Relevant bibliographies by topics / Transcriptome data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Transcriptome data'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Transcriptome data"

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Macrander, Jason, Jyothirmayi Panda, Daniel Janies, Marymegan Daly, and Adam M. Reitzel. "Venomix: a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data." PeerJ 6 (July 31, 2018): e5361. http://dx.doi.org/10.7717/peerj.5361.

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The advent of next-generation sequencing has resulted in transcriptome-based approaches to investigate functionally significant biological components in a variety of non-model organism. This has resulted in the area of “venomics”: a rapidly growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Ultimately, the transcriptomic portion of these analyses follows very similar pathways after transcriptome assembly often including candidate toxin identification using BLAST, expression level screening, protein sequence alignment, gene tree reconstruction, and characterization of potential toxin function. Here we describe the Python package Venomix, which streamlines these processes using common bioinformatic tools along with ToxProt, a publicly available annotated database comprised of characterized venom proteins. In this study, we use the Venomix pipeline to characterize candidate venom diversity in four phylogenetically distinct organisms, a cone snail (Conidae; Conus sponsalis), a snake (Viperidae; Echis coloratus), an ant (Formicidae; Tetramorium bicarinatum), and a scorpion (Scorpionidae; Urodacus yaschenkoi). Data on these organisms were sampled from public databases, with each original analysis using different approaches for transcriptome assembly, toxin identification, or gene expression quantification. Venomix recovered numerically more candidate toxin transcripts for three of the four transcriptomes than the original analyses and identified new toxin candidates. In summary, we show that the Venomix package is a useful tool to identify and characterize the diversity of toxin-like transcripts derived from transcriptomic datasets. Venomix is available at: https://bitbucket.org/JasonMacrander/Venomix/.
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Wang, Xinjun, Zhe Sun, Yanfu Zhang, et al. "BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data." Nucleic Acids Research 48, no. 11 (2020): 5814–24. http://dx.doi.org/10.1093/nar/gkaa314.

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Abstract Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.
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Ochsner, Scott A., Christopher M. Watkins, Apollo McOwiti, et al. "Transcriptomine, a web resource for nuclear receptor signaling transcriptomes." Physiological Genomics 44, no. 17 (2012): 853–63. http://dx.doi.org/10.1152/physiolgenomics.00033.2012.

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The nuclear receptor (NR) superfamily of ligand-regulated transcription factors directs ligand- and tissue-specific transcriptomes in myriad developmental, metabolic, immunological, and reproductive processes. The NR signaling field has generated a wealth of genome-wide expression data points, but due to deficits in their accessibility, annotation, and integration, the full potential of these studies has not yet been realized. We searched public gene expression databases and MEDLINE for global transcriptomic datasets relevant to NRs, their ligands, and coregulators. We carried out extensive, deep reannotation of the datasets using controlled vocabularies for RNA Source and regulating molecule and resolved disparate gene identifiers to official gene symbols to facilitate comparison of fold changes and their significance across multiple datasets. We assembled these data points into a database, Transcriptomine ( http://www.nursa.org/transcriptomine ), that allows for multiple, menu-driven querying strategies of this transcriptomic “superdataset,” including single and multiple genes, Gene Ontology terms, disease terms, and uploaded custom gene lists. Experimental variables such as regulating molecule, RNA Source, as well as fold-change and P value cutoff values can be modified, and full data records can be either browsed or downloaded for downstream analysis. We demonstrate the utility of Transcriptomine as a hypothesis generation and validation tool using in silico and experimental use cases. Our resource empowers users to instantly and routinely mine the collective biology of millions of previously disparate transcriptomic data points. By incorporating future transcriptome-wide datasets in the NR signaling field, we anticipate Transcriptomine developing into a powerful resource for the NR- and other signal transduction research communities.
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Yokoi, Kakeru, Takuya Tsubota, Akiya Jouraku, Hideki Sezutsu, and Hidemasa Bono. "Reference Transcriptome Data in Silkworm Bombyx mori." Insects 12, no. 6 (2021): 519. http://dx.doi.org/10.3390/insects12060519.

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Herein, we performed RNA-seq analysis of ten major tissues/subparts of silkworm larvae. The sequences were mapped onto the reference genome assembly and the reference transcriptome data were successfully constructed. The reference data provided a nearly complete sequence for sericin-1, a major silk gene with a complex structure. We also markedly improved the gene model for other genes. The transcriptomic expression was investigated in each tissue and a number of transcripts were identified that were exclusively expressed in tissues such as the testis. Transcripts strongly expressed in the midgut formed tight genomic clusters, suggesting that they originated from tandem gene duplication. Transcriptional factor genes expressed in specific tissues or the silk gland subparts were also identified. We successfully constructed reference transcriptome data in the silkworm and found that a number of transcripts showed unique expression profiles. These results will facilitate basic studies on the silkworm and accelerate its applications, which will contribute to further advances in lepidopteran and entomological research as well as the practical use of these insects.
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Kordonowy, Lauren L., and Matthew D. MacManes. "Characterization of a male reproductive transcriptome forPeromyscus eremicus(Cactus mouse)." PeerJ 4 (October 27, 2016): e2617. http://dx.doi.org/10.7717/peerj.2617.

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Rodents of the genusPeromyscushave become increasingly utilized models for investigations into adaptive biology. This genus is particularly powerful for research linking genetics with adaptive physiology or behaviors, and recent research has capitalized on the unique opportunities afforded by the ecological diversity of these rodents. Well characterized genomic and transcriptomic data is intrinsic to explorations of the genetic architecture responsible for ecological adaptations. Therefore, this study characterizes the transcriptome of three male reproductive tissues (testes, epididymis and vas deferens) ofPeromyscus eremicus(Cactus mouse), a desert specialist. The transcriptome assembly process was optimized in order to produce a high quality and substantially complete annotated transcriptome. This composite transcriptome was generated to characterize the expressed transcripts in the male reproductive tract ofP. eremicus,which will serve as a crucial resource for future research investigating our hypothesis that the male Cactus mouse possesses an adaptive reproductive phenotype to mitigate water-loss from ejaculate. This study reports genes under positive selection in the male Cactus mouse reproductive transcriptome relative to transcriptomes fromPeromyscus maniculatus(deer mouse) andMus musculus.Thus, this study expands upon existing genetic research in this species, and we provide a high quality transcriptome to enable further explorations of our proposed hypothesis for male Cactus mouse reproductive adaptations to minimize seminal fluid loss.
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Nishimura, Yuhei. "Drug discovery using transcriptome data." Folia Pharmacologica Japonica 149, no. 3 (2017): 138. http://dx.doi.org/10.1254/fpj.149.138.

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Reznikov, Leah R., David K. Meyerholz, Mahmoud Abou Alaiwa, et al. "The vagal ganglia transcriptome identifies candidate therapeutics for airway hyperreactivity." American Journal of Physiology-Lung Cellular and Molecular Physiology 315, no. 2 (2018): L133—L148. http://dx.doi.org/10.1152/ajplung.00557.2017.

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Mainstay therapeutics are ineffective in some people with asthma, suggesting a need for additional agents. In the current study, we used vagal ganglia transcriptome profiling and connectivity mapping to identify compounds beneficial for alleviating airway hyperreactivity (AHR). As a comparison, we also used previously published transcriptome data from sensitized mouse lungs and human asthmatic endobronchial biopsies. All transcriptomes revealed agents beneficial for mitigating AHR; however, only the vagal ganglia transcriptome identified agents used clinically to treat asthma (flunisolide, isoetarine). We also tested one compound identified by vagal ganglia transcriptome profiling that had not previously been linked to asthma and found that it had bronchodilator effects in both mouse and pig airways. These data suggest that transcriptome profiling of the vagal ganglia might be a novel strategy to identify potential asthma therapeutics.
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Cheng, Xuanjin, Junran Yan, Yongxing Liu, Jiahe Wang, and Stefan Taubert. "eVITTA: a web-based visualization and inference toolbox for transcriptome analysis." Nucleic Acids Research 49, W1 (2021): W207—W215. http://dx.doi.org/10.1093/nar/gkab366.

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Abstract Transcriptome profiling is essential for gene regulation studies in development and disease. Current web-based tools enable functional characterization of transcriptome data, but most are restricted to applying gene-list-based methods to single datasets, inefficient in leveraging up-to-date and species-specific information, and limited in their visualization options. Additionally, there is no systematic way to explore data stored in the largest transcriptome repository, NCBI GEO. To fill these gaps, we have developed eVITTA (easy Visualization and Inference Toolbox for Transcriptome Analysis; https://tau.cmmt.ubc.ca/eVITTA/). eVITTA provides modules for analysis and exploration of studies published in NCBI GEO (easyGEO), detailed molecular- and systems-level functional profiling (easyGSEA), and customizable comparisons among experimental groups (easyVizR). We tested eVITTA on transcriptomes of SARS-CoV-2 infected human nasopharyngeal swab samples, and identified a downregulation of olfactory signal transducers, in line with the clinical presentation of anosmia in COVID-19 patients. We also analyzed transcriptomes of Caenorhabditis elegans worms with disrupted S-adenosylmethionine metabolism, confirming activation of innate immune responses and feedback induction of one-carbon cycle genes. Collectively, eVITTA streamlines complex computational workflows into an accessible interface, thus filling the gap of an end-to-end platform capable of capturing both broad and granular changes in human and model organism transcriptomes.
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Londin, Eric R., Eleftheria Hatzimichael, Phillipe Loher, et al. "Towards a Reference Human Platelet Transcriptome: Evaluation Of Inter-Individual Correlations and Its Relationship With a Platelet Proteome." Blood 122, no. 21 (2013): 2297. http://dx.doi.org/10.1182/blood.v122.21.2297.2297.

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Abstract Next generation sequencing of RNA (RNA-seq) is an emerging technology that has so far been used successfully to profile the transcriptomes of several cell types and cell states. For the platelet transcriptome, RNA-seq descriptions exist for only a few subjects. Additionally, there have been no studies of the same individual’s transcriptome using two different technologies. As such, it has been unclear how well platelet transcriptomes correlate among different donors or across different RNA platforms, and what the transcriptomes’ relationship is with the platelet proteome. We generated RNA-seq profiles of the long RNA transcriptomes from the platelets of 10 healthy young males (5 white and 5 black). In addition to RNA-seq, we profiled the platelet messenger RNAs of the same 10 individuals using the Affymetrix GeneChip System. We observed that the abundance of platelet mRNA transcripts was highly correlated across the 10 individuals, a finding that was independent of race and of the employed technology. Additionally, our RNA-seq data showed that these high inter-individual correlations extend beyond mRNAs to several categories of non-coding RNAs. However, there was a notable exception: the category of pseudogenes exhibited a clear difference in expression by race. Comparison of our mRNA signatures with the only publicly available quantitative platelet proteome data showed that most (87.5%) identified platelet proteins had a detectable corresponding mRNA. Interestingly, there was also a high number of mRNAs that were present in the transcriptomes of all 10 individuals but had no representation in the proteome. Spearman correlation of the relative abundances for those platelet genes that were represented by both an mRNA and a protein, revealed an unexpectedly weak correlation between the transcriptome and the proteome. Further analysis of the overlapping and non-overlapping platelet mRNAs and proteins identified groups of genes with very distinct characteristics. Gene Ontology analysis of the respective gene identifiers revealed that the gene groups corresponded to distinct cellular processes, an interesting finding that provides novel insights for platelet biology. The very high inter-individual correlations of the transcriptome signatures across 10 different subjects representing two races together with the results of our analyses indicate that it is feasible to assemble a platelet mRNA-ome that can serve as a reference for future platelet transcriptomic studies of human health and disease. Disclosures: No relevant conflicts of interest to declare.
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Mořkovský, Libor, Jan Pačes, Jakub Rídl, and Radka Reifová. "Scrimer: designing primers from transcriptome data." Molecular Ecology Resources 15, no. 6 (2015): 1415–20. http://dx.doi.org/10.1111/1755-0998.12403.

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Dissertations / Theses on the topic "Transcriptome data"

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Liu, Zhe. "Machine annotation of genome and transcriptome data." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/17626.

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One of the key research topics of post-genome study is annotation of the gene with regards to specific function and biological processes. This can help us to understand the precise role that a gene or a group of genes carries. In this thesis, I developed techniques to automatically annotate genes on single gene and a group of genes levels. It is shown that these techniques improve our understanding of biological systems/diseases, and will aid drug discovery. In the first project, I attempted to achieve precise annotation for single genes. In the second and third projects, I performed annotations of a group of genes using pathway knowledge. I examined this problem from supervised and unsupervised learning aspects, respectively. The main contributions of the work are organized as follows: In gene annotation project, I built up an automated scheme to reconcile the term differences arising from the different automated annotation services. The method leaves less than 20% of the annotations for manual work. The generalization performance across other species is of a similar standard, again leaving less than 20% of the annotations for manual inspection. In addition, less than 10% of the results have different functions from EcoCyc results in E.coli genome annotation task. Overall, this method can significantly reduce human effort involvement (6 months’ work by several biologists for a bacterial genome) to resolve inconsistent gene annotations. Then I started from the current limitations of pathway analysis and presented a novel approach for pathway discovery. Enrichment analysis is the most popular approach to map gene expression profiling from genes to biological pathways. It is a powerful tool to identify pathways enriching of differentially expressed gene; however, it is unable to discover active/inhibitive pathways. In this study, I attempted to resolve this issue by integrative classification of KEGG and TF gene sets. I assumed that the pathways with good classification performance should be considered as the active/inhibitive pathways. Based on this hypothesis, I built up a generic approach to incorporate two types of biological data for active pathway discovery. The experimental results show that integration of transcription factor data boosts classification performance. In addition, this method identified relevant biological pathways, which are highly associated with tumour genesis and development. But they are ignored by Gene Set Enrichment Analysis, such as cancer pathway, inflammation and metabolic pathways. Furthermore, this method achieves comparable classification performance with the best-reported results. Lastly, I performed subtyping analysis of Rheumatoid Arthritis patients based on gene expression profiling. I revalidated the two clusters of patients based on two independent cohorts. The experimental results indicate that the subgroup structure does not correspond to the drug response status. In addition, I developed a pathway subtyping approach and achieved the same number of clusters as gene-level clustering results. The pathway clustering results show that one group of the patients has high proliferation and low inflammation response, while the other group has the reverse trend. It suggests that designing drugs with better trade-off between anti-inflammation and anti-proliferation for specific subgroup of patients may achieve better clinical outcomes.
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Östman, Josephine. "The fertile ovary transcriptome and proteome." Thesis, Uppsala universitet, Institutionen för kvinnors och barns hälsa, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447785.

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The Human Protein Atlas is an open-source database containing information about protein expression and location in the human cells,tissues and organs. The aim is to map all the proteins in humans using various biotechnology techniques such as antibody-based imaging, andRNA sequencing etc. Based on previous transcriptome analysis, 173 genes were shown to have an elevated expression in ovary compared to all other major tissue types in the human body. There is however no information regarding the expression in ovary during the reproductive years versus the post-menopausal years. In this thesis, the gene expression in ovaries of women in reproductive age was compared with women in post-menopausal age. 509 genes were found to have an at least 2-fold higher mean value RNA expression in the reproductive age group. 14 of these genes were analyzed further with antibody staining and multiplex immunofluorescence staining to localize the corresponding proteins. The results show that these genes are expressed in a variety of structures in the ovarian tissue, such as the oocyte, the granulosa cells and the corpus luteum. This thesis has demonstrated how data analysis can be used to find genes important for the ovary of women in reproductive age and in the future, this could aid research in female fertility.
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Mangul, Serghei. "Algorithms for Transcriptome Quantification and Reconstruction from RNA-Seq Data." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/71.

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Massively parallel whole transcriptome sequencing and its ability to generate full transcriptome data at the single transcript level provides a powerful tool with multiple interrelated applications, including transcriptome reconstruction, gene/isoform expression estimation, also known as transcriptome quantification. As a result, whole transcriptome sequencing has become the technology of choice for performing transcriptome analysis, rapidly replacing array-based technologies. The most commonly used transcriptome sequencing protocol, referred to as RNA-Seq, generates short (single or paired) sequencing tags from the ends of randomly generated cDNA fragments. RNA-Seq protocol reduces the sequencing cost and significantly increases data throughput, but is computationally challenging to reconstruct full-length transcripts and accurately estimate their abundances across all cell types. We focus on two main problems in transcriptome data analysis, namely, transcriptome reconstruction and quantification. Transcriptome reconstruction, also referred to as novel isoform discovery, is the problem of reconstructing the transcript sequences from the sequencing data. Reconstruction can be done de novo or it can be assisted by existing genome and transcriptome annotations. Transcriptome quantification refers to the problem of estimating the expression level of each transcript. We present a genome-guided and annotation-guided transcriptome reconstruction methods as well as methods for transcript and gene expression level estimation. Empirical results on both synthetic and real RNA-seq datasets show that the proposed methods improve transcriptome quantification and reconstruction accuracy compared to previous methods.
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Calviello, Lorenzo. "Detecting and quantifying the translated transcriptome with Ribo-seq data." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/18974.

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Die Untersuchung der posttranskriptionellen Genregulation erfordert eine eingehende Kenntnis vieler molekularer Prozesse, die auf RNA wirken, von der Prozessierung im Nukleus bis zur Translation und der Degradation im Zytoplasma. Mit dem Aufkommen von RNA-seq-Technologien können wir nun jeden dieser Schritte mit hohem Durchsatz und Auflösung verfolgen. Ribosome Profiling (Ribo-seq) ist eine RNA-seq-Technik, die darauf abzielt, die präzise Position von Millionen translatierender Ribosomen zu detektieren, was sich als ein wesentliches Instrument für die Untersuchung der Genregulation erweist. Allerdings ist die Interpretation von Ribo-seq-Profilen über das Transkriptom aufgrund der verrauschten Daten und unserer unvollständigen Kenntnis des translatierten Transkriptoms eine Herausforderung. In dieser Arbeit präsentiere ich eine Methode, um translatierte Regionen in Ribo-seq-Daten zu erkennen, wobei ein Spektralanalyse verwendet wird, die darauf abzielt, die ribosomale Translokation über die übersetzten Regionen zu erkennen. Die hohe Sensibilität und Spezifität unseres Ansatzes ermöglichten es uns, eine umfassende Darstellung der Translation über das menschlichen und pflanzlichen (Arabidopsis thaliana) Transkriptom zu zeichnen und die Anwesenheit bekannter und neu-identifizierter translatierter Regionen aufzudecken. Evolutionäre Konservierungsanalysen zusammen mit Hinweisen auf Proteinebene lieferten Einblicke in ihre Funktionen, von der Synthese von bisher unbekannter Proteinen einerseits, zu möglichen regulatorischen Rollen andererseits. Darüber hinaus zeigte die Quantifizierung des Ribo-seq-Signals über annotierte Genemodelle die Translation mehrerer Transkripte pro Gen, was die Verbindung zwischen Translations- und RNA-Überwachungsmechanismen offenbarte. Zusammen mit einem Vergleich verschiedener Ribo-seq-Datensätze in menschlichen und planzlichen Zellen umfasst diese Arbeit eine Reihe von Analysestrategien für Ribo-seq-Daten als Fenster in die vielfältigen Funktionen des exprimierten Transkriptoms.<br>The study of post-transcriptional gene regulation requires in-depth knowledge of multiple molecular processes acting on RNA, from its nuclear processing to translation and decay in the cytoplasm. With the advent of RNA-seq technologies we can now follow each of these steps with high throughput and resolution. Ribosome profiling (Ribo-seq) is a popular RNA-seq technique, which aims at monitoring the precise positions of millions of translating ribosomes, proving to be an essential tool in studying gene regulation. However, the interpretation of Ribo-seq profiles over the transcriptome is challenging, due to noisy data and to our incomplete knowledge of the translated transcriptome. In this Thesis, I present a strategy to detect translated regions from Ribo-seq data, using a spectral analysis approach aimed at detecting ribosomal translocation over the translated regions. The high sensitivity and specificity of our approach enabled us to draw a comprehensive map of translation over the human and Arabidopsis thaliana transcriptomes, uncovering the presence of known and novel translated regions. Evolutionary conservation analysis, together with large-scale proteomics evidence, provided insights on their functions, between the synthesis of previously unknown proteins to other possible regulatory roles. Moreover, quantification of Ribo-seq signal over annotated transcript structures exposed translation of multiple transcripts per gene, revealing the link between translation and RNA-surveillance mechanisms. Together with a comparison of different Ribo-seq datasets in human cells and in Arabidopsis thaliana, this work comprises a set of analysis strategies for Ribo-seq data, as a window into the manifold functions of the expressed transcriptome.
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Xu, Guorong. "Computational Pipeline for Human Transcriptome Quantification Using RNA-seq Data." ScholarWorks@UNO, 2011. http://scholarworks.uno.edu/td/343.

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The main theme of this thesis research is concerned with developing a computational pipeline for processing Next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate tens of millions of short reads for each DNA/RNA sample. The alignment of a large volume of short reads to a reference genome is a key step in NGS data analysis. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing useful information. In order to assist biomedical researchers to conveniently access essential information from NGS data files in SAM/BAM format, we have developed a Graphical User Interface (GUI) software tool named SAMMate to pipeline human transcriptome quantification. SAMMate allows researchers to easily process NGS data files in SAM/BAM format and is compatible with both single-end and paired-end sequencing technologies. It also allows researchers to accurately calculate gene expression abundance scores.
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Hu, Yin. "A NOVEL COMPUTATIONAL FRAMEWORK FOR TRANSCRIPTOME ANALYSIS WITH RNA-SEQ DATA." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/17.

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The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected alternative splicing variants, which circumvents the need of full transcript reconstruction and quantification. Beyond the scope of classical group-wise analysis, a clustering scheme is further described for mining prominent consistency among samples in transcription, breaking the restriction of presumed grouping. The performance of the framework has been demonstrated by a series of simulation studies and real datasets, including the Cancer Genome Atlas (TCGA) breast cancer analysis. The successful applications have suggested the unprecedented opportunity in using differential transcription analysis to reveal variations in the mRNA transcriptome in response to cellular differentiation or effects of diseases.
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Bécavin, Christophe. "Dimensionaly reduction and pathway network analysis of transcriptome data : application to T-cell characterization." Paris, Ecole normale supérieure, 2010. http://www.theses.fr/2010ENSUBS02.

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Kelso, Janet. "The development and application of informatics-based systems for the analysis of the human transcriptome." Thesis, University of the Western Cape, 2003. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_5101_1185442672.

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<p>Despite the fact that the sequence of the human genome is now complete it has become clear that the elucidation of the transcriptome is more complicated than previously expected. There is mounting evidence for unexpected and previously underestimated phenomena such as alternative splicing in the transcriptome. As a result, the identification of novel transcripts arising from the genome continues. Furthermore, as the volume of transcript data grows it is becoming increasingly difficult to integrate expression information which is from different sources, is stored in disparate locations, and is described using differing terminologies. Determining the function of translated transcripts also remains a complex task. Information about the expression profile &ndash<br>the location and timing of transcript expression &ndash<br>provides evidence that can be used in understanding the role of the expressed transcript in the organ or tissue under study, or in developmental pathways or disease phenotype observed.<br /> <br /> In this dissertation I present novel computational approaches with direct biological applications to two distinct but increasingly important areas of research in gene expression research. The first addresses detection and characterisation of alternatively spliced transcripts. The second is the construction of an hierarchical controlled vocabulary for gene expression data and the annotation of expression libraries with controlled terms from the hierarchies. In the final chapter the biological questions that can be approached, and the discoveries that can be made using these systems are illustrated with a view to demonstrating how the application of informatics can both enable and accelerate biological insight into the human transcriptome.</p>
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Johnson, Kristen. "Software for Estimation of Human Transcriptome Isoform Expression Using RNA-Seq Data." ScholarWorks@UNO, 2012. http://scholarworks.uno.edu/td/1448.

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The goal of this thesis research was to develop software to be used with RNA-Seq data for transcriptome quantification that was capable of handling multireads and quantifying isoforms on a more global level. Current software available for these purposes uses various forms of parameter alteration in order to work with multireads. Many still analyze isoforms per gene or per researcher determined clusters as well. By doing so, the effects of multireads are diminished or possibly wrongly represented. To address this issue, two programs, GWIE and ChromIE, were developed based on a simple iterative EM-like algorithm with no parameter manipulation. These programs are used to produce accurate isoform expression levels.
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Windhorst, Anita Cornelia [Verfasser]. "Transcriptome analysis in preterm infants developing bronchopulmonary dysplasia : data processing and statistical analysis of microarray data / Anita Cornelia Windhorst." Gießen : Universitätsbibliothek, 2015. http://d-nb.info/1078220395/34.

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Books on the topic "Transcriptome data"

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Wang, Yejun, and Ming-an Sun, eds. Transcriptome Data Analysis. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7710-9.

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author, Tuimala Jarno, Somervuo Panu author, Huss Mikael author, and Wong Garry author, eds. RNA-seq data analysis: A practical approach. CRC Press, Taylor & Francis Group, 2015.

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Bioinformatics in the post-genomic era: Genome, transcriptome, proteome, and information-based medicine. Addison-Wesley, 2005.

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Transcriptome Data Analysis: Methods and Protocols. Humana, 2018.

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Schadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.

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The life sciences are now a significant contributor to the ever expanding digital universe of data, and stand poised to lead in both the generation of big data and the realization of dramatic benefit from it. We can now score variations in DNA across whole genomes; RNA levels and alternative isoforms, metabolite levels, protein levels, and protein state information across the transcriptome, metabolome and proteome; methylation status across the methylome; and construct extensive protein–protein and protein–DNA interaction maps, all in a comprehensive fashion and at the scale of populations of individuals. This chapter describes a number of analytical approaches aimed at inferring causal relationships among variables in very large-scale datasets by leveraging DNA variation as a systematic perturbation source. The causal inference procedures are also demonstrated to enhance the ability to reconstruct truly predictive, probabilistic causal gene networks that reflect the biological processes underlying complex phenotypes like disease.
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Ellison, Aaron M., and Lubomír Adamec. The future of research with carnivorous plants. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779841.003.0029.

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The material presented in the chapters of Carnivorous Plants: Physiology, Ecology, and Evolution together provide a suite of common themes that could provide a framework for increasing progress in understanding carnivorous plants. All speciose genera would benefit from more robust, intra-generic classifications in a phylogenetic framework that uses a unified species concept. As more genomic, proteomic, and transcriptomic data accrue, new insights will emerge regarding trap biochemistry and regulation; interactions with commensals; and the importance of intraspecific variability on which natural selection works. Continued elaboration of field experiments will provide new insights into basic physiology; population biology; plant-animal and plant-microbe relationships; and evolutionary dynamics, all of which will aid conservation efforts and contribute to discussions of assisted migration as the climate continues to change.
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Book chapters on the topic "Transcriptome data"

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Cellerino, Alessandro, and Michele Sanguanini. "RNA-seq raw data processing." In Transcriptome Analysis. Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1_3.

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Cellerino, Alessandro, and Michele Sanguanini. "A primer on data distributions and their visualisation." In Transcriptome Analysis. Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1_1.

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Cannon, Johanna Taylor, and Kevin Michael Kocot. "Phylogenomics Using Transcriptome Data." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3774-5_4.

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Wang, Lingfei, and Tom Michoel. "Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8882-2_4.

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Sun, Ming-an, Xiaojian Shao, and Yejun Wang. "Microarray Data Analysis for Transcriptome Profiling." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7710-9_2.

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Gao, Shan. "Data Analysis in Single-Cell Transcriptome Sequencing." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_18.

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Castro-Melchor, Marlene, Huong Le, and Wei-Shou Hu. "Transcriptome Data Analysis for Cell Culture Processes." In Genomics and Systems Biology of Mammalian Cell Culture. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/10_2011_116.

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Seekaki, Panpaki, and Norichika Ogata. "Calculating Kolmogorov Complexity from the Transcriptome Data." In Intelligent Computing Theories and Application. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63312-1_46.

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Glebova, Olga, Yvette Temate-Tiagueu, Adrian Caciula, et al. "Transcriptome Quantification and Differential Expression from NGS Data." In Computational Methods for Next Generation Sequencing Data Analysis. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119272182.ch14.

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da Silva Filho, Reginaldo Inojosa, Ricardo Luis de Azevedo da Rocha, and Claudio Santos Oliveira. "Formal Language Model for Transcriptome and Proteome Data Integration." In Computational Science and Its Applications – ICCSA 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58814-4_60.

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Conference papers on the topic "Transcriptome data"

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Huacarpuma, R. C., M. T. Holanda, and M. E. M. T. Walter. "A conceptual data model for transcriptome project pipeline." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112349.

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Xu, Xiaoxiao, Arye Nehorai, and Joseph Dougherty. "Cell type specific analysis of human transcriptome data." In 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2012. http://dx.doi.org/10.1109/gensips.2012.6507737.

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Lindsay, James, Craig E. Nelson, and Ion I. Mandoiu. "Towards whole transcriptome deconvolution using single-cell data." In 2013 IEEE 3rd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2013. http://dx.doi.org/10.1109/iccabs.2013.6629234.

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Kwang, Kim Jong. "Brain tumor pathway identification by integrating transcriptome and interactome data." In the 2nd ACM Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2147805.2147918.

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Mangul, Serghei, Sahar Al Seesi, Ion Mandoiu, Adrian Caciula, Alex Zelikovsky, and Dumitru Brinza. "Transcriptome assembly and quantification from Ion Torrent RNA-Seq data." In 2013 IEEE 3rd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2013. http://dx.doi.org/10.1109/iccabs.2013.6629235.

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Chen, Chien Ming, Kuan Jung Lai, Tun Wen Pai, and Hao Teng Chang. "Transcriptome Data Visualization in Pathways with Application to Zebrafish Embryo Datasets." In 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2014. http://dx.doi.org/10.1109/cisis.2014.74.

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Li, Wei, Jianxing Feng, and Tao Jiang. "Workshop: Transcriptome assembly from RNA-Seq data: Objectives, algorithms and challenges." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729925.

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Afroze, Tamanna, Aura Rahman, Mrinmoy Sarkar, et al. "Analysis of RNA-Seq Data of 10000 Samples of Single-cell Transcriptome." In 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE). IEEE, 2019. http://dx.doi.org/10.1109/morse48060.2019.8998690.

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Singh, Vikas, Harsh Vardhan, Nishchal K. Verma, and Yan Cui. "Optimal Feature Selection using Fuzzy Combination of Feature Subset for Transcriptome Data." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491683.

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Ghaffari, Noushin, Jordi Abante, Raminder Singh, Philip D. Blood, and Charles D. Johnson. "Computational Considerations in Transcriptome Assemblies and Their Evaluation, using High Quality Human RNA-Seq data." In XSEDE16: Diversity, Big Data, and Science at Scale. ACM, 2016. http://dx.doi.org/10.1145/2949550.2949572.

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