title: High-resolution transcriptome analysis with long-read RNA sequencing.
abstract: RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2×75 bp and 2×262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, United States of America.
authors.forename: Hyunghoon
authors.initials: H
authors.lastname: Cho
authors.affiliation: Department of Genetics, Stanford University, Stanford, California, United States of America.
authors.forename: Joe
authors.initials: J
authors.lastname: Davis
authors.affiliation: Department of Pathology, Stanford University, Stanford, California, United States of America.
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Pathology, Stanford University, Stanford, California, United States of America.
authors.forename: Kevin S
authors.initials: KS
authors.lastname: Smith
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, United States of America; Department of Genetics, Stanford University, Stanford, California, United States of America.
authors.forename: Alexis
authors.initials: A
authors.lastname: Battle
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California, United States of America; Department of Genetics, Stanford University, Stanford, California, United States of America; Department of Pathology, Stanford University, Stanford, California, United States of America.
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Montgomery
mesh_terms: D000483:Alleles; D017398:Alternative Splicing; D045744:Cell Line, Tumor; D018076:DNA, Complementary / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D012313:RNA / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D059467:Transcriptome
chemical_list: D018076:DNA, Complementary; D012313:RNA