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bioRxiv2024

Engineered reactivity of a bacterial E1-like enzyme enables ATP-driven modification of protein C termini

In biological systems, ATP provides an energetic driving force for peptide bond formation, but protein chemists lack tools that emulate this strategy. Inspired by the eukaryotic ubiquitination cascade, we developed an ATP-driven platform for C-terminal activation and peptide ligation based on E. coli MccB, a bacterial ancestor of ubiquitin-activating (E1) enzymes that natively catalyzes C-terminal phosphoramidate bond formation. We show that MccB can act on non-native substrates to generate an O-AMPylated electrophile that can react with exogenous nucleophiles to form diverse C-terminal functional groups including thioesters, a versatile class of biological intermediates that have been exploited for protein semisynthesis. To direct this activity towards specific proteins of interest, we developed the Thioesterification C-terminal Handle (TeCH)-tag, a sequence that enables high-yield, ATP-driven protein bioconjugation via a thioester intermediate. By mining the natural diversity of the MccB family, we developed two additional MccB/TeCH-tag pairs that are mutually orthogonal to each other and to the E. coli system, facilitating the synthesis of more complex bioconjugates. Our method mimics the chemical logic of peptide bond synthesis that is widespread in biology for high-yield in vitro manipulation of protein structure with molecular precision.

biochemistry↗

bioRxiv2024

A Macroscopic Quantum Heat Engine: Nested Resonant Structures and the Entropic Stability of Life

Biological systems are fundamentally containers of thermally fluctuating atoms that through unknown mechanisms are structurally layered across many thermal scales from atoms to amino acids to primary, secondary, and tertiary structures to functional proteins to functional macromolecular assemblies and up. Understanding how chemical thermodynamics sc ales across these layered structures is central to describing biological structure and function. Muscle - with chemical thermodynamics well-defined on two different thermal scales - provides a clear solution to this problem. In 1938, A.V. Hill made the extraordinary observation that the mechanics and chemistry of muscle contraction is defined independent of the structural components of muscle, implying that the mechanics and chemistry of myosin motor proteins within muscle do not classically scale up to muscle. We have demonstrated experimentally that thermal scaling bridges Hills top-down thermodynamics and the bottom-up philosophy of molecular biologists. That is, with thermal scaling N individual myosin motor switches physically collapse into an ensemble of N myosin motor switches creating a functional entropy within the ensemble well defined by the statistical mechanics of a binary system of switches. This ensemble entropy stabilizes the resonant ensemble structure of muscle and energetically drives the irreversible kinetics and energetics of muscle contraction. Here I develop a general model of thermal scaling and show that it occurs when the ensemble state of a system is defined, at which point the number of ways constituent molecules can account for the ensemble state contributes to the entropy of the ensemble state. At that point, the statistical occupancy of molecular states physically replaces the physical occupancy of molecular states. This is not a classical mechanism and as shown here results in many quantum-like phenomena, which consistent with Hills observation means that biological function cannot be described by classical molecular mechanisms.

biophysics↗

bioRxiv2019

Dynamic interactions and intracellular fate of label-free GO within mammalian cells: role of lateral sheet size

Graphene oxide (GO) holds great potential for biomedical applications, however fundamental understanding of the way it interacts with biological systems is still lacking even though it is essential for successful clinical translation. In this study, we exploit intrinsic fluorescent properties of thin GO sheets to establish the relationship between lateral dimensions of the material, its cellular uptake mechanisms and intracellular fate over time. Label-free GO with distinct lateral dimensions, small (s-GO) and ultra-small (us-GO) were thoroughly characterised both in water and in biologically relevant cell culture medium. Interactions of the material with a range of non-phagocytic mammalian cell lines (BEAS-2B, NIH/3T3, HaCaT, 293T) were studied using a combination of complementary analytical techniques (confocal microscopy, flow cytometry and TEM). The uptake mechanism was initially interrogated using a range of pharmaceutical inhibitors and validated using polystyrene beads of different diameters (0.1 and 1 m). Subsequently, RNA-Seq was used to follow the changes in the uptake mechanism used to internalize s-GO flakes over time. Regardless of lateral dimensions, both types of GO were found to interact with the plasma membrane and to be internalized by a panel of cell lines studied. However, s-GO was internalized mainly via macropinocytosis while us-GO was mainly internalized via clathrin- and caveolae-mediated endocytosis. Importantly, we report the shift from macropinocytosis to clathrin-dependent endocytosis in the uptake of s-GO at 24 h, mediated by upregulation of mTORC1/2 pathway. Finally, we show that both s-GO and us-GO terminate in lysosomal compartments for up to 48 h. Our results offer an insight into the mechanism of interaction of GO with non-phagocytic cell lines over time that can be exploited for the design of biomedically-applicable 2D transport systems.

cell biology↗

bioRxiv2025

Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo

Lipid nanoparticles (LNPs) are the most clinically advanced nonviral RNA-delivery vehicles, though challenges remain in fully understanding how LNPs interact with biological systems. In vivo, proteins form an associated corona on LNPs that redefines their physicochemical properties and influences delivery outcomes. Despite its importance, the LNP protein corona is challenging to study owing to the technical difficulty of selectively recovering soft nanoparticles from biological samples. Herein, we developed a quantitative, label-free mass spectrometry-based proteomics approach to characterize the protein corona on LNPs. Critically, this protein corona isolation workflow avoids artifacts introduced by the presence of endogenous nanoparticles in human biofluids. We applied continuous density gradient ultracentrifugation for protein-LNP complex isolation, with mass spectrometry for protein identification normalized to protein composition in the biofluid alone. With this approach, we quantify proteins consistently enriched in the LNP corona including vitronectin, C-reactive protein, and alpha-2-macroglobulin. We explore the impact of these corona proteins on cell uptake and mRNA expression in HepG2 human liver cells, and find that, surprisingly, increased levels of cell uptake do not correlate with increased mRNA expression in part likely due to protein corona-induced lysosomal trafficking of LNPs. Our results underscore the need to consider the protein corona in the design of LNP-based therapeutics. Abstract Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/633942v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@2fef29org.highwire.dtl.DTLVardef@fe7613org.highwire.dtl.DTLVardef@1f9de01org.highwire.dtl.DTLVardef@15e5237_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Spatial probabilistic patterns of joint heat and water stress of chickpea in Australia

Quantitative characterisation of the crop environment for breeding and agronomic applications is commonly based on the cluster analysis of isolated climate factors, such as drought and temperature. The focus on individual climate factors fails to account for both the correlations between climate factors in space and time, and the contemporary models of dynamic biological systems where multiple interacting factors simultaneously shape the crop phenotype. With a focus on chickpea in Australia, our aims are to (1) assess the associations between climate factors and actual yield measured in 578 location -years; associations are investigated on a biologically relevant developmental scale; (2) establish the spatial, probabilistic patterns of multivariate environment types at regional and continental scales; and (3) evaluate the shifts in the frequency of environment types with climate change. We identified a syndrome of hot, dry and high vapour pressure deficit with three intensities of stress classified as low, medium and high. Measured yield, in the range from failed crop to 4 t ha-1, aligned more strongly with this multivariate syndrome than with isolated climate factors, with stronger associations at regional than continental scale. The frequency of stressful environment types increased with realised climate change and was most severe in the chickpea growing heartland of central Queensland; our models show that increased frequency of stress could be partially mitigated with earlier flowering varieties. Our findings will inform chickpea agronomy, phenotyping and breeding.

physiology↗

bioRxiv2024

The mechanism of spatial pattern transition in motile bacterial collectives

Understanding how individual behaviours contribute to collective actions is key in biological systems. In Myxococcus xanthus, a bacterial predator, swarming shifts to rippling patterns due to changes in the local environment near prey colonies. Through high-resolution microscopy and theoretical analysis, we demonstrate that two key properties drive this shift: local cellular alignment guided by an extracellular matrix and the ability of cells to reverse to resolve congestion. A tunable refractory period in the reversal system enables collective adaptation, allowing cells to synchronise in rippling and resolve congestion in swarming. These transitions occur without changes in genetic regulation but create stable spatial domains that promote local differentiation, a mechanism of spatial sorting that may be widespread in biology.

microbiology↗

bioRxiv2025

Generative design of novel bacteriophages with genome language models

Many important biological functions arise not from single genes, but from complex interactions encoded by entire genomes. Genome language models have emerged as a promising strategy for designing biological systems, but their ability to generate functional sequences at the scale of whole genomes has remained untested. Here, we report the first generative design of viable bacteriophage genomes. We leveraged frontier genome language models, Evo 1 and Evo 2, to generate whole-genome sequences with realistic genetic architectures and desirable host tropism, using the lytic phage {Phi}X174 as our design template. Experimental testing of AI-generated genomes yielded 16 viable phages with substantial evolutionary novelty. Cryo-electron microscopy revealed that one of the generated phages utilizes an evolutionarily distant DNA packaging protein within its capsid. Multiple phages demonstrate higher fitness than {Phi}X174 in growth competitions and in their lysis kinetics. A cocktail of the generated phages rapidly overcomes {Phi}X174-resistance in three E. coli strains, demonstrating the potential utility of our approach for designing phage therapies against rapidly evolving bacterial pathogens. This work provides a blueprint for the design of diverse synthetic bacteriophages and, more broadly, lays a foundation for the generative design of useful living systems at the genome scale.

synthetic biology↗

bioRxiv2026

Learning proteomic disease trajectories with flow matching

High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inherently dynamic and methods for reconstructing continuous proteome changes remain limited. Here, we introduce proteome velocity, a framework for inferring continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data using flow matching, in which a neural network learns velocity fields over proteome space. Proteome velocity estimates how rapidly and in which direction protein abundances change along a biological progression, such as disease. In mouse sepsis, covariate-conditioned velocity models resolved tissue- and pathogen-specific proteome trajectories and identified inflammatory proteins with distinct temporal activation patterns across infection routes and organ systems. In clinical COVID-19 plasma proteomes, inferred trajectories separated into distinct velocity programs associated with disease severity. These results show how generative trajectory models can transform cross-sectional proteomics data into interpretable, protein-resolved representations of molecular progression.

bioinformatics↗

bioRxiv2023

A scalable, data analytics workflow for image-based morphological profiles

Cell Painting is an established community-based, microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest annotated, publicly available dataset, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 100k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this paper, we have developed a data analytics workflow that is both scalable and computationally efficient, while providing significant, biologically relevant insights for biologists estimating and comparing the effects of different drug treatments. The two main objectives proposed include: 1) a simple, yet sophisticated, scalable data analytics metric that utilizes negative controls for comparing morphological cell profiles. We call this metric the equivalence score (Eq. score). 2) A workflow to identify and amplify subtle morphological image profile changes caused by drug treatments, compared to the negative controls. In summary, we provide a data analytics workflow to assist biologists in interpreting high-dimensional image features, not necessarily limited to morphological ones. This enhances the efficiency of drug candidate screening, thereby streamlining the drug development process. By increasing our understanding of using complex image-based data, we can decrease the cost and time to develop new, life-saving treatments. Author summaryMicroscopy-assays are often used to study cell responses to treatments in the search for new drugs. In this paper, we present a method that simplifies the understanding of the data generated from such assays. The data in this study consists of 750 morphological features, which describe the traits and characteristics of the cells, extracted from the images. By using untreated cells as a biological baseline, were able to detect subtle changes that occur in the treated cells. These changes are then transformed into an equivalence score (Eq. score), a metric that lets us compare the similarities among different treatments relative to our baseline of untreated cells. Our Eq. score approach transforms complex, high-dimensional data about cell morphology into something more interpretable and understandable. It reduces the "noise" in the features and highlights important changes, the "signal". Our method can be integrated into existing workflows, aiding researchers in understanding and interpreting complex morphological data derived from cell images more easily. Understanding cell morphology is crucial to deepening our knowledge of biological systems. Ultimately, this could contribute to the faster and more cost-effective development of new, life-saving treatments.

cell biology↗

bioRxiv2025

CoSiDeX: A hyperspectral fluorescent protein resource for highly multiplexed imaging

Fluorescent proteins (FPs) have revolutionized spatiotemporal observations in biology. Yet, the design of multiplexed assays remains constrained by limited spectral characterization and palette validation. Although over 1,000 FPs have been catalogued, systematic resources for characterizing their use in multiplexed approaches are lacking. Here we present a resource and methodology for selecting and decoding FPs in multiplexed imaging experiments. A library of forty-four FPs was built for rapid assembly into mammalian expression vectors and transposase-mediated integration. Hyperspectral imaging was performed for each FP and spectral space was characterized mathematically. To support experimental design and data interpretation, we developed the Cosine Similarity Decoder of XFP (CoSiDeX) toolbox, to predict spectrally resolvable FP palettes and decode and re-color hyperspectral images. Using this approach, we demonstrate live-cell imaging of 12 uniquely labeled clones. Our work offers a scalable platform for selecting optimal FP palettes for multiplex experiments, with broad utility across diverse biological systems and hyperspectral imaging techniques.

molecular biology↗

bioRxiv2016

DynOmics to identify delays and co-expression patterns across time course experiments

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in omics data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across omics data within an organism and for comparative gene expression analysis across organisms.

Bioinformatics↗

bioRxiv2021

The emergence of highly fit SARS-CoV-2 variants accelerated by recombination

The SARS-CoV-2 pandemic recently entered an alarming new phase with the emergence of the variants of concern (VOC) and understanding their biology is paramount to predicting future ones. Current efforts mainly focus on mutations in the spike glycoprotein (S), but changes in other regions of the viral proteome are likely key. We analyzed more than 900,000 SARS-CoV-2 genomes with a computational systems biology approach including a haplotype network and protein structural analyses to reveal lineage-defining mutations and their critical functional attributes. Our results indicate that increased transmission is promoted by epistasis, i.e., combinations of mutations in S and other viral proteins. Mutations in the non-S proteins involve immune-antagonism and replication performance, suggesting convergent evolution. Furthermore, adaptive mutations appear in geographically disparate locations, suggesting that either independent, repeat mutation events or recombination among different strains are generating VOC. We demonstrate that recombination is a stronger hypothesis, and may be accelerating the emergence of VOC by bringing together cooperative mutations. This emphasizes the importance of a global response to stop the COVID-19 pandemic.

evolutionary biology↗

bioRxiv2021

Identification of upstream transcription factor binding sites in orthologous genes using mixed Student's t-test statistics

BackgroundTranscription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive. Methodology/Principal FindingsThis study presents a computational tool, named "Grit", for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Students t-test statistical method that detects high-scoring conserved and non-conserved binding sites among species. The program performs sequence scanning at a rate of 3.2 Mb/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It marginally outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets. SignificanceGrit is a good alternative to current available motif scanning tools and is publicly available at http://www.thua45.cn/grit under an academic free license. Author SummaryLocating transcription factor-binding (TF-binding) site in the genome and identification their function is fundamental in understanding various biological processes. Improve the performance of the prediction tools is important because accurate TF-binding site prediction can save cost and time for wet-lab experiments. Also, genome wide TF-binding site prediction can provide new insights for transcriptome regulation in system biology perspective. This study developed a new TF-binding site prediction tool based on mixed Students t-test statistical method. The tool is amongst the top-ranked TF-binding site predictors, as such, it can help the researchers in TF-binding site identification and transcriptional regulation mechanism interpretation of genes.

bioinformatics↗

bioRxiv2018

KymoButler: A deep learning software for automated kymograph tracing and analysis

Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based \"one-click\" application for use by the wider scientific community. Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

cell biology↗

bioRxiv2026

High-speed atomic force-Raman microscopy

A comprehensive understanding of the mechanisms underlying biological systems requires correlative analysis of multiple complementary molecular properties through multidimensional measurements. High-speed atomic force microscopy (HS-AFM) is a powerful tool for elucidating biomolecular structural dynamics at the single-molecule level with high spatiotemporal resolution. However, structural information alone is often insufficient for fully understanding the biological function mechanisms. Here, we report high-speed atomic force-Raman microscopy (HS-AFRM), which enables multimodal measurements combining video-rate structural imaging with chemical-bond analysis. Raman spectroscopy is a powerful, non-invasive technique that probes molecular vibrations to provide chemical information. We achieved several key technical developments that facilitated the seamless integration of HS-AFM and micro-Raman spectroscopy, allowing reliable correlative measurements of structural and chemical information. We validated the versatility of the developed system using representative samples, including two-dimensional materials and a protein. Furthermore, we demonstrate probing of changes in the surrounding environment, which are inaccessible by HS-AFM alone. Multimodal measurements incorporating fluorescence spectroscopy were also demonstrated as an additional practical extension. This multimodal approach substantially enhances the analytical capability of HS-AFM, providing a powerful platform for revealing correlated structural and chemical properties across diverse research fields.

biophysics↗

bioRxiv2020

New insights into TNFα/PTP1B and PPARγ pathway through RNF213- a link between inflammation, obesity, insulin resistance and Moyamoya disease

Diabetic patients are always at a higher risk of ischemic diseases like coronary artery diseases. One such ischemic carotid artery disease is Moyamoya. Moyamoya disease (MMD) has been associated with diabetes Type-I and II and the causality was unclear. RNF213 is the major susceptible gene for MMD. To understand the association between diabetes mellitus and MMD we chose the major players from both the anomalies, insulin and RNF213. But before establishing a role of RNF213 in insulin regulating pathway we had to understand the involvement of RNF213 within different biological systems. For this we have adopted a preliminary computational approach to understand the prominent interactions of RNF213. Our first objective was to construct an interactome for RNF213. We have analyzed several curated databases and adapted a list of RNF213 interacting partners to develop its interactome. Then to understand the involvement of this interactome in biological functions we have analyzed major biological pathways, biological processes and prominent clusters related to this interactome through computational approach. Then to develop a pathway that might give clue for RNF213 involvement in insulin regulatory pathway we have validated the intercluster and intracluster predictions and identified a regulatory pathway for RNF213. RNF213 interactome was observed to be involved in adaptive immunity with 4 major clusters; one of the cluster involved TNFα. Immune system involves several pathways, and therefore at this point we have chosen an event-based strategy to obtain an explicit target. Immunity is mediated by many pro-inflammatory cytokines like TNFα. TNFα-mediated inflammation, obesity and insulin resistance are associated. Therefore we chose to explore the role of RNF213 in TNFα-mediated inflammation in macrophages and inflammation-mediated insulin-resistance in adipocytes. We have observed an enhancement of RNF213 gene expression by LPS mediated pro-inflammatory stimuli and suppression by PPARγ-mediated anti-inflammatory, insulin sensitizing stimuli in macrophages. A more significant response was observed in adipocytes as well. Administration of the pro-inflammatory cytokine TNFα was able to impede the reduction in RNF213 expression during adipogenesis and this effect was observed to be mediated by PTP1B. Inactivation of PTP1B abolished RNF213 expression which in turn enhanced the adipogenesis process through enhanced PPARγ. Constitutive expression of RNF213 suppressed the adipocyte differentiation by the inhibition of PPARγ. We could show the expression of RNF213 has been regulated by TNFα/PTP1B pathway and PPARγ. The constitutive expression of RNF213 during adipogenesis appears to be an adipostatic measure that obese patients acquire to inhibit further adipogenesis. This is verified in silico by analyzing the gene expression data obtained from Gene Expression Omnibus database, which showed a higher expression of RNF213 in adipose tissue samples of obese people. Overall this study gives new insights in the TNFα-mediated pathway in adipogenesis and suggests a role of RNF213 in adipogenesis via this pathway.View Full Text

bioinformatics↗

bioRxiv2021

Mining folded proteomes in the era of accurate structure prediction

Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins. Further, we explore the use of structure-based mining for functional inference.

bioinformatics↗

bioRxiv2022

Flaver: mining transcription factors in genome-wide transcriptome profiling data using weighted rank correlation statistics

BackgroundMining key transcription factors (TFs) in genome-wide transcriptome profiling data has been an active research area for many years and it has been partially solved by mathematically modelling the ranking orders of genes in the target gene-set for the TF of interest in the gene-list ranked by expression values, called gene-set enrichment analysis (GSEA). However, in some application scenarios the gene-set itself also has a rank attribute, such as the putative target gene-set predicted by the Grit software and other alternatives like FIMO and Pscan. New algorithms must be developed to analyze these data properly. Methodology/Principal FindingsBy implementing the weighted Kendalls tau statistic, we proposed a method for genome-wide transcriptome profiling data mining that can identify the key TFs orchestrating a profile. Theoretical properties of the proposed method were established, and its advantages over the GSEA approach were demonstrated when analyzing the RNA-Atlas datasets. The results showed that the top-rated TFs by our method always have experimentally supported evidences in the literatures. Benchmarking using gene ontology (GO) annotations in the AmiGO database indicated that the geometry performance (SQR_P) of our method is higher than GSEA in more than 14% of the cases. SignificanceThe developed method is suitable for analyzing the significance of overrepresentation of ranked gene-sets in a ranked gene-list. A software implementing the method, called "Flaver", was developed and is publicly available at http://www.thua45.cn/flaver under an academic free license. Author SummaryIdentification of the regulation roles of TFs in the transcriptome is fundamental in understanding various biological processes. Improve the performance of the prediction tools is important because accurate TF-mining in transcriptome data can finely improve the efficiency of wet-lab experiments. Also, genome wide TF-mining can provide new target genes for transcriptome regulation analysis in system biology perspective. This study developed a new TF-mining tool based on weighted rank correlation statistical method. The tool has better performance in analyzing ranked gene-set and ranked gene-list than its competitor, the GSEA tool. It can help the researchers in identification of the most important TFs in transcriptional data.

bioinformatics↗