SMaSH: A Benchmarking Toolkit for Human Genome Variant Calling

Motivation: Computational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call variants from human sequencing data disagree on many of their predictions, and current methods to evaluate accuracy and computational performance are ad-hoc and incomplete. Agreement on benchmarking variant calling methods would stimulate development of genomic processing tools and facilitate communication among researchers. Results: We propose SMaSH, a benchmarking methodology for evaluating human genome variant calling algorithms. We generate synthetic datasets, organize and interpret a wide range of existing benchmarking data for real genomes, and propose a set of accuracy and computational performance metrics for evaluating variant calling methods on this benchmarking data. Moreover, we illustrate the utility of SMaSH to evaluate the performance of some leading single nucleotide polymorphism (SNP), indel, and structural variant calling algorithms. Availability: We provide free and open access online to the SMaSH toolkit, along with detailed documentation, at smash.cs.berkeley.edu

Low-Rank Subspace Override for Unsupervised Domain Adaptation

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a particular task, such as visual adaptation, require a lot of computational time and data, which is not always guaranteed, have complex parameterization, or expensive optimization procedures. In this work, we present an approach that requires only a well-chosen snapshot of data to find a single domain invariant subspace. The subspace is calculated in closed form and overrides domain structures, which makes it fast and stable in parameterization. By employing low-rank techniques, we emphasize on descriptive characteristics of data. The presented idea is evaluated on various domain adaptation tasks such as text and image classification against state of the art domain adaptation approaches and achieves remarkable performance across all tasks

Towards the reconstruction of the genome-scale metabolic model of Lactobacillus acidophilus La-14

Lactobacillus acidophilus is a probiotic lactic acid bacterium used in food and dietary supplements for many years. However, despite its importance for industrial development and recognized health-promoting effects, no genome-scale metabolic model has been reported. A GSM model for L. acidophilus La-14 was developed, accounting 494 genes and 783 reactions. A genome annotation was performed to identify the metabolic potential of the bacterium. The biomass composition was determined based on information available in literature and previously published models. The model was validated by comparing in silico simulations with experimental data, regarding the aerobic and anaerobic growth. The reconstruction of the metabolic model has confirmed the fastidious requirements of L. acidophilus for amino acids, fatty acids, and vitamins. This model can be used for a better understanding of the metabolism of this bacterium and identification of industrially desirable compounds.This study was performed under the scope of the project “BIODATA.PT – Portuguese Biological Data Network” (ref. LISBOA-01-0145-FEDER-022231), funded by FCT/MCTES, through national funds of PIDDAC, Fundo Europeu de Desenvolvimento Regional (FEDER), Programa Operacional de Competitividade e Internacionalização (POCI) and Programa Operacional Regional de Lisboa (Lisboa 2020).info:eu-repo/semantics/publishedVersio

Outcome of Total Parathyroidectomy and Autotransplantation as Treatment of Secondary and Tertiary Hyperparathyroidism in Children and Adults

Contains fulltext : 89902.pdf (publisher's version ) (Closed access)BACKGROUND: Treatment safety and effectiveness of total parathyroidectomy and autotransplantation for secondary and tertiary hyperparathyroidism have been extensively proven in adults; the evidence for children, however, is scarce. Children and adolescents cannot simply be seen as young adults in the case of chronic kidney disease and hyperparathyroidism. The aim of this retrospective study was therefore, to evaluate whether parathyroidectomy with forearm autograft is as effective and safe in children and adolescents as in adults. METHODS: A group of 64 adults and 8 children and adolescents treated for secondary or tertiary hyperparathyroidism were retrieved from our database. The outcomes were compared on patient demographics, operation results, and blood parameters consisting of parathyroid hormone (PTH) and calcium levels. Our results were compared with all currently available articles on parathyroidectomy in children with secondary or tertiary hyperparathyroidism (n = 11). RESULTS: For adults, preoperative mean serum calcium was 2.67 +/- 0.29 mmol/l and mean parathyroid hormone (PTH) level was 120 +/- 86 pmol/l. For children, preoperative mean serum calcium was 2.62 +/- 0.20 mmol/l and mean parathyroid hormone (PTH) level was 80 +/- 38 pmol/l. Postoperative calcium and parathyroid hormone levels for adults dropped to 2.39 +/- 0.23 mmol/l and 30 +/- 53 pmol/l, respectively. Postoperative calcium and parathyroid hormone levels for children dropped to 2.41 +/- 0.16 mmol/l and 26 +/- 33 pmol/l, respectively. The effectiveness of parathyroidectomy with autotransplantation was 75% in children and 72% in adults. Thus, effectiveness did not differ significantly between children and adults. CONCLUSIONS: Combining the results of our own study with a literature review on pediatric parathyroidectomy, we conclude that parathyroidectomy and forearm autograft is as effective a treatment for secondary and tertiary hyperparathyroidism in children and adolescents as it is in adults.1 mei 201