642 research outputs found
Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations
No full textToday, it is well-known that in eukaryotic cells the complex interplay of transcription factors (TFs) bound to the DNA of promoters and enhancers is the basis for precise and specific control of transcription. Computational methods have been developed for the identification of potentially cooperating TFs through the co-occurrence of their binding sites (TFBSs). One challenge of these methods is the differentiation of TFBS pairs that are specific for a given sequence set from those that are ubiquitously appearing, rendering the results highly dependent on the choice of a proper background set. Here, we present an extension of our previous PC-TraFF approach that estimates the background co-occurrence of any TF pair by preserving the (oligo-) nucleotide composition and, thus, the core of TFBSs in the sequences of interest. Applying our approach to a simulated data set with implanted TFBS pairs, we could successfully identify them as sequence-set specific under a variety of conditions. When we analyzed the gene expression data sets of five breast cancer associated subtypes, the number of overlapping pairs could be dramatically reduced in comparison to our previous approach. As a result, we could identify potentially cooperating transcriptional regulators that are characteristic for each of the five breast cancer subtypes. This indicates that our approach is able to discriminate specific potential TF cooperations against ubiquitously occurring combinations. The results obtained with our method may help to understand the genetic programs governing specific biological processes such as the development of different tumor types
A promising approach towards precise animal weight monitoring using convolutional neural networks
No full textAn Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
No full textSensor technologies, such as the Global Navigation Satellite System (GNSS), produce huge amounts of data by tracking animal locations with high temporal resolution. Due to this high resolution, all animals show at least some co-occurrences, and the pure presence or absence of co-occurrences is not satisfactory for social network construction. Further, tracked animal contacts contain noise due to measurement errors or random co-occurrences. To identify significant associations, null models are commonly used, but the determination of an appropriate null model for GNSS data by maintaining the autocorrelation of tracks is challenging, and the construction is time and memory consuming. Bioinformaticians encounter phylogenetic background and random noise on sequencing data. They estimate this noise directly on the data by using the average product correction procedure, a method applied to information-theoretic measures. Using Global Positioning System (GPS) data of heifers in a pasture, we performed a proof of concept that this approach can be transferred to animal science for social network construction. The approach outputs stable results for up to 30% missing data points, and the predicted associations were in line with those of the null models. The effect of different distance thresholds for contact definition was marginal, but animal activity strongly affected the network structure
A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence
No full textThe knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen–Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC)
Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues
No full textTranscription factors (TFs) are a special class of DNA-binding proteins that orchestrate gene transcription by recruiting other TFs, co-activators or co-repressors. Their combinatorial interplay in higher organisms maintains homeostasis and governs cell identity by finely controlling and regulating tissue-specific gene expression. Despite the rich literature on the importance of cooperative TFs for deciphering the mechanisms of individual regulatory programs that control tissue specificity in several organisms such as human, mouse, or Drosophila melanogaster, to date, there is still need for a comprehensive study to detect specific TF cooperations in regulatory processes of cattle tissues. To address the needs of knowledge about specific combinatorial gene regulation in cattle tissues, we made use of three publicly available RNA-seq datasets and obtained tissue-specific gene (TSG) sets for ten tissues (heart, lung, liver, kidney, duodenum, muscle tissue, adipose tissue, colon, spleen and testis). By analyzing these TSG-sets, tissue-specific TF cooperations of each tissue have been identified. The results reveal that similar to the combinatorial regulatory events of model organisms, TFs change their partners depending on their biological functions in different tissues. Particularly with regard to preferential partner choice of the transcription factors STAT3 and NR2C2, this phenomenon has been highlighted with their five different specific cooperation partners in multiple tissues. The information about cooperative TFs could be promising: i) to understand the molecular mechanisms of regulating processes; and ii) to extend the existing knowledge on the importance of single TFs in cattle tissues.</div
Additional file 1 of PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information
No full textPC-TraFF analysis with different distance constrains. Significant pairs found by PC-TraFF using different distance constrains in the sequences of human RefSeq genes. (XLS 13 kb
Rosa Cornelia Veal Papers - Accession 1766
No full textThe Rosa Cornelia Veal Papers document the professional career, pedagogical philosophy, and personal life of Winthrop College Class of 1926 graduate Rosa Cornelia Veal (1904–1967), an educator whose work spanned elementary classroom instruction, teacher education, and curriculum development during the mid-twentieth century. The collection dates from 1904 to 1978, with the bulk of the materials concentrated between the 1930s and 1940s, corresponding to Veal’s tenure as an elementary school teacher and associate professor of elementary education at Ball State Teachers College.
The collection contains a substantial body of teaching materials that provide insight into classroom practices, instructional methods, and educational priorities of the period. These include lesson manuals, workbooks, visual teaching aids, curriculum guides, and daily classroom records documenting student progress, instructional planning, and classroom activities. Particularly significant are Veal’s classroom record books from 1936–1937 and her extensive use of Compton’s Pictured Teaching Materials, which illustrate the emphasis on visual learning and subject-based instruction in elementary education.Veal’s contributions as an author and curriculum developer are reflected in her published and unpublished writings, including instructional materials created for classroom use and children’s books she helped to author, most notably I Learn to Write. Also included are developmental manuals and religious instructional works prepared under her guidance, demonstrating her interest in holistic child development and literacy education.
Personal materials in the collection include journals and planners spanning two decades, which contain poetry, reflections on daily life, financial notes, reading excerpts, and personal observations. These writings provide a rare and intimate perspective on the professional and personal experiences of a woman educator during the early to mid-twentieth century. Biographical documents—such as probate records, census materials, school report cards, and family histories—further contextualize Veal’s life and career.
The collection also documents Veal’s professional affiliations and community involvement, including her leadership in the Muncie, Indiana branch of the Association for Childhood Education and her membership in the teaching sorority Delta Kappa Gamma. Related materials include correspondence, event ephemera, name cards, and organizational records. Photographs dating from the early twentieth century through the 1940s depict Veal, family members, unidentified individuals, and travel scenes, offering visual context to her personal and professional life.
Additionally, the collection includes a wide array of educational books used by Veal as both a student and educator, as well as miscellaneous ephemera reflecting intellectual, cultural, and everyday interests. Together, the Rosa Cornelia Veal Papers provide a rich resource for research on elementary education, teacher training, women educators, curriculum development, and the lived experience of professional women in education during the twentieth century.https://digitalcommons.winthrop.edu/manuscriptcollection_findingaids/2779/thumbnail.jp
Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming
No full textBackground: The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. Results: The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs. Conclusions: QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF's algorithm, we leveraged Compute Unified Device Architecture (CUDA).Deutsche Forschungsgemeinschaft (DFG) [WA 766/7-1
Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development
No full textTranscription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this study was to analyze co-occurring transcription factor binding sites (TFBSs) in a time series dataset from a new cell-culture model of human heart muscle development in order to identify common as well as specific co-occurring TFBS pairs in the promoter regions of regulated genes which can be essential to enhance cardiac tissue developmental processes. To this end, we separated available RNAseq dataset into five temporally defined groups: (i) mesoderm induction stage; (ii) early cardiac specification stage; (iii) late cardiac specification stage; (iv) early cardiac maturation stage; (v) late cardiac maturation stage, where each of these stages is characterized by unique differentially expressed genes (DEGs). To identify TFBS pairs for each stage, we applied the MatrixCatch algorithm, which is a successful method to deduce experimentally described TFBS pairs in the promoters of the DEGs. Although DEGs in each stage are distinct, our results show that the TFBS pair networks predicted by MatrixCatch for all stages are quite similar. Thus, we extend the results of MatrixCatch utilizing a Markov clustering algorithm (MCL) to perform network analysis. Using our extended approach, we are able to separate the TFBS pair networks in several clusters to highlight stage-specific co-occurences between TFBSs. Our approach has revealed clusters that are either common (NFAT or HMGIY clusters) or specific (SMAD or AP-1 clusters) for the individual stages. Several of these clusters are likely to play an important role during the cardiomyogenesis. Further, we have shown that the related TFs of TFBSs in the clusters indicate potential synergistic or antagonistic interactions to switch between different stages. Additionally, our results suggest that cardiomyogenesis follows the hourglass model which was already proven for Arabidopsis and some vertebrates. This investigation helps us to get a better understanding of how each stage of cardiomyogenesis is affected by different combination of TFs. Such knowledge may help to understand basic principles of stem cell differentiation into cardiomyocyte
Letter to Cornelia Bradford, Whittier House, from Lida Dodds, secretary to Emerson Miller.
No full textWhittier House scrapbooks document Whittier House programs, events, and anniversary celebrations through newspaper clippings, lecture fliers, newsletters, event programs, and ticket stubs. Newspaper clippings are primarily from the Jersey Journal. There is also Whittier House fundraising materials, including pamphlets, appeal letters, brochures, and postcards. The Whittier House Social Settlement, the first settlement house in New Jersey, was established in Jersey City, N.J. (Hudson County) in 1894. Founded by Cornelia Foster Bradford, who would remain with the organization as headworker until 1926, Whittier House was based on the settlement house, Toynbee Hall, in England. Whittier House provided various recreational and educational programs, along with much needed social services, for the immigrant populations of Jersey City. Many of these successful services were used as models for large-scale social reform movements through the state. In 1935, the Whittier House was taken over by the Boys' Club of Jersey City
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