Evidence map›Paper›PMID 28396519›Full record

ArticleGenome research2017

High-confidence coding and noncoding transcriptome maps.

Bo-Hyun You, Sang-Ho Yoon, Jin-Wu Nam

Abstract read
In one paragraph

Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Article
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  11. Tumor immune microenvironment lncRNAs.Briefings in bioinformatics · 2022
    Review
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  18. Illuminating lncRNA Function Through Target Prediction.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bo-Hyun YouDepartment of Life Science, College of Natural Sciences, Hanyang University, Seoul 133791, Republic of Korea.ORCID 0000-0002-1892-7790
Sang-Ho YoonDepartment of Life Science, College of Natural Sciences, Hanyang University, Seoul 133791, Republic of Korea.ORCID 0000-0003-2611-5554
Jin-Wu NamDepartment of Life Science, College of Natural Sciences, Hanyang University, Seoul 133791, Republic of Korea.ORCID 0000-0003-0047-3687

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Genome, HumanSoftwareTranscriptomeBenchmarkingChromosome MappingDatabases, GeneticHigh-Throughput Nucleotide SequencingHumansLikelihood FunctionsRNA, Long NoncodingTranscription Initiation SiteRNA, Long Noncoding

Identifiers

PMID28396519
PMCPMC5453319

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LicenceCC BY-NC
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.