A Quick Introduction to Version Control with Git and GitHub.
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ABSTRACT:
SUBMITTER: Blischak JD
PROVIDER: S-EPMC4718703 | biostudies-literature | 2016 Jan
REPOSITORIES: biostudies-literature
Publications
A Quick Introduction to Version Control with Git and GitHub.
Blischak John D JD Davenport Emily R ER Wilson Greg G
PLoS computational biology 20160119 1
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Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1004947.].
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Project description:The Salvador-Warts-Hippo (Hippo) pathway is a conserved regulator of organ size and is deregulated in human cancers. In epithelial tissues, the Hippo pathway is regulated by fundamental cell biological properties, such as polarity and adhesion, and coordinates these with tissue growth. Despite its importance in disease, development, and regeneration, the complete set of proteins that regulate Hippo signaling remain undefined. To address this, we used proteomics to identify proteins that bind to the Hippo (Hpo) kinase. Prominent among these were PAK-interacting exchange factor (known as Pix or RtGEF) and G-protein-coupled receptor kinase-interacting protein (Git). Pix is a conserved Rho-type guanine nucleotide exchange factor (Rho-GEF) homologous to Beta-PIX and Alpha-PIX in mammals. Git is the single Drosophila melanogaster homolog of the mammalian GIT1 and GIT2 proteins, which were originally identified in the search for molecules that interact with G-protein-coupled receptor kinases. Pix and Git form an oligomeric scaffold to facilitate sterile 20-like kinase activation and have also been linked to GTPase regulation. We show that Pix and Git regulate Hippo-pathway-dependent tissue growth in D. melanogaster and that they do this in parallel to the known upstream regulator Fat cadherin. Pix and Git influence activity of the Hpo kinase by acting as a scaffold complex, rather than enzymes, and promote Hpo dimerization and autophosphorylation of Hpo's activation loop. Therefore, we provide important new insights into an ancient signaling network that controls the growth of metazoan tissues.
| S-EPMC5558436 | biostudies-literature
Project description:There is a need for a screening tool with capacities of accurate detection of early mild cognitive impairment (MCI) and dementia and is suitable for use in a range of languages and cultural contexts. This research aims to evaluate the psychometric and diagnostic properties of the Taiwan version of Qmci (Qmci-TW) screen and to explore the discriminating ability of the Qmci-TW in differentiating among normal controls (NCs), MCI and dementia. Thirty-one participants with dementia and 36 with MCI and 35 NCs were recruited from a neurology department of regional hospital in Taiwan. Their results on the Qmci-TW, Taiwanese version of the Montreal Cognitive Assessment (MoCA), and Traditional Chinese version of the Mini-Mental State Examination (MMSE) were compared. For analysis, we used Cronbach's α, intraclass correlation coefficient, Spearman's ρ, Kruskal-Wallis test, receiver operating characteristic curve analysis, and multivariate analysis, as appropriate. The Qmci-TW exhibited satisfactory test-retest reliability, internal consistency, and interrater reliability as well as a strong positive correlation with results from the MoCA and MMSE. The optimal cut-off score on the Qmci-TW for differentiating MCI from NC was ≤ 51.5/100 and dementia from MCI was ≤ 31/100. The MoCA exhibited the highest accuracy in differentiating MCI from NC, followed by the Qmci-TW and then MMSE; whereas, the Qmci-TW and MMSE exhibited the same accuracy in differentiating dementia from MCI, followed by the MoCA. The Qmci-TW may be a useful clinical screening tool for a spectrum of cognitive impairments.
| S-EPMC6277119 | biostudies-literature
Project description:In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.
| S-EPMC6209220 | biostudies-literature
Project description:Summary"quaqc" allows for ATAC-seq-specific quality control and read filtering of NGS data with minimal processing time and extremely low memory overhead. An efficient scaling implementation allows for a wide range of use cases, from processing individual samples processed on personal laptops to handling thousands of samples processed in parallel on compute clusters. The helper R package "quaqcr" allows for interactive program execution and exploration of results.Availability and implementationSource code and documentation are freely available for download from https://github.com/bjmt/quaqc and https://github.com/bjmt/quaqcr under the GPLv3 license. "quaqc" is implemented in C and has been tested on both macOS and Linux. The "quaqcr" helper package only requires the R programming language. Fixed versions of the programs and code associated with this manuscript can be found at https://zenodo.org/records/13833437.
| S-EPMC11549019 | biostudies-literature
Project description:Whole genome methylation profiles of gastrointestinal tissues from cows positive for Mycobacterium avium spp. paratuberculosis and tissues from healthy cows
| PRJEB44789 | ENA
Project description:Background: Screening for post-stroke cognitive impairment (PSCI) is necessary because stroke increases the incidence of and accelerates premorbid cognitive decline. The Quick Mild Cognitive Impairment (Qmci) screen is a short, reliable and accurate cognitive screening instrument but is not yet validated in PSCI. We compared the diagnostic accuracy of a Chinese version of the Qmci screen (Qmci-CN) compared with the widely-used Chinese versions of the Montreal Cognitive Assessment (MoCA-CN) and Mini-Mental State Examination (MMSE-CN). Methods: We recruited 34 patients who had recovered from a stroke in rehabilitation unit clinics in 2 university hospitals in China: 11 with post-stroke dementia (PSD), 15 with post-stroke cognitive impairment no dementia (PSCIND), and 8 with normal cognition (NC). Classification was made based on clinician assessment supported by a neuropsychological battery, independent of the screening test scores. The Qmci-CN, MoCA-CN, and MMSE-CN screens were administered randomly by a trained rater, blind to the diagnosis. Results: The mean age of the sample was 63 ± 13 years and 61.8% were male. The Qmci-CN had statistically similar diagnostic accuracy in differentiating PSD from NC, an area under the curve (AUC) of 0.94 compared to 0.99 for the MoCA-CN (p = 0.237) and 0.99 for the MMSE-CN (p = 0.293). The Qmci-CN (AUC 0.91), MoCA-CN (AUC 0.94), and MMSE-CN (AUC 0.79) also had statistically similar accuracy in separating PSD from PSCIND. The MoCA-CN more accurately distinguished between PSCIND and normal cognition than the Qmci-CN (p = 0.015). Compared to the MoCA-CN, the administration times of the Qmci-CN (329s vs. 611s, respectively, p < 0.0001) and MMSE-CN (280 vs. 611s, respectively, p < 0.0001) were significantly shorter. Conclusion: The Qmci-CN is accurate in identifying PSD and separating PSD from PSCIND in patients post-stroke following rehabilitation and is comparable to the widely-used MoCA-CN, albeit with a significantly shorter administration time. The Qmci-CN had relatively poor accuracy in identifying PSCIND from NC and hence may lack accuracy for certain subgroups. However, given the small sample size, the study is under-powered to show superiority of one instrument over another. Further study is needed to confirm these findings in a larger sample size and in other settings (countries and languages).
| S-EPMC7982853 | biostudies-literature
Project description: Not available
| S-EPMC11633490 | biostudies-literature