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,
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac196/6569079
Here we present plotsr, an efficient tool to visualize structural similarities and rearrangements between genomes. It can be used to compare genomes on chromosome level or to zoom in on any selected region.
submitted by: Istvan Albert
GitHub - davetang/learning_vcf_file: Learning the Variant Call Format (github.com)
An exhaustive reference to all things VCF
submitted by: Istvan Albert
Somatic mutation rates scale with lifespan across mammals | Nature (www.nature.com)
Notably, the somatic mutation rate per year varied greatly across species and exhibited a strong inverse relationship with species lifespan, with no other life-history trait studied showing a comparable association.
submitted by: Istvan Albert
Cancers | Free Full-Text | A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data (www.mdpi.com)
Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling.
We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS.
submitted by: Istvan Albert
Got an RNA-seq dataset with 50, 100, 200+ samples? Plug it into a differential expression tool and hope for the best? No! You need to consider QC, EDA, and modeling technical variation, or else risk generating spurious results. A thread on papers, methods, and best practices: pic.twitter.com/p7Zn61QjHw
— Michael Love (@mikelove) April 11, 2022
Got an RNA-seq dataset with 50, 100, 200+ samples? Plug it into a differential expression tool and hope for the best? No! You need to consider QC, EDA, and modeling technical variation, or else risk generating spurious results. A thread on papers, methods, and best practices: pic.twitter.com/p7Zn61QjHw
— Michael Love (@mikelove) April 11, 2022submitted by: Istvan Albert
GitHub - refresh-bio/agc: Assembled Genomes Compressor (github.com)
Assembled Genomes Compressor (AGC) is a tool designed to compress collections of de-novo assembled genomes. It can be used for various types of datasets: short genomes (viruses) as well as long (humans).
The paper can be found at:
https://www.biorxiv.org/content/10.1101/2022.04.07.487441v1
submitted by: Istvan Albert
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