Evidence map›Paper›PMID 31874618›Full record

ArticleBMC bioinformatics2019

DTA-SiST: de novo transcriptome assembly by using simplified suffix trees.

Jin Zhao, Haodi Feng, Daming Zhu, Chi Zhang, Ying Xu

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2019. 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
0.3field-weighted citation impact, top 39% 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

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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, 4 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 3 institutions in 2 countries.

Jin ZhaoSchool of Computer Science and Technology, Shandong University, Binhai Road, Qingdao, Shandong, People's Republic of China.
Haodi FengSchool of Computer Science and Technology, Shandong University, Binhai Road, Qingdao, Shandong, People's Republic of China. fenghaodi@sdu.edu.cn.
Daming ZhuSchool of Computer Science and Technology, Shandong University, Binhai Road, Qingdao, Shandong, People's Republic of China.
Chi ZhangDepartment of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, IN, USA.
Ying XuDepartment of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA.
Shandong University of Science and Technology · CNIndiana University IndianapolisUniversity of Georgia · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlternative splicing allows the pre-mRNAs of a gene to be spliced into various mRNAs, which greatly increases the diversity of proteins. High-throughput sequencing of mRNAs has revolutionized our ability for transcripts reconstruction. However, the massive size of short reads makes de novo transcripts assembly an algorithmic challenge.

resultsWe develop a novel radical framework, called DTA-SiST, for de novo transcriptome assembly based on suffix trees. DTA-SiST first extends contigs by reads that have the longest overlaps with the contigs' terminuses. These reads can be found in linear time of the lengths of the reads through a well-designed suffix tree structure. Then, DTA-SiST constructs splicing graphs based on contigs for each gene locus. Finally, DTA-SiST proposes two strategies to extract transcript-representing paths: a depth-first enumeration strategy and a hybrid strategy based on length and coverage. We implemented the above two strategies and compared them with the state-of-the-art de novo assemblers on both simulated and real datasets. Experimental results showed that the depth-first enumeration strategy performs always better with recall and also better with precision for smaller datasets while the hybrid strategy leads with precision for big datasets.

conclusionsDTA-SiST performs more competitive than the other compared de novo assemblers especially with precision measure, due to the read-based contig extension strategy and the elegant transcripts extraction rules.

Indexed as

TranscriptomeAlgorithmsHigh-Throughput Nucleotide SequencingSoftwareAlternative splicingDe novo assemblyRNA-seq

Identifiers

PMID31874618
PMCPMC6929406
OpenAlexW2997326516

What OpenQuestion holds

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LicenceCC BY
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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.