Homolog.us - Frontier in Bioinformatics

Annual Bioinformatics Contest from the Rosalind Team

Formula Syntax in RNAseq Packages like DESeq2 or edgeR

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.

Rnaseq.work - Current APIs and Design Decisions

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.

Rnaseq.work - Plotting Functions in RNAseq-related Packages

Rnaseq.work - A Package with Clean APIs for Statistical Analysis of RNAseq Data

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.

Live Online Class - RNAseq Data Analysis using R

If you like to use R for RNAseq data analysis, please join our online class on Dec 1/8/15 at 10AM-1PM Pacific time. This module is designed for those from biology background.

Illumina Buys Pacbio, What Are the Implications?

Puzzling observations from various eukaryotic genomes (part III)

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.

How do the eukaryotic genomes evolve? (part II)

We are continuing our discussion of eukaryotic genome evolution based on Dan Graur’s “Molecular and Genome Evolution”. In this post, we look at two key measures - genome size and gene size.

How do the eukaryotic genomes evolve? (part I)

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.

Molecular and Genome Evolution by Dan Graur

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.

Practical Dynamic de Bruijn Graphs

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.

Finding SNPs from Long Noisy Reads

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.

A Terrific Post-doc Opportunity to Learn Bioinformatics

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.

Mantis and the Counting Quotient Filter

Salmonberry Genomics by High-school Students

A Generation Lost in the Bazaar

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 -

How does Multi-threaded Code Run in Assembly Language?

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 format.

Bioinformatics Contest - 2018

It is that time of the year again. Our friends from Rosalind, Stepik and Bioinformatics Institute are hosting another bioinformatics contest with qualifying round starting on Feb. 3rd. Details below.

A New Nemesis for Nanopore

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.

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