Chitayat syndrome: hyperphalangism, characteristic facies, hallux valgus and bronchomalacia results from a recurrent c.266A>G p.(Tyr89Cys) variant in the ERF gene.

BACKGROUND: In 1993, Chitayat et al., reported a newborn with hyperphalangism, facial anomalies, and bronchomalacia. We identified three additional families with similar findings. Features include bilateral accessory phalanx resulting in shortened index fingers; hallux valgus; distinctive face; respiratory compromise. OBJECTIVES: To identify the genetic aetiology of Chitayat syndrome and identify a unifying cause for this specific form of hyperphalangism. METHODS: Through ongoing collaboration, we had collected patients with strikingly-similar phenotype. Trio-based exome sequencing was first performed in Patient 2 through Deciphering Developmental Disorders study. Proband-only exome sequencing had previously been independently performed in Patient 4. Following identification of a candidate gene variant in Patient 2, the same variant was subsequently confirmed from exome data in Patient 4. Sanger sequencing was used to validate this variant in Patients 1, 3; confirm paternal inheritance in Patient 5. RESULTS: A recurrent, novel variant NM_006494.2:c.266A>G p.(Tyr89Cys) in ERF was identified in five affected individuals: de novo (patient 1, 2 and 3) and inherited from an affected father (patient 4 and 5). p.Tyr89Cys is an aromatic polar neutral to polar neutral amino acid substitution, at a highly conserved position and lies within the functionally important ETS-domain of the protein. The recurrent ERF c.266A>C p.(Tyr89Cys) variant causes Chitayat syndrome. DISCUSSION: ERF variants have previously been associated with complex craniosynostosis. In contrast, none of the patients with the c.266A>G p.(Tyr89Cys) variant have craniosynostosis. CONCLUSIONS: We report the molecular aetiology of Chitayat syndrome and discuss potential mechanisms for this distinctive phenotype associated with the p.Tyr89Cys substitution in ERF.

The UCSC genome browser database: update 2007.

The University of California, Santa Cruz Genome Browser Database contains, as of September 2006, sequence and annotation data for the genomes of 13 vertebrate and 19 invertebrate species. The Genome Browser displays a wide variety of annotations at all scales from the single nucleotide level up to a full chromosome and includes assembly data, genes and gene predictions, mRNA and EST alignments, and comparative genomics, regulation, expression and variation data. The database is optimized for fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. In the past year, 22 new assemblies and several new sets of human variation annotation have been released. New features include VisiGene, a fully integrated in situ hybridization image browser; phyloGif, for drawing evolutionary tree diagrams; a redesigned Custom Track feature; an expanded SNP annotation track; and many new display options. The Genome Browser, other tools, downloadable data files and links to documentation and other information can be found at http://genome.ucsc.edu/.

The UCSC Genome Browser Database: update 2006.

The University of California Santa Cruz Genome Browser Database (GBD) contains sequence and annotation data for the genomes of about a dozen vertebrate species and several major model organisms. Genome annotations typically include assembly data, sequence composition, genes and gene predictions, mRNA and expressed sequence tag evidence, comparative genomics, regulation, expression and variation data. The database is optimized to support fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. The Genome Browser displays a wide variety of annotations at all scales from single nucleotide level up to a full chromosome. The Table Browser provides direct access to the database tables and sequence data, enabling complex queries on genome-wide datasets. The Proteome Browser graphically displays protein properties. The Gene Sorter allows filtering and comparison of genes by several metrics including expression data and several gene properties. BLAT and In Silico PCR search for sequences in entire genomes in seconds. These tools are highly integrated and provide many hyperlinks to other databases and websites. The GBD, browsing tools, downloadable data files and links to documentation and other information can be found at http://genome.ucsc.edu/.

Novel small RNA-encoding genes in the intergenic regions of Escherichia coli.

BACKGROUND: Small, untranslated RNA molecules were identified initially in bacteria, but examples can be found in all kingdoms of life. These RNAs carry out diverse functions, and many of them are regulators of gene expression. Genes encoding small, untranslated RNAs are difficult to detect experimentally or to predict by traditional sequence analysis approaches. Thus, in spite of the rising recognition that such RNAs may play key roles in bacterial physiology, many of the small RNAs known to date were discovered fortuitously. RESULTS: To search the Escherichia coli genome sequence for genes encoding small RNAs, we developed a computational strategy employing transcription signals and genomic features of the known small RNA-encoding genes. The search, for which we used rather restrictive criteria, has led to the prediction of 24 putative sRNA-encoding genes, of which 23 were tested experimentally. Here we report on the discovery of 14 genes encoding novel small RNAs in E. coli and their expression patterns under a variety of physiological conditions. Most of the newly discovered RNAs are abundant. Interestingly, the expression level of a significant number of these RNAs increases upon entry into stationary phase. CONCLUSIONS: Based on our results, we conclude that small RNAs are much more widespread than previously imagined and that these versatile molecules may play important roles in the fine-tuning of cell responses to changing environments.

A protocol for the calibration of gamma cameras to estimate internal contamination in emergency situations.

The concern about accidents involving radioactive materials has led to the search of alternative methods to quickly identify and quantify radionuclides in workers and in the population. One of the options to face up an eventual demand for mass monitoring of internal contamination is the use of a nuclear medicine diagnostic equipment known as gamma camera, a device used to scan patients who have been administered specific amounts of radioactive materials for medical purposes. Although the gamma camera is used for image diagnosis, it can be calibrated with anthropomorphic phantoms or point sources for the quantification of radionuclide activities in the human body. This work presents a protocol for the calibration of gamma cameras for such application. In order to evaluate the suitability of this type of equipment, a gamma camera available in a public hospital located in Rio de Janeiro was calibrated for the in vivo measurement of 131I. The calibration includes the determination of detection efficiencies and minimum detectable activities for each radionuclide. The results show that the gamma camera presents enough sensitivity to detect activity levels corresponding to effective doses below 1 mSv. The protocol is the basis to establish a network of Nuclear Medicine Centres, located in public hospitals in eight countries of Latin America (Argentina, Brazil, Colombia, Cuba, Chile, Mexico, Peru and Uruguay) and in Spain that could be requested to collaborate in remediation actions in the event of an accident involving incorporation of radioactive materials. This protocol is one of the most significant outputs of the IAEA-ARCAL Project (RLA/9/049-LXXVIII) aimed to the Harmonization of Internal Dosimetry Procedures.

PromEC: An updated database of Escherichia coli mRNA promoters with experimentally identified transcriptional start sites.

PromEC is an updated compilation of Escherichia coli mRNA promoter sequences. It includes documentation on the location of experimentally identified mRNA transcriptional start sites on the E. coli chromosome, as well as the actual sequences in the promoter region. The database was updated as of July 2000 and includes 472 entries. PromEC is accessible at http://bioinfo.md.huji.ac. il/marg/promec

Studies on internal exposure doses received by the cuban population due to the intake of radionuclides from the environmental sources.

Studies for the determination of radionuclide concentrations in foodstuffs, water and air were carried out in Cuba for the estimation of annual committed effective doses to members of the public as a result of environmental radionuclides via ingestion and inhalation. As a result of these studies, it was possible to determine the concentrations of 226Ra, 210Pb, 210Po, 232Th, 90Sr and 137Cs in different food groups that constitute the diet of the Cuban population, as well as the 222Rn concentrations in air. Based on these results and using previously obtained results for doses due to the 40K body content, the annual committed effective doses due to the intake of studied radionuclides were estimated. An average value of 120+/-4 microSv y-1 was obtained for doses due to ingestion of food and water and the obtained value for 222Rn inhalation was 240+/-1 microSv y-1. Using the representative value obtained previously for 40K (150+/-40 microSv y-1) and assuming a dose of 50+/-50 microSv y-1 for the probable contribution of 220Rn by inhalation, a representative value of 560+/-20 microSv was estimated for the average annual committed effective doses due to ingestion and inhalation of radionuclides for the Cuban population. Obtained values are consistent with the expected results, taking into account the characteristics of Cuban exposure scenarios, with low-activity concentration levels in environmental objects and high air exchange rates in dwellings: These results are in the same order of magnitude as results obtained by other authors and the reference values established by the USNCEAR.

SECOND LATIN AMERICAN INTERCOMPARISON ON INTERNAL DOSE ASSESSMENT.

Internal dosimetry intercomparisons are essential for the verification of applied models and the consistency of results'. To that aim, the First Regional Intercomparison was organised in 2005, and that results led to the Second Regional Intercomparison Exercise in 2013, which was organised in the frame of the RLA 9/066 and coordinated by Autoridad Regulatoria Nuclear of Argentina. Four simulated cases covering intakes of (131)I, (137)Cs and Tritium were proposed. Ninteen centres from thirteen different countries participated in this exercise. This paper analyses the participants' results in this second exercise in order to test their skills and acquired knowledge, particularly in the application of the IDEAS Guidelines. It is important to highlight the increased number of countries that participated in this exercise compared with the first one and, furthermore, the improvement in the overall performance. The impact of the International Atomic Energy Agency (IAEA) Projects since 2003 has led to a significant enhancement of internal dosimetry capabilities that strengthen the radiation protection of workers.

Optimal amnesic probabilistic automata or how to learn and classify proteins in linear time and space.

Statistical modeling of sequences is a central paradigm of machine learning that finds multiple uses in computational molecular biology and many other domains. The probabilistic automata typically built in these contexts are subtended by uniform, fixed-memory Markov models. In practice, such automata tend to be unnecessarily bulky and computationally imposing both during their synthesis and use. Recently, D. Ron, Y. Singer, and N. Tishby built much more compact, tree-shaped variants of probabilistic automata under the assumption of an underlying Markov process of variable memory length. These variants, called Probabilistic Suffix Trees (PSTs) were subsequently adapted by G. Bejerano and G. Yona and applied successfully to learning and prediction of protein families. The process of learning the automaton from a given training set S of sequences requires theta(Ln2) worst-case time, where n is the total length of the sequences in S and L is the length of a longest substring of S to be considered for a candidate state in the automaton. Once the automaton is built, predicting the likelihood of a query sequence of m characters may cost time theta(m2) in the worst case. The main contribution of this paper is to introduce automata equivalent to PSTs but having the following properties: Learning the automaton, for any L, takes O (n) time. Prediction of a string of m symbols by the automaton takes O (m) time. Along the way, the paper presents an evolving learning scheme and addresses notions of empirical probability and related efficient computation, which is a by-product possibly of more general interest.

Assessment of the doses received by the Cuban population from 40K contained in the body: modelling based on a neural network.

Potassium-40 constitutes the main natural source of potassium present in the body, which influences the effective dose received by people. With the aim of assessing the contribution of this component to the doses received by the Cuban population, a study intended to assess the doses was developed. For this purpose, a representative sample of the Cuban population was selected according to age and sex. The measurements were made using the whole-body counter (WBC) of the Center for Radiation Protection and Hygiene (CPHR). For dose estimations, a uniform distribution of potassium for the whole body was assumed. The methodology used was the one recommended by the ICRP. The values of annual effective dose range between 93 and 209 microSv for females and between 123 and 212 microSv for males. The annual average effective dose for members of the public was estimated as 150 +/- 40 microSv, taking into account the experimental data and the specific features of the Cuban population. With the dose values obtained, it was possible to model dose estimates by means of a neural network, which was trained with the results obtained and using as starting data the sex, age, height and corporal weight of people studied. The modelobtained allows consideration of the anatomical features of each person in the estimation of doses.

PESNPdb: a comprehensive database of SNPs studied in association with pre-eclampsia.

Pre-eclampsia is a pregnancy specific disorder that can be life threatening for mother and child. Multiple studies have been carried out in an attempt to identify SNPs that contribute to the genetic susceptibility of the disease. Here we describe PESNPdb (http://bejerano.stanford.edu/pesnpdb), a database aimed at centralizing SNP and study details investigated in association with pre-eclampsia. We also describe a Placenta Disorders ontology that utilizes information from PESNPdb. The main focus of PESNPdb is to help researchers study the genetic complexity of pre-eclampsia through a user-friendly interface that encourages community participation.

Variations on probabilistic suffix trees: statistical modeling and prediction of protein families.

MOTIVATION: We present a method for modeling protein families by means of probabilistic suffix trees (PSTs). The method is based on identifying significant patterns in a set of related protein sequences. The patterns can be of arbitrary length, and the input sequences do not need to be aligned, nor is delineation of domain boundaries required. The method is automatic, and can be applied, without assuming any preliminary biological information, with surprising success. Basic biological considerations such as amino acid background probabilities, and amino acids substitution probabilities can be incorporated to improve performance. RESULTS: The PST can serve as a predictive tool for protein sequence classification, and for detecting conserved patterns (possibly functionally or structurally important) within protein sequences. The method was tested on the Pfam database of protein families with more than satisfactory performance. Exhaustive evaluations show that the PST model detects much more related sequences than pairwise methods such as Gapped-BLAST, and is almost as sensitive as a hidden Markov model that is trained from a multiple alignment of the input sequences, while being much faster.

Markovian domain fingerprinting: statistical segmentation of protein sequences.

MOTIVATION: Characterization of a protein family by its distinct sequence domains is crucial for functional annotation and correct classification of newly discovered proteins. Conventional Multiple Sequence Alignment (MSA) based methods find difficulties when faced with heterogeneous groups of proteins. However, even many families of proteins that do share a common domain contain instances of several other domains, without any common underlying linear ordering. Ignoring this modularity may lead to poor or even false classification results. An automated method that can analyze a group of proteins into the sequence domains it contains is therefore highly desirable. RESULTS: We apply a novel method to the problem of protein domain detection. The method takes as input an unaligned group of protein sequences. It segments them and clusters the segments into groups sharing the same underlying statistics. A Variable Memory Markov (VMM) model is built using a Prediction Suffix Tree (PST) data structure for each group of segments. Refinement is achieved by letting the PSTs compete over the segments, and a deterministic annealing framework infers the number of underlying PST models while avoiding many inferior solutions. We show that regions of similar statistics correlate well with protein sequence domains, by matching a unique signature to each domain. This is done in a fully automated manner, and does not require or attempt an MSA. Several representative cases are analyzed. We identify a protein fusion event, refine an HMM superfamily classification into the underlying families the HMM cannot separate, and detect all 12 instances of a short domain in a group of 396 sequences. CONTACT: jill@cs.huji.ac.il; tishby@cs.huji.ac.il.