scholarly journals MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

2020 ◽  
Author(s):  
Marek Cmero ◽  
Breon Schmidt ◽  
Ian J. Majewski ◽  
Paul G. Ekert ◽  
Alicia Oshlack ◽  
...  
Keyword(s):  
De Novo ◽  
Splice Variants ◽  
Lymphoblastic Leukemia ◽  
Differential Expression Analysis ◽  
Real Data ◽  
Tumour Suppressor Gene ◽  
Rna Seq ◽  
Structural Variants ◽  
Transcriptional Variants ◽  
Tandem Duplications

AbstractGenomic rearrangements can modify gene function by altering transcript sequences, and have been shown to be drivers in both cancer and rare diseases. Although there are now many methods to detect structural variants from Whole Genome Sequencing (WGS), RNA sequencing (RNA-seq) remains under-utilised as a technology for the detection of gene altering structural variants. Calling fusion genes from RNA-seq data is well established, but other transcriptional variants such as fusions with novel sequence, tandem duplications, large insertions and deletions, and novel splicing are difficult to detect using existing approaches.To identify all types of variants in transcriptomes, we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference free approach, which combines de novo assembly of transcripts with differential expression analysis, to identify up-regulated novel variants in a case sample.We validated MINTIE on simulated and real data sets and compared it with eight other approaches for finding novel transcriptional variants. We found MINTIE was able to detect all defined variant classes at high rates (>70%) while no other method was able to achieve this.We applied MINTIE to RNA-seq data from a cohort of acute lymphoblastic leukemia (ALL) patient samples and identified several novel clinically relevant variants, including an unpartnered recurrent fusion involving the tumour suppressor gene RB1, and variants in ALL-associated genes: tandem duplications in IKZF1 and PAX5, and novel splicing in ETV6. We further demonstrate the utility of MINTIE to identify rare disease variants using RNA-seq, including the discovery of an inter-chromosomal translocation in the DMD gene in a patient with muscular dystrophy. We posit that MINTIE will be able to identify new disease variants across a range of cancers and other disease types.

scholarly journals MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marek Cmero ◽  
Breon Schmidt ◽  
Ian J. Majewski ◽  
Paul G. Ekert ◽  
Alicia Oshlack ◽  
...  
Keyword(s):  
Differential Expression ◽  
De Novo Assembly ◽  
De Novo ◽  
Splice Variants ◽  
Differential Expression Analysis ◽  
New Disease ◽  
Fusion Genes ◽  
Rna Seq ◽  
Novel Variants ◽  
Transcriptional Variants

AbstractCalling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types.

scholarly journals nanotatoR: a tool for enhanced annotation of genomic structural variants

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Surajit Bhattacharya ◽  
Hayk Barseghyan ◽  
Emmanuèle C. Délot ◽  
Eric Vilain
Keyword(s):  
De Novo ◽  
Genome Mapping ◽  
Gene List ◽  
Sufficient Information ◽  
Rna Seq ◽  
Structural Variants ◽  
De Novo Genome Assembly ◽  
Pathogenic Variants ◽  
Increased Sensitivity

Abstract Background Whole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity. Results We developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient’s phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.). nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR’s annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM. Conclusions The extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

scholarly journals CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

2021 ◽  
Vol 17 (11) ◽  
pp. e1009631
Author(s):  
Raquel Linheiro ◽  
John Archer
Keyword(s):  
De Novo ◽  
Simulated Data ◽  
Real Data ◽  
Gene Families ◽  
Classification Systems ◽  
Whole Body ◽  
Cdna Libraries ◽  
Sequence Information ◽  
Rna Seq ◽  
High Quality

With the exponential growth of sequence information stored over the last decade, including that of de novo assembled contigs from RNA-Seq experiments, quantification of chimeric sequences has become essential when assembling read data. In transcriptomics, de novo assembled chimeras can closely resemble underlying transcripts, but patterns such as those seen between co-evolving sites, or mapped read counts, become obscured. We have created a de Bruijn based de novo assembler for RNA-Seq data that utilizes a classification system to describe the complexity of underlying graphs from which contigs are created. Each contig is labelled with one of three levels, indicating whether or not ambiguous paths exist. A by-product of this is information on the range of complexity of the underlying gene families present. As a demonstration of CStones ability to assemble high-quality contigs, and to label them in this manner, both simulated and real data were used. For simulated data, ten million read pairs were generated from cDNA libraries representing four species, Drosophila melanogaster, Panthera pardus, Rattus norvegicus and Serinus canaria. These were assembled using CStone, Trinity and rnaSPAdes; the latter two being high-quality, well established, de novo assembers. For real data, two RNA-Seq datasets, each consisting of ≈30 million read pairs, representing two adult D. melanogaster whole-body samples were used. The contigs that CStone produced were comparable in quality to those of Trinity and rnaSPAdes in terms of length, sequence identity of aligned regions and the range of cDNA transcripts represented, whilst providing additional information on chimerism. Here we describe the details of CStones assembly and classification process, and propose that similar classification systems can be incorporated into other de novo assembly tools. Within a related side study, we explore the effects that chimera’s within reference sets have on the identification of differentially expression genes. CStone is available at: https://sourceforge.net/projects/cstone/.

scholarly journals Differential Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

2020 ◽  
Author(s):  
Takayuki Osabe ◽  
Kentaro Shimizu ◽  
Koji Kadota
Keyword(s):  
Differential Expression ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Real Data ◽  
Gene Clustering ◽  
Rna Seq ◽  
Model Based ◽  
Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report a model-based clustering algorithm, MBCluster.Seq, that can be implemented using an R package for DE analysis.Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

scholarly journals nanotatoR: A tool for enhanced annotation of genomic structural variants

2020 ◽  
Author(s):  
Surajit Bhattacharya ◽  
Hayk Barseghyan ◽  
Emmanuèle C. Délot ◽  
Eric Vilain
Keyword(s):  
De Novo ◽  
Genome Mapping ◽  
Gene List ◽  
Sufficient Information ◽  
Rna Seq ◽  
Structural Variants ◽  
De Novo Genome Assembly ◽  
Pathogenic Variants ◽  
Increased Sensitivity

AbstractWhole genome sequencing is effective at identification of small variants but, because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity.We developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient’s phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.).nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR’s annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM. The extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

scholarly journals RNAtor: an Android-based application for biologists to plan RNA sequencing experiments

2016 ◽  
Author(s):  
Shruti Kane ◽  
Himanshu Garg ◽  
Neeraja M. Krishnan ◽  
Aditya Singh ◽  
Binay Panda
Keyword(s):  
Rna Sequencing ◽  
Mobile Applications ◽  
Mobile Application ◽  
Splice Variants ◽  
Real Data ◽  
Flow Cell ◽  
Rna Seq ◽  
Amount Of Information ◽  
Novel Transcripts ◽  
Google Play

AbstractRNA sequencing (RNA-seq) is a powerful technology for identification of novel transcripts (coding, non-coding and splice variants), understanding of transcript structures and estimation of gene and/or allelic expression. There are specific challenges that biologists face in determining the number of replicates to use, total number of sequencing reads to generate for detecting marginally differentially expressed transcripts and the number of lanes in a sequencing flow cell to use for the production of right amount of information. Although past studies attempted answering some of these questions, there is a lack of accessible and biologist-friendly mobile applications to answer these questions. Keeping this in mind, we have developed RNAtor, a mobile application for Android platforms, to aid biologists in correctly designing their RNA-seq experiments. The recommendations from RNAtor are based on simulations and real data.Availability and ImplementationThe Android version of RNAtor is available on Google Play Store and the code from GitHub (https://github.com/binaypanda/RNAtor).

scholarly journals De novo Transcriptome Assembly of Senna occidentalis Sheds Light on the Anthraquinone Biosynthesis Pathway

2022 ◽  
Vol 12 ◽  
Author(s):  
Sang-Ho Kang ◽  
Woo-Haeng Lee ◽  
Joon-Soo Sim ◽  
Niha Thaku ◽  
Saemin Chang ◽  
...  
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Differential Expression Analysis ◽  
Biosynthesis Pathway ◽  
Rna Seq ◽  
Pharmacological Activities ◽  
Secondary Metabolite Biosynthesis ◽  
Long Read ◽  
Gene Expression Levels

Senna occidentalis is an annual leguminous herb that is rich in anthraquinones, which have various pharmacological activities. However, little is known about the genetics of S. occidentalis, particularly its anthraquinone biosynthesis pathway. To broaden our understanding of the key genes and regulatory mechanisms involved in the anthraquinone biosynthesis pathway, we used short RNA sequencing (RNA-Seq) and long-read isoform sequencing (Iso-Seq) to perform a spatial and temporal transcriptomic analysis of S. occidentalis. This generated 121,592 RNA-Seq unigenes and 38,440 Iso-Seq unigenes. Comprehensive functional annotation and classification of these datasets using public databases identified unigene sequences related to major secondary metabolite biosynthesis pathways and critical transcription factor families (bHLH, WRKY, MYB, and bZIP). A tissue-specific differential expression analysis of S. occidentalis and measurement of the amount of anthraquinones revealed that anthraquinone accumulation was related to the gene expression levels in the different tissues. In addition, the amounts and types of anthraquinones produced differ between S. occidentalis and S. tora. In conclusion, these results provide a broader understanding of the anthraquinone metabolic pathway in S. occidentalis.

scholarly journals Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

2018 ◽  
Author(s):  
Pierre-Cyril Aubin-Frankowski ◽  
Jean-Philippe Vert
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
De Novo ◽  
State Of The Art ◽  
Real Data ◽  
Biological Processes ◽  
Rna Seq ◽  
Cell Cycles ◽  
Linear Differential ◽  
Cell Trajectories

AbstractSingle-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulation networks (GRN) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. In this work we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual data, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data.

scholarly journals Transcriptome characterization and expression profile of Coix lacryma-jobi L. in response to drought

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256875
Author(s):  
Guidong Miao ◽  
Yan Qin ◽  
Jihua Guo ◽  
Qingxia Zhang ◽  
Yingying Bao
Keyword(s):  
De Novo ◽  
Differential Expression Analysis ◽  
Global Gene Expression ◽  
Rna Seq ◽  
Transcriptional Responses ◽  
Drought Treatment ◽  
Short Read Sequencing ◽  
Real Time Quantitative Pcr ◽  
Polyadenylated Rna ◽  
Selection Of

Coix lacryma-jobi L. is a very important economic crop widely cultivated in Southeast Asia. Drought affects more than four million square kilometers every year, and is a significant factor limiting agricultural productivity. However, relatively little is known about how Coix lacryma-jobi L. responds to drought treatments. To obtain a detailed and comprehensive understanding of the mechanisms regulating the transcriptional responses of Coix lacryma-jobi L. to drought treatment, we employed high throughput short-read sequencing of cDNA prepared from polyadenylated RNA to explore global gene expression after a seven-day drought treatment. We generated a de novo assembled transcriptome comprising 65,480 unique sequences. Differential expression analysis based on RSEM-estimated transcript abundances identified 5,315 differentially expressed genes (DEGs) when comparing samples from plants following drought-treatment and from the appropriate controls. Among these, the transcripts for 3,460 genes were increased in abundance, whereas 1,855 were decreased. Real-time quantitative PCR for 5 transcripts confirmed the changes identified by RNA-Seq. The results provide a transcriptional overview of the changes in Coix lacryma-jobi L. in response to drought, and will be very useful for studying the function of associated genes and selection of molecular marker of Coix lacryma-jobi L in the future.

scholarly journals scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

2020 ◽  
Vol 36 (10) ◽  
pp. 3115-3123 ◽  
Author(s):  
Teng Fei ◽  
Tianwei Yu
Keyword(s):  
Single Cell ◽  
Differential Expression Analysis ◽  
Distance Matrix ◽  
Real Data ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Differential Expression

Abstract Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

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