scholarly journals NeoFuse: predicting fusion neoantigens from RNA sequencing data

2019 ◽  
Vol 36 (7) ◽  
pp. 2260-2261 ◽  
Author(s):  
Georgios Fotakis ◽  
Dietmar Rieder ◽  
Marlene Haider ◽  
Zlatko Trajanoski ◽  
Francesca Finotello
Keyword(s):  
Rna Sequencing ◽  
Human Leukocyte ◽  
Computational Prediction ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Computational Workflow ◽  
Hla Binding ◽  
Proteins And Peptides

Abstract Summary Gene fusions can generate immunogenic neoantigens that mediate anticancer immune responses. However, their computational prediction from RNA sequencing (RNA-seq) data requires deep bioinformatics expertise to assembly a computational workflow covering the prediction of: fusion transcripts, their translated proteins and peptides, Human Leukocyte Antigen (HLA) types, and peptide-HLA binding affinity. Here, we present NeoFuse, a computational pipeline for the prediction of fusion neoantigens from tumor RNA-seq data. NeoFuse can be applied to cancer patients’ RNA-seq data to identify fusion neoantigens that might expand the repertoire of suitable targets for immunotherapy. Availability and implementation NeoFuse source code and documentation are available under GPLv3 license at https://icbi.i-med.ac.at/NeoFuse/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals arcasHLA: high-resolution HLA typing from RNAseq

2019 ◽  
Vol 36 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe’er ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Biological Dataset ◽  
Hla Genotypes ◽  
High Level

Abstract Motivation The human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune di3orders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification. Results Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, we evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. Availability and implementation arcasHLA is available at https://github.com/RabadanLab/arcasHLA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

scholarly journals arcasHLA: high resolution HLA typing from RNA seq

2018 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe'er ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Mhc Class Ii ◽  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Hla Genes ◽  
Biological Dataset ◽  
High Level

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.

scholarly journals RsQTL: correlation of expressed SNVs with splicing using RNA-sequencing data

2019 ◽  
Author(s):  
Justin Sein ◽  
Liam F. Spurr ◽  
Pavlos Bousounis ◽  
N M Prashant ◽  
Hongyu Liu ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Tissue Expression ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Dynamic Nature ◽  
Dynamic Variation ◽  
Exon Junctions ◽  
Variant Allele Fraction ◽  
Allele Fraction

SummaryRsQTL is a tool for identification of splicing quantitative trait loci (sQTLs) from RNA-sequencing (RNA-seq) data by correlating the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA) with the proportion of molecules spanning local exon-exon junctions at loci with differential intron excision (percent spliced in, PSI). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx). RsQTL does not require matched DNA and can identify a subset of expressed sQTL loci. Due to the dynamic nature of VAFRNA, RsQTL is applicable for the assessment of conditional and dynamic variation-splicing relationships.Availability and implementationhttps://github.com/HorvathLab/[email protected] or [email protected] InformationRsQTL_Supplementary_Data.zip

scholarly journals Detection of aberrant events in RNA sequencing data

2020 ◽  
Author(s):  
Vicente A. Yépez ◽  
Christian Mertes ◽  
Michaela F. Mueller ◽  
Daniela S. Andrade ◽  
Leonhard Wachutka ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Rare Allele ◽  
Rna Seq ◽  
Sequencing Data ◽  
Aberrant Splicing ◽  
Aberrant Expression ◽  
Powerful Approach ◽  
Disease Entities ◽  
Computational Workflow ◽  
Assess Quality

Abstract RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects for individuals affected with a genetically undiagnosed rare disorder. Pioneer studies have shown that RNA-seq could increase diagnostic rates over DNA sequencing alone by 8% to 36 % depending on disease entities and probed tissues. To accelerate the adoption of RNA-seq among human genetic centers, detailed analysis protocols are now needed. Here, we present a step-by-step protocol that instructs how to robustly detect aberrant expression, aberrant splicing, and mono-allelic expression of a rare allele in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on DROP (Detection of RNA Outliers Pipeline), a modular computational workflow that integrates all analysis steps, can leverage parallel computing infrastructures, and generates browsable web page reports.

scholarly journals Rigorous Benchmarking of HLA Callers for RNA Sequencing Data

2021 ◽  
Author(s):  
Ram Ayyala ◽  
Junghyun Jung ◽  
Sergey Knyazev ◽  
SERGHEI MANGUL
Keyword(s):  
Rna Sequencing ◽  
Gold Standard ◽  
Large Scale ◽  
Human Leukocyte ◽  
Tissue Expression ◽  
Hla Typing ◽  
Read Length ◽  
Evaluation Metrics ◽  
Rna Seq ◽  
Sequencing Data

Although precise identification of the human leukocyte antigen (HLA) allele is crucial for various clinical and research applications, HLA typing remains challenging due to high polymorphism of the HLA loci. However, with Next-Generation Sequencing (NGS) data becoming widely accessible, many computational tools have been developed to predict HLA types from RNA sequencing (RNA-seq) data. However, there is a lack of comprehensive and systematic benchmarking of RNA-seq HLA callers using large-scale and realist gold standards. In order to address this limitation, we rigorously compared the performance of 12 HLA callers over 50,000 HLA tasks including searching 30 pairwise combinations of HLA callers and reference in over 1,500 samples. In each case, we produced evaluation metrics of accuracy that is the percentage of correctly predicted alleles (two and four-digit resolution) based on six gold standard datasets spanning 650 RNA-seq samples. To determine the influence of the relationship of the read length over the HLA region on prediction quality using each tool, we explored the read length effect by considering read length in the range 37-126 bp, which was available in our gold standard datasets. Moreover, using the Genotype-Tissue Expression (GTEx) v8 data, we carried out evaluation metrics by calculating the concordance of the same HLA type across different tissues from the same individual to evaluate how well the HLA callers can maintain consistent results across various tissues of the same individual. This study offers crucial information for researchers regarding appropriate choices of methods for an HLA analysis.

Curse: building expression atlases and co-expression networks from public RNA-Seq data

2018 ◽  
Vol 35 (16) ◽  
pp. 2880-2881 ◽  
Author(s):  
Dries Vaneechoutte ◽  
Klaas Vandepoele
Keyword(s):  
Rna Sequencing ◽  
State Of The Art ◽  
The State ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Valuable Resource ◽  
Sequencing Data ◽  
Imperfect Quality ◽  
Sequence Read Archive

Abstract Summary Public RNA-Sequencing (RNA-Seq) datasets are a valuable resource for transcriptome analyses, but their accessibility is hindered by the imperfect quality and presentation of their metadata and by the complexity of processing raw sequencing data. The Curse suite was created to alleviate these problems. It consists of an online curation tool named Curse to efficiently build compendia of experiments hosted on the Sequence Read Archive, and a lightweight pipeline named Prose to download and process the RNA-Seq data into expression atlases and co-expression networks. Curse networks showed improved linking of functionally related genes compared to the state-of-the-art. Availability and implementation Curse, Prose and their manuals are available at http://bioinformatics.psb.ugent.be/webtools/Curse/. Prose was implemented in Java. Supplementary information Supplementary data are available at Bioinformatics online.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

A Two-Stage Poisson Model for Testing RNA-Seq Data

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Paul L. Auer ◽  
Rebecca W Doerge
Keyword(s):  
Rna Sequencing ◽  
Statistical Approach ◽  
Poisson Model ◽  
Real Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Two Stage ◽  
Individual Gene ◽  
Unique Nature

RNA sequencing technology is providing data of unprecedented throughput, resolution, and accuracy. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. We introduce a simple and powerful statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.

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