powder electron paramagnetic resonance (EPR) spectra of gamma-ray-irradiated picromerite reveal three arsenic-associated oxyradicals: AsO42−, AsO32−, and AsO22−. The orientations of the principal 75As hyperfine directions of the AsO32− and AsO22− radicals match the bonding directions of the SO42− groups in the picromerite structure, further supporting substitutions of their respective diamagnetic precursors AsO43- and AsO33- for the sulfate group. These AsO42−, AsO32−, and AsO22− radicals in picromerite are similar but not identical to their counterparts in boussingaultite and gypsum, suggesting that sulfate minerals are capable of sequestrating both arsenate and arsenite, with important implications for understanding the fate and bioavailability of arsenic associated with agricultural applications of organic fertilizers.

Arsenic speciation in picromerite K2Mg(SO4)2•6H2O: Electron paramagnetic resonance and synchrotron X-ray absorption spectroscopic characterizations and implications for organic fertilizers

Picromerite K2Mg(SO4)2•6H2O (also known as schoenite, schönite, or sulfate of potash) formed from alkaline lakes by evaporation is an increasingly important chlorine-free fertilizer and has been used to produce other organic fertilizers such as arcanite K2SO4 and boussingaultite (NH4)2Mg(SO4)2•6H2O. Picromerite and boussingaultite, two Tutton’s salts, are also common secondary solid phases in diverse types of mine tailings and play important roles in controlling the mobility and bioavailability of various heavy metal(loid)s, including arsenic. In this study, picromerite crystals containing 64 ppm As have been synthesized at ambient conditions from aqueous solutions by slow evaporation. Arsenic K-edge X-ray absorption near-edge-structure (XANES) and extended X-ray absorption fine structure (EXAFS) data show that the dominant oxidation state is +5 and that As5+ occupies the S site in picromerite. Single-crystal and powder electron paramagnetic resonance (EPR) spectra of gamma-ray-irradiated picromerite reveal three arsenic-associated oxyradicals: AsO42−, AsO32−, and AsO22−. The orientations of the principal 75As hyperfine directions of the AsO32− and AsO22− radicals match the bonding directions of the SO42− groups in the picromerite structure, further supporting substitutions of their respective diamagnetic precursors AsO43- and AsO33- for the sulfate group. These AsO42−, AsO32−, and AsO22− radicals in picromerite are similar but not identical to their counterparts in boussingaultite and gypsum, suggesting that sulfate minerals are capable of sequestrating both arsenate and arsenite, with important implications for understanding the fate and bioavailability of arsenic associated with agricultural applications of organic fertilizers.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the ‘curse of dimensionality’, leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.