Portable nanopore analytics: are we there yet?

Oliva, Marco Antônio; Milicchio, Franco; King, Kaden; Benson, Grace; Boucher, Christina; Prosperi, Mattia

doi:10.1093/bioinformatics/btaa237

Cited by 25 publications

(16 citation statements)

References 35 publications

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“…Improvement of bioinformatics tools for detection of structural changes will address some of the challenges that were identified in this Project. Furthermore, the introduction of new sequencing technologies such as the Oxford Nanopore platform, which can provide long read sequences (>10 kb), will generate information permitting resolution of complex structural variants and enable inference of genome‐wide methylation profiles [39,40]. WGS will be more cost‐effective as a result, with shorter turn‐around times than those offered by current technologies.…”

Section: Discussionmentioning

confidence: 99%

Sarcoma and the 100,000 Genomes Project: our experience and changes to practice

Prendergast

Strobl

Cross

et al. 2020

The Journal of Pathology CR

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The largest whole genome sequencing (WGS) endeavour involving cancer and rare diseases was initiated in the UK in 2015 and ran for 5 years. Despite its rarity, sarcoma ranked third overall among the number of patients' samples sent for sequencing. Herein, we recount the lessons learned by a specialist sarcoma centre that recruited close to 1000 patients to the project, so that we and others may learn from our experience. WGS data was generated from 597 patients, but samples from the remaining approximately 400 patients were not sequenced. This was largely accounted for by unsuitability due to extensive necrosis, secondary to neoadjuvant radiotherapy or chemotherapy, or being placed in formalin. The number of informative genomes produced was reduced further by a PCR amplification step. We showed that this loss of genomic data could be mitigated by sequencing whole genomes from needle core biopsies. Storage of resection specimens at 4 C for up to 96 h overcame the challenge of freezing tissue out of hours including weekends. Removing access to formalin increased compliance to these storage arrangements. With over 70 different sarcoma subtypes described, WGS was a useful tool for refining diagnoses and identifying novel alterations. Genomes from 350 of the cohort of 597 patients were analysed in this study. Overall, diagnoses were modified for 3% of patients following review of the WGS findings. Continued refinement of the variant-calling bioinformatic pipelines is required as not all alterations were identified when validated against histology and standard of care diagnostic tests. Further research is necessary to evaluate the impact of germline mutations in patients with sarcoma, and sarcomas with evidence of hypermutation. Despite 50% of the WGS exhibiting domain 1 alterations, the number of patients with sarcoma who were eligible for clinical trials remains small, highlighting the need to revaluate clinical trial design.

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Section: Discussionmentioning

confidence: 99%

Sarcoma and the 100,000 Genomes Project: our experience and changes to practice

Prendergast

Strobl

Cross

et al. 2020

The Journal of Pathology CR

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“…Regardless the use of bias-handling, nonlinear ensemble classifiers like the RF can be computationally burdensome to run even after training. Given that bacterial sequencing is being increasingly done in real-time and mobile settings [15], development of fast classifiers is warranted, thus the choice of BLR seems favorable.…”

Section: Discussionmentioning

confidence: 99%

Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions

Prosperi¹,

Marini²,

Boucher³

et al. 2021

Preprint

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Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving pathogen mobility. By contrast, traditional phenotypic (antibiogram) testing cannot easily elucidate such information. Yet development of AMR prediction tools from genotype-phenotype data can be biased, since sampling is non-randomized. Sample provenience, period of collection, and species representation can confound the association of genetic traits with AMR. Thus, prediction models can perform poorly on new data with sampling distribution shifts. In this work -under an explicit set of causal assumptions -we evaluate the effectiveness of propensity-based rebalancing and confounding adjustment on AMR prediction using genotype-phenotype AMR data from the Pathosystems Resource Integration Center (PATRIC). We select bacterial genotypes (encoded as 𝑘-mer signatures, i.e. DNA fragments of length 𝑘), country, year, species, and AMR phenotypes for the tetracycline drug class, preparing test data with recent genomes coming from a single country. We test boosted logistic regression (BLR) and random forests (RF) with/without bias-handling. On 10,936 instances, we find evidence of species, location and year imbalance with respect to the AMR phenotype. The crude versus bias-adjusted change in effect of genetic signatures on AMR varies but only moderately (selecting the top 20,000 out of 40+ million 𝑘-mers). The area under the receiver operating characteristic (AUROC) of the RF (0.95) is comparable to that of BLR (0.94) on both out-of-bag samples from bootstrap and the external test (n=1,085), where AUROCs do not decrease. We observe a 1%-5% gain in AUROC with bias-handling compared to the sole use of

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“…However, some meta‐analyses are presenting positive outcomes 63 . Real‐time, more extensive analyses covering the whole genome are in principle possible using portable devices, but they are still under development 64 …”

Section: Precision Medicine In Clinical Practicementioning

confidence: 99%

“…63 Real-time, more extensive analyses covering the whole genome are in principle possible using portable devices, but they are still under development. 64 Researchers, clinicians, stakeholders, and, most significantly, patients with depression are disoriented in this uncertain landscape. As such, we are presenting a possible viewpoint.…”

Section: Precision Medicine In Clinical Practicementioning

confidence: 99%

Precision medicine in mood disorders

Serretti

2022

PCN Reports

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The choice of the most appropriate psychoactive medication for each of our patients is always a challenge. We can use more than 100 psychoactive drugs in the treatment of mood disorders, which can be prescribed either alone or in combination. Response and tolerability problems are common, and much trial and error is often needed before achieving a satisfactory outcome. Precision medicine is therefore needed for tailoring treatment to optimize outcome. Pharmacological, clinical, and demographic factors are important and informative, but biological factors may further inform and refine prediction. Twenty years after the first reports of gene variants modulating antidepressant response, we are now confronted with the prospect of routine clinical pharmacogenetic applications in the treatment of depression. The scientific community is divided into two camps: those who are enthusiastic and those who are skeptical. Although it appears clear that the benefit of existing tools is still not completely defined, at least in the case of central nervous system gene variants, this is not the case for metabolic gene variants, which is generally accepted. Cumulative scores encompassing many variants across the entire genome will soon predict psychiatric disorder liability and outcome. At present, precision medicine in mood disorders may be implemented using clinical and pharmacokinetic factors. In the near future, a genome‐wide composite genetic score in conjunction with clinical factors within each patient is the most promising approach for developing a more effective way to target treatment for patients suffering from mood disorders.

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Portable nanopore analytics: are we there yet?

Cited by 25 publications

References 35 publications

Sarcoma and the 100,000 Genomes Project: our experience and changes to practice

Sarcoma and the 100,000 Genomes Project: our experience and changes to practice

Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions

Precision medicine in mood disorders

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