69 research outputs found
Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells Supporting Tables
Single cell transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs). These tables contain supplementary results from differential gene and pathway analysis.<br
Benchmarking Undedicated Cloud Computing Providers for Analysis of Genomic Datasets
<div><p>A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (<i>E.coli</i> CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5–78.2) for <i>E.coli</i> and 53.5% (95% CI: 34.4–72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5–303.1) and 173.9% (95% CI: 134.6–213.1) more expensive for <i>E.coli</i> and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.</p></div
Directions and types of network transfers in our cloud-computing model.
<p>There are a variety of different network transfers between the nodes for each of the services in use in our model. Hadoop requires a bidirectional transmission of data between the master node and the slave nodes. This is required to coordinate the parallel processing of the cluster, and to allow for data transfer between nodes. Ganglia uses a unidirectional connection from the slave nodes to the master node to transfer the recorded metrics for storage and visualization. The persistent storage (provided by Amazon S3 (Simple Storage Service) or Google Storage, or an alternative method such as an FTP server) is accessed via the master node. The master node uses it to download input files for Crossbow, such as the manifest file and the reference Jar, and to use for persistent storage of the results of the Crossbow job as the instances destroy their storage on termination. Our local computer can also access the persistent storage via the Internet to allow access to upload the input files, or to download the results. The local computer needs to access the master node to initiate Crossbow. In EMR, this is replaced by a web interface and a JavaScript Object Notation Application Programming Interface (JSON API). In GCE, the user is required to remotely log in via Secure Shell (SSH) to commence the job.</p
Specification of used computational nodes for each system.
<p>∧Date accessed: April to June 2014; prior to this period, pricing was 0.520 in Amazon and Google respectively.</p>#<p>for each instance we added the minimum storage quota of 128 GB.</p><p>Specification of used computational nodes for each system.</p
Comparison of performance metrics for genomic alignment and SNP calling.
<p>All times are presented as hr:min:sec and remaining metrics are shown as mean ± standard deviation.</p><p>*Calculated by paired <i>t</i>-test.</p><p>Comparison of performance metrics for genomic alignment and SNP calling.</p
Comparison of undedicated cloud performance of Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances (panels a & c) versus Google Compute Engine (GCE) (panels b & d) for <i>E.coli</i> genome alignment and variant calling.
<p>In this two node cluster the total CPU percent for CPU idle (a and b) and waiting for disk input/output (c and d) is displayed. Note the shorter wall clock times for complete analysis on GCE compared to EMR.</p
Analytical pipeline demarcating each step required to complete the Crossbow job in the cloud.
<p>Analytical pipeline demarcating each step required to complete the Crossbow job in the cloud.</p
Comparison of undedicated cloud computing performances.
<p>The panel includes results of Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances (panels a & c) versus Google Compute Engine (GCE) (panels b & d) for human genome alignment and variant calling. In this 40 node cluster the total CPU percent for CPU idle (a and b) and waiting for disk input/output (c and d) is displayed. Note the greater consistency in performance of Crossbow, though generally longer wall clock times for complete analysis, on EMR compared to GCE.</p
Plot showing the study power of the selected SNPs in both (A) Australian (n = 232 cases and n = 288 controls) and (B) Nepalese (n = 106 cases and 204 controls) populations per-allele odds ratio.
<p>X-axis represents relative risk (range from 1.0 to 2.4) and Y-axis the power in percentage. Minor allele frequencies of each SNP are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067903#pone-0067903-t001" target="_blank">Table 1</a>.</p
Screening for Diabetic Eye Disease among Samoan Adults: A Pilot Study
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be found here<b>.</b> <a href="https://link.springer.com/article/10.1007/s40123-017-0092-8">https://link.springer.com/article/10.1007/s40123-017-0092-8</a></p><p></p>
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