Herald:The Biostar Herald for Monday, April 24, 2023

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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,

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https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad214/7131068

HMMER is a biological sequence analysis tool that uses profile hidden Markov models to search for sequence homologs. HMMER3 is developed and maintained by the Eddy/Rivas Laboratory at Harvard University.

pyhmmer is a Python package, implemented using the Cython language, that provides bindings to HMMER3. It directly interacts with the HMMER internals, which has the following advantages over CLI wrappers

submitted by: Istvan Albert

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Exhaustive benchmarking of de novo assembly methods for eukaryotic genomes | bioRxiv (www.biorxiv.org)

Here, we provide a comprehensive benchmark of 28 state-of-the-art assembly and polishing packages, in various combinations, when assembling two eukaryotic genomes using both next-generation (Illumina HiSeq) and third-generation (Oxford Nanopore and PacBio CLR) sequencing data, at both controlled and open levels of sequencing coverage. Recommendations are made for the most effective tools for each sequencing technology and the best performing combinations of methods, evaluated against common assessment metrics such as contiguity, computational performance, gene completeness, and reference reconstruction, across both organisms and across sequencing coverage depth

submitted by: Istvan Albert

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Been looking forward to this piece — (which I heard about when I was reporting on how funding agencies were backing away from GISAID recently)

It’s a wild tale about the shadowy founder of @gisaid Peter Bogner https://t.co/9mNa401Hvq

— Natasha Loder (@natashaloder) April 19, 2023

submitted by: Istvan Albert

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Hey, weren't you a co-author on that? https://t.co/sDBpU9UPnZ
Yes, I was, but the Nature editors neutered the letter to the point that I felt I could no longer in good conscience put my name on it. The most egregious (and final straw for me) was the insistence to change 1/n

— NimwegenLab (@NimwegenLab) April 20, 2023

submitted by: Istvan Albert

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In a recent paper, Shen et al. reported that most synonymous mutations in yeast genes were strongly deleterious. In a new preprint, we argue that this result is largely or entirely artifactual and arose from a lack of appropriate controls. https://t.co/KiV6eCa4Ux

— Leonid Kruglyak (@leonidkruglyak) July 15, 2022

submitted by: Istvan Albert

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No evidence that synonymous mutations in yeast genes are mostly deleterious | bioRxiv (www.biorxiv.org)

In a recent paper, Shen et al. reported that most mutations in the coding regions of 21 yeast genes were strongly deleterious, and that the distributions of fitness effects were similar for synonymous and nonsynonymous mutations. Taken at face value, these results would conflict with well-established findings from a broad range of fields and approaches. Here, we argue that these results arose from a lack of appropriate controls for the impacts of background genetic effects in edited strains

submitted by: Istvan Albert

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The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles | bioRxiv (www.biorxiv.org)

In this work, we address these questions by comprehensively analyzing imputations from 23 imputation models submitted to the ENCODE Imputation Challenge. We find that measuring the quality of imputations is significantly more challenging than reported in the literature, and is confounded by three factors: major distributional shifts that arise because of differences in data collection and processing over time, the amount of available data per cell type, and redundancy among performance measures.

submitted by: Istvan Albert

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1/ How to lie with statistics advanced edition...

This meta-study claims to overturn the long established pattern that moderate drinkers have lower mortality than abstainers. After adjusting for confounders there was "no significant reduction in mortality". Very misleading... https://t.co/9QVB9NWdmy pic.twitter.com/bMVGDIuVAl

— Doug Colkitt (🐊,🐊) (@0xdoug) April 6, 2023

TIL about reverse p-hacking, when you want a result to not-pass a p-value treshold

submitted by: Istvan Albert

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