.2019 Jul 19:8:1135.

doi: 10.12688/f1000research.19140.1. eCollection 2019.

Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich's ataxia, and other rare iron-related diseases

Gloria C Ferreira^#¹, Jenna Oberstaller^#², Renée Fonseca^#³, Thomas E Keller^#⁴, Swamy Rakesh Adapa^#², Justin Gibbons^#³, Chengqi Wang^#², Xiaoming Liu^#², Chang Li^#², Minh Pham^#⁵, Guy W Dayhoff Ii^#⁶, Linh M Duong^{7 8}, Luis Tañón Reyes⁹, Luciano Enrique Laratelli⁶, Douglas Franz⁶, Segun Fatumo¹⁰, Atm Golam Bari¹¹, Audrey Freischel⁸, Lindsey Fiedler⁷, Omkar Dokur¹¹, Krishna Sharma¹², Deborah Cragun², Ben Busby^#¹³, Rays H Y Jiang^#²

Affiliations

¹ Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, MDC 7, Tampa, FL, 33612, USA.
² Global and Planetary Health, College of Public Health, University of South Florida, USF Genomics Program, 3720 Spectrum Blvd, Tampa, FL, 33612, USA.
³ Morsani College of Medicine, University of South Florida, 12901 Bruce B Downs Blvd, Tampa, FL, 33612, USA.
⁴ University of South Florida, USF Genomics Program, 3720 Spectrum Blvd, Tampa, FL, 33612, USA.
⁵ Center for Urban Transportation Research, University of South Florida, 4202 E. Fowler Avenue, CUT100, Tampa, FL, 33620, USA.
⁶ Department of Chemistry, University of South Florida, 4202 E. Fowler Avenue, CHE 205, Tampa, FL, 33620-5250, USA.
⁷ College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd., MDC 56, Tampa, FL, 33612, USA.
⁸ Moffitt Cancer Center, Tampa, FL, 33612, USA.
⁹ Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, 4202 East Fowler Ave, ISA 2015 Tampa, FL, 33620, USA.
¹⁰ MRC/UVRI and LSHTM (Uganda Research Unit), Entebbe, Uganda.
¹¹ Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
¹² University of South Florida, Tampa, FL, 33620, USA.
¹³ National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, 20894-6075, USA.

^# Contributed equally.

PMID:31824661
PMCID: PMC6894363
DOI: 10.12688/f1000research.19140.1

Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich's ataxia, and other rare iron-related diseases

Gloria C Ferreira et al. F1000Res.2019.

.2019 Jul 19:8:1135.

doi: 10.12688/f1000research.19140.1. eCollection 2019.

Authors

Affiliations

¹ Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, MDC 7, Tampa, FL, 33612, USA.
² Global and Planetary Health, College of Public Health, University of South Florida, USF Genomics Program, 3720 Spectrum Blvd, Tampa, FL, 33612, USA.
³ Morsani College of Medicine, University of South Florida, 12901 Bruce B Downs Blvd, Tampa, FL, 33612, USA.
⁴ University of South Florida, USF Genomics Program, 3720 Spectrum Blvd, Tampa, FL, 33612, USA.
⁵ Center for Urban Transportation Research, University of South Florida, 4202 E. Fowler Avenue, CUT100, Tampa, FL, 33620, USA.
⁶ Department of Chemistry, University of South Florida, 4202 E. Fowler Avenue, CHE 205, Tampa, FL, 33620-5250, USA.
⁷ College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd., MDC 56, Tampa, FL, 33612, USA.
⁸ Moffitt Cancer Center, Tampa, FL, 33612, USA.
⁹ Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, 4202 East Fowler Ave, ISA 2015 Tampa, FL, 33620, USA.
¹⁰ MRC/UVRI and LSHTM (Uganda Research Unit), Entebbe, Uganda.
¹¹ Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
¹² University of South Florida, Tampa, FL, 33620, USA.
¹³ National Library of Medicine, 8600 Rockville Pike, Bethesda, MD, 20894-6075, USA.

^# Contributed equally.

PMID:31824661
PMCID: PMC6894363
DOI: 10.12688/f1000research.19140.1

Abstract

Background: Basic and clinical scientific research at the University of South Florida (USF) have intersected to support a multi-faceted approach around a common focus on rare iron-related diseases. We proposed a modified version of the National Center for Biotechnology Information's (NCBI) Hackathon-model to take full advantage of local expertise in building "Iron Hack", a rare disease-focused hackathon. As the collaborative, problem-solving nature of hackathons tends to attract participants of highly-diverse backgrounds, organizers facilitated a symposium on rare iron-related diseases, specifically porphyrias and Friedreich's ataxia, pitched at general audiences.Methods: The hackathon was structured to begin each day with presentations by expert clinicians, genetic counselors, researchers focused on molecular and cellular biology, public health/global health, genetics/genomics, computational biology, bioinformatics, biomolecular science, bioengineering, and computer science, as well as guest speakers from the American Porphyria Foundation (APF) and Friedreich's Ataxia Research Alliance (FARA) to inform participants as to the human impact of these diseases.Results: As a result of this hackathon, we developed resources that are relevant not only to these specific disease-models, but also to other rare diseases and general bioinformatics problems. Within two and a half days, "Iron Hack" participants successfully built collaborative projects to visualize data, build databases, improve rare disease diagnosis, and study rare-disease inheritance.Conclusions: The purpose of this manuscript is to demonstrate the utility of a hackathon model to generate prototypes of generalizable tools for a given disease and train clinicians and data scientists to interact more effectively.

Keywords: Ataxia; Bioinformatics; Clinical Informatics; Data Science; Friedreich’s Ataxia; Hackathon; Porphyria; Rare Diseases.

PubMed Disclaimer

Conflict of interest statement

No competing interests were disclosed.

Figures

**Figure 1.. UPWARD - Uniting People Working Against Rare Disease.**
UPWARD opens with a web interface designed to clearly communicate research and advocacy goals to the public, request consent and gather data in a HIPPA-compliant manner.

**Figure 2.. Overview of the Variants Discovery pipeline to report possible pathogenic variants associated with Mendelian diseases.**
Abbreviations: dbNSFP, database for nonsynonymous SNPs’ functional predictions; WGSA, whole genome sequencing annotator; HGMD, Human gene mutation database; eQTL, expression quantitative trait loci.

**Figure 3.. Flowchart for Massiveseq Methodology.**
The pipeline takes metadata from the Sequence Read Archive (SRA) and parses it for quality control (QC). The primary work takes place in a custom snakemake script that aligns sequences with Hisat2 and then quantifies transcripts with Stringtie in a parallelized fashion across available machines and cores.

**Figure 4.. The work flow chart for identifying abnormal genes based on RNA-Seq.**
After RNA-Seq is performed on a patient sample, the program searches the Genotype-Tissue Expression Project (GTEx) database for RNA-Seq data from the specific tissue potentially associated with the disease. Three methods are used for RNA-Seq normalization (Fragments per kilobase of transcript per million mapped reads (FPKM), transcripts per million mapped reads (TPM) and Differential gene expression analysis based on the negative binomial distribution (as implemented in DESeq)), and the data were fit to a Gaussian mixture model to remove noise within samples. The differentially expressed genes in the patient sample are finally captured by using the R program DESeq.

**Figure 5.. Phenotype-to-Genotype Mapping: Assessing combinatorial variant-contribution to disease phenotypes general workflow.**
Input data are variant-call files in .vcf format collected from patient samples. The feature-selection module collects all available annotation information for each identified variant, then narrows down to variants most likely to be associated with the phenotype based on user-specified parameters. These feature-selected variants are then analyzed for combinatorial contribution to the disease using the tools in the analysis module. The output of the analysis modules are tables and graphs that summarize the results.

**Figure 6.. Expression change of the UROS gene caused by eQTL SNP No. 1 across all tissue types in the Genotype-Tissue Expression Project (GTEx).**
There is significant down-regulation of UROS gene associated with this variant in all tissues (except ovary). NES: normalized effect size.

**Figure 7.. Expression change of the UROS gene caused by eQTL SNP No. 2 across all tissue types in the Genotype-Tissue Expression Project (GTEx).**
There is significant down-regulation of UROS gene associated with this variant in all tissues (except ovary). NES: normalized effect size.

**Figure 8.. Significance of up-regulated genes from metaseq analysis; red bar denotes .05 significance cutoff.**
Distribution of significance in downregulated genes from metaseq analysis; no genes were significant at 0.05 threshold.

Figure 9.. n = 20 genes are sampled here to compare different normalization method: Fragments per kilobase of transcript per million mapped reads (FPKM), transcripts per million mapped reads (TPM) and Differential gene expression analysis based on the negative binomial distribution (DESeq).

**Figure 10.. The Gaussian mixture model is implemented here to filter out noise.**
Hist plot shows the distribution of gene expression level for gene ‘CELSR2’ in 1671 different brain RNA-Seq samples. The Gaussian mixture model is fitted by the EM algorithm and the noise is filtered out by posterior probability bigger than 0.5.

**Figure 11.. Differential gene expression analysis based on the negative binomial distribution (DESeq) is used here to find differential expression genes between patient and database.**
A) Scatter plot shows significant differential genes (green dot, p-adj < 0.01).B) Boxplot shows top 10 abnormal genes in simulation compared with data from database.

See this image and copyright information in PMC

References

1. Cook A, Giunti P: Friedreich’s ataxia: clinical features, pathogenesis and management.Br Med Bull. 2017;124(1):19–30. 10.1093/bmb/ldx034 - DOI - PMC - PubMed
1. Balwani M, Desnick RJ: The porphyrias: advances in diagnosis and treatment.Blood. 2012;120(23):4496–4504. 10.1182/blood-2012-05-423186 - DOI - PMC - PubMed
1. Karim Z, Lyoumi S, Nicolas G, et al. : Porphyrias: A 2015 update.Clin Res Hepatol Gastroenterol. 2015;39(4):412–425. 10.1016/j.clinre.2015.05.009 - DOI - PubMed
1. Richter T, Nestler-Parr S, Babela R, et al. : Rare Disease Terminology and Definitions-A Systematic Global Review: Report of the ISPOR Rare Disease Special Interest Group.Value Health. 2015;18(6):906–914. 10.1016/j.jval.2015.05.008 - DOI - PubMed
1. Bissell DM, Anderson KE, Bonkovsky HL: Porphyria.N Engl J Med. 2017;377(9):862–872. 10.1056/NEJMra1608634 - DOI - PubMed

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Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich's ataxia, and other rare iron-related diseases

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Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich's ataxia, and other rare iron-related diseases

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

LinkOut - more resources

Full Text Sources

Medical

Research Materials