１ インフラを構築する（ＡＷＳにおけるインフラ；ＶＰＣを構成する；ＶＰＣとオンプレミス環境とを接続する） ２ ファイルオブジェクトを保存・共有・公開する（オブジェクトストレージＳ３の機能；ファイルストレージとして利用する；Ｗｅｂサーバーを構築する；信頼性とコストのバランスをとりたい） ３ アプリケーションサーバーを構築する（Ａｍａｚｏｎ ＥＣ２とＡＷＳ Ｌａｍｂｄａ；スケーラビリティーを高める；サーバーレスでプログラムを動かす；データベースサービスを活用する） ４ ＡＷＳシステムを管理する（リソース監視と異常検知・通報；耐障害性を高める仕組みとバックアップ＆リカバリー；構成管理）

Amazon Web Services完全ソリューションガイド

The increasing availability and decreasing cost of high-throughput sequencing has transformed academic medical microbiology, delivering an explosion in available genomes while also driving advances in bioinformatics. However, many microbiologists are unable to exploit the resulting large genomics datasets because they do not have access to relevant computational resources and to an appropriate bioinformatics infrastructure. Here, we present the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) facility, a shared computing infrastructure that has been designed from the ground up to provide an environment where microbiologists can share and reuse methods and data.

CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community

Vertebrate vision is accomplished through light-sensitive photopigments consisting of an opsin protein bound to a chromophore. In dim light, vertebrates generally rely on a single rod opsin [rhodopsin 1 (RH1)] for obtaining visual information. By inspecting 101 fish genomes, we found that three deep-sea teleost lineages have independently expanded their RH1 gene repertoires. Among these, the silver spinyfin (Diretmus argenteus) stands out as having the highest number of visual opsins in vertebrates (two cone opsins and 38 rod opsins). Spinyfins express up to 14 RH1s (including the most blueshifted rod photopigments known), which cover the range of the residual daylight as well as the bioluminescence spectrum present in the deep sea. Our findings present molecular and functional evidence for the recurrent evolution of multiple rod opsin-based vision in vertebrates.

https://science.sciencemag.org/content/sci/364/6440/588.full.pdf

Vision using multiple distinct rod opsins in deep-sea fishes.

Snapshots of computer systems that are stored and shared 'in the cloud' could make computational analyses more reproducible.

In silico research in the era of cloud

The recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research laboratory and into the front lines of surveillance and outbreak response requires user-friendly, reproducible and scalable pipelines that have been well validated. Single Nucleotide Variant Phylogenomics (SNVPhyl) is a bioinformatics pipeline for identifying high-quality single-nucleotide variants (SNVs) and constructing a whole-genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity, and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds or in the presence of contamination. SNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

SNVPhyl: a single nucleotide variant phylogenomics pipeline for microbial genomic epidemiology.

Background: Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. Results: We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. Conclusions: This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.

/pdf/genomics-virtual-laboratory-a-practical-bioinformatics-21byu5gbby.pdf

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

In weight training, correct exercise execution is crucial for maximizing its effectiveness and for preventing injuries. However, given the complexity of these movements, it is a challenge for trainees to know whether they are performing the exercise correctly. Considering the fact that wrong moves may result in life long injuries, it is important to design systems that can detect incorrect performances automatically. In this paper, we present a workflow to detect performance anomalies from only observations of the correct performance of an exercise by the trainee. We evaluated our algorithm on a benchmark data set for the biceps curl exercise, and also evaluated our system with a publicly available dataset, and showed that our method detects unseen anomalies in weight lifting exercises with 98 percent accuracy.

Detecting unseen anomalies in weight training exercises

In this paper, we demonstrate LiftSmart, a novel smart wearable to detect, track and analyse weight training activities. LiftSmart is the first wearable for weight training that is based on unsupervised machine learning techniques to eliminate the use of labelled data, which is expensive to collect, computationally intensive, and requires the tuning of multiple key parameters.

LiftSmart: a monitoring and warning wearable for weight trainers

Shape-Sphere: A metric space for analysing time series by their shape

Short bursts of repeating patterns [intervals of recurrence (IoR)] manifest themselves in many applications, such as in the time-series data captured from an athlete's movements using a wearable sensor while performing exercises. We present an efficient, online, one-pass, and real-time algorithm for finding and tracking IoR in a time-series data stream. We provide a detailed theoretical analysis of the behavior of any IoR and derive fundamental properties that can be used on real-world data streams. We show that why our method, unlike current state-of-the-art techniques, is robust to variations in repeats of the same pattern adjacent to each other. To evaluate our algorithm, we build a wearable device that runs our algorithm to conduct a user study. Our results show that our algorithm can detect intervals of repeating activities on edge devices with high accuracy (over 70% F1-Score) and in a real-time environment with only a 1.5-s lag. Our experimental results from real-world datasets demonstrate that our approach outperforms state-of-the-art algorithms in both accuracy and robustness to variations of the signal of recurrence.

Yousef Kowsar

Papers

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

Detecting unseen anomalies in weight training exercises

LiftSmart: a monitoring and warning wearable for weight trainers

Shape-Sphere: A metric space for analysing time series by their shape

An Online Unsupervised Dynamic Window Method to Track Repeating Patterns From Sensor Data.