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Istvan Albert,
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Genome-wide association studies | Nature Reviews Methods Primers (www.nature.com)
GWAS methods summary 2021
submitted by: zx8754
The mutational landscape of human somatic and germline cells | Nature (www.nature.com)
Over the course of an individual’s lifetime, normal human cells accumulate mutations. Here we compare the mutational landscape in 29 cell types from the soma and germline using multiple samples from the same individuals.
submitted by: Istvan Albert
The Specious Art of Single-Cell Genomics | bioRxiv (www.biorxiv.org)
Dimensionality reduction is standard practice for filtering noise and identifying relevant dimensions in large-scale data analyses. In biology, single-cell expression studies almost always begin with reduction to two or three dimensions to produce ‘all-in-one’ visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative analysis of cell relationships. However, there is little theoretical support for this practice
submitted by: Istvan Albert
On t-SNE & UMAP preserving structure: 1) we show massive distortion by examining what happens to equidistant cells and cell types. 2) neighbors aren’t preserved. 3) Biologically meaningful metrics are distorted. E.g., see below: pic.twitter.com/owUAFYyURM
— Lior Pachter (@lpachter) August 27, 2021
On t-SNE & UMAP preserving structure: 1) we show massive distortion by examining what happens to equidistant cells and cell types. 2) neighbors aren’t preserved. 3) Biologically meaningful metrics are distorted. E.g., see below: pic.twitter.com/owUAFYyURM
— Lior Pachter (@lpachter) August 27, 2021
Perhaps it’s time for everyone to say out loud what we’ve all known for some time, but have had difficulty admitting: t-SNE, UMAP and relatives are just specious art and we risk fooling ourselves when we start to believe in the mirages they present.
submitted by: Istvan Albert
Genome-wide association studies | Nature Reviews Methods Primers (www.nature.com)
In this Primer, we provide the reader with an introduction to GWAS, explaining their statistical basis and how they are conducted, describe state-of-the art approaches and discuss limitations and challenges, concluding with an overview of the current and future applications for GWAS results.
submitted by: Istvan Albert
Some musings on assigning a taxonomy to a sequence or genome – this usually involves some kind of sequence search (alignment or kmer) against a reference database – but what then?
— Mick W@tson (@BioMickWatson) July 30, 2021
Some musings on assigning a taxonomy to a sequence or genome – this usually involves some kind of sequence search (alignment or kmer) against a reference database – but what then?
— Mick W@tson (@BioMickWatson) July 30, 2021
submitted by: Istvan Albert
Benchmarking metagenomic classification tools for long-read sequencing data | bioRxiv (www.biorxiv.org)
The results show that off-the-shelf mappers such as Minimap2 or Ram are at least comparable with mapping-based classification tools in most accuracy measures while not being much slower than kmer based tools and requiring equal or less RAM. Majority of tested tools are prone to report organisms not present in datasets and underperform in the case of high presence of host’s genetic material.
submitted by: Istvan Albert
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