ABSTRACT
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 .
Subject(s)
RNA Splicing , RNA-Seq/methods , RNA/genetics , Animals , Base Sequence , Computational Biology/methods , Exons , Gene Expression Regulation , High-Throughput Nucleotide Sequencing , Humans , Mice , Sequence Analysis, RNA/methods , SoftwareABSTRACT
Public archives of next-generation sequencing data are growing exponentially, but the difficulty of marshaling this data has led to its underutilization by scientists. Here, we present ASCOT, a resource that uses annotation-free methods to rapidly analyze and visualize splice variants across tens of thousands of bulk and single-cell data sets in the public archive. To demonstrate the utility of ASCOT, we identify novel cell type-specific alternative exons across the nervous system and leverage ENCODE and GTEx data sets to study the unique splicing of photoreceptors. We find that PTBP1 knockdown and MSI1 and PCBP2 overexpression are sufficient to activate many photoreceptor-specific exons in HepG2 liver cancer cells. This work demonstrates how large-scale analysis of public RNA-Seq data sets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events.
Subject(s)
Alternative Splicing/genetics , Computational Biology/methods , Data Analysis , Photoreceptor Cells/cytology , RNA Splice Sites/genetics , Animals , Cell Line, Tumor , Gene Expression/genetics , Hep G2 Cells , Heterogeneous-Nuclear Ribonucleoproteins/genetics , High-Throughput Nucleotide Sequencing , Humans , Liver Neoplasms/genetics , Mice , Nerve Tissue Proteins/biosynthesis , Nerve Tissue Proteins/genetics , Neurons/cytology , Polypyrimidine Tract-Binding Protein/genetics , RNA-Binding Proteins/biosynthesis , RNA-Binding Proteins/genetics , Retina/cytology , Sequence Analysis, RNA/methodsABSTRACT
Motivation: General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners. Results: We implement these methods in new versions of Bowtie, Bowtie 2 and HISAT. We greatly improve thread scaling in many scenarios, including on the recent Intel Xeon Phi architecture. We also highlight how bottlenecks are exacerbated by variable-record-length file formats like FASTQ and suggest changes that enable superior scaling. Availability and implementation: Experiments for this study: https://github.com/BenLangmead/bowtie-scaling. Bowtie: http://bowtie-bio.sourceforge.net. Bowtie 2: http://bowtie-bio.sourceforge.net/bowtie2. HISAT: http://www.ccb.jhu.edu/software/hisat. Supplementary information: Supplementary data are available at Bioinformatics online.
