What’s new? – Computational Biology and Bioinformatics Research

Ten simple rules for funding scientific open source software

Scientific research increasingly relies on open-source software (OSS). Funding OSS development requires an intentional focus on issues of scholarly credit, unique forms of labor, maintenance, governance, and inclusive community-building. Such issues cut across different scientific disciplines, make them of interest to a variety of funders and institutions but may present challenges in understanding generalized needs. Here we present 10 simple rules for investing in scientific OSS and the teams who build and maintain it.

So you want to be a computational biologist?

The term 'computational biologist' can encompass several roles, including data analyst, data curator, database developer, statistician, mathematical modeler, bioinformatician, software developer, ontologist—and many more. What's clear is that computers are now essential components of modern biological research, and scientists are being asked to adopt new skills in computational biology and master new terminology.

Science in the age of selfies

A time traveler from 1915 arriving in 1965 would have been astonished by the advances in scientific theories and engineering technologies in the half century.

Leveraging Computational Biology for Plant Agriculture

Computational biology is an interdisciplinary branch of science that combines computer science and biology to analyze, model, and simulate biological processes. By leveraging the principles of computational biology, plant scientists can gain valuable insights into various aspects of plant agriculture. This article will explore how computational biology has been used to improve plant production, assess environmental impacts, and provide resources for data-driven decision-making.

AllInOne 1.9.0 is here!

Ready to hear news about #AllInOne Pre-Processing? AllInOne Pre-Processing is out with its new version (1.9.0) - it's packed with advanced features like Pearson correlation based on different independent variables! Bugs are also fixed!......................................................Consider a dataset that includes Location, Genotypes, Year, and Trial as independent variables and Yield as a dependent one (just translate variables based on your own area of expertise). You would like to see the correlation of Yield among locations in each Trial * Year interaction level All In One table. It could be time-consuming to get one table showing correlations among multiple variables, and this is exactly the point that AllInOne Pre-Processing can help you!