Evidence map›Paper›PMID 35136669›Full record

ArticleJournal of pathology informatics2022

SCHOOL: Software for Clinical Health in Oncology for Omics Laboratories.

Chelsea K Raulerson, Erika C Villa, Jeremy A Mathews, Benjamin Wakeland, Yan Xu, Jeffrey Gagan, Brandi L Cantarel

Open access · goldAbstract read
In one paragraph

Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 35% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  6. Endometrial polyps are non-neoplastic but harbor epithelial mutations in endometrial cancer drivers at low allelic frequencies.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022
    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

7 authors at 1 institution in 1 country.

Chelsea K RaulersonBioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Erika C VillaBioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Jeremy A MathewsBioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Benjamin WakelandBioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Yan XuDepartment of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Jeffrey GaganDepartment of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Brandi L CantarelBioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
The University of Texas Southwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bioinformatics analysis is a key element in the development of in-house next-generation sequencing assays for tumor genetic profiling that can include both tumor DNA and RNA with comparisons to matched-normal DNA in select cases. Bioinformatics analysis encompasses a computationally heavy component that requires a high-performance computing component and an assay-dependent quality assessment, aggregation, and data cleaning component. Although there are free, open-source solutions and fee-for-use commercial services for the computationally heavy component, these solutions and services can lack the options commonly utilized in increasingly complex genomic assays. Additionally, the cost to purchase commercial solutions or implement and maintain open-source solutions can be out of reach for many small clinical laboratories. Here, we present Software for Clinical Health in Oncology for Omics Laboratories (SCHOOL), a collection of genomics analysis workflows that (i) can be easily installed on any platform; (ii) run on the cloud with a user-friendly interface; and (iii) include the detection of single nucleotide variants, insertions/deletions, copy number variants (CNVs), and translocations from RNA and DNA sequencing. These workflows contain elements for customization based on target panel and assay design, including somatic mutational analysis with a matched-normal, microsatellite stability analysis, and CNV analysis with a single nucleotide polymorphism backbone. All of the features of SCHOOL have been designed to run on any computer system, where software dependencies have been containerized. SCHOOL has been built into apps with workflows that can be run on a cloud platform such as DNANexus using their point-and-click graphical interface, which could be automated for high-throughput laboratories.

Indexed as

BioinformaticscancerNGS

Identifiers

PMID35136669
PMCPMC8794024
OpenAlexW4205466482

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.