Herald:The Biostar Herald for Monday, February 10, 2025
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The Biostar Herald publishes user submitted links of bioinformatics relevance. It aims to provide a summary of interesting and relevant information you may have missed. You too can submit links here.

This edition of the Herald was brought to you by contribution from Istvan Albert, and was edited by Istvan Albert,


vcfsim: flexible simulation of all-sites VCFs with missing data | bioRxiv (www.biorxiv.org)

Here, we introduce an open-source command line tool, vcfsim, that interfaces with the popular coalescent simulation platform msprime and provides convenience functions for simulating all-sites VCFs with variable levels of ploidy and missing data. We show that the post-processed VCFs generated using vcfsim align precisely with population genetic expectations (i.e. are statistically identical to raw msprime output) and can accurately introduce missing data and varying ploidy levels, including the simulation of intraindividual ploidy variation (e.g. heterogametic sex chromosomes). We suggest vcfsim will be a useful tool for the benchmarking of new software tools, training of machine learning models, and the exploration of the effects of missing data in genomics data sets.

submitted by: Istvan Albert


R2: Genomics Analysis and Visualization Platform (hgserver1.amc.nl)

Welcome to R2, an open access online genomics datascience and discovery platform designed to assist the bio-medical researchers with limited to no Bioinformatics skills to perform data mining tasks in the omics field.

Our mission is to provide biomedical scientists with a comprehensive, user-friendly platform for analyzing, interpreting and visualizing genomic data, enabling them to make more informed decisions and advance their research in the field of genomics without the need for bioinformatics or coding expertise (nocode).

submitted by: Istvan Albert


A Bioinformatician, Computer Scientist, and Geneticist lead bioinformatic tool development - which one is better? (academic.oup.com)

Our results suggest that “Medical Informatics” outperforms all other fields in bioinformatic software accuracy, with a mean proportion of wins in accuracy rankings exceeding the null expectation. In contrast, tools developed by authors affiliated with” Bioinformatics” and” Engineering” fields tend to be less accurate. However, after correcting for multiple testing, no result is statistically significant (p > 0.05).

Editors' note: I see. The results suggest that field X is best, as long as you use the wrong stats. Alas, once you use statistics correctly, neither field wins. Ok. Gotcha.

submitted by: Istvan Albert


Collaborative Genomics Projects: A Comprehensive Guide (www.sciencedirect.com)

As the cost of genomic sequencing is decreasing, more and more researchers are leveraging genomic data to inform the biology of disease. The amount of genomic data generated is growing exponentially, and protocols need to be established for the long-term storage, dissemination, and regulation of these data for research. We aim to create a comprehensive guide to managing research projects involving genomic data, as learned through the evolution of The Cancer Genome Atlas program over the last decade. This project was primarily carried out in the United States, but the impact and lessons learned can be applied to an international audience.

submitted by: Istvan Albert


SoS - Script of Script (vatlab.github.io)

Script of Scripts (SoS) is a computational environment for the development and execution of scripts in multiple languages for daily computational research. It can be used to develop scripts to analyze data interactively in a Jupyter environment, and, with minimal effort, convert the scripts to a workflow that analyzes a large amount of data in batch mode.

Editor's note: basically it is a tool that allows one to a run a Jupyter Notebook as a script ... but then Jupyter notebooks were designed to avoid having to run obtuse scripts at the command line ... I guess the circle is now complete

submitted by: Istvan Albert


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I have been using SoS for more than a year now. In a conda environment the management of dependencies from R, Python, Jupyter and SoS is not the easiest. But once it's done the notebook works like a charm. It is still a bit obscure how and what type of variable can be transfered from R to Python via SoS or the other way around, so I just use HDF5 files to jump from one to another. However keeping R/Python/Bash cells in the same notebook is pretty neat !

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