Ten Simple Rules for Getting Help from Online Scientific Communities

The increasing complexity of research requires scientists to work at the intersection of multiple fields and to face problems for which their formal education has not prepared them. For example, biologists with no or little background in programming are now often using complex scripts to handle the results from their experiments; vice versa, programmers wishing to enter the world of bioinformatics must know about biochemistry, genetics, and other fields. In this context, communication tools such as mailing lists, web forums, and online communities acquire increasing importance. These tools permit scientists to quickly contact people skilled in a specialized field. A question posed properly to the right online scientific community can help in solving difficult problems, often faster than screening literature or writing to publication authors. The growth of active online scientific communities, such as those listed in Table S1, demonstrates how these tools are becoming an important source of support for an increasing number of researchers. Nevertheless, making proper use of these resources is not easy. Adhering to the social norms of World Wide Web communication—loosely termed “netiquette”—is both important and non-trivial. In this article, we take inspiration from our experience on Internet-shared scientific knowledge, and from similar documents such as “Asking the Questions the Smart Way” and “Getting Answers”, to provide guidelines and suggestions on how to use online communities to solve scientific problems

A Review of 2011 for PLoS Computational Biology

A Review of 2011 for <em>PLoS Computational Biology</em

Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2

The rapidly developing pandemic, known as coronavirus disease 2019 (COVID-19) and caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently spread across 213 countries and territories. This pandemic is a dire public health threat-particularly for those suffering from hypertension, cardiovascular diseases, pulmonary diseases, or diabetes; without approved treatments, it is likely to persist or recur. To facilitate the rapid discovery of inhibitors with clinical potential, we have applied ligand- and structure-based computational approaches to develop a virtual screening methodology that allows us to predict potential inhibitors. In this work, virtual screening was performed against two natural products databases, Super Natural II and Traditional Chinese Medicine. Additionally, we have used an integrated drug repurposing approach to computationally identify potential inhibitors of the main protease of SARS-CoV-2 in databases of drugs (both approved and withdrawn). Roughly 360,000 compounds were screened using various molecular fingerprints and molecular docking methods; of these, 80 docked compounds were evaluated in detail, and the 12 best hits from four datasets were further inspected via molecular dynamics simulations. Finally, toxicity and cytochrome inhibition profiles were computationally analyzed for the selected candidate compounds

Careers of an elite cohort of U.S. basic life science postdoctoral fellows and the influence of their mentor's citation record

<p>Abstract</p> <p>Background</p> <p>There is general agreement that the number of U.S. science PhDs being trained far exceeds the number of future academic positions. One suggested approach to this problem is to significantly reduce the number of PhD positions. A counter argument is that students are aware of the limited academic positions but have chosen a PhD track because it opens other, non-academic, opportunities. The latter view requires that students have objective information about what careers options will be available for them.</p> <p>Methods</p> <p>The scientific careers of the 1992-94 cohort of NIH National Institute of General Medical Sciences (NIGMS) Kirchstein-NRSA F32 postdoctoral fellows (PD) was determined by following their publications (PubMed), grants (NIH and NSF), and faculty and industry positions through 2009. These basic life science PDs receive support through individual grant applications and represent the most successful class of NIH PDs as judged by academic careers and grants. The sex dependence of the career and grant success and the influence of the PD mentor's citation record were also determined</p> <p>Results</p> <p>Of the 439 1992-94 NIGMS F32 fellows, the careers of 417 could be determined. Although females had significantly higher rates of dropping out of science (22% females, 9% males) there was no significant difference in the fraction of females that ended up as associate or full professors at research universities (22.8% females, 29.1% for males). More males then females ended up in industry (34% males, 22% females). Although there was no significant correlation between male grant success and their mentor's publication record (h index, citations, publications), there was a significant correlation for females. Females whose mentor's h index was in the top quartile were nearly 3 times as likely to receive a major grant as those whose mentors were in the bottom quartile (38.7% versus 13.3%).</p> <p>Conclusions</p> <p>Sixteen years after starting their PD, only 9% of males had dropped out of science. More females (28%) have dropped out of science, primarily because fewer went into industry positions. The mentor's publication record does not affect the future grant success of males but it has a dramatic effect on female grant success.</p

The role of mentorship in protege performance

The role of mentorship on protege performance is a matter of importance to academic, business, and governmental organizations. While the benefits of mentorship for proteges, mentors and their organizations are apparent, the extent to which proteges mimic their mentors' career choices and acquire their mentorship skills is unclear. Here, we investigate one aspect of mentor emulation by studying mentorship fecundity---the number of proteges a mentor trains---with data from the Mathematics Genealogy Project, which tracks the mentorship record of thousands of mathematicians over several centuries. We demonstrate that fecundity among academic mathematicians is correlated with other measures of academic success. We also find that the average fecundity of mentors remains stable over 60 years of recorded mentorship. We further uncover three significant correlations in mentorship fecundity. First, mentors with small mentorship fecundity train proteges that go on to have a 37% larger than expected mentorship fecundity. Second, in the first third of their career, mentors with large fecundity train proteges that go on to have a 29% larger than expected fecundity. Finally, in the last third of their career, mentors with large fecundity train proteges that go on to have a 31% smaller than expected fecundity.Comment: 23 pages double-spaced, 4 figure

mmView: a web-based viewer of the mmCIF format

<p>Abstract</p> <p>Background</p> <p>Structural biomolecular data are commonly stored in the PDB format. The PDB format is widely supported by software vendors because of its simplicity and readability. However, the PDB format cannot fully address many informatics challenges related to the growing amount of structural data. To overcome the limitations of the PDB format, a new textual format mmCIF was released in June 1997 in its version 1.0. mmCIF provides extra information which has the advantage of being in a computer readable form. However, this advantage becomes a disadvantage if a human must read and understand the stored data. While software tools exist to help to prepare mmCIF files, the number of available systems simplifying the comprehension and interpretation of the mmCIF files is limited.</p> <p>Findings</p> <p>In this paper we present mmView - a cross-platform web-based application that allows to explore comfortably the structural data of biomacromolecules stored in the mmCIF format. The mmCIF categories can be easily browsed in a tree-like structure, and the corresponding data are presented in a well arranged tabular form. The application also allows to display and investigate biomolecular structures via an integrated Java application Jmol.</p> <p>Conclusions</p> <p>The mmView software system is primarily intended for educational purposes, but it can also serve as a useful research tool. The mmView application is offered in two flavors: as an open-source stand-alone application (available from <url>http://sourceforge.net/projects/mmview</url>) that can be installed on the user's computer, and as a publicly available web server.</p

C-ME: A 3D Community-Based, Real-Time Collaboration Tool for Scientific Research and Training

The need for effective collaboration tools is growing as multidisciplinary proteome-wide projects and distributed research teams become more common. The resulting data is often quite disparate, stored in separate locations, and not contextually related. Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. C-ME stores the information in industry-standard databases that are immediately accessible by appropriate permission within the computer network directory service or anonymously across the internet through the C-ME application or through a web browser. The system addresses two important aspects of collaboration: context and information management. C-ME allows a researcher to use a 3-D atomic structure model or a 2-D image as a contextual basis on which to attach and share annotations to specific atoms or molecules or to specific regions of a 2-D image. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members