TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora.
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ABSTRACT:
SUBMITTER: Newman-Griffis D
PROVIDER: S-EPMC8212692 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation) between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community.FindingsHere we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file.ConclusionsThe online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs.surrey.ac.uk/RankProducts.
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Project description:BackgroundApplication of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques.ResultsWe designed and built TreeBuilder3D, an interactive viewer for visualizing the hierarchical relationships between expression profiles such as SAGE libraries or microarrays. The program allows loading expression data as plain text files and visualizing the relative differences of the analyzed datasets in 3-dimensional space using various distance metrics.ConclusionTreeBuilder3D provides a simple interface and has a small size. Written in Java, TreeBuilder3D is a platform-independent, open source application, which may be useful in analysis of large-scale gene expression data.
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Project description:BACKGROUND:System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. RESULTS:Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. CONCLUSIONS:Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.
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Project description:Transcriptomic analyses are broadly used in biomedical research calling for tools allowing biologists to be directly involved in data mining and interpretation. We present here GIANT, a Galaxy-based tool for Interactive ANalysis of Transcriptomic data, which consists of biologist-friendly tools dedicated to analyses of transcriptomic data from microarray or RNA-seq analyses. GIANT is organized into modules allowing researchers to tailor their analyses by choosing the specific set of tool(s) to analyse any type of preprocessed transcriptomic data. It also includes a series of tools dedicated to the handling of raw Affymetrix microarray data. GIANT brings easy-to-use solutions to biologists for transcriptomic data mining and interpretation.
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Project description:Literature-based Discovery (LBD) (a.k.a. Hypotheses Generation) is a systematic knowledge discovery process that elicit novel inferences about previously unknown scientific knowledge by rationally connecting complementary and non-interactive literature. Prompt identification of such novel knowledge is beneficial not only for researchers but also for various other stakeholders such as universities, funding bodies and academic publishers. Almost all the prior LBD research suffer from two major limitations. Firstly, the over-reliance of domain-dependent resources which restrict the models’ applicability to certain domains/problems. In this regard, we propose a generalisable LBD model that supports both cross-domain and cross-lingual knowledge discovery. The second persistent research deficiency is the mere focus of static snapshot of the corpus (i.e. ignoring the temporal evolution of topics) to detect the new knowledge. However, the knowledge in scientific literature changes dynamically and thus relying merely on static snapshot limits the model’s ability in capturing semantically meaningful connections. As a result, we propose a novel temporal model that captures semantic change of topics using diachronic word embeddings to unravel more accurate connections. The model was evaluated using the largest available literature repository to demonstrate the efficiency of the proposed cues towards recommending novel knowledge. Electronic supplementary material The online version of this chapter (10.1007/978-3-030-47436-2_25) contains supplementary material, which is available to authorized users.
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Project description:BackgroundMicroarray and DNA-sequencing based technologies continue to produce enormous amounts of data on gene expression. This data has great potential to illuminate our understanding of biology and medicine, but the data alone is of limited value without computational tools to allow human investigators to visualize and interpret it in the context of their problem of interest.ResultsWe created a web server called SHOE that provides an interactive, visual presentation of the available evidence of transcriptional regulation and gene co-expression to facilitate its exploration and interpretation. SHOE predicts the likely transcription factor binding sites in orthologous promoters of humans, mice, and rats using the combined information of 1) transcription factor binding preferences (position-specific scoring matrix (PSSM) libraries such as Transfac32, Jaspar, HOCOMOCO, ChIP-seq, SELEX, PBM, and iPS-reprogramming factor), 2) evolutionary conservation of putative binding sites in orthologous promoters, and 3) co-expression tendencies of gene pairs based on 1,714 normal human cells selected from the Gene Expression Omnibus Database.ConclusionSHOE enables users to explore potential interactions between transcription factors and target genes via multiple data views, discover transcription factor binding motifs on top of gene co-expression, and visualize genes as a network of gene and transcription factors on its native gadget GeneViz, the CellDesigner pathway analyzer, and the Reactome database to search the pathways involved. As we demonstrate here when using the CREB1 and Nf-?B datasets, SHOE can reliably identify experimentally verified interactions and predict plausible novel ones, yielding new biological insights into the gene regulatory mechanisms involved. SHOE comes with a manual describing how to run it on a local PC or via the Garuda platform ( www.garuda-alliance.org ), where it joins other popular gadgets such as the CellDesigner pathway analyzer and the Reactome database, as part of analysis workflows to meet the growing needs of molecular biologists and medical researchers. SHOE is available from the following URL http://ec2-54-150-223-65.ap-northeast-1.compute.amazonaws.com A video demonstration of SHOE can be found here: https://www.youtube.com/watch?v=qARinNb9NtE.
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Project description:Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA-protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA-protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA-protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA-protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA-protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu.
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