Analysis and visualization of chromosomal abnormalities in SNP data with SNPscan

BACKGROUND: A variety of diseases are caused by chromosomal abnormalities such as aneuploidies (having an abnormal number of chromosomes), microdeletions, microduplications, and uniparental disomy. High density single nucleotide polymorphism (SNP) microarrays provide information on chromosomal copy number changes, as well as genotype (heterozygosity and homozygosity). SNP array studies generate multiple types of data for each SNP site, some with more than 100,000 SNPs represented on each array. The identification of different classes of anomalies within SNP data has been challenging. RESULTS: We have developed SNPscan, a web-accessible tool to analyze and visualize high density SNP data. It enables researchers (1) to visually and quantitatively assess the quality of user-generated SNP data relative to a benchmark data set derived from a control population, (2) to display SNP intensity and allelic call data in order to detect chromosomal copy number anomalies (duplications and deletions), (3) to display uniparental isodisomy based on loss of heterozygosity (LOH) across genomic regions, (4) to compare paired samples (e.g. tumor and normal), and (5) to generate a file type for viewing SNP data in the University of California, Santa Cruz (UCSC) Human Genome Browser. SNPscan accepts data exported from Affymetrix Copy Number Analysis Tool as its input. We validated SNPscan using data generated from patients with known deletions, duplications, and uniparental disomy. We also inspected previously generated SNP data from 90 apparently normal individuals from the Centre d'Étude du Polymorphisme Humain (CEPH) collection, and identified three cases of uniparental isodisomy, four females having an apparently mosaic X chromosome, two mislabelled SNP data sets, and one microdeletion on chromosome 2 with mosaicism from an apparently normal female. These previously unrecognized abnormalities were all detected using SNPscan. The microdeletion was independently confirmed by fluorescence in situ hybridization, and a region of homozygosity in a UPD case was confirmed by sequencing of genomic DNA. CONCLUSION: SNPscan is useful to identify chromosomal abnormalities based on SNP intensity (such as chromosomal copy number changes) and heterozygosity data (including regions of LOH and some cases of UPD). The program and source code are available at the SNPscan website

Coherent optical phase transfer over a 32-km fiber with 1-s instability at 10−1710^{-17}

The phase coherence of an ultrastable optical frequency reference is fully maintained over actively stabilized fiber networks of lengths exceeding 30 km. For a 7-km link installed in an urban environment, the transfer instability is $6 \times 10^{-18}$ at 1-s. The excess phase noise of 0.15 rad, integrated from 8 mHz to 25 MHz, yields a total timing jitter of 0.085 fs. A 32-km link achieves similar performance. Using frequency combs at each end of the coherent-transfer fiber link, a heterodyne beat between two independent ultrastable lasers, separated by 3.5 km and 163 THz, achieves a 1-Hz linewidth.Comment: 4 pages, 4 figure

Coupling and stacking order of ReS2 atomic layers revealed by ultralow-frequency Raman spectroscopy

We investigate the ultralow-frequency Raman response of atomically thin ReS2, a special type of two-dimensional (2D) semiconductors with unique distorted 1T structure. Bilayer and few-layer ReS2 exhibit rich Raman spectra at frequencies below 50 cm-1, where a panoply of interlayer shear and breathing modes are observed. The emergence of these interlayer phonon modes indicate that the ReS2 layers are coupled and stacked orderly, in contrast to the general belief that the ReS2 layers are decoupled from one another. While the interlayer breathing modes can be described by a linear chain model as in other 2D layered crystals, the shear modes exhibit distinctive behavior due to the in-plane lattice distortion. In particular, the two shear modes in bilayer ReS2 are non-degenerate and well separated in the Raman spectrum, in contrast to the doubly degenerate shear modes in other 2D materials. By carrying out comprehensive first-principles calculations, we can account for the frequency and Raman intensity of the interlayer modes, and determine the stacking order in bilayer ReS2

Filamentous Aggregates Are Fragmented by the Proteasome Holoenzyme.

Filamentous aggregates (fibrils) are regarded as the final stage in the assembly of amyloidogenic proteins and are formed in many neurodegenerative diseases. Accumulation of aggregates occurs as a result of an imbalance between their formation and removal. Here we use single-aggregate imaging to show that large fibrils assembled from full-length tau are substrates of the 26S proteasome holoenzyme, which fragments them into small aggregates. Interestingly, although degradation of monomeric tau is not inhibited by adenosine 5'-(3-thiotriphosphate) (ATPγS), fibril fragmentation is predominantly dependent on the ATPase activity of the proteasome. The proteasome holoenzyme also targets fibrils assembled from α-synuclein, suggesting that its fibril-fragmenting function may be a general mechanism. The fragmented species produced by the proteasome shows significant toxicity to human cell lines compared with intact fibrils. Together, our results indicate that the proteasome holoenzyme possesses a fragmentation function that disassembles large fibrils into smaller and more cytotoxic species.Wellcome Trust, Sir Henry Wellcome Fellowship (101585/Z/13/Z) to Yu Y

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

Delivery of High-Stability Optical and Microwave Frequency Standards over an Optical Fiber Network

Optical and radio frequency standards located in JILA and National Institute of Standards and Technology (NIST) laboratories have been connected through a 3.45-km optical fiber link. An optical frequency standard based on an iodine-stabilized Nd:YAG laser at 1064 nm (with an instability of ∼4×10−14 at 1 s) has been transferred from JILA to NIST and simultaneously measured in both laboratories. In parallel, a hydrogen maser-based radio frequency standard (with an instability of ∼2.4×10−13 at 1 s) is transferred from NIST to JILA. Comparison between these frequency standards is made possible by the use of femtosecond frequency combs in both laboratories. The degradation of the optical and rf standards that are due to the instability in the transmission channel has been measured. Active noise cancellation is demonstrated to improve the transfer stability of the fiber link

Differentiating acute from chronic insomnia with machine learning from actigraphy time series data

Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices

A vertebrate case study of the quality of assemblies derived from next-generation sequences

The unparalleled efficiency of next-generation sequencing (NGS) has prompted widespread adoption, but significant problems remain in the use of NGS data for whole genome assembly. We explore the advantages and disadvantages of chicken genome assemblies generated using a variety of sequencing and assembly methodologies. NGS assemblies are equivalent in some ways to a Sanger-based assembly yet deficient in others. Nonetheless, these assemblies are sufficient for the identification of the majority of genes and can reveal novel sequences when compared to existing assembly references

One-carbon metabolism in cancer

Cells require one-carbon units for nucleotide synthesis, methylation and reductive metabolism, and these pathways support the high proliferative rate of cancer cells. As such, anti-folates, drugs that target one-carbon metabolism, have long been used in the treatment of cancer. Amino acids, such as serine are a major one-carbon source, and cancer cells are particularly susceptible to deprivation of one-carbon units by serine restriction or inhibition of de novo serine synthesis. Recent work has also begun to decipher the specific pathways and sub-cellular compartments that are important for one-carbon metabolism in cancer cells. In this review we summarise the historical understanding of one-carbon metabolism in cancer, describe the recent findings regarding the generation and usage of one-carbon units and explore possible future therapeutics that could exploit the dependency of cancer cells on one-carbon metabolism