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High-confidence coding and noncoding transcriptome maps
#MMPMID28396519
You BH
; Yoon SH
; Nam JW
Genome Res
2017[Jun]; 27
(6
): 1050-1062
PMID28396519
show ga
The advent of high-throughput RNA sequencing (RNA-seq) has led to the discovery
of unprecedentedly immense transcriptomes encoded by eukaryotic genomes. However,
the transcriptome maps are still incomplete partly because they were mostly
reconstructed based on RNA-seq reads that lack their orientations (known as
unstranded reads) and certain boundary information. Methods to expand the
usability of unstranded RNA-seq data by predetermining the orientation of the
reads and precisely determining the boundaries of assembled transcripts could
significantly benefit the quality of the resulting transcriptome maps. Here, we
present a high-performing transcriptome assembly pipeline, called CAFE, that
significantly improves the original assemblies, respectively assembled with
stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the
maximum likelihood estimation and by integrating information about transcription
start sites and cleavage and polyadenylation sites. Applying large-scale
transcriptomic data comprising 230 billion RNA-seq reads from the ENCODE, Human
BodyMap 2.0, The Cancer Genome Atlas, and GTEx projects, CAFE enabled us to
predict the directions of about 220 billion unstranded reads, which led to the
construction of more accurate transcriptome maps, comparable to the manually
curated map, and a comprehensive lncRNA catalog that includes thousands of novel
lncRNAs. Our pipeline should not only help to build comprehensive, precise
transcriptome maps from complex genomes but also to expand the universe of
noncoding genomes.
free
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