Searching for SNPs with cloud computing

Novel software utilizing cloud computing technology to cost-effectively align and map SNPs from a human genome in three

Clinical applications of next generation sequencing in cancer: From panels, to exomes, to genomes

This article will review recent impact of massively parallel next-generation sequencing (NGS) in our understanding and treatment of cancer. While whole exome sequencing (WES) remains popular and effective as a method of genetically profiling different cancers, advances in sequencing technology has enabled an increasing number of whole-genome based studies. Clinically, NGS has been used or is being developed for genetic screening, diagnostics, and clinical assessment. Though challenges remain, clinicians are in the early stages of using genetic data to make treatment decisions for cancer patients. As the integration of NGS in the study and treatment of cancer continues to mature, we believe that the field of cancer genomics will need to move towards more complete 100% genome sequencing. Current technologies and methods are largely limited to coding regions of the genome. A number of recent studies have demonstrated that mutations in non-coding regions may have direct tumorigenic effects or lead to genetic instability. Non-coding regions represent an important frontier in cancer genomics

Preparation and biomedical application of a non-polymer coated superparamagnetic nanoparticle

We report the preparation of a non-polymer coated superparamagnetic nanoparticle that is stable and biocompatible both in vitro and in vivo. The non-polymer, betaine, is a natural methylating agent in mammalian liver with active surface property. Upon systemic administration, the nanoparticle has preferential biodistribution in mammalian liver and exhibits good reduction of relaxivity time and negative enhancement for the detection of hepatoma nodules in rats using MRI. Our data demonstrate that the non-polymer coated superparamagnetic nanoparticle should have potential applications in biomedicine

Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems

In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches. However, given the proven power of latent factor models, some newer neural approaches incorporate them within more complex network architectures. One specific idea, recently put forward by several researchers, is to consider potential correlations between the latent factors, i.e., embeddings, by applying convolutions over the user-item interaction map. However, contrary to what is claimed in these articles, such interaction maps do not share the properties of images where Convolutional Neural Networks (CNNs) are particularly useful. In this work, we show through analytical considerations and empirical evaluations that the claimed gains reported in the literature cannot be attributed to the ability of CNNs to model embedding correlations, as argued in the original papers. Moreover, additional performance evaluations show that all of the examined recent CNN-based models are outperformed by existing non-neural machine learning techniques or traditional nearest-neighbor approaches. On a more general level, our work points to major methodological issues in recommender systems research.Comment: Source code available here: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluatio

PubMed related articles: a probabilistic topic-based model for content similarity

<p>Abstract</p> <p>Background</p> <p>We present a probabilistic topic-based model for content similarity called <it>pmra </it>that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ^®in MEDLINE ^®.</p> <p>Results</p> <p>The <it>pmra </it>retrieval model was compared against <it>bm25</it>, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of <it>pmra </it>over <it>bm25 </it>in terms of precision.</p> <p>Conclusion</p> <p>Our experiments suggest that the <it>pmra </it>model provides an effective ranking algorithm for related article search.</p

Susceptibility to tuberculosis is associated with variants in the ASAP1 gene encoding a regulator of dendritic cell migration

Human genetic factors predispose to tuberculosis (TB). We studied 7.6 million genetic variants in 5,530 people with pulmonary TB and in 5,607 healthy controls. In the combined analysis of these subjects and the follow-up cohort (15,087 TB patients and controls altogether), we found an association between TB and variants located in introns of the ASAP1 gene on chromosome 8q24 (P = 2.6 × 10−11 for rs4733781; P = 1.0 × 10−10 for rs10956514). Dendritic cells (DCs) showed high ASAP1 expression that was reduced after Mycobacterium tuberculosis infection, and rs10956514 was associated with the level of reduction of ASAP1 expression. The ASAP1 protein is involved in actin and membrane remodeling and has been associated with podosomes. The ASAP1-depleted DCs showed impaired matrix degradation and migration. Therefore, genetically determined excessive reduction of ASAP1 expression in M. tuberculosis–infected DCs may lead to their impaired migration, suggesting a potential mechanism of predisposition to TB

Human Plasma PeptideAtlas

Peptide identifications of high probability from 28 LC-MS/MS human serum and plasma experiments from eight different laboratories, carried out in the context of the HUPO Plasma Proteome Project, were combined and mapped to the EnsEMBL human genome. The 6929 distinct observed peptides were mapped to approximately 960 different proteins. The resulting compendium of peptides and their associated samples, proteins, and genes is made publicly available as a reference for future research on human plasma

Effect of rituximab on a salivary gland ultrasound score in primary Sjögren’s syndrome: results of the TRACTISS randomised double-blind multicentre substudy

Objectives To compare the effects of rituximab versus placebo on salivary gland ultrasound (SGUS) in primary Sjögren’s syndrome (PSS) in a multicentre, multiobserver phase III trial substudy. Methods Subjects consenting to SGUS were randomised to rituximab or placebo given at weeks 0, 2, 24 and 26, and scanned at baseline and weeks 16 and 48. Sonographers completed a 0–11 total ultrasound score (TUS) comprising domains of echogenicity, homogeneity, glandular definition, glands involved and hypoechoic foci size. Baseline-adjusted TUS values were analysed over time, modelling change from baseline at each time point. For each TUS domain, we fitted a repeated-measures logistic regression model to model the odds of a response in the rituximab arm (≥1-point improvement) as a function of the baseline score, age category, disease duration and time point. Results 52 patients (n=26 rituximab and n=26 placebo) from nine centres completed baseline and one or more follow-up visits. Estimated between-group differences (rituximab-placebo) in baseline-adjusted TUS were −1.2 (95% CI −2.1 to −0.3; P=0.0099) and −1.2 (95% CI −2.0 to −0.5; P=0.0023) at weeks 16 and 48. Glandular definition improved in the rituximab arm with an OR of 6.8 (95% CI 1.1 to 43.0; P=0.043) at week 16 and 10.3 (95% CI 1.0 to 105.9; P=0.050) at week 48. Conclusions We demonstrated statistically significant improvement in TUS after rituximab compared with placebo. This encourages further research into both B cell depletion therapies in PSS and SGUS as an imaging biomarker

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information