elPrep 4: A multithreaded framework for sequence analysis.

We present elPrep 4, a reimplementation from scratch of the elPrep framework for processing sequence alignment map files in the Go programming language. elPrep 4 includes multiple new features allowing us to process all of the preparation steps defined by the GATK Best Practice pipelines for variant calling. This includes new and improved functionality for sorting, (optical) duplicate marking, base quality score recalibration, BED and VCF parsing, and various filtering options. The implementations of these options in elPrep 4 faithfully reproduce the outcomes of their counterparts in GATK 4, SAMtools, and Picard, even though the underlying algorithms are redesigned to take advantage of elPrep's parallel execution framework to vastly improve the runtime and resource use compared to these tools. Our benchmarks show that elPrep executes the preparation steps of the GATK Best Practices up to 13x faster on WES data, and up to 7.4x faster for WGS data compared to running the same pipeline with GATK 4, while utilizing fewer compute resources.

Multi-gene assays: effect on chemotherapy use, toxicity and cost in estrogen receptor-positive early stage breast cancer.

AIM: To assess multi-gene assay (MGA) effects on chemotherapy use, toxicities, recurrences, and costs in estrogen receptor-positive early breast cancer. METHODS: Meta-analysis performed using data from public databases. Results: Studies included 12,202 women. Relative to no testing, chemotherapy use was higher with 12-gene and 70-gene and lower with PAM50 (commercial) and 21-gene MGAs. Overall, 1643 distant recurrences occurred with no testing, declining by 231 (21-gene), 121 (70-gene), 54 (12-gene) and 94 (PAM50); only the 21-gene assay resulted in no risk of increasing the number of distant recurrences. Relative to 'no testing', total cost of care declined only with 21-gene MGA. CONCLUSION: MGAs differ in chemotherapy use and related outcomes for women with estrogen receptor-positive early breast cancer.

Reducing animal sequencing redundancy by preferentially selecting animals with low-frequency haplotypes.

Many studies leverage targeted whole-genome sequencing (WGS) experiments to identify rare and causal variants within populations. As a natural consequence of their experimental design, many of these surveys tend to sequence redundant haplotype segments due to their high frequency in the base population, and the variants discovered within sequencing data are difficult to phase. We propose a new algorithm, called inverse weight selection (IWS), that preferentially selects individuals based on the cumulative presence of rare frequency haplotypes to maximize the efficiency of WGS surveys. To test the efficacy of this method, we used genotype data from 112,113 registered Holstein bulls derived from the US national dairy database. We demonstrate that IWS is at least 6.8% more efficient than previously published methods in selecting the least number of individuals required to sequence all haplotype segments ≥4% frequency in the US Holstein population. We also suggest that future surveys focus on sequencing homozygous haplotype segments as a first pass to achieve a 50% reduction in cost with an added benefit of phasing variant calls efficiently. Together, this new selection algorithm and experimental design suggestion significantly reduce the overall cost of variant discovery through WGS experiments, making surveys for causal variants influencing disease and production even more efficient.

The current use and attitudes towards tumor genome sequencing in breast cancer.

There is increasing availability of technologies that can interrogate the genomic landscape of an individual tumor; however, their impact on daily practice remains uncertain. We conducted a 28-item survey to investigate the current attitudes towards the integration of tumor genome sequencing in breast cancer management. A link to the survey was communicated via newsletters of several oncological societies, and dedicated mailing by academic research groups. Multivariable logistic regression modeling was carried out to determine the relationship between predictors and outcomes. 215 physicians participated to the survey. The majority were medical oncologists (88%), practicing in Europe (70%) and working in academic institutions (66%). Tumor genome sequencing was requested by 82 participants (38%), of whom 21% reported low confidence in their genomic knowledge, and 56% considered tumor genome sequencing to be poorly accessible. In multivariable analysis, having time allocated to research (OR 3.37, 95% CI 1.84-6.15, p < 0.0001), working in Asia (OR 5.76, 95% CI 1.57 - 21.15, p = 0.01) and having institutional guidelines for molecular sequencing (OR 2.09, 95% 0.99-4.42, p = 0.05) were associated with a higher probability of use. In conclusion, our survey indicates that tumor genome sequencing is sometimes used, albeit not widely, in guiding management of breast cancer patients.

Genetics: Clinical exome sequencing in neurology practice.

Clinical exome sequencing (CES) is becoming a standard tool for molecular diagnosis of genetic disorders, with a diagnostic yield of approximately 25%. New studies demonstrate the favourable diagnostic yield of CES for both early-onset and adult-onset neurogenetic disorders.These studies demonstrate the strengths, limitations and potential of CES in neurology practice.

[The place of functional genomics in oncological research]. / A funkcionális genomikai eszköztár szerepe az onkológiai kutatásokban.

The 1000 genomes project changed the way how we see the human genome. The rapid development of the deep sequencing technologies is raising several practical questions, and the way how we answer these questions will affect deeply the future of the oncological reseach in Hungary. In our manuscript we give a short overview of the results of the 1000 genomes project and we present the place of the functional genomic investigations between other genomic tools. Based on the recent development in the field we summarize the challenges that have to be addressed in the next couple of years.

Whole genome sequencing and evolutionary analysis of human respiratory syncytial virus A and B from Milwaukee, WI 1998-2010.

BACKGROUND: Respiratory Syncytial Virus (RSV) is the leading cause of lower respiratory-tract infections in infants and young children worldwide. Despite this, only six complete genome sequences of original strains have been previously published, the most recent of which dates back 35 and 26 years for RSV group A and group B respectively. METHODOLOGY/PRINCIPAL FINDINGS: We present a semi-automated sequencing method allowing for the sequencing of four RSV whole genomes simultaneously. We were able to sequence the complete coding sequences of 13 RSV A and 4 RSV B strains from Milwaukee collected from 1998-2010. Another 12 RSV A and 5 RSV B strains sequenced in this study cover the majority of the genome. All RSV A and RSV B sequences were analyzed by neighbor-joining, maximum parsimony and Bayesian phylogeny methods. Genetic diversity was high among RSV A viruses in Milwaukee including the circulation of multiple genotypes (GA1, GA2, GA5, GA7) with GA2 persisting throughout the 13 years of the study. However, RSV B genomes showed little variation with all belonging to the BA genotype. For RSV A, the same evolutionary patterns and clades were seen consistently across the whole genome including all intergenic, coding, and non-coding regions sequences. CONCLUSIONS/SIGNIFICANCE: The sequencing strategy presented in this work allows for RSV A and B genomes to be sequenced simultaneously in two working days and with a low cost. We have significantly increased the amount of genomic data that is available for both RSV A and B, providing the basic molecular characteristics of RSV strains circulating in Milwaukee over the last 13 years. This information can be used for comparative analysis with strains circulating in other communities around the world which should also help with the development of new strategies for control of RSV, specifically vaccine development and improvement of RSV diagnostics.

Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

BACKGROUND: The widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. RESULTS: We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. CONCLUSIONS: Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

The advent of personal genome sequencing.

Rapid technological advances are decreasing DNA sequencing costs and making it practical to undertake complete human genome sequencing on a large scale for the first time. Disease studies that involve sequencing hundreds of patient genomes are underway. The all-inclusive sequencing price per genome is expected to reach $1000 over the next few years and will likely decline further in the following years. This dramatic price decline will herald widespread personal genome sequencing and lead to significant improvements in human health and reduced health care costs. Key to realizing these benefits will be medical genomics' and systems biology's success in providing increasing contextual interpretation of biological and medical effects of the detected sequence variants in a genome. Given the substantial potential benefits and the manageability of the health and discrimination risks involved with the possible misuse of this information, we propose that governments and insurance companies support or even require personal genome sequencing. Critical to the widespread acceptance of personal genome sequencing, however, will be the need to educate physicians and the public about the realistic benefits and risks of such an analysis to prevent overinterpretation and misuse of this valuable information.