P-523 Whole-chromosome aneuploidies revealed by transcriptome of trophectoderm biopsied from human pre-implantation blastocyst

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
L Song ◽  
X Yanwen ◽  
C Bing ◽  
X Yan ◽  
Y Xiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
R Package ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Chromosomal Mosaicism ◽  
Rna Seq ◽  
Sequencing Data ◽  
Registration Number ◽  
Mrna Transcriptome

Abstract Study question Whether mRNA transcriptome of biopsied trophectoderm (TE) in human pre-implantation blastocyst can predict embryo karyotype? Summary answer mRNA transcriptome of biopsied TE can precisely predict whole-chromosome aneuploidies but not mosaicism or segmental aneuploidies. What is known already Karyotype of human pre-implantation blastocyst is well recognized by PGT-A. However, genome can’t demonstrate gene expression level which might infer the development potential of euploidy. Transcriptome of blastocyst by singe-cell RNA-seq has revealed the lineage segregation of human pre-implantation blastocyst. It is not known whether transcriptome of biopsied TE used in PGT-A can infer the karyotype of human pre-implantation blastocyst. Study design, size, duration A total of 74 TE samples were biopsied from 26 blastocysts which were donated from patients who underwent PGT at our Reproductive Medicine Center. All of these embryos have been previously diagnosed as aneuploidies (n = 19) or euploidies (n = 7) with monogenic disorder. Participants/materials, setting, methods The DNA and mRNA of all biopsied TEs were separated independently using a modified oligo-dT bead capture, followed by PGT-A of DNA and smart2-sequencing of mRNA (G&T-seq). Karyotype of biopsied TEs were confirmed with PGT-A performed in MiSeq system (Illumina) in our PGT laboratory with the use of next-generation sequencing. Data of transcriptome was analyzed using Rstudio and R package InferCNV to predict aneuploidies by referring to euploidies which were inferred with corresponding PGT-A results. Main results and the role of chance In human pre-implantation blastocyst, all whole-chromosome aneuploidies could be inferred by transcriptome of biopsied TE, which were consistent with PGT-A result. But chromosomal mosaicism or segmental aneuploidies were hard to be predicted precisely by transcriptome of TE. Limitations, reasons for caution The main limitation of this study lies in the inability to retrieve the exact copy number variations from mRNA transcription. Gene expression is in a great imbalance in such an early development of human pre-implantation blastocyst. Wider implications of the findings Our data suggest that mRNA transcriptome is enough for prediction of whole-chromosome aneuploidies. The method and value for predicting mosaicism and segmental aneuploidies by transcriptome should be further investigated. Trial registration number not applicable

P–523 Whole-chromosome aneuploidies revealed by transcriptome of trophectoderm biopsied from human pre-implantation blastocyst

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
L Song ◽  
X Yanwen ◽  
C Bing ◽  
X Yan ◽  
Y Xiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
R Package ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Chromosomal Mosaicism ◽  
Rna Seq ◽  
Sequencing Data ◽  
Registration Number ◽  
Mrna Transcriptome

Abstract Study question Whether mRNA transcriptome of biopsied trophectoderm (TE) in human pre-implantation blastocyst can predict embryo karyotype? Summary answer mRNA transcriptome of biopsied TE can precisely predict whole-chromosome aneuploidies but not mosaicism or segmental aneuploidies. What is known already Karyotype of human pre-implantation blastocyst is well recognized by PGT-A. However, genome can’t demonstrate gene expression level which might infer the development potential of euploidy. Transcriptome of blastocyst by singe-cell RNA-seq has revealed the lineage segregation of human pre-implantation blastocyst. It is not known whether transcriptome of biopsied TE used in PGT-A can infer the karyotype of human pre-implantation blastocyst. Study design, size, duration A total of 74 TE samples were biopsied from 26 blastocysts which were donated from patients who underwent PGT at our Reproductive Medicine Center. All of these embryos have been previously diagnosed as aneuploidies (n = 19) or euploidies (n = 7) with monogenic disorder. Participants/materials, setting, methods The DNA and mRNA of all biopsied TEs were separated independently using a modified oligo-dT bead capture, followed by PGT-A of DNA and smart2-sequencing of mRNA (G&T-seq). Karyotype of biopsied TEs were confirmed with PGT-A performed in MiSeq system (Illumina) in our PGT laboratory with the use of next-generation sequencing. Data of transcriptome was analyzed using Rstudio and R package InferCNV to predict aneuploidies by referring to euploidies which were inferred with corresponding PGT-A results. Main results and the role of chance In human pre-implantation blastocyst, all whole-chromosome aneuploidies could be inferred by transcriptome of biopsied TE, which were consistent with PGT-A result. But chromosomal mosaicism or segmental aneuploidies were hard to be predicted precisely by transcriptome of TE. Limitations, reasons for caution The main limitation of this study lies in the inability to retrieve the exact copy number variations from mRNA transcription. Gene expression is in a great imbalance in such an early development of human pre-implantation blastocyst. Wider implications of the findings: Our data suggest that mRNA transcriptome is enough for prediction of whole-chromosome aneuploidies. The method and value for predicting mosaicism and segmental aneuploidies by transcriptome should be further investigated. Trial registration number Not applicable

scholarly journals XenoCP: Cloud-based BAM cleansing tool for RNA and DNA from Xenograft

2019 ◽  
Author(s):  
Michael Rusch ◽  
Liang Ding ◽  
Sasi Arunachalam ◽  
Andrew Thrasher ◽  
Hongjian Jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Heterogeneity ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Link Type ◽  
Gene Expression Quantification ◽  
Expression Quantification ◽  
Generation Sequencing ◽  
Rna And Dna

ABSTRACTSummaryXenografts are important models for cancer research and the presence of mouse reads in xenograft next generation sequencing data can potentially confound interpretation of experimental results. We present an efficient, cloud-based BAM-to-BAM cleaning tool called XenoCP to remove mouse reads from xenograft BAM files. We show application of XenoCP in obtaining accurate gene expression quantification in RNA-seq and tumor heterogeneity in WGS of xenografts derived from brain and solid tumors.Availability and ImplementationSt. Jude Cloud (https://pecan.stjude.cloud/permalink/xenocp) and St. Jude Github (https://github.com/stjude/XenoCP)

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

scholarly journals TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2748 ◽  
Author(s):  
Andrea Komljenovic ◽  
Julien Roux ◽  
Marc Robinson-Rechavi ◽  
Frederic B. Bastian
Keyword(s):  
Gene Expression ◽  
Gene List ◽  
R Package ◽  
Rna Seq ◽  
Anatomical Structures ◽  
Enrichment Test ◽  
Expression Of Genes ◽  
Affymetrix Microarrays ◽  
New Gene

BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and reprocessed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures (“TopAnat”). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: http://www.bioconductor.org/packages/BgeeDB/

Hadoop-CNV-RF: a clinically validated and scalable copy number variation detection tool for next-generation sequencing data

2020 ◽  
Author(s):  
Getiria Onsongo ◽  
Ham Ching Lam ◽  
Matthew Bower ◽  
Bharat Thyagarajan
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Large Gene ◽  
Data Framework ◽  
Number Variation ◽  
Targeted Capture ◽  
Objective Detection ◽  
Gene Panels

Abstract Objective : Detection of small copy number variations (CNVs) in clinically relevant genes is routinely being used to aid diagnosis. We recently developed a tool, CNV-RF , capable of detecting small clinically relevant CNVs. CNV-RF was designed for small gene panels and did not scale well to large gene panels. On large gene panels, CNV-RF routinely failed due to memory limitations. When successful, it took about 2 days to complete a single analysis, making it impractical for routinely analyzing large gene panels. We need a reliable tool capable of detecting CNVs in the clinic that scales well to large gene panels. Results : We have developed Hadoop-CNV-RF, a scalable implementation of CNV-RF . Hadoop-CNV-RF is a freely available tool capable of rapidly analyzing large gene panels. It takes advantage of Hadoop, a big data framework developed to analyze large amounts of data. Preliminary results show it reduces analysis time from about 2 days to less than 4 hours and can seamlessly scale to large gene panels. Hadoop-CNV-RF has been clinically validated for targeted capture data and is currently being used in a CLIA molecular diagnostics laboratory. Its availability and usage instructions are publicly available at: https://github.com/getiria-onsongo/hadoop-cnvrf-public .

scholarly journals Association of intronic DNA methylation and hydroxymethylation alterations in the epigenetic etiology of dilated cardiomyopathy

2019 ◽  
Vol 317 (1) ◽  
pp. H168-H180 ◽  
Author(s):  
Ali M. Tabish ◽  
Mohammed Arif ◽  
Taejeong Song ◽  
Zaher Elbeck ◽  
Richard C. Becker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Dilated Cardiomyopathy ◽  
Cardiac Remodeling ◽  
Rna Seq ◽  
Wild Type ◽  
Kegg Pathways ◽  
Wild Type Phenotype ◽  
Gene Expression Levels

In this study, we investigated the role of DNA methylation [5-methylcytosine (5mC)] and 5-hydroxymethylcytosine (5hmC), epigenetic modifications that regulate gene activity, in dilated cardiomyopathy (DCM). A MYBPC3 mutant mouse model of DCM was compared with wild type and used to profile genomic 5mC and 5hmC changes by Chip-seq, and gene expression levels were analyzed by RNA-seq. Both 5mC-altered genes (957) and 5hmC-altered genes (2,022) were identified in DCM hearts. Diverse gene ontology and KEGG pathways were enriched for DCM phenotypes, such as inflammation, tissue fibrosis, cell death, cardiac remodeling, cardiomyocyte growth, and differentiation, as well as sarcomere structure. Hierarchical clustering of mapped genes affected by 5mC and 5hmC clearly differentiated DCM from wild-type phenotype. Based on these data, we propose that genomewide 5mC and 5hmC contents may play a major role in DCM pathogenesis. NEW & NOTEWORTHY Our data demonstrate that development of dilated cardiomyopathy in mice is associated with significant epigenetic changes, specifically in intronic regions, which, when combined with gene expression profiling data, highlight key signaling pathways involved in pathological cardiac remodeling and heart contractile dysfunction.

A-To-I RNA Editing By ADAR1 Is Essential For Hematopoiesis

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1199-1199 ◽  
Author(s):  
Brian Liddicoat ◽  
Robert Piskol ◽  
Alistair Chalk ◽  
Miyoko Higuchi ◽  
Peter Seeburg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Editing ◽  
Fetal Liver ◽  
Expression Profiles ◽  
In Utero ◽  
Tamoxifen Treatment ◽  
Rna Seq ◽  
Data Set ◽  
Hematopoietic Stem

Abstract The role of RNA and its regulation is becoming increasingly appreciated as a vital component of hematopoietic development. RNA editing by members of the Adenosine Deaminase Acting on RNA (ADAR) gene family is a form of post-transcriptional modification which converts genomically encoded adenosine to inosine (A-to-I) in double-stranded RNA. A-to-I editing by ADAR directly converts the sequence of the RNA substrate and can alter the structure, function, processing, and localization of the targeted RNA. ADAR1 is ubiquitously expressed and we have previously described essential roles in the development of hematopoietic and hepatic organs. Germline ablation of murine ADAR1 results in a significant upregulation of interferon (IFN) stimulated genes and embryonic death between E11.5 and E12.5 associated with fetal liver disintegration and failed hemopoiesis. To determine the biological importance of A-to-I editing by ADAR1, we generated an editing dead knock-in allele of ADAR1 (ADAR1E861A). Mice homozygous for the ADAR1E861A allele died in utero at ∼E13.5. The fetal liver (FL) was small and had significantly lower cellularity than in controls. Analysis of hemopoiesis demonstrated increased apoptosis and a loss of hematopoietic stem cells (HSC) and all mature lineages. Most notably erythropoiesis was severely impaired with ∼7-fold reduction across all erythrocyte progenitor populations compared to controls. These data are consistent with our previous findings that ADAR1 is essential for erythropoiesis (unpublished data) and suggest that the ADAR1E861A allele phenocopies the null allele in utero. To assess the requirement of A-to-I editing in adult hematopoiesis, we generated mice where we could somatically delete the wild-type ADAR1 allele and leave only ADAR1E861A expressed in HSCs (hScl-CreERAdar1fl/E861A). In comparison to hScl-CreERAdar1fl/+ controls, hScl-CreERAdar1fl/E861A mice were anemic and had severe leukopenia 20 days post tamoxifen treatment. Investigation of marrow hemopoiesis revealed a significant loss of all cells committed to the erythroid lineage in hScl-CreERAdar1fl/E861A mice, despite having elevated phenotypic HSCs. Upon withdrawal of tamoxifen diet, all blood parameters were restored to control levels within 12 weeks owing to strong selection against cells expressing only the ADAR1E861A allele. To understand the mechanism through which ADAR1 mediated A-to-I editing regulates hematopoiesis, RNA-seq was performed. Gene expression profiles showed that a loss of ADAR1 mediated A-to-I editing resulted in a significant upregulation of IFN signatures, consistent with the gene expression changes in ADAR1 null mice. To define substrates of ADAR1 we assessed A-to-I mismatches in the RNA-seq data sets. 3,560 previously known and 353 novel A-to-I editing sites were identified in our data set. However, no single editing substrate discovered could account for the IFN signature observed or the lethality of ADAR1E861A/E861A mice. These results demonstrate that ADAR1 mediated A-to-I editing is essential for the maintenance of both fetal and adult hemopoiesis in a cell-autonomous manner and a key suppressor of the IFN response in hematopoiesis. Furthermore the ADAR1E861A allele demonstrates the essential role of ADAR1 in vivo is A-to-I editing. Disclosures: Hartner: TaconicArtemis: Employment.

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