MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples

Abstract Summary Mistakes in linking a patient’s biological samples with their phenotype data can confound RNA-Seq studies. The current method for avoiding such sample mix-ups is to test for inconsistencies between biological data and known phenotype data such as sex. However, in DNA studies a common QC step is to check for unexpected relatedness between samples. Here, we extend this method to RNA-Seq, which allows the detection of duplicated samples without relying on identifying inconsistencies with phenotype data. Results We present RNASeq_similarity_matrix: an automated tool to generate a sequence similarity matrix from RNA-Seq data, which can be used to visually identify sample mix-ups. This is particularly useful when a study contains multiple samples from the same individual, but can also detect contamination in studies with only one sample per individual. Availability and implementation RNASeq_similarity_matrix has been made available as a documented GPL licensed Docker image on www.github.com/nicokist/RNASeq_similarity_matrix.

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Efficient RNA isoform identification and quantification from RNA-Seq data with network flows

Bioinformatics ◽

10.1093/bioinformatics/btu317 ◽

2014 ◽

Vol 30 (17) ◽

pp. 2447-2455 ◽

Cited By ~ 38

Author(s):

Elsa Bernard ◽

Laurent Jacob ◽

Julien Mairal ◽

Jean-Philippe Vert

Keyword(s):

Network Flows ◽

Rna Seq ◽

Identification And Quantification

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Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

10.1101/019067 ◽

2015 ◽

Cited By ~ 5

Author(s):

Abhinav Nellore ◽

Leonardo Collado-Torres ◽

Andrew E Jaffe ◽

José Alquicira-Hernández ◽

Jacob Pritt ◽

...

Keyword(s):

Statistical Analysis ◽

Web Services ◽

Open Source ◽

Rna Sequencing ◽

Open Source Software ◽

Rna Seq ◽

Spliced Alignment ◽

Amazon Web Services ◽

Scalable Analysis ◽

Multiple Samples

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 hours for US$0.91 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio.

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Sashimi plots: Quantitative visualization of alternative isoform expression from RNA-seq data

10.1101/002576 ◽

2014 ◽

Cited By ~ 3

Author(s):

Yarden Katz ◽

Eric T Wang ◽

Jacob Stilterra ◽

Schraga Schwartz ◽

Bang Wong ◽

...

Keyword(s):

Region Of Interest ◽

Splice Junction ◽

Genomic Region ◽

Rna Seq ◽

Mrna Isoforms ◽

Experimental Conditions ◽

Human Genes ◽

Quantitative Visualization ◽

Isoform Expression ◽

Multiple Samples

Analysis of RNA sequencing (RNA-Seq) data revealed that the vast majority of human genes express multiple mRNA isoforms, produced by alternative pre-mRNA splicing and other mechanisms, and that most alternative isoforms vary in expression between human tissues. As RNA-Seq datasets grow in size, it remains challenging to visualize isoform expression across multiple samples. We present Sashimi plots, a quantitative multi-sample visualization of RNA-Seq reads aligned to gene annotations, which enables quantitative comparison of isoform usage across samples or experimental conditions. Given an input annotation and spliced alignments of reads from a sample, a region of interest is visualized in a Sashimi plot as follows: (i) alignments in exons are represented as read densities (optionally normalized by length of genomic region and coverage), and (ii) splice junction reads are drawn as arcs connecting a pair of exons, where arc width is drawn proportional to the number of reads aligning to the junction.

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Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

10.1101/598748 ◽

2019 ◽

Cited By ~ 4

Author(s):

Yuanhua Huang ◽

Davis J McCarthy ◽

Oliver Stegle

Keyword(s):

Single Cell ◽

Real Data ◽

Experimental Designs ◽

Joint Analysis ◽

Rna Seq ◽

Computationally Efficient ◽

Primary Object ◽

Genotype Information ◽

Object Of Study ◽

Multiple Samples

AbstractThe joint analysis of multiple samples using single-cell RNA-seq is a promising experimental design, offering both increased throughput while allowing to account for batch variation. To achieve multi-sample designs, genetic variants that segregate between the samples in the pool have been proposed as natural barcodes for cell demultiplexing. Existing demultiplexing strategies rely on access to complete genotype data from the pooled samples, which greatly limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using simulations based on synthetic mixtures and results on real data, we demonstrate the robustness of our model and illustrate the utility of multi-sample experimental designs for common expression analyses.

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Intra-host site-specific polymorphisms of SARS-CoV-2 is consistent across multiple samples and methodologies

10.1101/2020.04.24.20078691 ◽

2020 ◽

Cited By ~ 5

Author(s):

Rebecca Rose ◽

David J. Nolan ◽

Samual Moot ◽

Amy Feehan ◽

Sissy Cross ◽

...

Keyword(s):

Evolutionary Dynamics ◽

Reference Genome ◽

Sequence Data ◽

Polymorphic Site ◽

Clinical Samples ◽

Rna Seq ◽

Multiple Alleles ◽

Sequencing Errors ◽

Longitudinal Sampling ◽

Multiple Samples

ABSTRACTDespite the potential relevance to clinical outcome, intra-host dynamics of SARS-CoV-2 are unclear. Here, we quantify and characterize intra-host variation in SARS-CoV-2 raw sequence data uploaded to SRA as of 14 April 2020, and compare results between two sequencing methods (amplicon and RNA-Seq). Raw fastq files were quality filtered and trimmed using Trimmomatic, mapped to the WuhanHu1 reference genome using Bowtie2, and variants called with bcftools mpileup. To ensure sufficient coverage, we only included samples with 10X coverage for >90% of the genome (n=406 samples), and only variants with a depth >=10. Derived (i.e. non-reference) alleles were found at 408 sites. The number of polymorphic sites (i.e. sites with multiple alleles) within samples ranged from 0-13, with 72% of samples (295/406) having at least one polymorphic site. Correlation between number of polymorphic sites and coverage was very low for both sequencing methods (R2 < 0.1, p < 0.05). Polymorphisms were observed >1 sample at 66 sites (range: 2-38 samples). The minor allele frequency (MAF) at each shared polymorphic site was 0.03% - 48.5%. 33/66 sites occurred in ORF1a1b, and 37/66 changes were non-synonymous. At 10/66 sites, derived alleles were found in samples sequenced using both methods. Polymorphic amplicon samples were found at 10/10 positions, while polymorphic RNA-Seq samples were found at 7/10 positions. In conclusion, our results suggest that intra-host variation is prevalent among clinical samples. While mutations resulting from amplification and/or sequencing errors cannot be excluded, the observation of shared polymorphic sites with high MAF across multiple samples and sequencing methods is consistent with true underlying variation. Further investigation into intra-host evolutionary dynamics, particularly with longitudinal sampling, is critical for broader understanding of disease progression.

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Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape

10.1101/2021.08.13.456196 ◽

2021 ◽

Author(s):

Luke Zappia ◽

Fabian J Theis

Keyword(s):

Single Cell ◽

Open Science ◽

Software Tools ◽

Field Analysis ◽

Analysis Tool ◽

Tracking Data ◽

Rna Seq ◽

Science Practices ◽

Single Cell Rna Sequencing ◽

Multiple Samples

Recent years have seen a revolution in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq). As the number, size and complexity of scRNA-seq datasets continue to increase, so does the number of computational methods and software tools for extracting meaning from them. Since 2016 the scRNA-tools database has catalogued software tools for analysing scRNA-seq data. With the number of tools in the database passing 1000, we take this opportunity to provide an update on the state of the project and the field. Analysis of five years of analysis tool tracking data clearly shows the evolution of the field, and that the focus of developers has moved from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find evidence that open science practices reward developers with increased recognition and help accelerate the field.

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A multi-sample approach increases the accuracy of transcript assembly

Nature Communications ◽

10.1038/s41467-019-12990-0 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Li Song ◽

Sarven Sabunciyan ◽

Guangyu Yang ◽

Liliana Florea

Keyword(s):

Statistical Models ◽

Rna Seq ◽

Weighted Voting ◽

Large Numbers ◽

Splice Graph ◽

Voting Scheme ◽

Functional Analyses ◽

Multiple Samples ◽

Programming Algorithms ◽

Transcript Assembly

Abstract Transcript assembly from RNA-seq reads is a critical step in gene expression and subsequent functional analyses. Here we present PsiCLASS, an accurate and efficient transcript assembler based on an approach that simultaneously analyzes multiple RNA-seq samples. PsiCLASS combines mixture statistical models for exonic feature selection across multiple samples with splice graph based dynamic programming algorithms and a weighted voting scheme for transcript selection. PsiCLASS achieves significantly better sensitivity-precision tradeoff, and renders precision up to 2-3 fold higher than the StringTie system and Scallop plus TACO, the two best current approaches. PsiCLASS is efficient and scalable, assembling 667 GEUVADIS samples in 9 h, and has robust accuracy with large numbers of samples.

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Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

Genome Biology ◽

10.1186/s13059-019-1865-2 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 13

Author(s):

Yuanhua Huang ◽

Davis J. McCarthy ◽

Oliver Stegle

Keyword(s):

Single Cell ◽

Real Data ◽

Experimental Designs ◽

Rna Seq ◽

Computationally Efficient ◽

Primary Object ◽

Genotype Information ◽

Object Of Study ◽

Multiple Samples ◽

Pooled Samples

AbstractMultiplexed single-cell RNA-seq analysis of multiple samples using pooling is a promising experimental design, offering increased throughput while allowing to overcome batch variation. To reconstruct the sample identify of each cell, genetic variants that segregate between the samples in the pool have been proposed as natural barcode for cell demultiplexing. Existing demultiplexing strategies rely on availability of complete genotype data from the pooled samples, which limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using pools based on synthetic mixtures and results on real data, we demonstrate the robustness of Vireo and illustrate the utility of multiplexed experimental designs for common expression analyses.

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MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies

BioMed Research International ◽

10.1155/2015/318064 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6

Author(s):

Pankaj Kumar ◽

Anna Halama ◽

Shahina Hayat ◽

Anja M. Billing ◽

Manish Gupta ◽

...

Keyword(s):

Time Series ◽

Gene Expression Omnibus ◽

Case Control ◽

Rna Seq ◽

Web Interface ◽

Level Information ◽

Drug Treatments ◽

Multiple Samples ◽

Significant Effort ◽

Project Level

The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe “MetaRNA-Seq,” a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level.

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