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VM VirtualBox is a free and open-source hypervisor software that allows users to run multiple operating systems on a single computer. It provides a platform for creating and managing virtual machines, enabling users to run different operating systems, applications, and environments simultaneously on the same physical machine.

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Lab products found in correlation

9 protocols using vm virtualbox

1

Benchmarking BLAST on BOINC Grid

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The specification of the computer used for BLAST analysis was Intel® Core™ i7-4500U CPU @2.40 GHz with 8.00 GB of RAM. To simulate resources used per one workunit on BOINC grid system, hardware virtualization was required to allocate one processing unit and 1.00 GB of RAM. The virtualization was done by using Oracle® VM VirtualBox (version 5.0.8). The operating system for virtualization was Microsoft® Windows 7 (32-bit).
Pinthong W., Muangruen P., Suriyaphol P, & Mairiang D. (2016). A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model. PeerJ, 4, e2248.
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2

Comparative Analysis of Staphylococcal Phage Genomes

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All staphylococcal phage genomes were analysed with Phamerator to group genes into phams - gene products of related proteins [56 (link)]. The number of phams generated was used as a second metric to determine phage grouping. First, a SQL database was created locally and customized for incorporation of information present in GenBank files. Second, phage genomes were imported into Phamerator to assign phams with kclust. Phamerator identifies conserved domains in all genes using the NCBI conserved domain database [57 (link)]. Analysis were performed on Intel-based PCs with the Windows 7 operating system with a Virtual Machine (Oracle VM Virtual Box) running the Ubuntu 16.04 operating system for execution of Phamerator python scripts in Linux command line. Data manipulation and adjustments in the database scheme were made with MySQL language queries. All the python code created is accessible upon request.
SplitsTree network was used to visualize the relationship of shared gene content between staphylococcal phage genomes [58 (link), 59 (link)]. Phams generated by Phamerator were scored by the presence/absence. Protein repertoire relatedness was used in SplitsTree to visualize and analysed the evolutionary data, using network functionality.
Oliveira H., Sampaio M., Melo L.D., Dias O., Pope W.H., Hatfull G.F, & Azeredo J. (2019). Staphylococci phages display vast genomic diversity and evolutionary relationships. BMC Genomics, 20, 357.
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3

Tract Volume and Diffusion Analyses in Blindness

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Statistical analyses were performed in SAS University Edition running on an Oracle VM VirtualBox. In order to remove the potential effects of head size, residuals of total intracranial volume (as calculated in FreeSurfer) were computed for tract volume (Sanfilipo et al., 2004 (link); Pintzka et al., 2015 ). Note that the means of the residuals average to zero, resulting in negative and positive values. Equality of variances was assessed with a folded F-test. If variances were equal, a two-sample pooled t-test was used, while a Satterthwaite t-test was used in instances of unequal variance. Bonferroni correction was used for multiple comparisons, and results were considered significant at a threshold of p < 0.05. Finally, a series of t-tests were used to examine the possible effect of hemispheric laterality within each group on tract volume and mean QA measures of the arcuate and uncinate fasciculi.
As a secondary analysis, along tract variations in QA between blind and sighted groups were assessed for each reconstructed fasciculus (Colby et al., 2012 (link)). A permutation analysis using 10,000 permutations was used to control for family wise error and multiple comparisons.
Bauer C.M., Cattaneo Z, & Merabet L.B. (2018). Early Blindness is Associated with Increased Volume of the Uncinate Fasciculus. The European journal of neuroscience, 47(5), 427-432.
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4

Grid Computing System Implementation

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A computer training room with 49 desktop computers was used for grid system implementation. The room was accessible from 5 pm to 8 am, after its regular operating hours. The specification of each computer was Intel® Core™ i5 CPU 660 @3.47 GHz with 4.00 GB of RAM. The operating system of all computers was Microsoft® Windows 7 (32-bit). Every computer was connected to local area network. One computer was assigned as a host machine for the BOINC project server. This computer had Oracle® VM VirtualBox (version 5.0.8) installed to virtualize hardware for the project server. The virtual machine image of the BOINC project server (version April 12, 2014 on Debian Linux/GNU version 7) was downloaded from BOINC webpage (https://boinc.berkeley.edu/dl/debian-7-boinc-server-140412.7z). One processing unit and 512 MB of RAM were allocated for the project server. Other computers were assigned as client machines with BOINCManager (version 7.4.42; https://boinc.berkeley.edu/dl/boinc_7.4.42_windows_intelx86.exe) installed.
Pinthong W., Muangruen P., Suriyaphol P, & Mairiang D. (2016). A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model. PeerJ, 4, e2248.
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5

Comparative Evaluation of Genome Assembly Algorithms

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Seven assemblers (Table 3), which represent 5 different assembly algorithm strategies, were selected to assemble paired-end and single-end data sets.
All the selected assemblers were executed on the virtual machine, which was designed using Oracle VM VirtualBox with 2 VCPU, 4 GB of RAM memory and 64-bit Linux Ubuntu Server 14.04 operating system (supplementary file 1).
Khan A.R., Pervez M.T., Babar M.E., Naveed N, & Shoaib M. (2018). A Comprehensive Study of De Novo Genome Assemblers: Current Challenges and Future Prospective. Evolutionary Bioinformatics Online, 14, 1176934318758650.
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6

Structural and Functional Connectivity Analysis in Sighted and Blind Individuals

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To assess differences in connectivity (HARDI and resting state fMRI) between individuals in the sighted and blind groups, a two-tailed Mann-Whitney U exact test was performed for each connection between pairs of ROIs in MATLAB. This resulted in a total of 45 (i.e. 10 × 9/2=45) connections. A False Discovery Rate (FDR) analysis was performed at a rate of q=0.05 to correct for multiple comparisons within the structural and functional connectivity matrices39 . Statistical analyses on tract-based data were performed in SAS University Edition running on an Oracle VM VirtualBox. In order to correct for the potential effects of head size, residuals of total intracranial volume (as calculated in FreeSurfer) were calculated for tract volume, tract length, and number of fibers. Because of the small sample size and assumed unequal variance, a two-sided Exact Wilcoxon two-sample test was used. Significance was set at p < 0.05.
Bauer C.M, & Papadelis C. (2019). Alterations in the Structural and Functional Connectivity of the Visuomotor Network of Children with Periventricular Leukomalacia. Seminars in pediatric neurology, 31, 48-56.
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7

Integrative NMR Virtual Machine Setup

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In addition, we make all the software components of Integrative NMR available on a virtual machine. An ISO-formatted 64-bit disk image of Ubuntu MATE 15.04 was downloaded from the Ubuntu MATE web page (http://ubuntu-mate.org) and installed in an ORACLE VM VirtualBox (http://www.virtualbox.org). The software components of Integrative NMR were installed and optimized on this virtual machine. Then, the virtual disk image was exported to Open Virtualization Archive (OVA) format. In addition, we used the 7-zip file compression program (http://www.7-zip.org) to prepare a separated compressed version of the virtual machine for 32-bit operating systems that cannot download files larger than 2 GB from a web browser.
Lee W., Cornilescu G., Dashti H., Eghbalnia H.R., Tonelli M., Westler W.M., Butcher S.E., Henzler-Wildman K.A, & Markley J.L. (2016). Integrative NMR for biomolecular research. Journal of Biomolecular Nmr, 64, 307-332.
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8

Mitochondrial DNA Repeat Motif Analysis

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Repeats were detected by a script written in R (vR-3.6.3). Briefly, to find all repeats with N basepairs (bps), the mtDNA light strand is truncated by 0 to N bps and each of the N truncated mtDNAs is then split every N bps. This generates every possible substring (and thus repeat) of length N. In the next step, duplicate strings are removed. Afterwards we can find DR (a substring with at least two matches in the mtDNA), MR (at least one match in the mtDNA and on its reverse), IR (at least one match in the mtDNA and on its reverse-complement) and ER motifs (at least one match in the mtDNA and on its complement). Overlapping and duplicate repeats were not counted for the correlation between repeats and MLS. The code for the analyses performed in this paper can be found on github (pabisk/aging_triplex2).
Unless stated otherwise, all analyses were performed in R. G-quadruplex motifs were detected by the pqsfinder package (v2. Triplexator was run on a virtual machine in an Oracle VM VirtualBox (v6.1) in -ss mode on the human mitochondrial genome and its reverse complement, the results were combined and overlapping motifs from the output were removed. We used the web interface of nBMST to detect mirror repeats/triplexes (v1.0).
Pabis K. (2020). Triplex and other DNA motifs show motif-specific associations with mitochondrial DNA deletions and species lifespan.
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9

Molecular Analysis of Berry DNA in Flies

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All data was considered binomial based on whether an individual fly was positive or negative for berry DNA for the given primer. Treatments tested were temperature regime, sex, day, and whether the individual fly was positive or negative. No variables were considered random. Data were analyzed using SAS version 9.4 University Edition run on Oracle VM VirtualBox, and all data conformed to the necessary assumptions. The Proc Logistic command was used to run a logistic regression of all treatments except field-trapped flies. "Day" was considered a continuous variable for these analyses.
For the field transects, treatments included the trap location, sex, and whether an individual fly was positive or negative. No variables were considered random. Data were analyzed using a Proc Glimmix command to run a binomial regression of all treatments, adding the "dist = binomial" and "link = logit" to our model statement.
Kraft L., Sit T.L., Diepenbrock L.M., Ashrafi H., Aryal R., Fernandez G.E., & Burrack H.J. (2021). Detection of Fruit Meals Within Laboratory-Raised and Field-Trapped Adult Drosophila suzukii (Diptera: Drosophilidae) Guts. Frontiers in Ecology and Evolution, 9.
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