Evidence map›Paper›PMID 26090362›Full record

ReviewFrontiers in bioengineering and biotechnology2015

Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques.

Dario Veneziano, Giovanni Nigita, Alfredo Ferro

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

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  9. Noncoding RNAs in apoptosis: identification and function.Turkish journal of biology = Turk biyoloji dergisi · 2022
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  17. Dysregulation of different classes of tRNA fragments in chronic lymphocytic leukemia.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
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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.

Dario VenezianoDepartment of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, OH , USA.
Giovanni NigitaDepartment of Molecular Virology, Immunology and Medical Genetics, The Ohio State University , Columbus, OH , USA.
Alfredo FerroDepartment of Clinical and Molecular Biomedicine, University of Catania , Catania , Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The majority of the human transcriptome is defined as non-coding RNA (ncRNA), since only a small fraction of human DNA encodes for proteins, as reported by the ENCODE project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long non-coding RNA, have been classified, each with its own three-dimensional folding and specific function. As ncRNAs are highly abundant in living organisms and have been discovered to play important roles in many biological processes, there has been an ever increasing need to investigate the entire ncRNAome in further unbiased detail. Recently, the advent of next-generation sequencing (NGS) technologies has substantially increased the throughput of transcriptome studies, allowing an unprecedented investigation of ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also emerging. The huge amount of transcript data produced by NGS has progressively required the development and implementation of suitable bioinformatics workflows, complemented by knowledge-based approaches, to identify, classify, and evaluate the expression of hundreds of ncRNAs in normal and pathological conditions, such as cancer. In this mini-review, we present and discuss current bioinformatics advances in the development of such computational approaches to analyze and classify the ncRNA component of human transcriptome sequence data obtained from NGS technologies.

Indexed as

bioinformaticscircRNAlncRNAmiRNARNA-seq

Identifiers

PMID26090362
PMCPMC4453482

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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.