QIIME (Quantitative Insights Into Microbial Ecology) is one of the most popular open-source bioinformatics suite for performing metagenome, 16S rRNA amplicon and Internal Transcribed Spacer (ITS) data analysis. Although, it is very comprehensive and powerful tool, it lacks a method to provide publication ready taxonomic pie charts. The script plot_taxa_summary.py bundled with QIIME generate a html file and a folder containing taxonomic pie chart and legend as separate images. The images have randomly generated alphanumeric names. Therefore, it is difficult to associate the pie chart with the legend and the corresponding sample identifier. Even if the option to have the legend within the html file is selected while executing plot_taxa_summary.py, it is very tedious to crop a complete image (having both the pie chart and the legend) due to unequal image sizes. It requires a lot of time to manually prepare the pie charts for multiple samples for publication purpose. Moreover, there are chances of error while identifying the pie chart and legend pair due to random alphanumeric names of the images. To bypass all these bottlenecks and make this process efficient, we have developed a python based program, prepare_taxa_charts.py, to automate the renaming, cropping and merging of taxonomic pie chart and corresponding legend image into a single, good quality publication ready image. This program not only augments the functionality of plot_taxa_summary.py but is also very fast in terms of CPU time and user friendly.
heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout.
Targeted sequencing using next-generation sequencing technologies is currently being rapidly adopted for clinical sequencing and cancer marker tests. However, no existing bioinformatics tool is available for the analysis and visualization of multiple targeted sequencing datasets. In the present study, we use cancer panel targeted sequencing datasets generated by the Life Technologies Ion Personal Genome Machine (PGM) Sequencer as an example to illustrate how to develop an automated pipeline for the comparative analyses of multiple datasets. Cancer Panel Analysis Pipeline (CPAP) uses standard output files from variant calling software to generate a distribution map of SNPs among all of the samples in a circular diagram generated by Circos. The diagram is hyper-linked to a dynamic HTML table that allows the users to identify target SNPs by using different filters. CPAP also integrates additional information about the identified SNPs by linking to an integrated SQL database compiled from SNP-related databases, including dbSNP, 1000 Genomes Project, COSMIC and dbNSFP. CPAP only takes 17 minutes to complete a comparative analysis of 500 datasets. CPAP not only provides an automated platform for the analysis of multiple cancer panel datasets but can also serve as a model for any customized targeted sequencing project. This article is protected by copyright. All rights reserved.
Public accessibility of biomedical articles from PubMed Central reduces journal readership–retrospective cohort analysis
- FASEB journal : official publication of the Federation of American Societies for Experimental Biology
- Published over 5 years ago
Does PubMed Central-a government-run digital archive of biomedical articles-compete with scientific society journals? A longitudinal, retrospective cohort analysis of 13,223 articles (5999 treatment, 7224 control) published in 14 society-run biomedical research journals in nutrition, experimental biology, physiology, and radiology between February 2008 and January 2011 reveals a 21.4% reduction in full-text hypertext markup language (HTML) article downloads and a 13.8% reduction in portable document format (PDF) article downloads from the journals' websites when U.S. National Institutes of Health-sponsored articles (treatment) become freely available from the PubMed Central repository. In addition, the effect of PubMed Central on reducing PDF article downloads is increasing over time, growing at a rate of 1.6% per year. There was no longitudinal effect for full-text HTML downloads. While PubMed Central may be providing complementary access to readers traditionally underserved by scientific journals, the loss of article readership from the journal website may weaken the ability of the journal to build communities of interest around research papers, impede the communication of news and events to scientific society members and journal readers, and reduce the perceived value of the journal to institutional subscribers.-Davis, P. M. Public accessibility of biomedical articles from PubMed Central reduces journal readership-retrospective cohort analysis.
The original version of this Article contained an error in the title, which was incorrectly given as ‘APRDX1 mutant allele causes a MMACHC secondary epimutation in cblC patients’. This has now been corrected in both the PDF and HTML versions of the Article to read ‘A PRDX1 mutant allele causes a MMACHC secondary epimutation in cblC patients’.
Nearly everyone in society uses the Internet in one form or another. The Internet is heralded as an efficient way of providing mental health treatments and services. However, some people are still excluded from using Internet-enabled technology through lack of resources, skills, and confidence.
The original version of this Article contained an error in Eq. 1. The arrows between the symbols “T” and “B”, and “B” and “T”, were written “↔” but should have been “→”, and incorrectly read: IEBIC=IEBAC+ISEE+I(e↔h)+IEBICT↔B+IESEEB↔T The correct from of the Eq. 1 is as follows:IEBIC=IEBAC+ISEE+I(e↔h)+IEBICT→B+IESEEB→T This has now been corrected in both the PDF and HTML versions of the article.
Many studies have provided evidence for the effectiveness of Internet-based stand-alone interventions for mental disorders. A newer form of intervention combines the strengths of face-to-face (f2f) and Internet approaches (blended interventions).