ReviewChest2020
A Bioinformatics Crash Course for Interpreting Genomics Data.
Review in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- The potential mechanisms of neuroblastoma in children based on bioinformatics big data.Translational pediatrics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Reductions in genotyping costs and improvements in computational power have made conducting genome-wide association studies (GWAS) standard practice for many complex diseases. GWAS is the assessment of genetic variants across the genome of many individuals to determine which, if any, genetic variants are associated with a specific trait. As with any analysis, there are evolving best practices that should be followed to ensure scientific rigor and reliability in the conclusions. This article presents a brief summary for many of the key bioinformatics considerations when either planning or evaluating GWAS. This review is meant to serve as a guide to those without deep expertise in bioinformatics and GWAS and give them tools to critically evaluate this popular approach to investigating complex diseases. In addition, a checklist is provided that can be used by investigators to evaluate whether a GWAS has appropriately accounted for the many potential sources of bias and generally followed current best practices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.