Evidence map›Paper›PMID 23892401›Full record

ArticleNucleic acids research2013

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts.

Liang Sun, Haitao Luo, Dechao Bu, Guoguang Zhao, Kuntao Yu, Changhai Zhang, Yuanning Liu, Runsheng Chen, Yi Zhao

Abstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,133 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,133citing papers in PubMed, 1 pooled it
–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

1,133 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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1,073 more citing papers are in PubMed but not listed here.

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

9 authors.

Liang SunBioinformatics Research Group, Advanced Computing Research Laboratory, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Laboratory of Bioinformatics and Non-coding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Haitao Luo
Dechao Bu
Guoguang Zhao
Kuntao Yu
Changhai Zhang
Yuanning Liu
Runsheng Chen
Yi Zhao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci.

Indexed as

SoftwareAnimalsGene Expression ProfilingHumansMicePongoProteinsRNA, Long NoncodingSequence Analysis, RNAProteinsRNA, Long Noncoding

Identifiers

PMID23892401
PMCPMC3783192

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.