ReviewHuman genetics2020
Identifying disease-causing mutations in genomes of single patients by computational approaches.
Review in Human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 13 citations in OpenAlex.
- Revisiting retinal and macular degeneration in the genomics era.Nature reviews. Genetics · 2026Review
- Validation of genetic variants from NGS data using deep convolutional neural networks.BMC bioinformatics · 2023Article
- A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity.American journal of human genetics · 2021Article
- Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants.Human genetics · 2021Article
- Rare Diseases of the Oral Cavity, Neck, and Pharynx.Laryngo- rhino- otologie · 2021Review
- Artificial intelligence and the hunt for immunological disorders.Current opinion in allergy and clinical immunology · 2020Review
- The human genetic determinism of life-threatening infectious diseases: genetic heterogeneity and physiological homogeneity?Human genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last decade next generation sequencing (NGS) has been extensively used to identify new pathogenic mutations and genes causing rare genetic diseases. The efficient analyses of NGS data is not trivial and requires a technically and biologically rigorous pipeline that addresses data quality control, accurate variant filtration to minimize false positives and false negatives, and prioritization of the remaining genes based on disease genomics and physiological knowledge. This review provides a pipeline including all these steps, describes popular software for each step of the analysis, and proposes a general framework for the identification of causal mutations and genes in individual patients of rare genetic diseases.
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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.