Herald:The Biostar Herald for Monday, April 24, 2023
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13 months ago
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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,


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

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

submitted by: Istvan Albert

submitted by: Istvan Albert

submitted by: Istvan Albert

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

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

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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