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PubMed2002

Quantitation of residual WBCs in filtered blood components by high-throughput, real-time kinetic PCR.

BACKGROUND: The effort to eliminate transfusion complications associated with WBCs has led to the widespread use of filters able to reduce WBC concentrations to <or=0.1 WBC per microL blood. This has necessitated sensitive QC methods to quantitate residual WBCs in filtered units. One fast, effective method is DNA amplification using real-time kinetic PCR (kPCR). STUDY DESIGN AND METHODS: Two methods of preparation of standards were compared and used for the optimization of quantitative kPCR. The first involved spiking genomic DNA cell lysate into a diluent, followed by a series of 1 in 10 dilutions. The second involved spiking serial 1 in 10 dilutions of WBCs into twice-filtered fresh whole blood. Two hundred fifty filtered frozen whole-blood samples were amplified in duplicate to show the kPCR assay's reproducibility. Another 359 filtered frozen whole blood samples were used to compare data from kPCR with data from a standard PCR protocol using (32)P-labeled probe and autoradiography. All specimens were amplified for conserved HLA DQ(alpha) sequences. RESULTS: Standards prepared by both methods gave reproducible and equivalent results. Quantitation of standards representing a dynamic range of 8 x 10(o) to 8 x 10(5) WBCs per mL, yielded standard deviations ranging from 0.59 cycle to 1.04 cycles (a one-cycle increase is equivalent to a twofold increase in WBC concentration). The scatter graph of the 250 samples tested in duplicate by kPCR generated a slope of 1.0122 and an R(2) value of 0.9265. The comparison of kPCR and (32)P-probe hybridization results on 359 clinical samples gave a scatter-graph slope of 0.9428 and an R(2) value of 0.8718, indicating excellent agreement of the methods over a 4-log dynamic range. CONCLUSION: kPCR is a high-throughput, sensitive assay that could prove useful in routine quality assurance of the WBC reduction process.

Computer Systems↗

PubMed2026

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗

PubMed2005

Large scale hierarchical clustering of protein sequences.

BACKGROUND: Searching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to. RESULTS: We report on our developments in grouping all known protein sequences hierarchically into superfamily and family clusters. Our graph-based algorithms take into account the topology of the sequence space induced by the data itself to construct a biologically meaningful partitioning. We have applied our clustering procedures to a non-redundant set of about 1,000,000 sequences resulting in a hierarchical clustering which is being made available for querying and browsing at http://systers.molgen.mpg.de/. CONCLUSIONS: Comparisons with other widely used clustering methods on various data sets show the abilities and strengths of our clustering methods in producing a biologically meaningful grouping of protein sequences.

Algorithms↗

PubMed2000

Correspondence analysis of genes and tissue types and finding genetic links from microarray data.

In this paper, we propose and use two novel procedures for the analysis of microarray gene expression data. The first is correspondence analysis which visualizes the relationship between genes and tissues as two 2 dimensional graphs, oriented so that distances between genes are preserved, distances between tissues are preserved, and so that genes which primarily distinguish certain types of tissue are spatially close to those tissues. For the inference of genetic links, partial correlations rather than correlations are the key issue. A partial correlation between i and j is the relationship between i and j after the effect of surrounding genes has been subtracted out of their pairwise correlation. This leads to the area of graphical modeling. A limitation of the graphical modeling approach is that the correlation matrix of expression profiles between genes is degenerate whenever the number of genes to be analyzed exceeds the number of distinct expression measurements. This can cause considerable problems, as calculation of partial correlations typically uses the inverse of the correlation matrix. To avoid this limitation, we propose two practical multiple regression procedures with variable selection to measure the net, screened, relationship between pairs of genes. Possible biases arising from the analysis of a subset of genes from the genome are examined in the worked examples. It seems that both these approaches are more natural ways of analyzing gene expression data than the currently popular approach of two way clustering.

Carbonates↗

PubMed2025

IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.

MOTIVATION: Developing computational tools for integrative analysis across multiple types of omics data has been of immense importance in cancer molecular biology and precision medicine research. While recent advancements have yielded integrative prediction solutions for multi-omics data, these methods lack a comprehensive and cohesive understanding of the rationale behind their specific predictions. To shed light on personalized medicine and unravel previously unknown characteristics within integrative analysis of multi-omics data, we introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications, named Integrative Graph Convolutional Networks (IGCN). RESULTS: To demonstrate the superiority of IGCN, we compare its performance with other state-of-the-art approaches across different cancer subtype and biomedical classification tasks. Our experimental results show that our proposed model outperforms the state-of-the-art and baseline methods. IGCN identifies which types of omics data receive more emphasis for each patient when predicting a specific class. Additionally, IGCN has the capability to pinpoint significant biomarkers from a range of omics data types. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/bozdaglab/IGCN.

Humans↗

PubMed1999

Cytological, genetic and evolutionary functions of chiasmata based on chiasma graph analysis.

The nature of the chiasma as a cytological parameter for analysing cross-over was reexamined quantitatively by an improved chiasma graph method. It was reconfirmed in Mus platythrix (n =13) that interstitial chiasmata at diakinesis are distributed randomly and almost uniformly along bivalents except for the centromere and telomere regions. The size of these chiasma blank regions was consistently 0.8% of the total length of haploid autosomes in all chromosomes. There was a minimum value of chiasma interference distance between two adjacent chiasmata, which was constantly 1.8% in all chromosomes. The chiasma frequency at diakinesis was 20.1+/-2. 0 by the conventional method including terminal chiasmata. However, the primed in situ labeling technique revealed that terminal chiasmata were mostly telomere-telomere associations. From these data and also from recent molecular data we concluded that the terminal chiasma is cytologically functional for ensuring the normal disjunction of bivalents at anaphase I, but genetically non-functional for shuffling genes. The chiasma frequency excluding terminal chiasmata was 14.6+/-1.8. Reexamination of the chiasma frequency of 106 animal species revealed that the chiasma frequency increased linearly in proportion to the haploid chromosome number in spite of remarkable difference in their genome size. The increase in chiasma frequency would be evolution-adaptive, because gene shuffling is expected to be accelerated in species with high chromosome numbers.

Animals↗

PubMed2003

Cross-platform comparison and visualisation of gene expression data using co-inertia analysis.

BACKGROUND: Rapid development of DNA microarray technology has resulted in different laboratories adopting numerous different protocols and technological platforms, which has severely impacted on the comparability of array data. Current cross-platform comparison of microarray gene expression data are usually based on cross-referencing the annotation of each gene transcript represented on the arrays, extracting a list of genes common to all arrays and comparing expression data of this gene subset. Unfortunately, filtering of genes to a subset represented across all arrays often excludes many thousands of genes, because different subsets of genes from the genome are represented on different arrays. We wish to describe the application of a powerful yet simple method for cross-platform comparison of gene expression data. Co-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships in multiple datasets which contain the same samples. CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses. RESULTS: We illustrate the power of CIA for cross-platform analysis of gene expression data by using it to identify the main common relationships in expression profiles on a panel of 60 tumour cell lines from the National Cancer Institute (NCI) which have been subjected to microarray studies using both Affymetrix and spotted cDNA array technology. The co-ordinates of the CIA projections of the cell lines from each dataset are graphed in a bi-plot and are connected by a line, the length of which indicates the divergence between the two datasets. Thus, CIA provides graphical representation of consensus and divergence between the gene expression profiles from different microarray platforms. Secondly, the genes that define the main trends in the analysis can be easily identified. CONCLUSIONS: CIA is a robust, efficient approach to coupling of gene expression datasets. CIA provides simple graphical representations of the results making it a particularly attractive method for the identification of relationships between large datasets.

Breast Neoplasms↗

PubMed2003

Haplotyping as perfect phylogeny: a direct approach.

A full haplotype map of the human genome will prove extremely valuable as it will be used in large-scale screens of populations to associate specific haplotypes with specific complex genetic-influenced diseases. A haplotype map project has been announced by NIH. The biological key to that project is the surprising fact that some human genomic DNA can be partitioned into long blocks where genetic recombination has been rare, leading to strikingly fewer distinct haplotypes in the population than previously expected (Helmuth, 2001; Daly et al., 2001; Stephens et al., 2001; Friss et al., 2001). In this paper we explore the algorithmic implications of the no-recombination in long blocks observation, for the problem of inferring haplotypes in populations. This assumption, together with the standard population-genetic assumption of infinite sites, motivates a model of haplotype evolution where the haplotypes in a population are assumed to evolve along a coalescent, which as a rooted tree is a perfect phylogeny. We consider the following algorithmic problem, called the perfect phylogeny haplotyping problem (PPH), which was introduced by Gusfield (2002) - given n genotypes of length m each, does there exist a set of at most 2n haplotypes such that each genotype is generated by a pair of haplotypes from this set, and such that this set can be derived on a perfect phylogeny? The approach taken by Gusfield (2002) to solve this problem reduces it to established, deep results and algorithms from matroid and graph theory. Although that reduction is quite simple and the resulting algorithm nearly optimal in speed, taken as a whole that approach is quite involved, and in particular, challenging to program. Moreover, anyone wishing to fully establish, by reading existing literature, the correctness of the entire algorithm would need to read several deep and difficult papers in graph and matroid theory. However, as stated by Gusfield (2002), many simplifications are possible and the list of "future work" in Gusfield (2002) began with the task of developing a simpler, more direct, yet still efficient algorithm. This paper accomplishes that goal, for both the rooted and unrooted PPH problems. It establishes a simple, easy-to-program, O(nm(2))-time algorithm that determines whether there is a PPH solution for input genotypes and produces a linear-space data structure to represent all of the solutions. The approach allows complete, self-contained proofs. In addition to algorithmic simplicity, the approach here makes the representation of all solutions more intuitive than in Gusfield (2002), and solves another goal from that paper, namely, to prove a nontrivial upper bound on the number of PPH solutions, showing that that number is vastly smaller than the number of haplotype solutions (each solution being a set of n pairs of haplotypes that can generate the genotypes) when the perfect phylogeny requirement is not imposed.

Algorithms↗

PubMed2025

scMGCL: accurate and efficient integration representation of single-cell multi-omics data.

MOTIVATION: Single-cell multi-omics data integration is essential for understanding cellular states and disease mechanisms, yet integrating heterogeneous data modalities remains a challenge. We present scMGCL, a graph contrastive learning framework for robust integration of single-cell ATAC-seq and RNA-seq data. Our approach leverages self-supervised learning on cell-cell similarity graphs, in which each modality's graph structure serves as an augmentation for the other. This cross-modality contrastive paradigm enables the learning of biologically meaningful, shared representations while preserving modality-specific features. RESULTS: Benchmarking against state-of-the-art methods demonstrates that scMGCL outperforms others in cell-type clustering, label transfer accuracy, and preservation of marker-gene correlations. Additionally, scMGCL significantly improves computational efficiency, reducing runtime and memory usage. The method's effectiveness is further validated through extensive analyses of cell-type similarity and functional consistency, providing a powerful tool for multi-omics data exploration. AVAILABILITY AND IMPLEMENTATION: Code and datasets are released at https://github.com/zlCreator/scMGCL.

Single-Cell Analysis↗

PubMed2025

soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.

Humans↗

PubMed2005

The use of edge-betweenness clustering to investigate biological function in protein interaction networks.

BACKGROUND: This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters. RESULTS: Protein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions. CONCLUSION: This method provides a useful tool for rapid screening of small to medium size protein interaction datasets.

Algorithms↗

PubMed2006

Assessing the effects of human mixing patterns on human immunodeficiency virus-1 interhost phylogenetics through social network simulation.

Geneticists seeking to understand HIV-1 evolution among human hosts generally assume that hosts represent a panmictic population. Social science research demonstrates that the network patterns over which HIV-1 spreads are highly nonrandom, but the effect of these patterns on the genetic diversity of HIV-1 and other sexually transmitted pathogens has yet to be thoroughly examined. In addition, interhost phylogenetic models rarely account explicitly for genetic diversity arising from intrahost dynamics. This study outlines a graph-theoretic framework (exponential random graph modeling, ERGM) for the estimation, inference, and simulation of dynamic partnership networks. This approach is used to simulate HIV-1 transmission and evolution under eight mixing patterns resembling those observed in empirical human populations, while simultaneously incorporating intrahost viral diversity. Models of parametric growth fit panmictic populations well, yielding estimates of total viral effective population on the order of the product of infected host size and intrahost effective viral population size. Populations exhibiting patterns of nonrandom mixing differ more widely in estimates of effective population size they yield, however, and reconstructions of population dynamics can exhibit severe errors if panmixis is assumed. I discuss implications for HIV-1 phylogenetics and the potential for ERGM to provide a general framework for addressing these issues.

Evolution, Molecular↗

PubMed2026

ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.

MOTIVATION: Spatial multi-omics technologies jointly profile transcriptomes, proteins and chromatin accessibility in situ, enabling integrative analysis of tissue organization across molecular layers. However, most existing graph-based integration methods rely on independently constructed modality-specific k-nearest-neighbor graphs. When auxiliary modalities are sparse or noisy, these graphs can become topologically discordant, propagate spurious edges, weaken cross-modal alignment, and reduce spatial domain resolution. RESULTS: We present Anchored RNA for Integrated Spatial Embedding (ARISE), an RNA expression anchored framework for spatial multi-omics integration. ARISE defines a shared-edge topology by intersecting RNA feature-similarity and spatial-proximity graphs, encodes auxiliary modalities on this common scaffold, and integrates them through inside-out hierarchical fusion. We further show theoretically that graph intersection minimizes false-positive edges within a broad class of k-of-r graph fusion rules, providing a principled basis for topology anchoring. Across various spatial multi-omics benchmarks spanning simulated and real datasets in bi-modal and tri-modal settings, ARISE improves spatial domain identification, cross-modal consistency, and preservation of tissue structure relative to existing methods. Furthermore, the learned representation supports biologically meaningful downstream analyses, including marker-based domain annotation, pathway enrichment, and cis-regulatory inference, indicating that ARISE yields a robust and interpretable framework for spatial multi-omics integration. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XiangxiangWang-code/ARISE. The archived version used in this study is available at https://doi.org/10.6084/m9.figshare.32686137.v2.

Multiomics↗

PubMed2005

Reconstructing pedigrees: a combinatorial perspective.

A pedigree is a directed graph that displays the relationship between individuals according to their parentage. We derive a combinatorial result that shows how any pedigree-up to individuals who have no extant (present-day) ancestors-can be reconstructed from (sex-labelled) pedigrees that describe the ancestry of single extant individuals and pairs of extant individuals. Furthermore, this reconstruction can be done in polynomial time. We also provide an example to show that the corresponding reconstruction result does not hold for pedigrees that are not sex-labelled. We then show how any pedigree can also be reconstructed from two functions that just describe certain circuits in the pedigree. Finally, we obtain an enumeration result for pedigrees that is relevant to the question of how many segregating sites are needed to reconstruct pedigrees.

Animals↗

PubMed2004

A parallel graph decomposition algorithm for DNA sequencing with nanopores.

MOTIVATION: With the potential availability of nanopore devices that can sense the bases of translocating single-stranded DNA (ssDNA), it is likely that 'reads' of length approximately 10(5) will be available in large numbers and at high speed. We address the problem of complete DNA sequencing using such reads. We assume that approximately 10(2) copies of a DNA sequence are split into single strands that break into randomly sized pieces as they translocate the nanopore in arbitrary orientations. The nanopore senses and reports each individual base that passes through, but all information about orientation and complementarity of the ssDNA subsequences is lost. Random errors (both biological and transduction) in the reads create further complications. RESULTS: We have developed an algorithm that addresses these issues. It can be considered an extreme variation of the well-known Eulerian path approach. It searches over a space of de Bruijn graphs until it finds one in which (a) the impact of errors is eliminated and (b) both possible orientations of the two ssDNA sequences can be identified separately and unambiguously. Our algorithm is able to correctly reconstruct real DNA sequences of the order of 10(6) bases (e.g. the bacterium Mycoplasma pneumoniae) from simulated erroneous reads on a modest workstation in about 1 h. We describe, and give measured timings of, a parallel implementation of this algorithm on the Cray Multithreaded Architecture (MTA-2) supercomputer, whose architecture is ideally suited to this 'unstructured' problem. Our parallel implementation is crucial to the problem of rapidly sequencing long DNA sequences and also to the situation where multiple nanopores are used to obtain a high-bandwidth stream of reads.

Algorithms↗

PubMed1993

LODVIEW: a computer program for the graphical evaluation of lod score results in exclusion mapping of human disease genes.

For linkage analysis projects aimed at mapping hereditary disease genes in humans, hundreds of highly polymorphic microsatellite markers which can be typed by PCR (PCR markers) have become available. With this technical improvement, the availability of a technique allowing for transparency in the handling of rapidly generated lod score data is becoming important. We present a computer program LODVIEW for the graphical representation of lod score data. It is designed for the input of lod score data generated with the LINKAGE package or similar programs. LODVIEW consists of 24 preformatted files, one for each chromosome. Each file contains a table for the input of lod score data and a file for the graphical representation of the data, which will show automatically any entry that is made in the respective input table. The program provides the user with published PCR marker information pre-entered into a table and graph at the correct positions corresponding to the genetic distances between markers. The graphical display of LODVIEW allows for the rapid evaluation of lod score results calculated from PCR markers on each chromosome. The following information can be obtained from the graphical display at one glance: (i) Regions of exclusion (Z(theta) < -2) and nonexclusion, (ii) markers with positive lod scores, (iii) the distribution of positive and negative lod scores among the families examined (indication of genetic heterogeneity), (iv) multipoint lod scores, and (v) the availability of PCR markers in regions of interest. The program is continually updated for novel PCR marker information from the literature. The program will help to efficiently monitor and direct the progress of exclusion mapping projects.

Chromosome Mapping↗

PubMed2006

Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks.

MOTIVATION: The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets. RESULTS: We demonstrate a new method ('load points') to rank the enzymes/metabolites in the metabolic network and propose a model to determine and rank the biochemical lethality in metabolic networks (enzymes/metabolites) through 'choke points'. Based on an extended form of the graph theory model of metabolic networks, metabolite structural information was used to calculate the k-shortest paths between metabolites (the presence of more than one competing path between substrate and product). On the basis of these paths and connectivity information, load points were calculated and used to empirically rank the importance of metabolites/enzymes in the metabolic network. The load point analysis emphasizes the role that the biochemical structure of a metabolite, rather than its connectivity (hubs), plays in the conversion pathway. In order to identify potential drug targets (based on the biochemical lethality of metabolic networks), the concept of choke points and load points was used to find enzymes (edges) which uniquely consume or produce a particular metabolite (nodes). A non-pathogenic bacterial strain Bacillus subtilis 168 (lactic acid producing bacteria) and a related pathogenic bacterial strain Bacillus anthracis Sterne (avirulent but toxigenic strain, producing the toxin Anthrax) were selected as model organisms. The choke point strategy was implemented on the pathogen bacterial network of B.anthracis Sterne. Potential drug targets are proposed based on the analysis of the top 10 choke points in the bacterial network. A comparative study between the reported top 10 bacterial choke points and the human metabolic network was performed. Further biological inferences were made on results obtained by performing a homology search against the human genome. AVAILABILITY: The load and choke point modules are introduced in the Pathway Hunter Tool (PHT), the basic version of which is available on http://www.pht.uni-koeln.de.

Bacillus↗

PubMed2005

Colorectal cancer "methylator phenotype": fact or artifact?

It has been proposed that human colorectal tumors can be classified into two groups: one in which methylation is rare, and another with methylation of several loci associated with a "CpG island methylated phenotype (CIMP)," characterized by preferential proximal location in the colon, but otherwise poorly defined. There is considerable overlap between this putative methylator phenotype and the well-known mutator phenotype associated with microsatellite instability (MSI). We have examined hypermethylation of the promoter region of five genes (DAPK, MGMT, hMLH1, p16INK4a, and p14ARF) in 106 primary colorectal cancers. A graph depicting the frequency of methylated loci in the series of tumors showed a continuous, monotonically decreasing distribution quite different from the previously claimed discontinuity. We observed a significant association between the presence of three or more methylated loci and the proximal location of the tumors. However, if we remove from analysis the tumors with hMLH1 methylation or those with MSI, the significance vanishes, suggesting that the association between multiple methylations and proximal location was indirect due to the correlation with MSI. Thus, our data do not support the independent existence of the so-called methylator phenotype and suggest that it rather may represent a statistical artifact caused by confounding of associations.

Adaptor Proteins, Signal Transducing↗