Herald:The Biostar Herald for Monday, December 12, 2022
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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,

EPI2ME Labs Workflows | EPI2ME Labs Blog (labs.epi2me.io)

EPI2ME Labs maintains a collection of bioinformatics workflows tailored to Oxford Nanopore Technologies long-read sequencing data. They are curated and actively maintained by experts in long-read sequence analysis..

submitted by: Istvan Albert

Structural variants and the SAM format - the long (reads) and short (reads) of it (cmdcolin.github.io)

The SAM specification is pretty amazing (https://samtools.github.io/hts-specs/SAMv1.pdf), but it is also fairly terse and abstract. True understanding might come from playing with real-world data. I will try to relay some things I have learned over the years, with a bit of a focus on how SAM file concepts can relate to structural variants.

submitted by: Istvan Albert

submitted by: Istvan Albert

submitted by: Istvan Albert

Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias | PLOS Biology (journals.plos.org)

Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization.

submitted by: Istvan Albert

Impact of gene annotation choice on the quantification of RNA-seq data | BMC Bioinformatics | Full Text (bmcbioinformatics.biomedcentral.com)

In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

submitted by: Istvan Albert

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