Evidence map›Paper›PMID 36379355›Full record

ReviewJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2023

A Clinician's Guide to Bioinformatics for Next-Generation Sequencing.

Nicholas Bradley Larson, Ann L Oberg, Alex A Adjei, Liguo Wang

Abstract readReview
In one paragraph

Review in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Article
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  5. [Application of puncture tissue rinse solution in the detection ofZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
    Article
  6. Article
  7. Cell-Free DNA-Based Theranostics for Inflammatory Disorders.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  8. Translational lung cancer research · 2026
    Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Cerebrospinal Fluid-Derived Genomic Alterations Tracking Glioma.Journal of molecular neuroscience : MN · 2025
    Article
  19. Article
  20. 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

4 authors.

Nicholas Bradley LarsonDivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, Minnesota. Electronic address: Larson.Nicholas@mayo.edu.
Ann L ObergDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Alex A AdjeiTaussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio.
Liguo WangDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI AKHILESH PANDEY · 1985 to 2026
$151.3M
Statistics CoreU10CA180882 · NCI · MAYO CLINIC ROCHESTER · PI Sumithra Jay Mandrekar · 2014 to 2026
$115.0M
Use of microfluidic tumor cultures to enable clinical trials of therapies for ovarian cancerP50CA136393 · NCI · MAYO CLINIC ROCHESTER · PI ELLEN L. GOODE · 2009 to 2026
$37.0M
Tissue CoreP50CA102701 · NCI · MAYO CLINIC ROCHESTER · PI MUKHOPADHYAY, DEBABRATA · 2004 to 2018
$32.7M
Role of Vav1 protooncogene in pancreatic cancerP20CA102701 · NCI · MAYO CLINIC COLL OF MEDICINE, ROCHESTER · PI PETERSEN, GLORIA M. · 2003 to 2003
$1.5M
NCI NIH HHS P20 CA102701NCI NIH HHS P30 CA015083NCI NIH HHS P50 CA102701NCI NIH HHS P50 CA136393NCI NIH HHS U10 CA180882
6 · The paper itself

Abstract

Next-generation sequencing (NGS) technologies are high-throughput methods for DNA sequencing and have become a widely adopted tool in cancer research. The sheer amount and variety of data generated by NGS assays require sophisticated computational methods and bioinformatics expertise. In this review, we provide background details of NGS technology and basic bioinformatics concepts for the clinician investigator interested in cancer research applications, with a focus on DNA-based approaches. We introduce the general principles of presequencing library preparation, postsequencing alignment, and variant calling. We also highlight the common variant annotations and NGS applications for other molecular data types. Finally, we briefly discuss the revealed utility of NGS methods in NSCLC research and study design considerations for research studies that aim to leverage NGS technologies for clinical care.

Indexed as

Lung NeoplasmsComputational BiologyDNAHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNADNABioinformaticsDNANext-generation sequencingReview

Identifiers

PMID36379355
PMCPMC9870988

What OpenQuestion holds

Textmetadata
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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.