An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research.   The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at  https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research.   The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at  https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

The clinical and genetic characteristics of permanent neonatal diabetes (PNDM) in the state of Qatar

Background

Neonatal diabetes mellitus (NDM) is a rare condition that occurs within the first six months of life. Permanent NDM (PNDM) is caused by mutations in specific genes that are known for their expression at early and/or late stages of pancreatic beta‐ cell development, and are either involved in beta‐cell survival, insulin processing, regulation, and release. The native population in Qatar continues to practice consanguineous marriages that lead to a high level of homozygosity. To our knowledge, there is no previous report on the genomics of NDM among the Qatari population. The aims of the current study are to identify patients with NDM diagnosed between 2001 and 2016, and examine their clinical and genetic characteristics.

Methods

To calculate the incidence of PNDM, all patients with PNDM diagnosed between 2001 and 2016 were compared to the total number of live births over the 16‐year‐period. Whole Genome Sequencing (WGS) was used to investigate the genetic etiology in the PNDM cohort.

Results

PNDM was diagnosed in nine (n = 9) patients with an estimated incidence rate of 1:22,938 live births among the indigenous Qatari. Seven different mutations in six genes (PTF1A, GCK, SLC2A2, EIF2AK3, INS, and HNF1B) were identified. In the majority of cases, the genetic etiology was part of a previously identified autosomal recessive disorder. Two novel de novo mutations were identified in INS and HNF1B.

Conclusion

Qatar has the second highest reported incidence of PNDM worldwide. A majority of PNDM cases present as rare familial autosomal recessive disorders. Pancreas associated transcription factor 1a (PTF1A) enhancer deletions are the most common cause of PNDM in Qatar, with only a few previous cases reported in the literature.

Accelerating next generation sequencing data analysis with system level optimizations

Next generation sequencing (NGS) data analysis is highly compute intensive. In-memory computing, vectorization, bulk data transfer, CPU frequency scaling are some of the hardware features in the modern computing architectures. To get the best execution time and utilize these hardware features, it is necessary to tune the system level parameters before running the application. We studied the GATK-HaplotypeCaller which is part of common NGS workflows, that consume more than 43% of the total execution time. Multiple GATK 3.x versions were benchmarked and the execution time of HaplotypeCaller was optimized by various system level parameters which included: (i) tuning the parallel garbage collection and kernel shared memory to simulate in-memory computing, (ii) architecture-specific tuning in the PairHMM library for vectorization, (iii) including Java 1.8 features through GATK source code compilation and building a runtime environment for parallel sorting and bulk data transfer (iv) the default 'on-demand' mode of CPU frequency is over-clocked by using 'performance-mode' to accelerate the Java multi-threads. As a result, the HaplotypeCaller execution time was reduced by 82.66% in GATK 3.3 and 42.61% in GATK 3.7. Overall, the execution time of NGS pipeline was reduced to 70.60% and 34.14% for GATK 3.3 and GATK 3.7 respectively.

Transcript annotation prioritization and screening system (TrAPSS) for mutation screening

When searching for disease-causing mutations with polymerase chain reaction (PCR)-based methods, candidate genes are usually screened in their entirety, exon by exon. Genomic resources (i.e. www.ncbi.nih.gov, www.ensembl.org, and genome.ucsc.edu) largely support this paradigm for mutation screening by making it easy to view and access sequence data associated with genes in their genomic context. However, the administrative burden of conducting mutation screening in potentially hundreds of genes and thousands of exons in thousands of patients is significant, even with the use of public genome resources. For example, the manual design of oligonucleotide primers for all exons of the 10 Leber's congenital amaurosis (LCA) genes (149 exons) represents a significant information management challenge. The Transcript Annotation Prioritization and Screening System (TrAPSS) is designed to accelerate mutation screening by (1) providing a gene-based local cache of candidate disease genes in a genomic context, (2) automating tasks associated with optimizing candidate disease gene screening and information management, and (3) providing the implementation of an algorithmic technique to utilize large amounts of heterogeneous genome annotation (e.g. conserved protein functional domains) so as to prioritize candidate genes.