Evidence map›Paper›PMID 27051465›Full record

ArticleBioData mining2016

Distinguishing highly similar gene isoforms with a clustering-based bioinformatics analysis of PacBio single-molecule long reads.

Ma Liang, Castle Raley, Xin Zheng, Geetha Kutty, Emile Gogineni, Brad T Sherman, Qiang Sun, Xiongfong Chen, Thomas Skelly, Kristine Jones and 11 more

Open access · goldAbstract read
In one paragraph

Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

  1. AFrontiers in genetics · 2021
    Article
  2. Article
  3. Long-read amplicon denoising.Nucleic acids research · 2019
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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

21 authors at 5 institutions in 1 country.

Ma Liang *Critical Care Medicine Department, Clinical Center, Frederick, MD USA.
Castle Raley *Leidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Xin Zheng *Leidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Geetha KuttyCritical Care Medicine Department, Clinical Center, Frederick, MD USA.
Emile GogineniCritical Care Medicine Department, Clinical Center, Frederick, MD USA.
Brad T ShermanLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Qiang SunLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Xiongfong ChenLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Thomas SkellyLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Kristine JonesLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Robert StephensLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Bin ZhouCenter of Information Technology, National Institutes of Health (NIH), Bethesda, MD USA.
William LauCenter of Information Technology, National Institutes of Health (NIH), Bethesda, MD USA.
Calvin JohnsonCenter of Information Technology, National Institutes of Health (NIH), Bethesda, MD USA.
Tomozumi ImamichiLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Minkang JiangLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Robin DewarLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Richard A LempickiLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Bao TranLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Joseph A KovacsCritical Care Medicine Department, Clinical Center, Frederick, MD USA.
Da Wei HuangLeidos BioMedical Research, Inc., Frederick National Laboratory for Cancer Research, NIH, Frederick, MD USA.
Frederick National Laboratory for Cancer Research · USLeidos (United States) · USNational Institutes of Health · USCenter for Information Technology · USNational Cancer Institute · US

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
The Characterization of Pneumocystis Surface AntigensZIACL000037 · CLC · CLINICAL CENTER · PI KOVACS, JOSEPH A · 2009 to 2018
$0k
CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

backgroundGene isoforms are commonly found in both prokaryotes and eukaryotes. Since each isoform may perform a specific function in response to changing environmental conditions, studying the dynamics of gene isoforms is important in understanding biological processes and disease conditions. However, genome-wide identification of gene isoforms is technically challenging due to the high degree of sequence identity among isoforms. Traditional targeted sequencing approach, involving Sanger sequencing of plasmid-cloned PCR products, has low throughput and is very tedious and time-consuming. Next-generation sequencing technologies such as Illumina and 454 achieve high throughput but their short read lengths are a critical barrier to accurate assembly of highly similar gene isoforms, and may result in ambiguities and false joining during sequence assembly. More recently, the third generation sequencer represented by the PacBio platform offers sufficient throughput and long reads covering the full length of typical genes, thus providing a potential to reliably profile gene isoforms. However, the PacBio long reads are error-prone and cannot be effectively analyzed by traditional assembly programs.

resultsWe present a clustering-based analysis pipeline integrated with PacBio sequencing data for profiling highly similar gene isoforms. This approach was first evaluated in comparison to de novo assembly of 454 reads using a benchmark admixture containing 10 known, cloned msg genes encoding the major surface glycoprotein of Pneumocystis jirovecii. All 10 msg isoforms were successfully reconstructed with the expected length (~1.5 kb) and correct sequence by the new approach, while 454 reads could not be correctly assembled using various assembly programs. When using an additional benchmark admixture containing 22 known P. jirovecii msg isoforms, this approach accurately reconstructed all but 4 these isoforms in their full-length (~3 kb); these 4 isoforms were present in low concentrations in the admixture. Finally, when applied to the original clinical sample from which the 22 known msg isoforms were cloned, this approach successfully identified not only all known isoforms accurately (~3 kb each) but also 48 novel isoforms.

conclusionsPacBio sequencing integrated with the clustering-based analysis pipeline achieves high-throughput and high-resolution discrimination of highly similar sequences, and can serve as a new approach for genome-wide characterization of gene isoforms and other highly repetitive sequences.

Indexed as

Bioinformatics analysisGene isoformsMajor surface glycoproteinNGSPacBioPneumocystisRepetitive sequencesUclust

Identifiers

PMID27051465
PMCPMC4820869
OpenAlexW2320370483

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.