The need for speed in accurate and whole genome data analysis.
The challenge: To assess sensitivity/recall, precision, computation time, and disk footprint of four bioinformatic pipelines by performed alignment and variant calling for the reference short-read WGS data of NA12878 and the Ashkenazim trio.
The outcome: Genalice Map has achieved ultra- rapid speed and superior low disk footprint with BWA/ GATK-like sensitivity, thus enabling efficient (re)analyses of ever-increasing amounts of WGS data.
Population Calling module to boost variant calling in trio-sequencing.
The challenge: In family trio-sequencing it is essential to find a perfect balance between genotyping errors and variant calling sensitivity.
The solution: Genalice Population gives you the complete flexibility to boost high quality variants in your trio-sequencing data.
Chances and challenges of high-throughput sequencing (HTS) of Mendelian disorders
Roeland Van Ham (KeyGene) talks about his experience with Genalice Map in the AgriGenomics setting.
“Through the technology implemented in Genalice Map, there are specular reductions in the footprint which is a huge problem and a huge challenge…”
Comprehensive real life workflow for rapid diagnosis of critically ill children.
The challenge: Rare genetic conditions are frequent risk factors for, or direct causes of, organ failure requiring paediatric intensive care unit (PICU) support. There is therefore a need to provide a rapid genetic diagnosis to inform clinical management. To date, Whole Genome Sequencing (WGS) approaches have proved successful in diagnosing a proportion of children with rare diseases, but results may take months to report or require the use of equipment and practices not compatible with a clinical diagnostic setting.
The outcome: Using the Genalice system, the researchers were able to significantly reduce the processing time from raw sequence data (FASTQ format) to text files containing lists of variants from the reference sequence (VCF files) from up to 144 hours (using a standard GATK pipeline) to 60 minutes per trio. To ensure the increased processing speed did not adversely affect the accuracy of variant calls the researchers processed the Genome in a Bottle (GIAB) reference sample under exactly the same conditions as our RaPS samples.
Pioneering an efficient migration of 13,000 whole genomes.
The challenge: Migrating a dataset of 13,000 whole genomes focused on the identification of the genetic basis of rare diseases with emphasis on cardiovascular disorders to the latest human reference standard GRCh38.
The solution: Analysis of 13,000 whole genomes shows that GRCh38 delivers better coverage and significantly more variants without detriment to quality. Alignment and variant calling for GRCh38 was completed in 20 days using 10 compute nodes.
Variant calling quality enhancement within a large breeding population.
The challenge: The massive increase in data production in agrigenomics creates growing computational and storage challenges in a time and budget constraint environment.
The solution: By implementing Genalice Population, KeyGene was able to reduce processing times by over 100 fold, whereas former hardware requirements of a cluster >1,000 cores decreased to a single machine with 12 cores. The module further increased the accuracy and efficiency for SNP variation of a large breeding population of highly homozygous individuals, derived from complex crosses between multiple parental lines.
A comparison of the GATK and Genalice Map joint genotyping callers on a Swedish population of WGS data samples.
The challenge: Ambitious whole human genome sequencing initiatives like the SweGen project need population-wide variant call software that can reliably and quickly re-analyse hundreds or even thousands of WGS sequencing runs. Using two joint variant callers (GATK and Genalice Map) with a random subset of the SweGen data (100 samples) the researchers calculated several statistical descriptors to compare the two call-sets and their variant callers.
The outcome: The researchers conclude that the two methods give a highly comparable variant set, but differ substantially in time and resources when it comes to processing. They further see additional and potentially interesting variants called by Genalice in low mappable regions of the human genome.
High quality variants from 1,000 Drosophila genomes in a day.
The challenge: Mapping and variant calling of large full genome cohorts are time consuming and leave little time to actually analyse variants.
The solution: Realise a fast data processing of more than 1,000 Drosophila genomes in a single day. The speed-up realised with Genalice Population allows scientists to mine data and find the hidden gems in the variant calls.
Interview with Hywel Williams (UCL Great Ormond Street Institute of Child Health).
“We incorporated one of the (Genalice) servers into our cluster, which allowed us to drastically reduce the sample processing time, down to an average of 20 minutes per sample, meaning we could process a full trio in just one hour. Work performed by us ‘in house’ and by others has shown that this time reduction does not come at the price of accuracy or sensitivity.”