Evidence map›Paper›PMID 28873323›Full record

ArticleBioengineered2017

Survey of gene splicing algorithms based on reads.

Xiuhua Si, Qian Wang, Lei Zhang, Ruo Wu, Jiquan Ma

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineered, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 49% of its field
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 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

5 authors at 1 institution in 1 country.

Xiuhua Sia Department of Computer Science & Technology , Heilongjiang University , Harbin , China.ORCID http://orcid.org/0000-0002-5528-2813
Qian Wangb Shandong Aerospace Institute of Electronic Technology , Yantai , China.
Lei Zhanga Department of Computer Science & Technology , Heilongjiang University , Harbin , China.ORCID http://orcid.org/0000-0003-4088-2426
Ruo Wua Department of Computer Science & Technology , Heilongjiang University , Harbin , China.
Jiquan Maa Department of Computer Science & Technology , Heilongjiang University , Harbin , China.
Heilongjiang University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene splicing is the process of assembling a large number of unordered short sequence fragments to the original genome sequence as accurately as possible. Several popular splicing algorithms based on reads are reviewed in this article, including reference genome algorithms and de novo splicing algorithms (Greedy-extension, Overlap-Layout-Consensus graph, De Bruijn graph). We also discuss a new splicing method based on the MapReduce strategy and Hadoop. By comparing these algorithms, some conclusions are drawn and some suggestions on gene splicing research are made.

Indexed as

AlgorithmsGenome, BacterialHigh-Throughput Nucleotide SequencingSequence Analysis, DNASoftwareDe Bruijn graphgene splicingHadoopMapReduceread

Identifiers

PMID28873323
PMCPMC5736332
OpenAlexW2751827973

What OpenQuestion holds

Textmetadata
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.