Evidence map›Paper›PMID 30850377›Full record

ArticleG3 (Bethesda, Md.)2019

TSD: A Computational Tool To Study the Complex Structural Variants Using PacBio Targeted Sequencing Data.

Guofeng Meng, Ying Tan, Yue Fan, Yan Wang, Guang Yang, Gregory Fanning, Yang Qiu

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
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  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Structural variation in the sequencing era.Nature reviews. Genetics · 2020
    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

7 authors.

Guofeng MengJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China menggf@gmail.com.ORCID 0000-0002-5418-3821
Ying TanJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
Yue FanJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
Yan WangJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
Guang YangJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
Gregory FanningJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
Yang QiuJanssen R&D China, 4560 Jinke Road, Shanghai 201210, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PacBio sequencing is a powerful approach to study DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of endogenous or exogenous sequences. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at: https://github.com/menggf/tsd.

Indexed as

SoftwareAlgorithmsCell LineComputational BiologyGenetic VariationGenomicsHepatitis B virusHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNAVirus Integrationstructural variants long reads genomic structure PacBio

Identifiers

PMID30850377
PMCPMC6505135

What OpenQuestion holds

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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.