To see the other types of publications on this topic, follow the link: Transcriptome data.
Journal articles on the topic 'Transcriptome data'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Transcriptome data.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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/.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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.
5
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.
Full textAbstract:
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.
6
Nishimura, Yuhei. "Drug discovery using transcriptome data." Folia Pharmacologica Japonica 149, no. 3 (2017): 138. http://dx.doi.org/10.1254/fpj.149.138.
Full text
7
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.
Full textAbstract:
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.
8
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.
Full textAbstract:
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.
9
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.
Full textAbstract:
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.
10
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.
Full text
11
Han, Henry, and Ying Liu. "Transcriptome marker diagnostics using big data." IET Systems Biology 10, no. 1 (2016): 41–48. http://dx.doi.org/10.1049/iet-syb.2015.0026.
Full text
12
Barral-Arca, Ruth, Alberto Gómez-Carballa, Miriam Cebey-López, Xabier Bello, Federico Martinón-Torres, and Antonio Salas. "A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus." International Journal of Molecular Sciences 21, no. 5 (2020): 1831. http://dx.doi.org/10.3390/ijms21051831.
Full textAbstract:
Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infection.
13
Caurcel, Carlos, Dominik R. Laetsch, Richard Challis, Sujai Kumar, Karim Gharbi, and Mark Blaxter. "MolluscDB: a genome and transcriptome database for molluscs." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1825 (2021): 20200157. http://dx.doi.org/10.1098/rstb.2020.0157.
Full textAbstract:
As sequencing becomes more accessible and affordable, the analysis of genomic and transcriptomic data has become a cornerstone of many research initiatives. Communities with a focus on particular taxa or ecosystems need solutions capable of aggregating genomic resources and serving them in a standardized and analysis-friendly manner. Taxon-focussed resources can be more flexible in addressing the needs of a research community than can universal or general databases. Here, we present MolluscDB, a genome and transcriptome database for molluscs. MolluscDB offers a rich ecosystem of tools, including an Ensembl browser, a BLAST server for homology searches and an HTTP server from which any dataset present in the database can be downloaded. To demonstrate the utility of the database and verify the quality of its data, we imported data from assembled genomes and transcriptomes of 22 species, estimated the phylogeny of Mollusca using single-copy orthologues, explored patterns of gene family size change and interrogated the data for biomineralization-associated enzymes and shell matrix proteins. MolluscDB provides an easy-to-use and openly accessible data resource for the research community. This article is part of the Theo Murphy meeting issue ‘Molluscan genomics: broad insights and future directions for a neglected phylum’.
14
Meger, Yakov, Ekaterina Vodiasova, and Anastasiya Lantushenko. "Impact of sequencing data filtering on the quality of de novo transcriptome assembly." E3S Web of Conferences 270 (2021): 01014. http://dx.doi.org/10.1051/e3sconf/202127001014.
Full textAbstract:
There are many assemblers with different algorithms that are used for de novo transcriptome assembly. At the same time, the filtering stage, which is one of the key stages, also has several approaches and algorithms. However, to date, there are only few studies on the effect of the degree of filtration on the de novo transcriptome assembly, specially for single-end reads. In this paper, we analyzed transcriptomes obtained using two of the most common software (rnaSPADES and Trinity), and also applied various approaches to the stage of filtering reads. The key differences between the two assemblies were shown and the parameters that were sensitive to the degree of filtering and the length of the input reads were identified. An efficient two-stage filtering algorithm was also proposed, which allows one to preserve the volume of input data as much as possible with the required quality of all reads after filtering and trimming.
15
Chakraborty, Sandeep. "RNA-seq assembler artifacts can bias expression counts and differential expression analysis - case study on the chickpea transcriptome emphasizes importance of freely accessible data for reproducibility." F1000Research 5 (December 6, 2016): 2394. http://dx.doi.org/10.12688/f1000research.9667.2.
Full textAbstract:
The unprecedented volume of genomic and transcriptomic data analyzed by software pipelines makes verification of inferences based on such data, albeit theoretically possible, a challenging proposition. The availability of intermediate data can immensely aid re-validation efforts. One such example is the transcriptome, assembled from raw RNA-seq reads, which is frequently used for annotation and quantification of genes transcribed. The quality of the assembled transcripts influences the accuracy of inferences based on them. Here the publicly available transcriptome from Cicer arietinum (ICC4958; Desi chickpea, http://www.nipgr.res.in/ctdb.html)1 was analyzed using YeATS2. This revealed that a majority of the highly expressed transcripts (HET) encoded multiple genes, strongly indicating that the counts may have been biased by the merging of different transcripts. TC00004 is ranked in the top five HET for all five tissues analyzed here, and encodes both a retinoblastoma-binding-like protein (E-value=0) and a senescence-associated protein (E-value= 5e-108). Fragmented transcripts are another source of error. The ribulose bisphosphate carboxylase small chain (RBCSC) protein is split into two transcripts with an overlapping amino acid sequence "ASNGGRVHC", TC13991 and TC23009, with length 201 and 332 nucleotides and expression counts 17.90 and 1403.8, respectively. The huge difference in counts indicates an erroneous normalization algorithm in determining counts. It is well known that RBCSC is highly expressed and expectedly TC23009 ranks fifth among HETs in the shoot. Furthermore, some transcripts are split into open reading frames that map to the same protein, although this should not have any significant bearing on the counts. It is proposed that studies analyzing differential expression based on the transcriptome should consider these artifacts, and providing intermediate assembled transcriptomes should be mandatory, possibly with a link to the raw sequence data (Bioproject).
16
Salazar, Juan Alfonso, Cristian Vergara-Pulgar, Claudia Jorquera, et al. "De Novo Transcriptome Sequencing in Kiwifruit (Actinidia chinensis var. deliciosa (A Chev) Liang et Ferguson) and Development of Tissue-Specific Transcriptomic Resources." Agronomy 11, no. 5 (2021): 919. http://dx.doi.org/10.3390/agronomy11050919.
Full textAbstract:
Kiwifruit (Actinidia chinensis var. deliciosa (A Chev) Liang et Ferguson) is a sub-tropical vine species from the Actinidiaceae family native to China. This species has an allohexaploid genome (from diploid and autotetraploid parents), contained in 174 chromosomes producing a climacteric and fleshy fruit called kiwifruit. Currently, only a small body of transcriptomic and proteomic data are available for A. chinensis var. deliciosa. In this low molecular knowledge context, the main goal of this study is to construct a tissue-specific de novo transcriptome assembly, generating differential expression analysis among these specific tissues, to obtain new useful transcriptomic information for a better knowledge of vegetative, floral and fruit growth in this species. In this study, we have analyzed different whole transcriptomes from shoot, leaf, flower bud, flower and fruit at four development stages (7, 50, 120 and 160 days after flowering; DAF) in kiwifruit obtained through RNA-seq sequencing. The first version of the developed A. chinensis var. deliciosa de novo transcriptome contained 142,025 contigs (x¯ = 1044 bp, N50 = 1133 bp). Annotation was performed with BLASTX against the TAIR10 protein database, and we found an annotation proportion of 35.6% (50,508), leaving 64.4% (91,517) of the contigs without annotation. These results represent a reference transcriptome for allohexaploid kiwifruit generating a database of A. chinensis var. deliciosa genes related to leaf, flower and fruit development. These results provided highly valuable information identifying over 20,000 exclusive genes including all tissue comparisons, which were associated with the proteins involved in different biological processes and molecular functions.
17
Mora-Márquez, Fernando, José Luis Vázquez-Poletti, Víctor Chano, Carmen Collada, Álvaro Soto, and Unai López de Heredia. "Hardware Performance Evaluation of De novo Transcriptome Assembly Software in Amazon Elastic Compute Cloud." Current Bioinformatics 15, no. 5 (2020): 420–30. http://dx.doi.org/10.2174/1574893615666191219095817.
Full textAbstract:
Background: Bioinformatics software for RNA-seq analysis has a high computational requirement in terms of the number of CPUs, RAM size, and processor characteristics. Specifically, de novo transcriptome assembly demands large computational infrastructure due to the massive data size, and complexity of the algorithms employed. Comparative studies on the quality of the transcriptome yielded by de novo assemblers have been previously published, lacking, however, a hardware efficiency-oriented approach to help select the assembly hardware platform in a cost-efficient way. Objective: We tested the performance of two popular de novo transcriptome assemblers, Trinity and SOAPdenovo-Trans (SDNT), in terms of cost-efficiency and quality to assess limitations, and provided troubleshooting and guidelines to run transcriptome assemblies efficiently. Methods: We built virtual machines with different hardware characteristics (CPU number, RAM size) in the Amazon Elastic Compute Cloud of the Amazon Web Services. Using simulated and real data sets, we measured the elapsed time, cost, CPU percentage and output size of small and large data set assemblies. Results: For small data sets, SDNT outperformed Trinity by an order the magnitude, significantly reducing the time duration and costs of the assembly. For large data sets, Trinity performed better than SDNT. Both the assemblers provide good quality transcriptomes. Conclusion: The selection of the optimal transcriptome assembler and provision of computational resources depend on the combined effect of size and complexity of RNA-seq experiments.
18
Zhu, Jiang, Fuhong He, Jing Wang, and Jun Yu. "Modeling Transcriptome Based on Transcript-Sampling Data." PLoS ONE 3, no. 2 (2008): e1659. http://dx.doi.org/10.1371/journal.pone.0001659.
Full text
19
Othman, Roohaida, Afiq Adham Abd Rasib, Mohammad Akhmal Ilias, Suganthi Murthy, Najihah Ismail, and Nursyuhaida Mohd Hanafi. "Transcriptome data of the carrageenophyte Eucheuma denticulatum." Data in Brief 24 (June 2019): 103824. http://dx.doi.org/10.1016/j.dib.2019.103824.
Full text
20
Baek, Dong-Yeob, Jin-Ho Yoo, Youngbok Lee, et al. "ArrayQue: The comprehensive transcriptome data analysis tool." BioChip Journal 6, no. 4 (2012): 314–18. http://dx.doi.org/10.1007/s13206-012-6402-2.
Full text
21
Yu, Pingjian, and Wei Lin. "Single-cell Transcriptome Study as Big Data." Genomics, Proteomics & Bioinformatics 14, no. 1 (2016): 21–30. http://dx.doi.org/10.1016/j.gpb.2016.01.005.
Full text
22
Tuna, Salih, and Mahesan Niranjan. "Inference from Low Precision Transcriptome Data Representation." Journal of Signal Processing Systems 58, no. 3 (2009): 267–79. http://dx.doi.org/10.1007/s11265-009-0363-2.
Full text
23
Shnier, Daniel, Mircea A. Voineagu, and Irina Voineagu. "Persistent homology analysis of brain transcriptome data in autism." Journal of The Royal Society Interface 16, no. 158 (2019): 20190531. http://dx.doi.org/10.1098/rsif.2019.0531.
Full textAbstract:
Persistent homology methods have found applications in the analysis of multiple types of biological data, particularly imaging data or data with a spatial and/or temporal component. However, few studies have assessed the use of persistent homology for the analysis of gene expression data. Here we apply persistent homology methods to investigate the global properties of gene expression in post-mortem brain tissue (cerebral cortex) of individuals with autism spectrum disorders (ASD) and matched controls. We observe a significant difference in the geometry of inter-sample relationships between autism and healthy controls as measured by the sum of the death times of zero-dimensional components and the Euler characteristic. This observation is replicated across two distinct datasets, and we interpret it as evidence for an increased heterogeneity of gene expression in autism. We also assessed the topology of gene-level point clouds and did not observe significant differences between ASD and control transcriptomes, suggesting that the overall transcriptome organization is similar in ASD and healthy cerebral cortex. Overall, our study provides a novel framework for persistent homology analyses of gene expression data for genetically complex disorders.
24
Van Etten, Julia, Alexander Shumaker, Tali Mass, Hollie M. Putnam, and Debashish Bhattacharya. "Transcriptome analysis provides a blueprint of coral egg and sperm functions." PeerJ 8 (August 18, 2020): e9739. http://dx.doi.org/10.7717/peerj.9739.
Full textAbstract:
Background Reproductive biology and the evolutionary constraints acting on dispersal stages are poorly understood in many stony coral species. A key piece of missing information is egg and sperm gene expression. This is critical for broadcast spawning corals, such as our model, the Hawaiian species Montipora capitata, because eggs and sperm are exposed to environmental stressors during dispersal. Furthermore, parental effects such as transcriptome investment may provide a means for cross- or trans-generational plasticity and be apparent in egg and sperm transcriptome data. Methods Here, we analyzed M. capitata egg and sperm transcriptomic data to address three questions: (1) Which pathways and functions are actively transcribed in these gametes? (2) How does sperm and egg gene expression differ from adult tissues? (3) Does gene expression differ between these gametes? Results We show that egg and sperm display surprisingly similar levels of gene expression and overlapping functional enrichment patterns. These results may reflect similar environmental constraints faced by these motile gametes. We find significant differences in differential expression of egg vs. adult and sperm vs. adult RNA-seq data, in contrast to very few examples of differential expression when comparing egg vs. sperm transcriptomes. Lastly, using gene ontology and KEGG orthology data we show that both egg and sperm have markedly repressed transcription and translation machinery compared to the adult, suggesting a dependence on parental transcripts. We speculate that cell motility and calcium ion binding genes may be involved in gamete to gamete recognition in the water column and thus, fertilization.
25
Bono, Hidemasa, and Kiichi Hirota. "Meta-Analysis of Hypoxic Transcriptomes from Public Databases." Biomedicines 8, no. 1 (2020): 10. http://dx.doi.org/10.3390/biomedicines8010010.
Full textAbstract:
Hypoxia is the insufficiency of oxygen in the cell, and hypoxia-inducible factors (HIFs) are central regulators of oxygen homeostasis. In order to obtain functional insights into the hypoxic response in a data-driven way, we attempted a meta-analysis of the RNA-seq data from the hypoxic transcriptomes archived in public databases. In view of methodological variability of archived data in the databases, we first manually curated RNA-seq data from appropriate pairs of transcriptomes before and after hypoxic stress. These included 128 human and 52 murine transcriptome pairs. We classified the results of experiments for each gene into three categories: upregulated, downregulated, and unchanged. Hypoxic transcriptomes were then compared between humans and mice to identify common hypoxia-responsive genes. In addition, meta-analyzed hypoxic transcriptome data were integrated with public ChIP-seq data on the known human HIFs, HIF-1 and HIF-2, to provide insights into hypoxia-responsive pathways involving direct transcription factor binding. This study provides a useful resource for hypoxia research. It also demonstrates the potential of a meta-analysis approach to public gene expression databases for selecting candidate genes from gene expression profiles generated under various experimental conditions.
26
Kim, Hani Jieun, Yingxin Lin, Thomas A. Geddes, Jean Yee Hwa Yang, and Pengyi Yang. "CiteFuse enables multi-modal analysis of CITE-seq data." Bioinformatics 36, no. 14 (2020): 4137–43. http://dx.doi.org/10.1093/bioinformatics/btaa282.
Full textAbstract:
Abstract Motivation Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand–receptor interaction analysis and interactive web-based visualization of CITE-seq data. Results We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand–receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. Availability and implementation CiteFuse is freely available at http://shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at https://github.com/SydneyBioX/CiteFuse/ as an R package. Contact pengyi.yang@sydney.edu.au Supplementary information Supplementary data are available at Bioinformatics online.
27
Chakraborty, Sandeep. "RNA-seq assembler artifacts can bias expression counts and differential expression analysis - application of YeATS on the chickpea transcriptome." F1000Research 5 (September 27, 2016): 2394. http://dx.doi.org/10.12688/f1000research.9667.1.
Full textAbstract:
Background: The unprecedented volume of genomic and transcriptomic data analyzed by software pipelines makes verification of inferences based on such data, albeit theoretically possible, a challenging proposition. The availability of intermediate data can immensely aid re-validation efforts. One such example is the transcriptome, assembled from raw RNA-seq reads, which is frequently used for annotation and quantification of genes transcribed. The quality of the assembled transcripts influences the accuracy of inferences based on them. Method: Here the publicly available transcriptome from Cicer arietinum (ICC4958; Desi chickpea, http://www.nipgr.res.in/ctdb.html) was analyzed using YeATS. Results and Conclusion: The analysis revealed that a majority of the highly expressed transcripts (HET) encoded multiple genes, strongly indicating that the counts may have been biased by the merging of different transcripts. TC00004 is ranked in the top five HET for all five tissues analyzed here, and encodes both a retinoblastoma-binding-like protein (E-value=0) and a senescence-associated protein (E-value= 5e-108). Fragmented transcripts are another source of error. The ribulose bisphosphate carboxylase small chain (RBCSC) protein is split into two transcripts with an overlapping amino acid sequence ”ASNGGRVHC”, TC13991 and TC23009, with length 201 and 332 nucleotides and expression counts 17.90 and 1403.8, respectively. The huge difference in counts indicates an erroneous normalization algorithm in determining counts. It is well known that RBCSC is highly expressed and expectedly TC23009 ranks fifth among HETs in the shoot. Furthermore, some transcripts are split into open reading frames that map to the same protein, although this should not have any significant bearing on the counts. It is proposed that studies analyzing differential expression based on the transcriptome should consider these artifacts, and providing intermediate assembled transcriptomes should be mandatory, possibly with a link to the raw sequence data (Bioproject).
28
Zheng, Yi, and Fangqing Zhao. "Visualization of circular RNAs and their internal splicing events from transcriptomic data." Bioinformatics 36, no. 9 (2020): 2934–35. http://dx.doi.org/10.1093/bioinformatics/btaa033.
Full textAbstract:
Abstract Summary Circular RNAs (circRNAs) are proved to have unique compositions and splicing events distinct from canonical mRNAs. However, there is no visualization tool designed for the exploration of complex splicing patterns in circRNA transcriptomes. Here, we present CIRI-vis, a Java command-line tool for quantifying and visualizing circRNAs by integrating the alignments and junctions of circular transcripts. CIRI-vis can be applied to visualize the internal structure and isoform abundance of circRNAs and perform circRNA transcriptome comparison across multiple samples. Availability and implementation https://sourceforge.net/projects/ciri/files/CIRI-vis. Supplementary information Supplementary data are available at Bioinformatics online.
29
Tang, Shizhen, Aron S. Buchman, Philip L. De Jager, David A. Bennett, Michael P. Epstein, and Jingjing Yang. "Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia." PLOS Genetics 17, no. 4 (2021): e1009482. http://dx.doi.org/10.1371/journal.pgen.1009482.
Full textAbstract:
Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.
30
Albrecht, Daniela, Olaf Kniemeyer, Axel A. Brakhage, Matthias Berth, and Reinhard Guthke. "Integration of Transcriptome and Proteome Data from Human-Pathogenic Fungi by Using a Data Warehouse." Journal of Integrative Bioinformatics 4, no. 1 (2007): 51–63. http://dx.doi.org/10.1515/jib-2007-52.
Full textAbstract:
Summary A data warehouse for the integrated storage and visualisation of genome and experimental transcriptome and proteome data of human-pathogenic fungi was established. It provides tools for uploading images and corresponding data from microarray experiments, two-dimensional (2D) gel experiments and mass spectrometry (MS) analyses. All data are cross-linked. A user can find out, on which gels in the database an interesting protein was detected. Additionally, he can see on which microarrays the corresponding mRNA had been spotted and whether these spots show interesting intensity values. So the data warehouse enables an integrated analysis of both transcriptome and proteome data. Some of the uploaded data were transcriptome and proteome time series data of temperature shift experiments obtained from Aspergillus fumigatus. Several proteins were differentially regulated at different times after the temperature shift. For a couple of them also the respective transcripts were found to be differentially expressed. For even more of those proteins the transcripts did not show differential regulation and vice versa. So both kinds of data clearly complement each other and should be analysed together.
31
Gonzalez-Ibeas, Daniel, Pedro J. Martinez-Garcia, Randi A. Famula, et al. "Assessing the Gene Content of the Megagenome: Sugar Pine (Pinus lambertiana)." G3 Genes|Genomes|Genetics 6, no. 12 (2016): 3787–802. http://dx.doi.org/10.1534/g3.116.032805.
Full textAbstract:
Abstract Sugar pine (Pinus lambertiana Douglas) is within the subgenus Strobus with an estimated genome size of 31 Gbp. Transcriptomic resources are of particular interest in conifers due to the challenges presented in their megagenomes for gene identification. In this study, we present the first comprehensive survey of the P. lambertiana transcriptome through deep sequencing of a variety of tissue types to generate more than 2.5 billion short reads. Third generation, long reads generated through PacBio Iso-Seq have been included for the first time in conifers to combat the challenges associated with de novo transcriptome assembly. A technology comparison is provided here to contribute to the otherwise scarce comparisons of second and third generation transcriptome sequencing approaches in plant species. In addition, the transcriptome reference was essential for gene model identification and quality assessment in the parallel project responsible for sequencing and assembly of the entire genome. In this study, the transcriptomic data were also used to address questions surrounding lineage-specific Dicer-like proteins in conifers. These proteins play a role in the control of transposable element proliferation and the related genome expansion in conifers.
32
Rychel, Kevin, Katherine Decker, Anand V. Sastry, Patrick V. Phaneuf, Saugat Poudel, and Bernhard O. Palsson. "iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning." Nucleic Acids Research 49, no. D1 (2020): D112—D120. http://dx.doi.org/10.1093/nar/gkaa810.
Full textAbstract:
Abstract Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.
33
Petegrosso, Raphael, Zhuliu Li, and Rui Kuang. "Machine learning and statistical methods for clustering single-cell RNA-sequencing data." Briefings in Bioinformatics 21, no. 4 (2019): 1209–23. http://dx.doi.org/10.1093/bib/bbz063.
Full textAbstract:
Abstract Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics. Availability All the source code and data are available at https://github.com/kuanglab/single-cell-review.
34
Mukhin, A. M., M. A. Genaev, D. A. Rasskazov, S. A. Lashin, and D. A. Afonnikov. "RDBMS and NOSQL Based Hybrid Technology for Transcriptome Data Structuring and Processing." Mathematical Biology and Bioinformatics 15, no. 2 (2020): 455–70. http://dx.doi.org/10.17537/2020.15.455.
Full textAbstract:
The transcriptome sequencing experiment (RNA-seq) has become almost a routine procedure for studying both model organisms and crops. As a result of bioinformatics processing of such experimental output, huge heterogeneous data are obtained, representing nucleotide sequences of transcripts, amino acid sequences, and their structural and functional annotation. It is important to present the data obtained to a wide range of researchers in the form of databases. This article proposes a hybrid approach to creating molecular genetic databases that contain information about transcript sequences and their structural and functional annotation. The essence of the approach consists in the simultaneous storing both structured and weakly structured data in the database. The technology was used to implement a database of transcriptomes of agricultural plants. This paper discusses the features of implementing this approach and examples of generating both simple and complex queries to such a database in the SQL language. The OORT database is freely available at https://oort.cytogen.ru/.
35
Kaisers, Wolfgang, Johannes Ptok, Holger Schwender, and Heiner Schaal. "Validation of Splicing Events in Transcriptome Sequencing Data." International Journal of Molecular Sciences 18, no. 6 (2017): 1110. http://dx.doi.org/10.3390/ijms18061110.
Full text
36
Galbraith, S. J., L. M. Tran, and J. C. Liao. "Transcriptome network component analysis with limited microarray data." Bioinformatics 22, no. 15 (2006): 1886–94. http://dx.doi.org/10.1093/bioinformatics/btl279.
Full text
37
Hack, C. J. "Integrated transcriptome and proteome data: The challenges ahead." Briefings in Functional Genomics and Proteomics 3, no. 3 (2004): 212–19. http://dx.doi.org/10.1093/bfgp/3.3.212.
Full text
38
Nothnagel, Michael, Andreas Wolf, Alexander Herrmann, et al. "Statistical inference of allelic imbalance from transcriptome data." Human Mutation 32, no. 1 (2010): 98–106. http://dx.doi.org/10.1002/humu.21396.
Full text
39
Yonekura-Sakakibara, Keiko, Atsushi Fukushima, and Kazuki Saito. "Transcriptome data modeling for targeted plant metabolic engineering." Current Opinion in Biotechnology 24, no. 2 (2013): 285–90. http://dx.doi.org/10.1016/j.copbio.2012.10.018.
Full text
40
Sseruwagi, Peter, James Wainaina, Joseph Ndunguru, et al. "The first transcriptomes from field-collected individual whiteflies (Bemisia tabaci, Hemiptera: Aleyrodidae)." Gates Open Research 1 (December 28, 2017): 16. http://dx.doi.org/10.12688/gatesopenres.12783.1.
Full textAbstract:
Background: Bemisia tabaci species (B. tabaci), or whiteflies, are the world’s most devastating insect pests. They cause billions of dollars (US) of damage each year, and are leaving farmers in the developing world food insecure. Currently, all publically available transcriptome data for B. tabaci are generated from pooled samples, which can lead to high heterozygosity and skewed representation of the genetic diversity. The ability to extract enough RNA from a single whitefly has remained elusive due to their small size and technological limitations. Methods: In this study, we optimised the single whitefly RNA extraction procedure, and sequenced the transcriptome of four individual adult Sub-Saharan Africa (SSA1) B. tabaci. Transcriptome sequencing resulted in 39-42 million raw reads. De novo assembly of trimmed reads yielded between 65,000-162,000 transcripts across B. tabaci transcriptomes. Results: Bayesian phylogenetic analysis of mitochondrion cytochrome I oxidase (mtCOI) grouped the four whiteflies within the SSA1 clade. BLASTn searches on the four transcriptomes identified five endosymbionts; the primary endosymbiont Portiera aleyrodidarum and four secondary endosymbionts: Arsenophonus, Wolbachia, Rickettsia, and Cardinium spp. that were predominant across all four SSA1 B. tabaci samples with prevalence levels between 54.1-75%. Amino acid alignments of the NusG gene of P. aleyrodidarum for the SSA1 B. tabaci transcriptomes of samples WF2 and WF2b revealed an eleven amino acid residue deletion that was absent in samples WF1 and WF2a. Comparison of the protein structure of the NusG protein from P. aleyrodidarum in SSA1 with known NusG structures showed the deletion resulted in a shorter D loop. Conclusions: The use of field-collected specimens means time and money will be saved in future studies using single whitefly transcriptomes in monitoring vector and viral interactions. Our method is applicable to any small organism where RNA quantity has limited transcriptome studies.
41
Sseruwagi, Peter, James Wainaina, Joseph Ndunguru, et al. "The first transcriptomes from field-collected individual whiteflies (Bemisia tabaci, Hemiptera: Aleyrodidae)." Gates Open Research 1 (February 13, 2018): 16. http://dx.doi.org/10.12688/gatesopenres.12783.2.
Full textAbstract:
Background: Bemisia tabaci species (B. tabaci), or whiteflies, are the world’s most devastating insect pests. They cause billions of dollars (US) of damage each year, and are leaving farmers in the developing world food insecure. Currently, all publically available transcriptome data for B. tabaci are generated from pooled samples, which can lead to high heterozygosity and skewed representation of the genetic diversity. The ability to extract enough RNA from a single whitefly has remained elusive due to their small size and technological limitations. Methods: In this study, we optimised a single whitefly RNA extraction procedure, and sequenced the transcriptome of four individual adult Sub-Saharan Africa 1 (SSA1) B. tabaci. Transcriptome sequencing resulted in 39-42 million raw reads. De novo assembly of trimmed reads yielded between 65,000-162,000 Contigs across B. tabaci transcriptomes. Results: Bayesian phylogenetic analysis of mitochondrion cytochrome I oxidase (mtCOI) grouped the four whiteflies within the SSA1 clade. BLASTn searches on the four transcriptomes identified five endosymbionts; the primary endosymbiont Portiera aleyrodidarum and four secondary endosymbionts: Arsenophonus, Wolbachia, Rickettsia, and Cardinium spp. that were predominant across all four SSA1 B. tabaci samples with prevalence levels of between 54.1 to 75%. Amino acid alignments of the NusG gene of P. aleyrodidarum for the SSA1 B. tabaci transcriptomes of samples WF2 and WF2b revealed an eleven amino acid residue deletion that was absent in samples WF1 and WF2a. Comparison of the protein structure of the NusG protein from P. aleyrodidarum in SSA1 with known NusG structures showed the deletion resulted in a shorter D loop. Conclusions: The use of field-collected specimens means time and money will be saved in future studies using single whitefly transcriptomes in monitoring vector and viral interactions. Our method is applicable to any small organism where RNA quantity has limited transcriptome studies.
42
Sseruwagi, Peter, James Wainaina, Joseph Ndunguru, et al. "The first transcriptomes from field-collected individual whiteflies (Bemisia tabaci, Hemiptera: Aleyrodidae): a case study of the endosymbiont composition." Gates Open Research 1 (March 8, 2018): 16. http://dx.doi.org/10.12688/gatesopenres.12783.3.
Full textAbstract:
Background: Bemisia tabaci species (B. tabaci), or whiteflies, are the world’s most devastating insect pests. They cause billions of dollars (US) of damage each year, and are leaving farmers in the developing world food insecure. Currently, all publically available transcriptome data for B. tabaci are generated from pooled samples, which can lead to high heterozygosity and skewed representation of the genetic diversity. The ability to extract enough RNA from a single whitefly has remained elusive due to their small size and technological limitations. Methods: In this study, we optimised a single whitefly RNA extraction procedure, and sequenced the transcriptome of four individual adult Sub-Saharan Africa 1 (SSA1) B. tabaci. Transcriptome sequencing resulted in 39-42 million raw reads. De novo assembly of trimmed reads yielded between 65,000-162,000 Contigs across B. tabaci transcriptomes. Results: Bayesian phylogenetic analysis of mitochondrion cytochrome I oxidase (mtCOI) grouped the four whiteflies within the SSA1 clade. BLASTn searches on the four transcriptomes identified five endosymbionts; the primary endosymbiont Portiera aleyrodidarum and four secondary endosymbionts: Arsenophonus, Wolbachia, Rickettsia, and Cardinium spp. that were predominant across all four SSA1 B. tabaci samples with prevalence levels of between 54.1 to 75%. Amino acid alignments of the NusG gene of P. aleyrodidarum for the SSA1 B. tabaci transcriptomes of samples WF2 and WF2b revealed an eleven amino acid residue deletion that was absent in samples WF1 and WF2a. Comparison of the protein structure of the NusG protein from P. aleyrodidarum in SSA1 with known NusG structures showed the deletion resulted in a shorter D loop. Conclusions: The use of field-collected specimens means time and money will be saved in future studies using single whitefly transcriptomes in monitoring vector and viral interactions. Our method is applicable to any small organism where RNA quantity has limited transcriptome studies.
43
Wu, Wenjing, Zhiqiang Li, Shijun Zhang, Yunling Ke, and Yahui Hou. "Transcriptome response to elevated atmospheric CO2concentration in the Formosan subterranean termite,Coptotermes formosanusShiraki (Isoptera: Rhinotermitidae)." PeerJ 4 (October 4, 2016): e2527. http://dx.doi.org/10.7717/peerj.2527.
Full textAbstract:
BackgroundCarbon dioxide (CO2) is a pervasive chemical stimulus that plays a critical role in insect life, eliciting behavioral and physiological responses across different species. High CO2concentration is a major feature of termite nests, which may be used as a cue for locating their nests. Termites also survive under an elevated CO2concentration. However, the mechanism by which elevated CO2concentration influences gene expression in termites is poorly understood.MethodsTo gain a better understanding of the molecular basis involved in the adaptation to CO2concentration, a transcriptome ofCoptotermes formosanusShiraki was constructed to assemble the reference genes, followed by comparative transcriptomic analyses across different CO2concentration (0.04%, 0.4%, 4% and 40%) treatments.Results(1) Based on a high throughput sequencing platform, we obtained approximately 20 GB of clean data and revealed 189,421 unigenes, with a mean length and an N50 length of 629 bp and 974 bp, respectively. (2) The transcriptomic response ofC. formosanusto elevated CO2levels presented discontinuous changes. Comparative analysis of the transcriptomes revealed 2,936 genes regulated among 0.04%, 0.4%, 4% and 40% CO2concentration treatments, 909 genes derived from termites and 2,027 from gut symbionts. Genes derived from termites appears selectively activated under 4% CO2level. In 40% CO2level, most of the down-regulated genes were derived from symbionts. (3) Through similarity searches to data from other species, a number of protein sequences putatively involved in chemosensory reception were identified and characterized inC. formosanus, including odorant receptors, gustatory receptors, ionotropic receptors, odorant binding proteins, and chemosensory proteins.DiscussionWe found that most genes associated with carbohydrate metabolism, energy metabolism, and genetic information processing were regulated under different CO2concentrations. Results suggested that termites adapt to ∼4% CO2level and their gut symbionts may be killed under high CO2level. We anticipate that our findings provide insights into the transcriptome dynamics of CO2responses in termites and form the basis to gain a better understanding of regulatory networks.
44
Kroll, Jose E., Jihoon Kim, Lucila Ohno-Machado, and Sandro J. de Souza. "Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data." PeerJ 3 (November 19, 2015): e1419. http://dx.doi.org/10.7717/peerj.1419.
Full textAbstract:
Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.
45
Hynst, Jakub, Karla Plevova, Lenka Radova, Vojtech Bystry, Karol Pal, and Sarka Pospisilova. "Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants." PeerJ 7 (June 12, 2019): e7071. http://dx.doi.org/10.7717/peerj.7071.
Full textAbstract:
Background Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. Methods We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. Results We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. Discussion Byapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.
46
Xu, Zhongneng, and Shuichi Asakawa. "Physiological RNA dynamics in RNA-Seq analysis." Briefings in Bioinformatics 20, no. 5 (2018): 1725–33. http://dx.doi.org/10.1093/bib/bby045.
Full textAbstract:
Abstract Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.
47
Ortiz, Randy, Priyanka Gera, Christopher Rivera, and Juan C. Santos. "Pincho: A Modular Approach to High Quality De Novo Transcriptomics." Genes 12, no. 7 (2021): 953. http://dx.doi.org/10.3390/genes12070953.
Full textAbstract:
Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console.
48
Dargahi, Daryanaz, Richard D. Swayze, Leanna Yee, et al. "A Pan-Cancer Analysis of Alternative Splicing Events Reveals Novel Tumor-Associated Splice Variants of Matriptase." Cancer Informatics 13 (January 2014): CIN.S19435. http://dx.doi.org/10.4137/cin.s19435.
Full textAbstract:
High-throughput transcriptome sequencing allows identification of cancer-related changes that occur at the stages of transcription, pre-messenger RNA (mRNA), and splicing. In the current study, we devised a pipeline to predict novel alternative splicing (AS) variants from high-throughput transcriptome sequencing data and applied it to large sets of tumor transcriptomes from The Cancer Genome Atlas (TCGA). We identified two novel tumor-associated splice variants of matriptase, a known cancer-associated gene, in the transcriptome data from epithelial-derived tumors but not normal tissue. Most notably, these variants were found in 69% of lung squamous cell carcinoma (LUSC) samples studied. We confirmed the expression of matriptase AS transcripts using quantitative reverse transcription PCR (qRT-PCR) in an orthogonal panel of tumor tissues and cell lines. Furthermore, flow cytometric analysis confirmed surface expression of matriptase splice variants in chinese hamster ovary (CHO) cells transiently transfected with cDNA encoding the novel transcripts. Our findings further implicate matriptase in contributing to oncogenic processes and suggest potential novel therapeutic uses for matriptase splice variants.
49
Rydenfelt, Mattias, Bertram Klinger, Martina Klünemann, and Nils Blüthgen. "SPEED2: inferring upstream pathway activity from differential gene expression." Nucleic Acids Research 48, W1 (2020): W307—W312. http://dx.doi.org/10.1093/nar/gkaa236.
Full textAbstract:
Abstract Extracting signalling pathway activities from transcriptome data is important to infer mechanistic origins of transcriptomic dysregulation, for example in disease. A popular method to do so is by enrichment analysis of signature genes in e.g. differentially regulated genes. Previously, we derived signatures for signalling pathways by integrating public perturbation transcriptome data and generated a signature database called SPEED (Signalling Pathway Enrichment using Experimental Datasets), for which we here present a substantial upgrade as SPEED2. This web server hosts consensus signatures for 16 signalling pathways that are derived from a large number of transcriptomic signalling perturbation experiments. When providing a gene list of e.g. differentially expressed genes, the web server allows to infer signalling pathways that likely caused these genes to be deregulated. In addition to signature lists, we derive ‘continuous’ gene signatures, in a transparent and automated fashion without any fine-tuning, and describe a new algorithm to score these signatures.
50
Mercatelli, Daniele, Nicola Balboni, Francesca De Giorgio, Emanuela Aleo, Caterina Garone, and Federico Manuel Giorgi. "The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow." Methods and Protocols 4, no. 2 (2021): 28. http://dx.doi.org/10.3390/mps4020028.
Full textAbstract:
Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research.