Journal articles on the topic 'Transcriptomics Data Sets'
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Alkhateeb, Abedalrhman, Iman Rezaeian, Siva Singireddy, Dora Cavallo-Medved, Lisa A. Porter, and Luis Rueda. "Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer." Cancer Informatics 18 (January 2019): 117693511983552. http://dx.doi.org/10.1177/1176935119835522.
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Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.
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Bauer, Chris, Karol Stec, Alexander Glintschert, Kristina Gruden, Christian Schichor, Michal Or-Guil, Joachim Selbig, and Johannes Schuchhardt. "BioMiner: Paving the Way for Personalized Medicine." Cancer Informatics 14 (January 2015): CIN.S20910. http://dx.doi.org/10.4137/cin.s20910.
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Personalized medicine is promising a revolution for medicine and human biology in the 21st century. The scientific foundation for this revolution is accomplished by analyzing biological high-throughput data sets from genomics, transcriptomics, proteomics, and metabolomics. Currently, access to these data has been limited to either rather simple Web-based tools, which do not grant much insight or analysis by trained specialists, without firsthand involvement of the physician. Here, we present the novel Web-based tool “BioMiner,” which was developed within the scope of an international and interdisciplinary project (SYSTHER†) and gives access to a variety of high-throughput data sets. It provides the user with convenient tools to analyze complex cross-omics data sets and grants enhanced visualization abilities. BioMiner incorporates transcriptomic and cross-omics high-throughput data sets, with a focus on cancer. A public instance of BioMiner along with the database is available at http://systherDB.microdiscovery.de/ , login and password: “systher”; a tutorial detailing the usage of BioMiner can be found in the Supplementary File.
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Elhossiny, Ahmed M., Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, and Marina Pasca Di Magliano. "Abstract A006: Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion." Cancer Research 82, no. 22_Supplement (November 15, 2022): A006. http://dx.doi.org/10.1158/1538-7445.panca22-a006.
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Abstract Pancreatic Cancer Ductal Adenocarcinoma (PDAC) is one of the deadliest cancers with 5-year survival of 11%. Understanding the intratumor heterogeneity is a pivotal piece to unravel the complexity of PDAC. While single cell RNASeq identifies the heterogeneous cell populations within the tumor tissue, spatially characterizing the transcriptomic profile of neoplastic and pre-neoplastic populations within the tissue remains a challenge, as the spatial dimension is usually lost upon tissue dissociation. We identified an approach to integrate spatial transcriptomics data with single cell RNASeq data. To characterize the cell populations within the tissue we performed single cell RNASeq on disease pathology-free pancreas tissue and primary PDAC samples. We profiled the transcriptomic profile of Acinar, Ductal, Acinar-to-Ductal (ADM), and Pancreatic Intraepithelial Neoplasia (PanINs) regions of interest (ROIs) across the tissue using the Nanostring GeoMx platform. Differential gene expression analysis using linear mixed-effect models of the cell-type specific ROIs defined pan-marker gene sets for each cell type, which were mapped to UMAP projections of single cell RNA sequencing data using AUCell scoring. As expected, the acinar pan-markers gene set derived from the spatial transcriptomics mapped to the manually annotated acinar population in the single cell data. On the other hand, Ductal, ADM, and PanIN pan-marker gene sets were mapped to distinct clusters that previously were not well-defined by single cell sequencing. The analysis coupled with orthogonal validation using RNAScope revealed gene signatures uniquely specific to ADM lesions and PanINs, respectively. Interestingly, our list included known markers as well as novel findings, supporting the validity of the findings. Furthermore, RNA velocity analysis using scVelo revealed a trajectory of cell evolution originating from acinar cells passing through the newly-defined ADM population and ending towards the ductal population derived from tumor samples. Overall, this integration approach of spatial and single cell transcriptomics can further define the characteristics that differentiate neoplastic and pre-neoplastic populations, as well as the potential drivers for tumorigenesis that could be therapeutically targeted. Citation Format: Ahmed M. Elhossiny, Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, Marina Pasca Di Magliano. Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A006.
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Alsagaby, Suliman. "Integration of Proteomics and Transcriptomics Data Sets Identifies Prognostic Markers in Chronic Lymphocytic Leukemia." Majmaah Journal of Health Sciences 7, no. 2 (2019): 1. http://dx.doi.org/10.5455/mjhs.2019.01.002.
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Hamey, Fiona K., and Berthold Göttgens. "Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets." Experimental Hematology 78 (October 2019): 11–20. http://dx.doi.org/10.1016/j.exphem.2019.08.009.
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Vahlensieck, Christian, Cora S. Thiel, Jan Adelmann, Beatrice A. Lauber, Jennifer Polzer, and Oliver Ullrich. "Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data." International Journal of Molecular Sciences 22, no. 16 (August 6, 2021): 8451. http://dx.doi.org/10.3390/ijms22168451.
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Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.
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Visentin, Luca, Giorgia Scarpellino, Giorgia Chinigò, Luca Munaron, and Federico Alessandro Ruffinatti. "BioTEA: Containerized Methods of Analysis for Microarray-Based Transcriptomics Data." Biology 11, no. 9 (September 13, 2022): 1346. http://dx.doi.org/10.3390/biology11091346.
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Tens of thousands of gene expression data sets describing a variety of model organisms in many different pathophysiological conditions are currently stored in publicly available databases such as the Gene Expression Omnibus (GEO) and ArrayExpress (AE). As microarray technology is giving way to RNA-seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring, at the same time, the reproducibility of the results. To meet this need, here we present bioTEA (biological Transcript Expression Analyzer), a novel software tool that combines ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in a single text file, which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of the analysis. Overall, these features make bioTEA an invaluable tool for both bioinformaticians and wet-lab biologists interested in transcriptomics. BioTEA is free and open-source.
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Bisht, Vartika, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos, and Animesh Acharjee. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (May 28, 2021): 5763. http://dx.doi.org/10.3390/ijms22115763.
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Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.
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Nehme, Ali, Hassan Dakik, Frédéric Picou, Meyling Cheok, Claude Preudhomme, Hervé Dombret, Juliette Lambert, et al. "Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature." Blood Advances 4, no. 20 (October 27, 2020): 5322–35. http://dx.doi.org/10.1182/bloodadvances.2020002042.
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Abstract Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.
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Di Filippo, Marzia, Dario Pescini, Bruno Giovanni Galuzzi, Marcella Bonanomi, Daniela Gaglio, Eleonora Mangano, Clarissa Consolandi, Lilia Alberghina, Marco Vanoni, and Chiara Damiani. "INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation." PLOS Computational Biology 18, no. 2 (February 7, 2022): e1009337. http://dx.doi.org/10.1371/journal.pcbi.1009337.
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Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
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Hoekzema, Renee S., Lewis Marsh, Otto Sumray, Thomas M. Carroll, Xin Lu, Helen M. Byrne, and Heather A. Harrington. "Multiscale Methods for Signal Selection in Single-Cell Data." Entropy 24, no. 8 (August 13, 2022): 1116. http://dx.doi.org/10.3390/e24081116.
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Analysis of single-cell transcriptomics often relies on clustering cells and then performing differential gene expression (DGE) to identify genes that vary between these clusters. These discrete analyses successfully determine cell types and markers; however, continuous variation within and between cell types may not be detected. We propose three topologically motivated mathematical methods for unsupervised feature selection that consider discrete and continuous transcriptional patterns on an equal footing across multiple scales simultaneously. Eigenscores (eigi) rank signals or genes based on their correspondence to low-frequency intrinsic patterning in the data using the spectral decomposition of the Laplacian graph. The multiscale Laplacian score (MLS) is an unsupervised method for locating relevant scales in data and selecting the genes that are coherently expressed at these respective scales. The persistent Rayleigh quotient (PRQ) takes data equipped with a filtration, allowing the separation of genes with different roles in a bifurcation process (e.g., pseudo-time). We demonstrate the utility of these techniques by applying them to published single-cell transcriptomics data sets. The methods validate previously identified genes and detect additional biologically meaningful genes with coherent expression patterns. By studying the interaction between gene signals and the geometry of the underlying space, the three methods give multidimensional rankings of the genes and visualisation of relationships between them.
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Gillenwater, Lucas A., Shahab Helmi, Evan Stene, Katherine A. Pratte, Yonghua Zhuang, Ronald P. Schuyler, Leslie Lange, et al. "Multi-omics subtyping pipeline for chronic obstructive pulmonary disease." PLOS ONE 16, no. 8 (August 25, 2021): e0255337. http://dx.doi.org/10.1371/journal.pone.0255337.
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Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.
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Liu, Qi, Quanhu Sheng, Jie Ping, Marisol Adelina Ramirez, Ken S. Lau, Robert J. Coffey, and Yu Shyr. "scRNABatchQC: multi-samples quality control for single cell RNA-seq data." Bioinformatics 35, no. 24 (August 2, 2019): 5306–8. http://dx.doi.org/10.1093/bioinformatics/btz601.
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Abstract Summary Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. Availability and implementation scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. Supplementary information Supplementary data are available at Bioinformatics online.
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Gao, Bei, Hui-Wen Lue, Jennifer Podolak, Sili Fan, Ying Zhang, Archana Serawat, Joshi J. Alumkal, Oliver Fiehn, and George V. Thomas. "Multi-Omics Analyses Detail Metabolic Reprogramming in Lipids, Carnitines, and Use of Glycolytic Intermediates between Prostate Small Cell Neuroendocrine Carcinoma and Prostate Adenocarcinoma." Metabolites 9, no. 5 (April 26, 2019): 82. http://dx.doi.org/10.3390/metabo9050082.
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As the most common cancer in men, prostate cancer is molecularly heterogeneous. Contributing to this heterogeneity are the poorly understood metabolic adaptations of the two main types of prostate cancer, i.e., adenocarcinoma and small cell neuroendocrine carcinoma (SCNC), the latter being more aggressive and lethal. Using transcriptomics, untargeted metabolomics and lipidomics profiling on LASCPC-01 (prostate SCNC) and LNCAP (prostate adenocarcinoma) cell lines, we found significant differences in the cellular phenotypes of the two cell lines. Gene set enrichment analysis on the transcriptomics data showed 62 gene sets were upregulated in LASCPC-01, while 112 gene sets were upregulated in LNCAP. ChemRICH analysis on metabolomics and lipidomics data revealed a total of 25 metabolite clusters were significantly different. LASCPC-01 exhibited a higher glycolytic activity and lower levels of triglycerides, while the LNCAP cell line showed increases in one-carbon metabolism as an exit route of glycolytic intermediates and a decrease in carnitine, a mitochondrial lipid transporter. Our findings pinpoint differences in prostate neuroendocrine carcinoma versus prostate adenocarcinoma that could lead to new therapeutic targets in each type.
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Nesterenko, Maksim, and Aleksei Miroliubov. "From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)." F1000Research 11 (May 27, 2022): 583. http://dx.doi.org/10.12688/f1000research.110492.1.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, the system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there is no genomic and transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: externa and main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for both P. reticulata and Rhizocephala. Based on the results obtained, the spatial heterogeneity of protein-coding genes expression in different regions of P. reticulata adult female body was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in the metamorphosis”, even in their reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of this amazing parasites.
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Nesterenko, Maksim, and Aleksei Miroliubov. "From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)." F1000Research 11 (January 9, 2023): 583. http://dx.doi.org/10.12688/f1000research.110492.2.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, a system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there are no genomic or transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: the externa, and the main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite’s body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for any rhizocephalan. Based on the results obtained, the spatial heterogeneity of protein-coding gene expression in different regions of the adult female body of P. reticulata was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in their metamorphosis”, even at the reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of these extraordinary parasites.
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Rue-Albrecht, Kevin, Federico Marini, Charlotte Soneson, and Aaron T. L. Lun. "iSEE: Interactive SummarizedExperiment Explorer." F1000Research 7 (June 14, 2018): 741. http://dx.doi.org/10.12688/f1000research.14966.1.
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Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.
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Bentham, Robert B., Kevin Bryson, and Gyorgy Szabadkai. "MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections." Nucleic Acids Research 45, no. 15 (July 14, 2017): 8712–30. http://dx.doi.org/10.1093/nar/gkx590.
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Newton, Richard, and Lorenz Wernisch. "A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships." PLoS ONE 9, no. 8 (August 22, 2014): e105522. http://dx.doi.org/10.1371/journal.pone.0105522.
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Ma, Lingjie, Sheng-Wei Ma, Qingyan Deng, Yang Yuan, Zhaoyan Wei, Haiyan Jia, and Zhengqiang Ma. "Identification of Wheat Inflorescence Development-Related Genes Using a Comparative Transcriptomics Approach." International Journal of Genomics 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/6897032.
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Inflorescence represents the highly specialized plant tissue producing the grains. Although key genes regulating flower initiation and development are conserved, the mechanism regulating fertility is still not well explained. To identify genes and gene network underlying inflorescence morphology and fertility of bread wheat, expressed sequence tags (ESTs) from different tissues were analyzed using a comparative transcriptomics approach. Based on statistical comparison of EST frequencies of individual genes in EST pools representing different tissues and verification with RT-PCR and RNA-seq data, 170 genes of 59 gene sets predominantly expressed in the inflorescence were obtained. Nearly one-third of the gene sets displayed differentiated expression profiles in terms of their subgenome orthologs. The identified genes, most of which were predominantly expressed in anthers, encode proteins involved in wheat floral identity determination, anther and pollen development, pollen-pistil interaction, and others. Particularly, 25 annotated gene sets are associated with pollen wall formation, of which 18 encode enzymes or proteins participating in lipid metabolic pathway, including fatty acid ω-hydroxylation, alkane and fatty alcohol biosynthesis, and glycerophospholipid metabolism. We showed that the comparative transcriptomics approach was effective in identifying genes for reproductive development and found that lipid metabolism was particularly active in wheat anthers.
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Novikova, S. E., and V. G. Zgoda. "Transcriptomics and proteomics in studies of induced differentiation of leukemia cells." Biomeditsinskaya Khimiya 61, no. 5 (2015): 529–44. http://dx.doi.org/10.18097/pbmc20156105529.
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Induced differentiation of leukemia cells is in the focus of basic and applied biomedical studies medicine and biology for more than 30 years. During this period specific regulatory molecules involved in the maturation process have been identified by biochemical and molecular biological methods. Recent developments of high-throughput transcriptomic and proteomic techniques made it possible to analyze large sets of mRNA and proteins; this resulted in identification of functionally important signal transduction pathways and networks of molecular interactions, and thus extent existing knowledge on the molecular mechanisms of induced differentiation. Despite significant advances in mechanisms of induced differentiation, many problems related to the molecular mechanism of cell maturation, a phenomenon of therapeutic resistance of leukemic cells need better understanding and thus require further detailed study. Transcriptomics and proteomics methods provide a suitable methodological platform for the implementation of such studies. This review highlights the use of transcriptomic and proteomic methods in studies aimed at various aspects of the induced differentiation. Special attention is paid to the employment of the systems approach for investigation of various aspects of cell maturation. The use of the systems approach in studies of induced differentiation is an important step for the transition from the formal data accumulation on expression of mRNA and proteins towards creating models of biological processes in silico.
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Sinha, Saurabh, Jun Song, Richard Weinshilboum, Victor Jongeneel, and Jiawei Han. "KnowEnG: a knowledge engine for genomics." Journal of the American Medical Informatics Association 22, no. 6 (July 22, 2015): 1115–19. http://dx.doi.org/10.1093/jamia/ocv090.
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Abstract We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of “Knowledge Engine for Genomics” (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the “Knowledge Network” that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.
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Landeira-Viñuela, Alicia, Paula Díez, Pablo Juanes-Velasco, Quentin Lécrevisse, Alberto Orfao, Javier De Las Rivas, and Manuel Fuentes. "Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model." Biomolecules 11, no. 12 (November 26, 2021): 1776. http://dx.doi.org/10.3390/biom11121776.
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Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.
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Bennett, Jason, Mikhail Pomaznoy, Akul Singhania, and Bjoern Peters. "A metric for evaluating biological information in gene sets and its application to identify co-expressed gene clusters in PBMC." PLOS Computational Biology 17, no. 10 (October 6, 2021): e1009459. http://dx.doi.org/10.1371/journal.pcbi.1009459.
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Recent technological advances have made the gathering of comprehensive gene expression datasets a commodity. This has shifted the limiting step of transcriptomic studies from the accumulation of data to their analyses and interpretation. The main problem in analyzing transcriptomics data is that the number of independent samples is typically much lower (<100) than the number of genes whose expression is quantified (typically >14,000). To address this, it would be desirable to reduce the gathered data’s dimensionality without losing information. Clustering genes into discrete modules is one of the most commonly used tools to accomplish this task. While there are multiple clustering approaches, there is a lack of informative metrics available to evaluate the resultant clusters’ biological quality. Here we present a metric that incorporates known ground truth gene sets to quantify gene clusters’ biological quality derived from standard clustering techniques. The GECO (Ground truth Evaluation of Clustering Outcomes) metric demonstrates that quantitative and repeatable scoring of gene clusters is not only possible but computationally lightweight and robust. Unlike current methods, it allows direct comparison between gene clusters generated by different clustering techniques. It also reveals that current cluster analysis techniques often underestimate the number of clusters that should be formed from a dataset, which leads to fewer clusters of lower quality. As a test case, we applied GECO combined with k-means clustering to derive an optimal set of co-expressed gene modules derived from PBMC, which we show to be superior to previously generated modules generated on whole-blood. Overall, GECO provides a rational metric to test and compare different clustering approaches to analyze high-dimensional transcriptomic data.
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Evgeniou, Michail, Juan Manuel Sacnun, Klaus Kratochwill, and Paul Perco. "A Meta-Analysis of Human Transcriptomics Data in the Context of Peritoneal Dialysis Identifies Novel Receptor-Ligand Interactions as Potential Therapeutic Targets." International Journal of Molecular Sciences 22, no. 24 (December 10, 2021): 13277. http://dx.doi.org/10.3390/ijms222413277.
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Peritoneal dialysis (PD) is one therapeutic option for patients with end-stage kidney disease (ESKD). Molecular profiling of samples from PD patients using different Omics technologies has led to the discovery of dysregulated molecular processes due to PD treatment in recent years. In particular, a number of transcriptomics (TX) datasets are currently available in the public domain in the context of PD. We set out to perform a meta-analysis of TX datasets to identify dysregulated receptor-ligand interactions in the context of PD-associated complications. We consolidated transcriptomics profiles from twelve untargeted genome-wide gene expression studies focusing on human cell cultures or samples from human PD patients. Gene set enrichment analysis was used to identify enriched biological processes. Receptor-ligand interactions were identified using data from CellPhoneDB. We identified 2591 unique differentially expressed genes in the twelve PD studies. Key enriched biological processes included angiogenesis, cell adhesion, extracellular matrix organization, and inflammatory response. We identified 70 receptor-ligand interaction pairs, with both interaction partners being dysregulated on the transcriptional level in one of the investigated tissues in the context of PD. Novel receptor-ligand interactions without prior annotation in the context of PD included BMPR2-GDF6, FZD4-WNT7B, ACKR2-CCL2, or the binding of EPGN and EREG to the EGFR, as well as the binding of SEMA6D to the receptors KDR and TYROBP. In summary, we have consolidated human transcriptomics datasets from twelve studies in the context of PD and identified sets of novel receptor-ligand pairs being dysregulated in the context of PD that warrant investigation in future functional studies.
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Bazile, Jeanne, Florence Jaffrezic, Patrice Dehais, Matthieu Reichstadt, Christophe Klopp, Denis Laloe, and Muriel Bonnet. "Molecular signatures of muscle growth and composition deciphered by the meta-analysis of age-related public transcriptomics data." Physiological Genomics 52, no. 8 (August 1, 2020): 322–32. http://dx.doi.org/10.1152/physiolgenomics.00020.2020.
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The lean-to-fat ratio is a major issue in the beef meat industry from both carcass and meat production perspectives. This industrial perspective has motivated meat physiologists to use transcriptomics technologies to decipher mechanisms behind fat deposition within muscle during the time course of muscle growth. However, synthetic biological information from this volume of data remains to be produced to identify mechanisms found in various breeds and rearing practices. We conducted a meta-analysis on 10 transcriptomic data sets stored in public databases, from the longissimus thoracis of five different bovine breeds divergent by age. We updated gene identifiers on the last version of the bovine genome (UCD1.2), and the 715 genes common to the 10 studies were subjected to the meta-analysis. Of the 238 genes differentially expressed (DEG), we identified a transcriptional signature of the dynamic regulation of glycolytic and oxidative metabolisms that agrees with a known shift between those two pathways from the animal puberty. We proposed some master genes of the myogenesis, namely MYOG and MAPK14, as probable regulators of the glycolytic and oxidative metabolisms. We also identified overexpressed genes related to lipid metabolism (APOE, LDLR, MXRA8, and HSP90AA1) that may contribute to the expected enhanced marbling as age increases. Lastly, we proposed a transcriptional signature related to the induction (YBX1) or repression (MAPK14, YWAH, ERBB2) of the commitment of myogenic progenitors into the adipogenic lineage. The relationships between the abundance of the identified mRNA and marbling values remain to be analyzed in a marbling biomarkers discovery perspectives.
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Deblais, Loïc, Dipak Kathayat, Yosra A. Helmy, Gary Closs, and Gireesh Rajashekara. "Translating ‘big data’: better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry." Animal Health Research Reviews 21, no. 1 (January 7, 2020): 15–35. http://dx.doi.org/10.1017/s1466252319000124.
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AbstractRecent technological advances has led to the generation, storage, and sharing of colossal sets of information (‘big data’), and the expansion of ‘omics’ in science. To date, genomics/metagenomics, transcriptomics, proteomics, and metabolomics are arguably the most ground breaking approaches in food and public safety. Here we review some of the recent studies of foodborne pathogens (Campylobacter spp., Salmonella spp., and Escherichia coli) in poultry using big data. Genomic/metagenomic approaches have reveal the importance of the gut microbiota in health and disease. They have also been used to identify, monitor, and understand the epidemiology of antibiotic-resistance mechanisms and provide concrete evidence about the role of poultry in human infections. Transcriptomics studies have increased our understanding of the pathophysiology and immunopathology of foodborne pathogens in poultry and have led to the identification of host-resistance mechanisms. Proteomic/metabolomic approaches have aided in identifying biomarkers and the rapid detection of low levels of foodborne pathogens. Overall, ‘omics' approaches complement each other and may provide, at least in part, a solution to our current food-safety issues by facilitating the development of new rapid diagnostics, therapeutic drugs, and vaccines to control foodborne pathogens in poultry. However, at this time most ‘omics' approaches still remain underutilized due to their high cost and the high level of technical skills required.
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Bastian, Frederic B., Julien Roux, Anne Niknejad, Aurélie Comte, Sara S. Fonseca Costa, Tarcisio Mendes de Farias, Sébastien Moretti, et al. "The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals." Nucleic Acids Research 49, no. D1 (October 10, 2020): D831—D847. http://dx.doi.org/10.1093/nar/gkaa793.
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Abstract Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as ‘healthy’ or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.
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Koduru, Srinivas V. "Abstract 2291: microRNA/mRNA integrated analysis of multiple myeloma transcriptomics." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2291. http://dx.doi.org/10.1158/1538-7445.am2022-2291.
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Abstract Myeloma is plasma cell disorder, mostly effects adults over 60 years. Non-coding RNAs are emerging field and play vital role in development of disease. Major non-coding RNAs are miRNAs, lncRNAs, circRNAs and sn/snoRNAs. We analyzed restricted and publically available RNA-seq and small RNA-seq data sets for biomarkers identification and their involvement in myeloma. We obtained restricted data from “BluePrint”, which contains 11 myeloma plasma cells and 4 normal tonsil plasma cells (EGAS00001001110). We identified 1534 genes are differentially regulated (2-fold cut-off, >10-FC 218 genes). Top 10 upregulated genes were: EDNRB (1246-FC), SCUBE1 (737-FC), MC4R (691-FC), NDNF (601-FC), PTGS2 (567-FC), GPRC5D (531-FC), MFAP3L (467-FC), CCND1 (381-FC), CXCL12 (344-FC) and BTBD3 (326-FC) ; top 10 downregulated were: EBF1 (-111-FC), HLA-DRB1(-133-FC), CPXM1 (-171-FC), LOC642131 (-171-FC), IGHV1OR15-3 (-171-FC), HLA-DRB5 (-204-FC), PRAMENP (-210-FC), DTX1 (-220-FC), CD22 (-337-FC) and RFTN1 (-356-FC). Publically available small RNA-seq data downloaded and analyzed for miRNAs, lncRNAs, circRNAs and sn/snoRNAs which contains 3 healthy donor’s plasma cells and 3 newly diagnosed myeloma patient plasma cells (PRJNA377345). We used mirDIP portal to analyze miRNA and mRNA differentially expressed data, predicated from more than 13 databases showed major role of miR-152-3p (targets 28 mRNAs), miR-93-5p (targets 19 mRNAs), miR-301a-3p (targets 13 mRNAs), miR-29c-3p (targets 12 mRNAs), and miR-144-3p (targets 9 mRNAs). Integrated analysis can provide valuable information from the transcriptomics data and effect of miRNAs on mRNAs. Citation Format: Srinivas V. Koduru. microRNA/mRNA integrated analysis of multiple myeloma transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2291.
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Filippi, Alexandru, and Maria-Magdalena Mocanu. "Mining TCGA Database for Genes with Prognostic Value in Breast Cancer." International Journal of Molecular Sciences 24, no. 2 (January 13, 2023): 1622. http://dx.doi.org/10.3390/ijms24021622.
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The aim of the study was to use transcriptomics data to identify genes associated with advanced/aggressive breast cancer and their effect on survival outcomes. We used the publicly available The Cancer Genome Atlas (TCGA) database to obtain RNA sequence data from patients with less than five years survival (Poor Prognosis, n = 101), patients with greater than five years survival (Good Prognosis, n = 200), as well as unpaired normal tissue data (normal, n = 105). The data analyses performed included differential expression between groups and selection of subsets of genes, gene ontology, cell enrichment analysis, and survival analyses. Gene ontology results showed significantly reduced enrichment in gene sets related to tumor immune microenvironment in Poor Prognosis and cell enrichment analysis confirmed significantly reduced numbers of macrophages M1, CD8 T cells, plasma cells and dendritic cells in samples in the Poor Prognosis samples compared with Good Prognosis. A subset of 742 genes derived from differential expression analysis as well as genes coding for immune checkpoint molecules was evaluated for their effect on overall survival. In conclusion, this study may contribute to the better understanding of breast cancer transcriptomics and provide possible targets for further research and eventual therapeutic interventions.
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Rams, Mona, and Tim Conrad. "Dictionary learning for transcriptomics data reveals type-specific gene modules in a multi-class setting." it - Information Technology 62, no. 3-4 (May 27, 2020): 119–34. http://dx.doi.org/10.1515/itit-2019-0048.
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AbstractExtracting information from large biological datasets is a challenging task, due to the large data size, high-dimensionality, noise, and errors in the data. Gene expression data contains information about which gene products have been formed by a cell, thus representing which genes have been read to activate a particular biological process. Understanding which of these gene products can be related to which processes can for example give insights about how diseases evolve and might give hints about how to fight them.The Next Generation RNA-sequencing method emerged over a decade ago and is nowadays state-of-the-art in the field of gene expression analyses. However, analyzing these large, complex datasets is still a challenging task. Many of the existing methods do not take into account the underlying structure of the data.In this paper, we present a new approach for RNA-sequencing data analysis based on dictionary learning. Dictionary learning is a sparsity enforcing method that has widely been used in many fields, such as image processing, pattern classification, signal denoising and more. We show how for RNA-sequencing data, the atoms in the dictionary matrix can be interpreted as modules of genes that either capture patterns specific to different types, or else represent modules that are reused across different scenarios. We evaluate our approach on four large datasets with samples from multiple types. A Gene Ontology term analysis, which is a standard tool indicated to help understanding the functions of genes, shows that the found gene-sets are in agreement with the biological context of the sample types. Further, we find that the sparse representations of samples using the dictionary can be used to identify type-specific differences.
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Osier, Nicole D., Christopher C. Imes, Heba Khalil, Jamie Zelazny, Ann E. Johansson, and Yvette P. Conley. "Symptom Science." Biological Research For Nursing 19, no. 1 (September 20, 2016): 18–27. http://dx.doi.org/10.1177/1099800416666716.
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Omics approaches, including genomics, transcriptomics, proteomics, epigenomics, microbiomics, and metabolomics, generate large data sets. Once they have been used to address initial study aims, these large data sets are extremely valuable to the greater research community for ancillary investigations. Repurposing available omics data sets provides data to address research questions, generate and test hypotheses, replicate findings, and conduct mega-analyses. Many well-characterized, longitudinal, epidemiological studies collected extensive phenotype data related to symptom occurrence and severity. While the main phenotype of interest for many of these studies was often not symptom related, these data were collected to better understand the primary phenotype of interest. A search for symptom data (i.e., cognitive impairment, fatigue, gastrointestinal distress/nausea, sleep, and pain) in the database of genotypes and phenotypes (dbGaP) revealed many studies that collected symptom and omics data. There is thus a real possibility for nurse scientists to be able to look at symptom data over time from thousands of individuals and use omics data to identify key biological underpinnings that account for the development and severity of symptoms without recruiting participants or generating any new data. The purpose of this article is to introduce the reader to resources that provide omics data to the research community for repurposing, provide guidance on using these databases, and encourage the use of these data to move symptom science forward.
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Locard-Paulet, Marie, Oana Palasca, and Lars Juhl Jensen. "Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies." PLOS Computational Biology 18, no. 10 (October 6, 2022): e1010604. http://dx.doi.org/10.1371/journal.pcbi.1010604.
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Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.
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Pollard, Alice E., and David Carling. "Thermogenic adipocytes: lineage, function and therapeutic potential." Biochemical Journal 477, no. 11 (June 12, 2020): 2071–93. http://dx.doi.org/10.1042/bcj20200298.
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Metabolic inflexibility, defined as the inability to respond or adapt to metabolic demand, is now recognised as a driving factor behind many pathologies associated with obesity and the metabolic syndrome. Adipose tissue plays a pivotal role in the ability of an organism to sense, adapt to and counteract environmental changes. It provides a buffer in times of nutrient excess, a fuel reserve during starvation and the ability to resist cold-stress through non-shivering thermogenesis. Recent advances in single-cell RNA sequencing combined with lineage tracing, transcriptomic and proteomic analyses have identified novel adipocyte progenitors that give rise to specialised adipocytes with diverse functions, some of which have the potential to be exploited therapeutically. This review will highlight the common and distinct functions of well-known adipocyte populations with respect to their lineage and plasticity, as well as introducing the most recent members of the adipocyte family and their roles in whole organism energy homeostasis. Finally, this article will outline some of the more preliminary findings from large data sets generated by single-cell transcriptomics of mouse and human adipose tissue and their implications for the field, both for discovery and for therapy.
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Altaf-Ul-Amin, Md, Tetsuo Katsuragi, Tetsuo Sato, and Shigehiko Kanaya. "A Glimpse to Background and Characteristics of Major Molecular Biological Networks." BioMed Research International 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/540297.
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Recently, biology has become a data intensive science because of huge data sets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. These huge datasets have paved the way for system-level analysis of the processes and subprocesses of the cell. For system-level understanding, initially the elements of a system are connected based on their mutual relations and a network is formed. Among omics researchers, construction and analysis of biological networks have become highly popular. In this review, we briefly discuss both the biological background and topological properties of major types of omics networks to facilitate a comprehensive understanding and to conceptualize the foundation of network biology.
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Andreatta, M., and SJ Carmona. "P03.21 Projecting T cells into a reference transcriptomic atlas to interpret antitumor immune responses." Journal for ImmunoTherapy of Cancer 8, Suppl 2 (October 2020): A30.2—A31. http://dx.doi.org/10.1136/jitc-2020-itoc7.59.
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BackgroundSingle-cell transcriptomics is a transformative technology to explore heterogeneous cell populations such as T cells, one of our most potent weapons against cancer and viral infections. Recent advances in this technology and the computational tools developed in their wake provide unique opportunities to build reference cell atlases that can be used to interpret new single-cell RNA-sequencing (scRNA-seq) data and systematically compare data sets derived from different models or therapeutic conditions.Materials and MethodsWe have developed ProjecTILs (https://github.com/carmonalab/ProjecTILs), a novel computational method to project new data sets into a reference map of T cells, enabling their direct comparison in a stable, annotated system of coordinates. ProjecTILs enables the classification of query cells into curated, discrete states, but also over a continuous space of intermediate states. We illustrate the projection of several data sets from recent publications over two cross-study murine T cell reference atlases: the first describing tumor-infiltrating T lymphocytes (TILs), the second characterizing acute and chronic viral infection.ResultsProjecTILs accurately predicted the effects of multiple perturbations, including the ablation of genes controlling T cell differentiation, such as Tox, Ptpn2, miR-155 and Regnase-1, and identified novel gene programs that were altered in these cells (such as a Lag3-Klrc1 inhibitory module), revealing mechanisms of action behind these immunotherapeutic targets and opening new opportunities for the identification of novel targets. By comparing multiple samples over the same reference map, and across alternative embeddings, our method allows exploring the effect of cellular perturbations (e.g. as the result of therapy or genetic engineering) in terms of transcriptional states and altered genetic programs.ConclusionsThe proposed computational method will likely contribute to reveal the mechanisms of action of experimental immunotherapies and guide novel therapeutic interventions in cancer and beyond.Disclosure InformationM. Andreatta: None. S.J. Carmona: None.
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Raffel, Simon, Jenny Hansson, Lutz Christoph, Daniel Klimmeck, Nina Cabezas-Wallscheid, Eike C. Buss, Christian Thiede, et al. "Identification Of Novel Markers Of Human AML Stem Cells Using High Resolution Proteomics and Transcriptomics." Blood 122, no. 21 (November 15, 2013): 4194. http://dx.doi.org/10.1182/blood.v122.21.4194.4194.
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Abstract Acute Myeloid Leukemia (AML) is a hierarchically organized clonal malignant disorder with leukemia stem cells (LSC) at its apex. LSC have self-renewal activity and generate leukemic progeny, which make up the majority of leukemic cells. LSC can be quiescent and reside in specific niches in the bone marrow, rendering them resistant to conventional chemotherapy approaches. LSC are considered the source of relapse and thus further strategies to eradicate LSC are pivotal to improve patient outcomes of this dismal disease. LSC present within cell populations can be detected by their capacity to re-initiate the leukemia after xenotransplantation into immune-compromised mice. However, using current methods, it is neither possible to prospectively isolate pure functional LSC nor distinguish them reliably from normal hematopoietic stem cells (HSC). In order to search for novel LSC-specific markers, we applied state-of-the-art proteomics and gene expression profiling by next-generation sequencing (RNA-Seq) to LSC-containing and LSC-free cell fractions from primary AML patient samples. To define functional LSC we FACS-sorted primary patient samples of different AML subtypes according to surface expression of CD34 and CD38 and transplanted each of the resulting four cell populations into conditioned NSG recipients. Thirteen AML samples showed human leukemic engraftment in at least one of the subsets, dissecting LSC-containing and LSC-free subpopulations within the same patient. AML engraftment was mainly observed within the CD34+CD38- fraction, but several cases showed LSC activity also in the CD34+CD38+ fraction or even in the CD34- subsets. As healthy age matched controls, we collected samples of bone marrow from individuals without hematological conditions older than 60 years, who underwent hip replacement surgery. Hematopoietic stem and progenitor cells (HSPC, Lineage-CD34+CD38-) were FACS-sorted and included into the transcriptome and proteome analyses. Hierarchical clustering of transcriptomic data revealed that the similarity between LSC-containing and LSC-free subpopulations within the same patients was greater than the similarity of LSC and non-LSC fractions across different patients. As expected, AMLs with the same molecular subtype clustered together. Gene Set Enrichment Analysis showed enrichment of known LSC- and other stem cell gene sets in the LSC-containing fractions when compared to non-LSC fractions. Comparison of the expression pattern of LSC-containing fractions with healthy HSPC revealed distinct expression of previously proposed LSC markers including CD47, TIM-3, CD25, CD99, CD97, CD123 and CSF-1R. In addition, our approach allowed us to identify several differentially expressed new cell surface proteins, which may serve as novel marker candidates for AML LSC. Quantitative proteomic analysis was performed by employing tandem mass tag labeling and high-resolution mass spectrometry. Using this approach, approximately 7,000 proteins were quantified from LSC-containing and LSC-free fractions from several individual AML samples of different subtypes. Importantly, our data include many low abundance proteins or others known to be difficult to detect by mass spectrometry, such as transcription factors and membrane proteins. Statistical analysis revealed a number of candidate proteins distinguishing the LSC-containing and LSC-free fractions. Data sets derived from the RNA-Seq and proteomics approaches will be presented and both data sets will be bioinformatically integrated towards a comprehensive expression signature of normal and leukemic stem cells. Disclosures: No relevant conflicts of interest to declare.
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Lorrain, Cécile, Clémence Marchal, Stéphane Hacquard, Christine Delaruelle, Jérémy Pétrowski, Benjamin Petre, Arnaud Hecker, Pascal Frey, and Sébastien Duplessis. "The Rust Fungus Melampsora larici-populina Expresses a Conserved Genetic Program and Distinct Sets of Secreted Protein Genes During Infection of Its Two Host Plants, Larch and Poplar." Molecular Plant-Microbe Interactions® 31, no. 7 (July 2018): 695–706. http://dx.doi.org/10.1094/mpmi-12-17-0319-r.
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Mechanisms required for broad-spectrum or specific host colonization of plant parasites are poorly understood. As a perfect illustration, heteroecious rust fungi require two alternate host plants to complete their life cycles. Melampsora larici-populina infects two taxonomically unrelated plants, larch, on which sexual reproduction is achieved, and poplar, on which clonal multiplication occurs, leading to severe epidemics in plantations. We applied deep RNA sequencing to three key developmental stages of M. larici-populina infection on larch: basidia, pycnia, and aecia, and we performed comparative transcriptomics of infection on poplar and larch hosts, using available expression data. Secreted protein was the only significantly overrepresented category among differentially expressed M. larici-populina genes between the basidial, the pycnial, and the aecial stages, highlighting their probable involvement in the infection process. Comparison of fungal transcriptomes in larch and poplar revealed a majority of rust genes were commonly expressed on the two hosts and a fraction exhibited host-specific expression. More particularly, gene families encoding small secreted proteins presented striking expression profiles that highlight probable candidate effectors specialized on each host. Our results bring valuable new information about the biological cycle of rust fungi and identify genes that may contribute to host specificity.
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Jessa, Selin, Nisha Kabir, Maria Vladoiu, Steven Hébert, Michael D. Taylor, Nada Jabado, and Claudia L. Kleinman. "TBIO-15. MODELING DEVELOPMENTAL GENE EXPRESSION DYNAMICS AT CELLULAR RESOLUTION TO INTERPRET PEDIATRIC BRAIN TUMOR TRANSCRIPTIONAL PROGRAMS." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii469. http://dx.doi.org/10.1093/neuonc/noaa222.842.
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Abstract A central challenge in understanding the biology of pediatric brain tumors is defining the cellular and molecular context where oncogenesis occurs. We hypothesize that spatiotemporally restricted cell types are uniquely susceptible to specific genetic alterations, which alter normal neurodevelopmental programs and ultimately lead to oncogenesis. The resulting tumors retain some transcriptomic features of their lineage of origin. To delineate these origins, we assembled a densely sampled developmental time course of the mouse forebrain and pons, doubling our recently published single-cell atlas. This dataset comprises >100,000 cells at 9 timepoints from E10-P6. However, while single cell transcriptomics reveal rich gene dynamics during cell differentiation, interpretation of individual genes can be challenging due to data sparsity. Leveraging this time-series, we present strategies to model and visualize the expression of a given gene across differentiation of distinct lineages. We demonstrate an interactive web app to interrogate the expression of genes or gene sets during brain development, extract temporally correlated genes, and search active transcription factor regulatory modules. Finally, we profile the expression of core transcriptional programs of several pediatric brain tumor entities during development. Our analyses reveal genes with restricted expression patterns that elucidate tumor etiology. More broadly, these resources harness single cell data to enable exploration of neurodevelopmental gene programs with great relevance to pediatric brain tumor oncogenesis.
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Gim, Jungsoo, Sungho Won, and Taesung Park. "Conditional estimation of local pooled dispersion parameter in small-sample RNA-Seq data improves differential expression test." Journal of Bioinformatics and Computational Biology 14, no. 05 (October 2016): 1644006. http://dx.doi.org/10.1142/s0219720016440066.
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High throughput sequencing technology in transcriptomics studies contribute to the understanding of gene regulation mechanism and its cellular function, but also increases a need for accurate statistical methods to assess quantitative differences between experiments. Many methods have been developed to account for the specifics of count data: non-normality, a dependence of the variance on the mean, and small sample size. Among them, the small number of samples in typical experiments is still a challenge. Here we present a method for differential analysis of count data, using conditional estimation of local pooled dispersion parameters. A comprehensive evaluation of our proposed method in the aspect of differential gene expression analysis using both simulated and real data sets shows that the proposed method is more powerful than other existing methods while controlling the false discovery rates. By introducing conditional estimation of local pooled dispersion parameters, we successfully overcome the limitation of small power and enable a powerful quantitative analysis focused on differential expression test with the small number of samples.
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Chan, Mark Y., Motakis Efthymios, Sock Hwee Tan, John W. Pickering, Richard Troughton, Christopher Pemberton, Hee-Hwa Ho, et al. "Prioritizing Candidates of Post–Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics." Circulation 142, no. 15 (October 13, 2020): 1408–21. http://dx.doi.org/10.1161/circulationaha.119.045158.
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Background: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery. Methods: We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF. Results: In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well. Conclusions: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.
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Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: interactive visualization of scRNA-seq data." Bioinformatics 36, no. 7 (November 25, 2019): 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.
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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhang, Wei, Brian X. Leon-Ricardo, Bas van Schooten, Steven M. Van Belleghem, Brian A. Counterman, W. Owen McMillan, Marcus R. Kronforst, and Riccardo Papa. "Comparative Transcriptomics Provides Insights into Reticulate and Adaptive Evolution of a Butterfly Radiation." Genome Biology and Evolution 11, no. 10 (September 13, 2019): 2963–75. http://dx.doi.org/10.1093/gbe/evz202.
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Abstract Butterfly eyes are complex organs that are composed of a diversity of proteins and they play a central role in visual signaling and ultimately, speciation, and adaptation. Here, we utilized the whole eye transcriptome to obtain a more holistic view of the evolution of the butterfly eye while accounting for speciation events that co-occur with ancient hybridization. We sequenced and assembled transcriptomes from adult female eyes of eight species representing all major clades of the Heliconius genus and an additional outgroup species, Dryas iulia. We identified 4,042 orthologous genes shared across all transcriptome data sets and constructed a transcriptome-wide phylogeny, which revealed topological discordance with the mitochondrial phylogenetic tree in the Heliconius pupal mating clade. We then estimated introgression among lineages using additional genome data and found evidence for ancient hybridization leading to the common ancestor of Heliconius hortense and Heliconius clysonymus. We estimated the Ka/Ks ratio for each orthologous cluster and performed further tests to demonstrate genes showing evidence of adaptive protein evolution. Furthermore, we characterized patterns of expression for a subset of these positively selected orthologs using qRT-PCR. Taken together, we identified candidate eye genes that show signatures of adaptive molecular evolution and provide evidence of their expression divergence between species, tissues, and sexes. Our results demonstrate: 1) greater evolutionary changes in younger Heliconius lineages, that is, more positively selected genes in the cydno–melpomene–hecale group as opposed to the sara–hortense–erato group, and 2) suggest an ancient hybridization leading to speciation among Heliconius pupal-mating species.
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Krebs, Kristi, and Lili Milani. "Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery." Annual Review of Pharmacology and Toxicology 63, no. 1 (January 20, 2023): 65–76. http://dx.doi.org/10.1146/annurev-pharmtox-051421-111324.
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A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.
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Reed, Megan R., A. Geoffrey Lyle, Annick De Loose, Leena Maddukuri, Katrina Learned, Holly C. Beale, Ellen T. Kephart, et al. "A Functional Precision Medicine Pipeline Combines Comparative Transcriptomics and Tumor Organoid Modeling to Identify Bespoke Treatment Strategies for Glioblastoma." Cells 10, no. 12 (December 2, 2021): 3400. http://dx.doi.org/10.3390/cells10123400.
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Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30–50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.
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Chung, Matthew, Preston J. Basting, Rayanna S. Patkus, Alexandra Grote, Ashley N. Luck, Elodie Ghedin, Barton E. Slatko, et al. "A Meta-Analysis of Wolbachia Transcriptomics Reveals a Stage-Specific Wolbachia Transcriptional Response Shared Across Different Hosts." G3 Genes|Genomes|Genetics 10, no. 9 (September 1, 2020): 3243–60. http://dx.doi.org/10.1534/g3.120.401534.
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Abstract Wolbachia is a genus containing obligate, intracellular endosymbionts with arthropod and nematode hosts. Numerous studies have identified differentially expressed transcripts in Wolbachia endosymbionts that potentially inform the biological interplay between these endosymbionts and their hosts, albeit with discordant results. Here, we re-analyze previously published Wolbachia RNA-Seq transcriptomics data sets using a single workflow consisting of the most up-to-date algorithms and techniques, with the aim of identifying trends or patterns in the pan-Wolbachia transcriptional response. We find that data from one of the early studies in filarial nematodes did not allow for robust conclusions about Wolbachia differential expression with these methods, suggesting the original interpretations should be reconsidered. Across datasets analyzed with this unified workflow, there is a general lack of global gene regulation with the exception of a weak transcriptional response resulting in the upregulation of ribosomal proteins in early larval stages. This weak response is observed across diverse Wolbachia strains from both nematode and insect hosts suggesting a potential pan-Wolbachia transcriptional response during host development that diverged more than 700 million years ago.
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Everett, Logan J., Deepak Mav, Dhiral P. Phadke, Michele R. Balik-Meisner, and Ruchir R. Shah. "Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq Accuracy." Bioinformatics and Biology Insights 16 (January 2022): 117793222210952. http://dx.doi.org/10.1177/11779322221095216.
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High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.
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Wei, Fu-Jin, Saneyoshi Ueno, Tokuko Ujino-Ihara, Maki Saito, Yoshihiko Tsumura, Yuumi Higuchi, Satoko Hirayama, Junji Iwai, Tetsuji Hakamata, and Yoshinari Moriguchi. "Construction of a reference transcriptome for the analysis of male sterility in sugi (Cryptomeria japonica D. Don) focusing on MALE STERILITY 1 (MS1)." PLOS ONE 16, no. 2 (February 25, 2021): e0247180. http://dx.doi.org/10.1371/journal.pone.0247180.
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Sugi (Cryptomeria japonica D. Don) is an important conifer used for afforestation in Japan. As the genome of this species is 11 Gbps, it is too large to assemble within a short timeframe. Transcriptomics is one approach that can address this deficiency. Here we designed a workflow consisting of three stages to de novo assemble transcriptome using Oases and Trinity. The three transcriptomic stage used were independent assembly, automatic and semi-manual integration, and refinement by filtering out potential contamination. We identified a set of 49,795 cDNA and an equal number of translated proteins. According to the benchmark set by BUSCO, 87.01% of cDNAs identified were complete genes, and 78.47% were complete and single-copy genes. Compared to other full-length cDNA resources collected by Sanger and PacBio sequencers, the extent of the coverage in our dataset was the highest, indicating that these data can be safely used for further studies. When two tissue-specific libraries were compared, there were significant expression differences between male strobili and leaf and bark sets. Moreover, subtle expression difference between male-fertile and sterile libraries were detected. Orthologous genes from other model plants and conifer species were identified. We demonstrated that our transcriptome assembly output (CJ3006NRE) can serve as a reference transcriptome for future functional genomics and evolutionary biology studies.
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Lachmann, Alexander, Zhuorui Xie, and Avi Ma’ayan. "blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation." Bioinformatics 38, no. 8 (February 10, 2022): 2356–57. http://dx.doi.org/10.1093/bioinformatics/btac076.
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Abstract Motivation The identification of pathways and biological processes from differential gene expression is central for interpretation of data collected by transcriptomics assays. Gene set enrichment analysis (GSEA) is the most commonly used algorithm to calculate the significance of the relevancy of an annotated gene set with a differential expression signature. To compute significance, GSEA implements permutation tests which are slow and inaccurate for comparing many differential expression signatures to thousands of annotated gene sets. Results Here, we present blitzGSEA, an algorithm that is based on the same running sum statistic as GSEA, but instead of performing permutations, blitzGSEA approximates the enrichment score probabilities based on Gamma distributions. blitzGSEA achieves significant improvement in performance compared with prior GSEA implementations, while approximating small P-values more accurately. Availability and implementation The data, a python package, together with all source code, and a detailed user guide are available from GitHub at: https://github.com/MaayanLab/blitzgsea. Supplementary information Supplementary data are available at Bioinformatics online.
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Thistlethwaite, Lillian R., Varduhi Petrosyan, Xiqi Li, Marcus J. Miller, Sarah H. Elsea, and Aleksandar Milosavljevic. "CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models." PLOS Computational Biology 17, no. 1 (January 29, 2021): e1008550. http://dx.doi.org/10.1371/journal.pcbi.1008550.
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We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, “Connect the Dots”, a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.