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 GenoMax, Istvan Albert, Starlitnightly, and was edited by Istvan Albert,
Hey Steve, What do you think of the rebuttal - https://t.co/cv44CxCnio
— Jack A Gilbert (he/him/his) (@gilbertjacka) August 1, 2023
Hey Steve, What do you think of the rebuttal - https://t.co/cv44CxCnio
— Jack A Gilbert (he/him/his) (@gilbertjacka) August 1, 2023submitted by: Istvan Albert
Major, fatal errors found in the data and methods of a 2020 paper in @Nature, including millions of reads mis-identified as bacteria. The "cancer microbiome" in this study was simply not there. @abrahamgihawi @elapertea @YuchenGe1 @JenniferLu717 https://t.co/z5Aja84kiR
— Steven Salzberg 💙💛 (@StevenSalzberg1) August 1, 2023
Major, fatal errors found in the data and methods of a 2020 paper in @Nature, including millions of reads mis-identified as bacteria. The "cancer microbiome" in this study was simply not there. @abrahamgihawi @elapertea @YuchenGe1 @JenniferLu717 https://t.co/z5Aja84kiR
— Steven Salzberg 💙💛 (@StevenSalzberg1) August 1, 2023submitted by: Istvan Albert
Major data analysis errors invalidate cancer microbiome findings | bioRxiv (www.biorxiv.org)
We re-analyzed the data from a recent large-scale study that reported strong correlations between microbial organisms and 33 different cancer types, and that created machine learning predictors with near-perfect accuracy at distinguishing among cancers [...] Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong.
submitted by: Istvan Albert
GitHub - Starlitnightly/omicverse: A python library for multi omics included bulk and single cell RNA-seq analysis. (github.com)
OmicVerse is the fundamental package for multi omics included bulk and single cell analysis with Python. The original name of the omicverse was Pyomic, but we wanted to address a whole universe of transcriptomics, so we changed the name to OmicVerse, it aimed to solve all task in RNA-seq.
The OmicVerse documentation website provides an application programming interface (API) reference for each algorithm, along with tutorials describing the functionality and limitations of each algorithm and its interaction with other bulk/single-seq tools. These tutorials are also accessible through Google Colab, providing a free computing environment to support pipeline studies.
Our aim was to create an ecosystem for bulk/single-seq analysis and beautiful visualization within Python environments. Users can perform comprehensive transcriptome analysis using a single programming language, leveraging the machine-learning models and expertise of the Python community. As OmicVerse continues to evolve, we anticipate ongoing updates that incorporate new algorithms, features, and models. OmicVerse is expected to benefit the bulk/single-seq community by facilitating the prototyping of new models, establishing standards for the analysis of trans omics, and enhancing the scientific discovery pipeline.
submitted by: Starlitnightly
Comprehensive Assessment of Eleven de novo HiFi Assemblers on Complex Eukaryotic Genomes and Metagenomes | bioRxiv (www.biorxiv.org)
Assessment of read assemblers for eukarotic genomes.
submitted by: GenoMax
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