Ladder-seq, association of genes with cancer and more
1 - A new approach to RNA-seq: Ladder-seq
As time goes, past technologies are refined. This is the case with (bulk) RNA-seq. Ladder-seq is a new technology that includes read length information during the sequencing steps, improving transcript quantification and data analysis.
Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data | Nature Biotechnology
The quality of RNA sequencing transcriptomes is improved when mRNAs are separated by length.
2 - Genes and association with cancer
Cancer is a highly disruptive disease and can be deadly. In 1971, Richard Nixon, the USA president at the time, declared the war on cancer. Ever since a lot of money has been invested to combat all types of cancer. By trying to understand and finding possible treatments, a huge amount of genes are analysed. The assay below argues that eventually all genes might be associated to cancer.
https://www.sciencedirect.com/science/article/pii/S0168952521002663
3 - Focusing only on metrics: is it any good?
Metrics can be a good way to quantify some processes. What happens when people start gaming the metrics? The assay below discuss about metrics and how nefastous they can be.
Campbell’s Law: The Dark Side of Metric Fixation
When organizations optimize metrics at the cost of all else, they expose themselves to metric corruption. Ultimately, as the Facebook scandal illustrates, they may fail their users and their business goals.
4 - Randomized clinical trials and clinical practice
Some clinicians criticize the use of RCT, saying that they do not reflect the practice. In this essay, Frank Harrell explain the value of random clinical trials and other experimental designs to test the efficacy of treatments.
Randomized Clinical Trials Do Not Mimic Clinical Practice, Thank Goodness | Statistical Thinking
What clinicians learn from clinical practice, unless they routinely do n-of-one studies, is based on comparisons of unlikes. Then they criticize like-vs-like comparisons from randomized trials for not being generalizable.
5 - tidybulk: transcriptomic analysis with a tidy flavor
The tidyverse is an amazing ecosystem of packages in R to handle data. The tidybulk package extends the tidyverse for transcriptomics, allowing the user to do differential expression analysis, cell type deconvolution and much more in your standard tidy pipelines.