Popular RNAseq packages often use the formula notation in R. For example, the DESeq package uses it in the design parameter, whereas edgeR creates its design matrix by expanding a formula with “model.matrix”. The formula syntax seems to confuse many users of these libraries.
As mentioned in an earlier post, I have been working on a R library for RNAseq data analysis. The goal of this library is to provide clean, easy-to-remember functions for data analysis. In this post, I will describe the functional options chosen for the rna_visualize function for plotting of data. I will also discuss the design and coding challenges encountered during this implementation.
Over the last couple of months, I have been working on and off on a new R package for statistical analysis of RNAseq data. A number of popular and excellent packages (e.g. edgeR, DEseq, DEseq2, limma-voom, sleuth, etc.) exist to solve this problem, and they all use different mathematical methods to find statistically significant genes.
We are continuing our discussion of eukaryotic genome evolution based on Dan Graur’s “Molecular and Genome Evolution”. In this post, we present a number of puzzling observations in various eukaryotic genomes. The title of each section also includes the page number of Graur’s book, where the observation is reported.
In the previous post, I wrote about the book “Molecular and Genome Evolution” by Dan Graur. It contains thirteen chapters as shown below. Chapters 7-11 may be considered the heart of the book, where Graur discusses how the genomes evolve and how new genes come into existence. Among those, the chapters 6-8 present three mechanisms for genome evolution, namely DNA duplication, molecular tinkering and mobile elements. Subsequently, chapters 10 and 11 discuss evolutionary aspects of the prokaryotic and the eukaryotic genomes respectively.
Over the last two weeks, I have been reading Dan Graur’s book titled “Molecular and Genome Evolution”. This is a fantastic book that everyone should read before starting to work on any genome-related project. For the benefit of our readers, I will share some comments in this short post. If time permits, I will later follow up with a longer post on the book.
The de Bruijn graphs are immensely helpful in assembling Illumina sequences, but they often occupy massive amounts of memory, especially for large raw datasets. Our readers interested in representing de Bruijn graphs in compact space should not miss a recent paper by Victoria Crawford, Alan Kuhnle, Christina Boucher, Rayan Chikhi and Travis Gagie. The paper is published in Bioinformatics, but the journal link is not open-source.
In the past, the major attention of algorithm developers working on long noisy reads (Pacbio, Nanopore) had been directed to noise correction and genome assembly. Now that the costs have come down, users are looking into other applications, including finding SNPs.
Here is a great opportunity to learn cutting-edge algorithms in bioinformatics. Heng Li, who developed several popular NGS bioinformatics programs like Samtools, BWA and Minimap, is moving to Dana Farber Cancer Institute. He is hiring new post-docs to work with him.
Often I download newly published bioinformatics programs or libraries from the github into my Windows laptop and try to compile them within its Cygwin UNIX environment. Over the years, I noticed that those C/C++ codes tend to fall into two distinct categories -
In the traditional model of computing, programmers write their codes in C or other high-level
(i.e. human-readable) languages. Then a compiler (e.g. gcc) converts that code into assembly
and machine (byte) instructions. This is because the microprocessor can understand only 0s and 1s,
whereas the humans tend go crazy trying to make sense of such code. The assembly language is a
happy compromise between the two. It presents the machine or byte-instructions in human-readable
Investor warning: The following post is for entertainment purposes only, and should not be considered as financial advice of any sort.
In Feb 2016, we made a forecast that Oxford Nanopore would go out of business by the end of 2017. That did not happen, and we do deserve to get an ‘F’ for that forecast. We would also like to take this opportunity to make our readers aware of a relevant (and highly controversial) investment research report that came out recently.
For those interested in trying out the cutting-edge tools in ancestry research on real data, I
am open-sourcing my own genotype information in this github project
along with all analysis steps. You need to install two programs - plink and admixture. Then by following
the steps given in the README file, you should be able to find the geographic origin of the given sample,
(which is me).