240 DIFFERENCES IN GLOBAL TRANSCRIPTOME PROFILE OF BOVINE BLASTOCYSTS DERIVED FROM SUPEROVULATED OR SYNCHRONIZED CYCLIC HEIFERS

2010 ◽  
Vol 22 (1) ◽  
pp. 278
Author(s):  
A. Gad ◽  
M. Hoelker ◽  
F. Rings ◽  
N. Ghanem ◽  
D. Salilew-Wondim ◽  
...  
Keyword(s):  
Data Analysis ◽  
Transcript Abundance ◽  
R Package ◽  
Transcriptome Profile ◽  
Metabolic Processes ◽  
Cell Stage ◽  
Endoscopic Method ◽  
Response To Stress ◽  
Uterine Environment ◽  
Transfer Method

Estrus synchronization and superovulation are the most widely used procedures in embryo transfer technology. However, changes in the oviduct and uterine environment due to these procedures and the subsequent influence on embryos have not yet been investigated. This study was con- ducted to investigate the effect of oviduct environment of only synchronized or superovulated cyclic heifers on the gene expression profile of blastocysts. Bovine Affymetrix array analysis was performed using 2 groups of blastocysts. The first group was bovine blastocysts produced after superovulation of Simmental heifers (n = 9) using 8 consecutive FSH injections over 4 days in decreasing doses (in total, 300-400 mg of FSH equivalent according to body weight) and flushed at Day 7 by nonsurgical endoscopic method. The second group was bovine blastocysts derived from synchronized Simmental heifers (n = 4) after transfer of 2-cell stage embryos from superovulated donor Simmental heifers (n = 9) by nonsurgical transvaginal endoscopy tubal transfer method. Total RNA was extracted from 3 pools of embryos from each experimental group (6 embryos per pool). A total of 6 biotin-labeled cRNA samples were hybridized on 6 bovine Affymetrix arrays. Data analysis was performed using LIMMA written on R package, which maintained the Bioconductor. Array data analysis revealed a total of 454 transcripts to be differen- tially expressed (P < 0.05, fold change >2) between the 2 groups. Of these, 429 and 25 were up- and down-regulated, respectively, in blastocysts derived from superovulated heifers compared with those derived from synchronized animals. Genes involved in response to stress (HSPA14 and HSPE1), cellular and metabolic processes (CPSF3, ATPIF1, POMP, and MDH2), translation (RPS17, EEF1B2, and EIF4E), and cell commu- nication (FN1, KRT18, and DSG2) were found to be enriched in blastocysts derived from superovulated animals. On the other hand, protein metabolic processes related genes (CLGN) were found to be enriched in blastocysts derived from the synchronized group. The KEGG analysis of the differentially expressed genes showed that the ribosome and oxidative phosphorylation pathways are the dominant pathways and genes involved in these pathways are greatly abundant in the blastocysts derived from superovulated animals. Quantitative real-time PCR has confirmed the transcript abundance of 7 out of 8 genes selected for validation. In conclusion, blastocysts cultured in synchronized animals post 2-cell stage showed significant differences in transcriptome profile compared with their counterparts that remained in superovulated heifers until Day 7. Further functional analysis of some selected candidate genes could give new insights into mechanisms regulating the ability of embryos to survive after transfer.

scholarly journals BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

2021 ◽  
Author(s):  
Darawan Rinchai ◽  
Jessica Roelands ◽  
Mohammed Toufiq ◽  
Wouter Hendrickx ◽  
Matthew C Altman ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
R Package ◽  
Supplementary Information ◽  
Illustrative Case ◽  
Bioinformatic Tools ◽  
Transcriptional Module ◽  
Wide Range ◽  
Downstream Analysis ◽  
Computing Module ◽  
Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Functional data analysis techniques to improve the generalizability of near-infrared spectral data for monitoring mosquito populations

2020 ◽  
Author(s):  
Pedro M. Esperança ◽  
Dari F. Da ◽  
Ben Lambert ◽  
Roch K. Dabiré ◽  
Thomas S. Churcher
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Near Infrared ◽  
R Package ◽  
Mosquito Vector ◽  
Modelling Framework ◽  
Functional Representation ◽  
Infrared Spectral ◽  
Generalised Linear Modelling

AbstractNear infrared spectroscopy is increasingly being used as an economical method to monitor mosquito vector populations in support of disease control. Despite this rise in popularity, strong geographical variation in spectra has proven an issue for generalising predictions from one location to another. Here, we use a functional data analysis approach—which models spectra as smooth curves rather than as a discrete set of points—to develop a method that is robust to geographic heterogeneity. Specifically, we use a penalised generalised linear modelling framework which includes efficient functional representation of spectra, spectral smoothing and regularisation. To ensure better generalisation of model predictions from one training set to another, we use cross-validation procedures favouring smoother representation of spectra. To illustrate the performance of our approach, we collected spectra for field-caught specimens of Anopheles gambiae complex mosquitoes – the most epidemiologically important vector species on the planet – in two sites in Burkina Faso. Using these spectra, we show how models trained on data from one site can successfully classify morphologically identical sibling species in another site, over 250km away. Whilst we apply our framework to species prediction, our unified statistical framework can, alternatively, handle regression analysis (for example, to determine mosquito age) and other types of multinomial classification (for example, to determine infection status). To make our methods readily available for field entomologists, we have created an open-source R package mlevcm. All data used is publicly also available.

Radiation-induced enhancement of jasmonic acid levels

1994 ◽  
Vol 102 ◽  
pp. 303-306
Author(s):  
H. Sprinz ◽  
C. von Sonntag ◽  
U. Franck ◽  
O. Miersch ◽  
H. Dahlhelm
Keyword(s):  
Jasmonic Acid ◽  
Plant Hormone ◽  
Positive Stimulus ◽  
Ionising Radiation ◽  
Physiological Effects ◽  
Low Doses ◽  
Metabolic Processes ◽  
Radiation Induced ◽  
Response To Stress

In recent years, some experiments with low doses of ionising radiation have been performed, with reproducible results, which indicate a positive stimulus rather than a deletirious effect (Decker & Degner 1983; Wolff 1989).The aim of our own studies was to establish whether low doses of ionising radiation can influence metabolism by means of a modification of the concentration of a plant hormone, in this particular case jasmonate. The multiple physiological effects caused by the plant bioregulator jasmonate, including response to stress, suggest their essential involvement in central genetic and metabolic processes (Sembdner & Parthier 1993).

TRIM28 maintains genome imprints and regulates development of porcine SCNT embryos

Reproduction ◽  
2021 ◽  
Vol 161 (4) ◽  
pp. 411-424
Author(s):  
Yanhui Zhai ◽  
Meng Zhang ◽  
Xinglan An ◽  
Sheng Zhang ◽  
Xiangjie Kong ◽  
...  
Keyword(s):  
Somatic Cell Nuclear Transfer ◽  
Nuclear Transfer ◽  
Transcript Abundance ◽  
Dna Demethylation ◽  
Epigenetic Variability ◽  
Promoter Regions ◽  
Cell Stage ◽  
Genome Wide ◽  
Developmental Progression ◽  
Imprinting Gene

Pre-implantation embryos undergo genome-wide DNA demethylation, however certain regions, like imprinted loci remain methylated. Further, the mechanisms ensuring demethylation resistance by TRIM28 in epigenetic reprogramming remain poorly understood. Here, TRIM28 was knocked down in oocytes, and its effects on porcine somatic cell nuclear transfer (SCNT) embryo development was examined. Our results showed that SCNT embryos constructed from TRIM28 knockdown oocytes had significantly lower cleavage (53.9 ± 3.4% vs 64.8 ± 2.7%) and blastocyst rates (12.1 ± 4.3% vs 19.8 ± 1.9%) than control-SCNT embryos. The DNA methylation levels at the promoter regions of the imprinting gene IGF2 and H19 were significantly decreased in the 4-cell stage, and the transcript abundance of other imprinting gene was substantially increased. We also identified an aberrant two-fold decrease in the expression of CXXC1and H3K4me3 methyltransferase (ASH2L and MLL2), and the signal intensity of H3K4me3 had a transient drop in SCNT 2-cell embryos. Our results indicated that maternal TRIM28 knockdown disrupted the genome imprints and caused epigenetic variability in H3K4me3 levels, which blocked the transcription activity of zygote genes and affected the normal developmental progression of porcine SCNT embryos.

scholarly journals Vertical and horizontal integration of multi-omics data with miodin

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Benjamin Ulfenborg
Keyword(s):  
Data Analysis ◽  
R Package ◽  
Heterogeneous Data ◽  
Omics Data ◽  
Technical Expertise ◽  
Horizontal Integration ◽  
Level Data ◽  
Genomics And Proteomics ◽  
Health And Disease ◽  
Molecular Layers

Abstract Background Studies on multiple modalities of omics data such as transcriptomics, genomics and proteomics are growing in popularity, since they allow us to investigate complex mechanisms across molecular layers. It is widely recognized that integrative omics analysis holds the promise to unlock novel and actionable biological insights into health and disease. Integration of multi-omics data remains challenging, however, and requires combination of several software tools and extensive technical expertise to account for the properties of heterogeneous data. Results This paper presents the miodin R package, which provides a streamlined workflow-based syntax for multi-omics data analysis. The package allows users to perform analysis of omics data either across experiments on the same samples (vertical integration), or across studies on the same variables (horizontal integration). Workflows have been designed to promote transparent data analysis and reduce the technical expertise required to perform low-level data import and processing. Conclusions The miodin package is implemented in R and is freely available for use and extension under the GPL-3 license. Package source, reference documentation and user manual are available at https://gitlab.com/algoromics/miodin.

scholarly journals Knockdown NRPC2, 3, 8, NRPABC1 and NRPABC2 Affects RNAPIII Activity and Disrupts Seed Development in Arabidopsis

2021 ◽  
Vol 22 (21) ◽  
pp. 11314
Author(s):  
Hailiang Zhao ◽  
Yao Qin ◽  
Ziyi Xiao ◽  
Kun Liang ◽  
Dianming Gong ◽  
...  
Keyword(s):  
Seed Development ◽  
Ribosome Biogenesis ◽  
Expression Patterns ◽  
Rna Polymerase Iii ◽  
5S Rrna ◽  
Rna Seq ◽  
Metabolic Processes ◽  
Control Seed ◽  
Genes Encoding ◽  
Response To Stress

RNA polymerase III (RNAPIII) contains 17 subunits forming 4 functional domains that control the different stages of RNAPIII transcription and are dedicated to the synthesis of small RNAs such as 5S rRNA and tRNAs. Here, we identified 23 genes encoding these subunits in Arabidopsis (Arabidopsis thaliana) and further analyzed 5 subunits (NRPC2, NRPC3, NRPC8, NRPABC1, and NRPABC2) encoded by 6 genes with different expression patterns and belonging to different sub-complexes. The knockdown of these genes repressed the expression of 5S rRNA and tRNAs, causing seed developmental arrest at different stages. Among these knockdown mutants, RNA-seq analysis revealed 821 common differentially expressed genes (DEGs), significantly enriched in response to stress, abscisic acid, cytokinins, and the jasmonic acid signaling pathway. Weighted gene co-expression network analysis (WGCNA) revealed several hub genes involved in embryo development, carbohydrate metabolic and lipid metabolic processes. We identified numerous unique DEGs between the mutants belonging to pathways, including cell proliferation, ribosome biogenesis, cell death, and tRNA metabolic processes. Thus, NRPC2, NRPC3, NRPC8, NRPABC1, and NRPABC2 control seed development in Arabidopsis by influencing RNAPIII activity and, thus, hormone signaling. Reduced expression of these subunit genes causes an insufficient accumulation of the total RNAPIII, leading to the phenotypes observed following the genetic knockdown of these subunits.

scholarly journals Random forest based similarity learning for single cell RNA sequencing data

2018 ◽  
Author(s):  
Maziyar Baran Pouyan ◽  
Dennis Kostka
Keyword(s):  
Data Analysis ◽  
Random Forest ◽  
Single Cells ◽  
R Package ◽  
Similarity Learning ◽  
Sequencing Data ◽  
Genome Wide ◽  
Step Procedure ◽  
Exploratory Data ◽  
Cell Cell

AbstractMotivationGenome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore obtaining accurate cell–cell similarities from scRNA-seq data is critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task. Furthermore, abundance and type of noise present in scRNA-seq datasets suggest that application of generic methods, or of methods developed for bulk RNA-seq data, is likely suboptimal.ResultsHere we present RAFSIL, a random forest based approach to learn cell–cell similarities from scRNA-seq data. RAFSIL implements a two-step procedure, where feature construction geared towards scRNA-seq data is followed by similarity learning. It is designed to be adaptable and expandable, and RAFSIL similarities can be used for typical exploratory data analysis tasks like dimension reduction, visualization, and clustering. We show that our approach compares favorably with current methods across a diverse collection of datasets, and that it can be used to detect and highlight unwanted technical variation in scRNA-seq datasets in situations where other methods fail. Overall, RAFSIL implements a flexible approach yielding a useful tool that improves the analysis of scRNA-seq data.Availability and ImplementationThe RAFSIL R package is available online at www.kostkalab.net/software.html

scholarly journals csdR, an R package for differential co-expression analysis

2021 ◽  
Author(s):  
Jakob P. Pettersen ◽  
Eivind Almaas
Keyword(s):  
Data Analysis ◽  
Expression Analysis ◽  
R Package ◽  
Ease Of Use ◽  
The Other ◽  
Gene Pairs ◽  
Computational Performance ◽  
Parallel Speedup ◽  
Multiple Processing ◽  
Number Of Iterations

AbstractBackgroundDifferential co-expression network analysis has become an important tool to gain understanding of biological phenotypes and diseases. The CSD algorithm is a method to generate differential co-expression networks by comparing gene co-expressions from two different conditions. Each of the gene pairs is assigned conserved (C), specific (S) and differentiated (D) scores based on the co-expression of the gene pair between the two conditions. The result of the procedure is a network where the nodes are genes and the links are the gene pairs with the highest C-, S-, and D-scores. However, the existing CSD-implementations suffer from poor computational performance, difficult user procedures and lack of documentation.ResultsWe created the R-package csdR aimed at reaching good performance together with ease of use, sufficient documentation, and with the ability to play well with other tools for data analysis. csdR was benchmarked on a realistic dataset with 20, 645 genes. After verifying that the chosen number of iterations gave sufficient robustness, we tested the performance against the two existing CSD implementations. csdR was superior in performance to one of the implementations, whereas the other did not run. Our implementation can utilize multiple processing cores. However, we were unable to achieve more than ∼ 2.7 parallel speedup with saturation reached at about 10 cores.ConclusionsThe results suggest that csdR is a useful tool for differential co-expression analysis and is able to generate robust results within a workday on datasets of realistic sizes when run on a workstation or compute server.

scholarly journals Identifying differential isoform abundance with RATs: a universal tool and a warning

2017 ◽  
Author(s):  
Kimon Froussios ◽  
Kira Mourão ◽  
Gordon G. Simpson ◽  
Geoffrey J. Barton ◽  
Nick J. Schurch
Keyword(s):  
Transcript Abundance ◽  
Pcr Primers ◽  
R Package ◽  
Original Study ◽  
Transcript Expression ◽  
Rna Seq ◽  
Qrt Pcr ◽  
False Discovery ◽  
Alignment Free ◽  
Go Terms

AbstractMotivationThe biological importance of changes in gene and transcript expression is well recognised and is reflected by the wide variety of tools available to characterise these changes. Regulation via Differential Transcript Usage (DTU) is emerging as an important phenomenon. Several tools exist for the detection of DTU from read alignment or assembly data, but options for detection of DTU from alignment-free quantifications are limited.ResultsWe present an R package named RATs – (Relative Abundance of Transcripts) – that identifies DTU transcriptome-wide directly from transcript abundance estimations. RATs is agnostic to quantification methods and exploits bootstrapped quantifications, if available, to inform the significance of detected DTU events. RATs contextualises the DTU results and shows good False Discovery performance (median FDR ≤0.05) at all replication levels. We applied RATs to a human RNA-seq dataset associated with idiopathic pulmonary fibrosis with three DTU events validated by qRT-PCR. RATs found all three genes exhibited statistically significant changes in isoform proportions based on Ensembl v60 annotations, but the DTU for two were not reliably reproduced across bootstrapped quantifications. RATs also identified 500 novel DTU events that are enriched for eleven GO terms related to regulation of the response to stimulus, regulation of immune system processes, and symbiosis/parasitism. Repeating this analysis with the Ensembl v87 annotation showed the isoform abundance profiles of two of the three validated DTU genes changed radically. RATs identified 414 novel DTU events that are enriched for five GO terms, none of which are in common with those previously identified. Only 141 of the DTU evens are common between the two analyses, and only 8 are among the 248 reported by the original study. Furthermore, the original qRT-PCR probes no longer match uniquely to their original transcripts, calling into question the interpretation of these data. We suggest parallel full-length isoform sequencing, annotation pre-filtering and sequencing of the transcripts captured by qRT-PCR primers as possible ways to improve the validation of RNA-seq results in future experiments.AvailabilityThe package is available through Github at https://github.com/bartongroup/Rats.

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