Search

Unlabelled: Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches. There are often systematic but unpredictable biases from batch-to-batch, so proper randomization of biologically relevant traits across batches is crucial for distinguishing true biological differences from experimental artifacts. When a large number of traits are biologically relevant, as is common for clinical studies of patients with varying sex, age, genotype and medical background, proper randomization can be extremely difficult to prepare by hand, especially because traits may affect biological inferences, such as differential expression, in a combinatorial manner. Here we present ARTS (automated randomization of multiple traits for study design), which aids researchers in study design by automatically optimizing batch assignment for any number of samples, any number of traits and any batch size., Availability and Implementation: ARTS is implemented in Perl and is available at github.com/mmaiensc/ARTS. ARTS is also available in the Galaxy Tool Shed, and can be used at the Galaxy installation hosted by the UIC Center for Research Informatics (CRI) at galaxy.cri.uic.edu., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

It is clinically useful to distinguish between two types of hereditary nephrogenic diabetes insipidus (NDI): a 'pure' type characterized by loss of water only and a complex type characterized by loss of water and ions. Patients with congenital NDI bearing mutations in the vasopressin 2 receptor gene, AVPR2, or in the aquaporin-2 gene, AQP2, have a pure NDI phenotype with loss of water but normal conservation of sodium, potassium, chloride and calcium. Patients with hereditary hypokalemic salt-losing tubulopathies have a complex phenotype with loss of water and ions. They have polyhydramnios, hypercalciuria and hypo- or isosthenuria and were found to bear KCNJ1 (ROMK) and SLC12A1 (NKCC2) mutations. Patients with polyhydramnios, profound polyuria, hyponatremia, hypochloremia, metabolic alkalosis and sensorineural deafness were found to bear BSND mutations. These clinical phenotypes demonstrate the critical importance of the proteins ROMK, NKCC2 and Barttin to transfer NaCl in the medullary interstitium and thereby to generate, together with urea, a hypertonic milieu. This editorial describes two new developments: (i) the genomic information provided by the sequencing of the AQP2 gene is key to the routine care of these patients, and, as in other genetic diseases, reduces health costs and provides psychological benefits to patients and families and (ii) the expression of AQP2 mutants in Xenopus oocytes and in polarized renal tubular cells recapitulates the clinical phenotypes and reveals a continuum from severe loss of function with urinary osmolalities <150 mOsm/kg H2O to milder defects with urine osmolalities >200 mOsm/kg H2O.

Oligonucleotide microarrays have been applied to microbial surveillance and discovery where highly multiplexed assays are required to address a wide range of genetic targets. Although printing density continues to increase, the design of comprehensive microbial probe sets remains a daunting challenge, particularly in virology where rapid sequence evolution and database expansion confound static solutions. Here, we present a strategy for probe design based on protein sequences that is responsive to the unique problems posed in virus detection and discovery. The method uses the Protein Families database (Pfam) and motif finding algorithms to identify oligonucleotide probes in conserved amino acid regions and untranslated sequences. In silico testing using an experimentally derived thermodynamic model indicated near complete coverage of the viral sequence database.

Objective: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method., Methods: An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts., Results: Recall of the system for UMLS coding of all terms was .77 (95% CI.72-.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-.91), and precision of the experts ranged from .61 to .91., Conclusion: Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.