scholarly journals recount3: summaries and queries for large-scale RNA-seq expression and splicing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christopher Wilks ◽  
Shijie C. Zheng ◽  
Feng Yong Chen ◽  
Rone Charles ◽  
Brad Solomon ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Large Scale ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Resources ◽  
Link Type ◽  
Private Data ◽  
Exon Junctions ◽  
Human And Mouse

AbstractWe present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new analysis pipeline. To facilitate access to the data, we provide the and R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

scholarly journals recount3: summaries and queries for large-scale RNA-seq expression and splicing

2021 ◽  
Author(s):  
Christopher Wilks ◽  
Shijie C. Zheng ◽  
Feng Yong Chen ◽  
Rone Charles ◽  
Brad Solomon ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Large Scale ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Resources ◽  
Private Data ◽  
Exon Junctions ◽  
Human And Mouse

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

scholarly journals SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

2018 ◽  
Author(s):  
Xianwen Ren ◽  
Liangtao Zheng ◽  
Zemin Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Random Projection ◽  
Rna Seq ◽  
Sequencing Data ◽  
Computational Framework ◽  
Human Blood Cells ◽  
Single Cell Rna Sequencing ◽  
Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

scholarly journals RNA sequencing data: hitchhiker's guide to expression analysis

2018 ◽  
Author(s):  
Koen Van Den Berge ◽  
Katharina Hembach ◽  
Charlotte Soneson ◽  
Simone Tiberi ◽  
Lieven Clement ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Large Scale ◽  
Science Studies ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Types ◽  
The Past ◽  
Long Read ◽  
Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

scholarly journals Building an RNA Sequencing Transcriptome of the Central Nervous System

2016 ◽  
Vol 22 (6) ◽  
pp. 579-592 ◽  
Author(s):  
Xiaomin Dong ◽  
Yanan You ◽  
Jia Qian Wu
Keyword(s):  
Gene Expression ◽  
Central Nervous System ◽  
Nervous System ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Cell ◽  
Rna Seq ◽  
The Central Nervous System

The composition and function of the central nervous system (CNS) is extremely complex. In addition to hundreds of subtypes of neurons, other cell types, including glia (astrocytes, oligodendrocytes, and microglia) and vascular cells (endothelial cells and pericytes) also play important roles in CNS function. Such heterogeneity makes the study of gene transcription in CNS challenging. Transcriptomic studies, namely the analyses of the expression levels and structures of all genes, are essential for interpreting the functional elements and understanding the molecular constituents of the CNS. Microarray has been a predominant method for large-scale gene expression profiling in the past. However, RNA-sequencing (RNA-Seq) technology developed in recent years has many advantages over microarrays, and has enabled building more quantitative, accurate, and comprehensive transcriptomes of the CNS and other systems. The discovery of novel genes, diverse alternative splicing events, and noncoding RNAs has remarkably expanded the complexity of gene expression profiles and will help us to understand intricate neural circuits. Here, we discuss the procedures and advantages of RNA-Seq technology in mammalian CNS transcriptome construction, and review the approaches of sample collection as well as recent progress in building RNA-Seq-based transcriptomes from tissue samples and specific cell types.

scholarly journals Efficient management and analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr

2017 ◽  
Author(s):  
Florian Privé ◽  
Hugues Aschard ◽  
Michael G.B. Blum
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Genomic Data ◽  
Supplementary Information ◽  
Risk Scores ◽  
Analysis Pipeline ◽  
Polygenic Risk ◽  
Link Type ◽  
Genome Wide ◽  
R Packages

AbstractMotivation:Genome-wide datasets produced for association studies have dramatically increased in size over the past few years, with modern datasets commonly including millions of variants measured in dozens of thousands of individuals. This increase in data size is a major challenge severely slowing down genomic analyses. Specialized software for every part of the analysis pipeline have been developed to handle large genomic data. However, combining all these software into a single data analysis pipeline might be technically difficult.Results:Here we present two R packages, bigstatsr and bigsnpr, allowing for management and analysis of large scale genomic data to be performed within a single comprehensive framework. To address large data size, the packages use memory-mapping for accessing data matrices stored on disk instead of in RAM. To perform data pre-processing and data analysis, the packages integrate most of the tools that are commonly used, either through transparent system calls to existing software, or through updated or improved implementation of existing methods. In particular, the packages implement a fast derivation of Principal Component Analysis, functions to remove SNPs in Linkage Disequilibrium, and algorithms to learn Polygenic Risk Scores on millions of SNPs. We illustrate applications of the two R packages by analysing a case-control genomic dataset for the celiac disease, performing an association study and computing Polygenic Risk Scores. Finally, we demonstrate the scalability of the R packages by analyzing a simulated genome-wide dataset including 500,000 individuals and 1 million markers on a single desktop computer.Availability:https://privefl.github.io/bigstatsr/ & https://privefl.github.io/bigsnpr/Contact:[email protected] & [email protected] information:Supplementary data are available at Bioinformatics online.

scholarly journals Slinker: Visualising novel splicing events in RNA-Seq data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1255
Author(s):  
Breon Schmidt ◽  
Marek Cmero ◽  
Paul Ekert ◽  
Nadia Davidson ◽  
Alicia Oshlack
Keyword(s):  
Rare Disease ◽  
Human Genome ◽  
Rna Sequencing ◽  
Reference Genome ◽  
Data Driven ◽  
Rna Seq ◽  
Bioinformatics Pipeline ◽  
Link Type ◽  
Muscular Disorders ◽  
Data Driven Approach

Visualisation of the transcriptome relative to a reference genome is fraught with sparsity. This is due to RNA sequencing (RNA-Seq) reads being predominantly mapped to exons that account for just under 3% of the human genome. Recently, we have used exon-only references, superTranscripts, to improve visualisation of aligned RNA-Seq data through the omission of supposedly unexpressed regions such as introns. However, variation within these regions can lead to novel splicing events that may drive a pathogenic phenotype. In these cases, the loss of information in only retaining annotated exons presents significant drawbacks. Here we present Slinker, a bioinformatics pipeline written in Python and Bpipe that uses a data-driven approach to assemble sample-specific superTranscripts. At its core, Slinker uses Stringtie2 to assemble transcripts with any sequence across any gene. This assembly is merged with reference transcripts, converted to a superTranscript, of which rich visualisations are made through Plotly with associated annotation and coverage information. Slinker was validated on five novel splicing events of rare disease samples from a cohort of primary muscular disorders. In addition, Slinker was shown to be effective in visualising deletion events within transcriptomes of tumour samples in the important leukemia gene, IKZF1. Slinker offers a succinct visualisation of RNA-Seq alignments across typically sparse regions and is freely available on Github.

scholarly journals LSTrAP-Cloud: A User-friendly Cloud Computing Pipeline to Infer Co-functional and Regulatory Networks

2020 ◽  
Author(s):  
Qiao Wen Tan ◽  
William Goh ◽  
Marek Mutwil
Keyword(s):  
Rna Sequencing ◽  
Gene Function ◽  
Regulatory Networks ◽  
Large Scale ◽  
Function Prediction ◽  
Sequencing Data ◽  
Gene Function Prediction ◽  
Analysis Pipeline ◽  
User Friendly

AbstractAs genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes that they might be interested in. LSTrAP-Cloud is based on Google Colaboratory and provides user-friendly tools that process and quality-control RNA sequencing data streamed from the European Sequencing Archive. LSTRAP-Cloud outputs a gene co-expression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters and transcription factors involved in the synthesis, transport and regulation of nicotine can be identified using our pipeline.

scholarly journals Genotype-free demultiplexing of pooled single-cell RNA-seq

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jun Xu ◽  
Caitlin Falconer ◽  
Quan Nguyen ◽  
Joanna Crawford ◽  
Brett D. McKinnon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Genetic Differences ◽  
Rna Seq ◽  
Link Type ◽  
Genotype Information ◽  
Single Cell Rna Sequencing ◽  
Pooled Samples

AbstractA variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit

scholarly journals Trans-NanoSim characterizes and simulates nanopore RNA-seq data

2019 ◽  
Author(s):  
Saber Hafezqorani ◽  
Chen Yang ◽  
Ka Ming Nip ◽  
René L Warren ◽  
Inanc Birol
Keyword(s):  
Rapid Development ◽  
Cost Effective ◽  
Effective Alternative ◽  
Rna Seq ◽  
Link Type ◽  
Cost Effective Alternative ◽  
Analytical Tools ◽  
Human And Mouse

AbstractWe introduce Trans-NanoSim, the first tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-seq data. Through benchmarking on sets of nanopore reads from human and mouse reference transcriptomes, we show the robustness of Trans-NanoSim in capturing the characteristics of nanopore cDNA and direct RNA reads. As a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim would facilitate the rapid development of analytical tools for nanopore RNA-seq data. Trans-NanoSim is freely accessible at https://github.com/bcgsc/NanoSim

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

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