scholarly journals Alignment-free filtering for cfNA fusion fragments

2019 ◽  
Vol 35 (14) ◽  
pp. i225-i232 ◽  
Author(s):  
Xiao Yang ◽  
Yasushi Saito ◽  
Arjun Rao ◽  
Hyunsung John Kim ◽  
Pranav Singh ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Cell Line ◽  
De Novo ◽  
High Sensitivity ◽  
Detection Methods ◽  
Rna Seq ◽  
Sequencing Data ◽  
Alignment Free ◽  
Fusion Detection ◽  
High Depth

Abstract Motivation Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers. Results AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data. Availability and implementation AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

scholarly journals Fusion detection and quantification by pseudoalignment

2017 ◽  
Author(s):  
Páll Melsted ◽  
Shannon Hateley ◽  
Isaac Charles Joseph ◽  
Harold Pimentel ◽  
Nicolas Bray ◽  
...  
Keyword(s):  
De Novo ◽  
Chromosomal Rearrangements ◽  
Clinical Use ◽  
Gene Fusions ◽  
Fusion Genes ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcript Quantification ◽  
Novel Approach ◽  
Fusion Detection

RNA sequencing in cancer cells is a powerful technique to detect chromosomal rearrangements, allowing for de novo discovery of actively expressed fusion genes. Here we focus on the problem of detecting gene fusions from raw sequencing data, assembling the reads to define fusion transcripts and their associated breakpoints, and quantifying their abundances. Building on the pseudoalignment idea that simplifies and accelerates transcript quantification, we introduce a novel approach to fusion detection based on inspecting paired reads that cannot be pseudoaligned due to conflicting matches. The method and software, called pizzly, filters false positives, assembles new transcripts from the fusion reads, and reports candidate fusions. With pizzly, fusion detection from raw RNA-Seq reads can be performed in a matter of minutes, making the program suitable for the analysis of large cancer gene expression databases and for clinical use. pizzly is available at https://github.com/pmelsted/pizzly

scholarly journals Fusion-Bloom: fusion detection in assembled transcriptomes

2019 ◽  
Vol 36 (7) ◽  
pp. 2256-2257
Author(s):  
Readman Chiu ◽  
Ka Ming Nip ◽  
Inanc Birol
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Variant Calling ◽  
Detection Sensitivity ◽  
Detection Methods ◽  
Supplementary Information ◽  
Rna Seq ◽  
Data Types ◽  
Creative Commons ◽  
Fusion Detection

Abstract Summary Presence or absence of gene fusions is one of the most important diagnostic markers in many cancer types. Consequently, fusion detection methods using various genomics data types, such as RNA sequencing (RNA-seq) are valuable tools for research and clinical applications. While information-rich RNA-seq data have proven to be instrumental in discovery of a number of hallmark fusion events, bioinformatics tools to detect fusions still have room for improvement. Here, we present Fusion-Bloom, a fusion detection method that leverages recent developments in de novo transcriptome assembly and assembly-based structural variant calling technologies (RNA-Bloom and PAVFinder, respectively). We benchmarked Fusion-Bloom against the performance of five other state-of-the-art fusion detection tools using multiple datasets. Overall, we observed Fusion-Bloom to display a good balance between detection sensitivity and specificity. We expect the tool to find applications in translational research and clinical genomics pipelines. Availability and implementation Fusion-Bloom is implemented as a UNIX Make utility, available at https://github.com/bcgsc/pavfinder and released under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/. Supplementary information Supplementary data are available at Bioinformatics online.

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 Sequence diversity and evolution of an iflavirus family associated with ticks

2020 ◽  
Author(s):  
Romain Daveu ◽  
Caroline Hervet ◽  
Louane Sigrist ◽  
Davide Sassera ◽  
Aaron Jex ◽  
...  
Keyword(s):  
Cell Line ◽  
Complete Genome ◽  
Evolutionary Dynamics ◽  
De Novo ◽  
Rna Viruses ◽  
Ixodes Scapularis ◽  
Fine Scale ◽  
Rna Seq ◽  
Strong Positive Selection ◽  
Synonymous Mutations

AbstractWe studied a family of iflaviruses, a group of RNA viruses frequently found in arthropods, focusing on viruses associated with ticks. Our aim was to bring insight on the evolutionary dynamics of this group of viruses, which may interact with the biology of ticks. We explored systematically de novo RNA-Seq assemblies available for species of ticks which allowed to identify nine new genomes of iflaviruses. The phylogeny of virus sequences was not congruent with that of the tick hosts, suggesting recurrent host changes across tick genera along evolution. We identified five different variants with a complete or near-complete genome in Ixodes ricinus. These sequences were closely related, which allowed a fine-scale estimation of patterns of substitutions: we detected a strong excess of synonymous mutations suggesting evolution under strong positive selection. ISIV, a sequence found in the ISE6 cell line of Ixodes scapularis, was unexpectedly nearidentical with I. ricinus variants, suggesting a contamination of this cell line by I. ricinus material. Overall, our work constitutes a step in the understanding of the interactions between this family of viruses and ticks.

Computational Approaches for Transcriptome Assembly Based on Sequencing Technologies

2020 ◽  
Vol 15 (1) ◽  
pp. 2-16
Author(s):  
Yuwen Luo ◽  
Xingyu Liao ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Critical Role ◽  
High Sensitivity ◽  
Biological Properties ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Massive Sequencing ◽  
Generation Sequencing

Transcriptome assembly plays a critical role in studying biological properties and examining the expression levels of genomes in specific cells. It is also the basis of many downstream analyses. With the increase of speed and the decrease in cost, massive sequencing data continues to accumulate. A large number of assembly strategies based on different computational methods and experiments have been developed. How to efficiently perform transcriptome assembly with high sensitivity and accuracy becomes a key issue. In this work, the issues with transcriptome assembly are explored based on different sequencing technologies. Specifically, transcriptome assemblies with next-generation sequencing reads are divided into reference-based assemblies and de novo assemblies. The examples of different species are used to illustrate that long reads produced by the third-generation sequencing technologies can cover fulllength transcripts without assemblies. In addition, different transcriptome assemblies using the Hybrid-seq methods and other tools are also summarized. Finally, we discuss the future directions of transcriptome assemblies.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
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.

scholarly journals RNA-Seq analysis of early transcriptional responses to activation in the leukaemic Jurkat E6.1 T cell line

2020 ◽  
Vol 5 ◽  
pp. 42 ◽  
Author(s):  
Suet Ling Felce ◽  
Gillian Farnie ◽  
Michael L. Dustin ◽  
James H. Felce
Keyword(s):  
T Cell ◽  
Cell Line ◽  
Cell Biology ◽  
Cell Receptor ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcriptional Responses ◽  
Human T Cells ◽  
T Cell Biology ◽  
Whole Transcriptome

Background: The leukaemia-derived Jurkat E6.1 cell line has been used as a model T cell in the study of many aspects of T cell biology, most notably activation in response to T cell receptor (TCR) engagement. Methods: We present whole-transcriptome RNA-Sequencing data for Jurkat E6.1 cells in the resting state and two hours post-activation via TCR and CD28. We compare early transcriptional responses in the presence and absence of the chemokines CXCL12 and CCL19, and perform a basic comparison between observed transcriptional responses in Jurkat E6.1 cells and those in primary human T cells using publicly deposited data. Results: Jurkat E6.1 cells have many of the hallmarks of standard T cell transcriptional responses to activation, but lack most of the depth of responses in primary cells. Conclusions: These data indicate that Jurkat E6.1 cells hence represent only a highly simplified model of early T cell transcriptional responses.

scholarly journals Circall: fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dat Thanh Nguyen ◽  
Quang Thinh Trac ◽  
Thi-Hau Nguyen ◽  
Ha-Nam Nguyen ◽  
Nir Ohad ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Simulated Data ◽  
High Sensitivity ◽  
Circular Rna ◽  
Computational Time ◽  
Circular Rnas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Mapping Algorithm ◽  
False Discovery Rate Method

Abstract Background Circular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA sequencing (RNA-seq) focus mostly on improving mapping quality of reads supporting the back-splicing junction (BSJ) of a circRNA to eliminate false positives (FPs). We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, thus increasing the rate of FP circRNAs. Results We have developed Circall, a novel circRNA detection method from RNA-seq. Circall controls the FPs using a robust multidimensional local false discovery rate method based on the length and expression of circRNAs. It is computationally highly efficient by using a quasi-mapping algorithm for fast and accurate RNA read alignments. We applied Circall on two simulated datasets and three experimental datasets of human cell-lines. The results show that Circall achieves high sensitivity and precision in the simulated data. In the experimental datasets it performs well against current leading methods. Circall is also substantially faster than the other methods, particularly for large datasets. Conclusions With those better performances in the detection of circRNAs and in computational time, Circall facilitates the analyses of circRNAs in large numbers of samples. Circall is implemented in C++ and R, and available for use at https://www.meb.ki.se/sites/biostatwiki/circall and https://github.com/datngu/Circall.

scholarly journals TrancriptomeReconstructoR: 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

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.

scholarly journals Chromatin and RNA Maps Reveal Regulatory Long Noncoding RNAs in Mouse

2015 ◽  
Vol 36 (5) ◽  
pp. 809-819 ◽  
Author(s):  
Gireesh K. Bogu ◽  
Pedro Vizán ◽  
Lawrence W. Stanton ◽  
Miguel Beato ◽  
Luciano Di Croce ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
De Novo ◽  
Noncoding Rnas ◽  
Mouse Cell ◽  
Long Noncoding Rnas ◽  
Chromatin State ◽  
Rna Seq ◽  
Gene Encoding ◽  
Protein Coding

Discovering and classifying long noncoding RNAs (lncRNAs) across all mammalian tissues and cell lines remains a major challenge. Previously, mouse lncRNAs were identified using transcriptome sequencing (RNA-seq) data from a limited number of tissues or cell lines. Additionally, associating a few hundred lncRNA promoters with chromatin states in a single mouse cell line has identified two classes of chromatin-associated lncRNA. However, the discovery and classification of lncRNAs is still pending in many other tissues in mouse. To address this, we built a comprehensive catalog of lncRNAs by combining known lncRNAs with high-confidence novel lncRNAs identified by mapping andde novoassembling billions of RNA-seq reads from eight tissues and a primary cell line in mouse. Next, we integrated this catalog of lncRNAs with multiple genome-wide chromatin state maps and found two different classes of chromatin state-associated lncRNAs, including promoter-associated (plncRNAs) and enhancer-associated (elncRNAs) lncRNAs, across various tissues. Experimental knockdown of an elncRNA resulted in the downregulation of the neighboring protein-codingKdm8gene, encoding a histone demethylase. Our findings provide 2,803 novel lncRNAs and a comprehensive catalog of chromatin-associated lncRNAs across different tissues in mouse.

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