Evidence map›Paper›PMID 30310253›Full record

ArticleBioinformation2018

Towards Personalized Medicine: An Improved

Cindy Huang, Vichetra Sam, Sophie Du, Tuan Le, Anthony Fletcher, William Lau, Kathleen Meyer, Esther Asaki, Da Wei Huang, Calvin Johnson

Abstract read
In one paragraph

Article in Bioinformation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
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

10 authors.

Cindy HuangCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Vichetra SamCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Sophie DuCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Tuan LeCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Anthony FletcherCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
William LauCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Kathleen MeyerCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Esther AsakiCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Da Wei HuangCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.
Calvin JohnsonCenter for Information Technology, National Institutes of Health, Bethesda, Maryland 10891.

Funding

Text Analytics, Machine Learning & High Performance ComputingZIHCT000200 · CIT · CENTER FOR INFORMATION TECHNOLOGY · PI JOHNSON, CALVIN A · 2009 to 2019
$25.5M
Computational Tools For Bioinformatics And Genome AnalysisZIHCT000260 · CIT · CENTER FOR INFORMATION TECHNOLOGY · PI JOHNSON, CALVIN A · 2016 to 2019
$1.9M
6 · The paper itself

Abstract

The third-generation sequencing technology, PacBio, has shown an ability to sequence the HIV virus amplicons in their full length. The long read of PaBio offers a distinct advantage to comprehensively understand the virus evolution complexity at quasispecies level (i.e. maintaining linkage information of variants) comparing to the short reads from Illumina shotgun sequencing. However, due to the highnoise nature of the PacBio reads, it is still a challenge to build accurate contigs at high sensitivity. Most of previously developed NGS assembly tools work with the assumption that the input reads are fairly accurate, which is largely true for the data derived from Sanger or Illumina technologies. When applying these tools on PacBio high-noise reads, they are largely driven by noise rather than true signal eventually leading to poor results in most cases. In this study, we propose the de novo assembly procedure, which comprises a positivefocused strategy, and linkage-frequency noise reduction so that it is more suitable for PacBio high-noise reads. We further tested the unique de novo assembly procedure on HIV PacBio benchmark data and clinical samples, which accurately assembled dominant and minor populations of HIV quasispecies as expected. The improved de novo assembly procedure shows potential ability to promote PacBio technology in the field of HIV drug-resistance clinical detection, as well as in broad HIV phylogenetic studies.

Indexed as

De Novo AssemblyHIVPacBioquasispecies

Identifiers

PMID30310253
PMCPMC6166399

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.