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

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/.

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Identification and Expression Analysis of the Genes Involved in the Raffinose Family Oligosaccharides Pathway of Phaseolus vulgaris and Glycine max

Plants ◽

10.3390/plants10071465 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1465

Author(s):

Ramon de Koning ◽

Raphaël Kiekens ◽

Mary Esther Muyoka Toili ◽

Geert Angenon

Keyword(s):

Common Bean ◽

Seed Development ◽

Expression Analysis ◽

De Novo ◽

Expression Patterns ◽

Gene Families ◽

Rna Seq ◽

Raffinose Family Oligosaccharides ◽

Specific Expression ◽

Raffinose Synthase

Raffinose family oligosaccharides (RFO) play an important role in plants but are also considered to be antinutritional factors. A profound understanding of the galactinol and RFO biosynthetic gene families and the expression patterns of the individual genes is a prerequisite for the sustainable reduction of the RFO content in the seeds, without compromising normal plant development and functioning. In this paper, an overview of the annotation and genetic structure of all galactinol- and RFO biosynthesis genes is given for soybean and common bean. In common bean, three galactinol synthase genes, two raffinose synthase genes and one stachyose synthase gene were identified for the first time. To discover the expression patterns of these genes in different tissues, two expression atlases have been created through re-analysis of publicly available RNA-seq data. De novo expression analysis through an RNA-seq study during seed development of three varieties of common bean gave more insight into the expression patterns of these genes during the seed development. The results of the expression analysis suggest that different classes of galactinol- and RFO synthase genes have tissue-specific expression patterns in soybean and common bean. With the obtained knowledge, important galactinol- and RFO synthase genes that specifically play a key role in the accumulation of RFOs in the seeds are identified. These candidate genes may play a pivotal role in reducing the RFO content in the seeds of important legumes which could improve the nutritional quality of these beans and would solve the discomforts associated with their consumption.

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TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

10.21203/rs.3.rs-131404/v1 ◽

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.

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SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data

BioMed Research International ◽

10.1155/2015/780519 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Yuxiang Tan ◽

Yann Tambouret ◽

Stefano Monti

Keyword(s):

Sample Size ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Simulated Data ◽

Real Data ◽

Rna Seq ◽

Sequencing Data ◽

Detection Algorithms ◽

Fusion Detection

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

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

10.1101/464479 ◽

2018 ◽

Cited By ~ 4

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.

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Ion channel profiling of the Lymnaea stagnalis ganglia via transcriptome analysis

10.21203/rs.3.rs-31358/v1 ◽

2020 ◽

Author(s):

Nan Dong ◽

Julia Bandura ◽

Zhaolei Zhang ◽

Yan Wang ◽

Karine Labadie ◽

...

Keyword(s):

Ion Channels ◽

Reference Genome ◽

Lymnaea Stagnalis ◽

De Novo ◽

Model Organisms ◽

Sequence Length ◽

Pond Snail ◽

Sequence Information ◽

Functional Domain ◽

Rna Seq

Abstract Background. The pond snail Lymnaea stagnalis (L. stagnalis) has been widely used as a model organism in neurobiology, ecotoxicology, and parasitology due to the relative simplicity of its CNS. However, its usefulness is restricted by a limited availability of transcriptome data. While sequence information for the L. stagnalis CNS transcripts has been obtained from EST library and a de novo RNA-seq assembly, the quality of these assemblies is limited by a combination of low coverage of EST libraries, the fragmented nature of de novo assemblies, and lack of reference genome. Results. In this study, taking advantage of the recent availability of the L. stagnalis reference genome, we generated an RNA-seq library from the adult L. stagnalis CNS, using a combination of genome-guided and de novo assembly programs to identify 17,832 protein-coding L. stagnalis transcripts. We combined our library with existing resources to produce a transcript set with greater sequence length, completeness, and diversity than previously available ones. Using our assembly and functional domain analysis, we profiled L. stagnalis CNS transcripts encoding ion channels and ionotropic receptors, which are key proteins for CNS function, and compared their sequences to other vertebrate and invertebrate model organisms. Interestingly, L. stagnalis transcripts encoding numerous putative Ca2+ channels showed the most sequence similarity to those of mouse, zebrafish, Xenopus tropicalis, fruit fly, and C. elegans, suggesting that many calcium channel-related signaling pathways may be evolutionarily conserved. Conclusions. Our study provides the most thorough characterization to date of the L. stagnalis transcriptome and provides insights into differences between vertebrates and invertebrates in CNS transcript diversity, according to function and protein class. Furthermore, this study is, to the best of our knowledge, the first to provide a complete characterization of the ion channels of a single species, opening new avenues for future research on fundamental neurobiological processes.

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De novo assembly and analysis of the transcriptome of the Dermacentor marginatus genes differentially expressed after blood-feeding and long-term starvation

10.21203/rs.3.rs-57342/v4 ◽

2020 ◽

Author(s):

Ercha Hu ◽

Yuan Meng ◽

Ying Ma ◽

Ruiqi Song ◽

Zhengxiang Hu ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Blood Feeding ◽

Differentially Expressed ◽

Whole Body ◽

Sequence Information ◽

Transcriptome Data ◽

Rna Seq ◽

Dermacentor Marginatus

Abstract Background: The ixodid tick Dermacentor marginatus is a vector of many pathogens wide spread in Eurasia. Studies of gene sequence on many tick species have greatly increased the information on tick protective antigen which might have the potential to function as effective vaccine candidates or drug targets for eco-friendly acaricide development. In the current study, RNA-seq was applied to identify D. marginatus sequences and analyze differentially expressed unigenes.Methods: To obtain a broader picture of gene sequences and changes in expression level, RNA-seq was performed to obtain the whole-body transcriptome data of D. marginatus adult female ticks after engorgement and long-term starvation. Subsequently, the real-time quantitative PCR (RT-qPCR) was applied to validate the RNA-seq data.Results: RNA-seq produced 30,251 unigenes, of which 32% were annotated. Gene expression was compared among groups that differed by status as newly molted, starved and engorged female adult ticks. Nearly one third of the unigenes in each group were differentially expressed compared to the other two groups, and the most numerous were genes encoding proteins involved in catalytic and binding activities and apoptosis. Selected up-regulated differentially expressed genes in each group were associated to protein, lipids, carbohydrate and chitin metabolism. Blood-feeding and long-term starvation also caused genes differentially expressed in the defense response and antioxidant response. RT-qPCR results indicated 6 differentially expressed transcripts showed similar trends in expression changes with RNA-seq results confirming that the gene expression profiles in transcriptome data is in consistent with RT-qPCR validation.Conclusions: Obtaining the sequence information of D. marginatus and characterizing the expression pattern of the genes involved in blood-feeding and during starvation would be helpful in understanding molecular physiology of D. marginatus and provides data for anti-tick vaccine and drug development for controlling the tick.

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TrancriptomeReconstructoR: data-driven annotation of complex transcriptomes

10.1101/2020.12.10.418897 ◽

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

AbstractBackgroundThe 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.ResultsWe 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.ConclusionsOur 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.

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Lep-Anchor: automated construction of linkage map anchored haploid genomes

Bioinformatics ◽

10.1093/bioinformatics/btz978 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2359-2364 ◽

Cited By ~ 4

Author(s):

Pasi Rastas

Keyword(s):

Linkage Map ◽

De Novo ◽

Simulated Data ◽

Real Data ◽

Significant Loss ◽

Linkage Maps ◽

Additional Information ◽

Correct Orientation ◽

Genome Assemblies ◽

Speed Accuracy

Abstract Motivation Linkage mapping provides a practical way to anchor de novo genome assemblies into chromosomes and to detect chimeric or otherwise erroneous contigs. Such anchoring improves with higher number of markers and individuals, as long as the mapping software can handle all the information. Recent software Lep-MAP3 can robustly construct linkage maps for millions of genotyped markers and on thousands of individuals, providing optimal maps for genome anchoring. For such large datasets, automated and robust genome anchoring tool is especially valuable and can significantly reduce intensive computational and manual work involved. Results Here, we present a software Lep-Anchor (LA) to anchor genome assemblies automatically using dense linkage maps. As the main novelty, it takes into account the uncertainty of the linkage map positions caused by low recombination regions, cross type or poor mapping data quality. Furthermore, it can automatically detect and cut chimeric contigs, and use contig–contig, single read or alternative genome assembly alignments as additional information on contig order and orientations and to collapse haplotype contigs. We demonstrate the performance of LA using real data and show that it outperforms ALLMAPS on anchoring completeness and speed. Accuracy-wise LA and ALLMAPS are about equal, but at the expense of lower completeness of ALLMAPS. The software Chromonomer was faster than the other two methods but has major limitations and is lower in accuracy. We also show that with additional information, such as contig–contig and read alignments, the anchoring completeness can be improved by up to 70% without significant loss in accuracy. Based on simulated data, we conclude that the anchoring accuracy can be improved by utilizing information about map position uncertainty. Accuracy is the rate of contigs in correct orientation and completeness is the number contigs with inferred orientation. Availability and implementation Lep-Anchor is available with the source code under GNU general public license from http://sourceforge.net/projects/lep-anchor. All the scripts and code used to produce the reported results are included with Lep-Anchor.

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SPARSim single cell: a count data simulator for scRNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz752 ◽

2019 ◽

Cited By ~ 2

Author(s):

Giacomo Baruzzo ◽

Ilaria Patuzzi ◽

Barbara Di Camillo

Keyword(s):

Single Cell ◽

Count Data ◽

Simulated Data ◽

Real Data ◽

R Package ◽

Supplementary Information ◽

Rna Seq ◽

Distribution Of Zeros ◽

New Methods ◽

Research Fields

Abstract Motivation Single cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data. Results In this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data. Availability and implementation SPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim. Supplementary information Supplementary data are available at Bioinformatics online.

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flexiMAP: a regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btaa854 ◽

2020 ◽

Author(s):

Krzysztof J Szkop ◽

David S Moss ◽

Irene Nobeli

Keyword(s):

Simulated Data ◽

Alternative Polyadenylation ◽

Real Data ◽

R Package ◽

Supplementary Information ◽

Beta Regression ◽

Rna Seq ◽

Good Balance ◽

Flexible Modeling ◽

Specificity And Sensitivity

Abstract Motivation We present flexible Modeling of Alternative PolyAdenylation (flexiMAP), a new beta-regression-based method implemented in R, for discovering differential alternative polyadenylation events in standard RNA-seq data. Results We show, using both simulated and real data, that flexiMAP exhibits a good balance between specificity and sensitivity and compares favourably to existing methods, especially at low fold changes. In addition, the tests on simulated data reveal some hitherto unrecognized caveats of existing methods. Importantly, flexiMAP allows modeling of multiple known covariates that often confound the results of RNA-seq data analysis. Availability and implementation The flexiMAP R package is available at: https://github.com/kszkop/flexiMAP. Scripts and data to reproduce the analysis in this paper are available at: https://doi.org/10.5281/zenodo.3689788. Supplementary information Supplementary data are available at Bioinformatics online.

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