Posted on Sat 04 May 2024 in how-to • Tagged with R, shiny, app, RNAseq, bioinformatics, data-visualization
How do you communicate results of an analysis? What tools do you use? Scientists that work in the wet lab are accustomed to firing up excel or some instrument-specific software and working with their own data. For genomics or other types of experiments in biology that result in large datasets …
Posted on Sun 21 April 2024 in how-to • Tagged with bioinformatics, data-visualization, rnaseq
One of the, if not the, most common downstream analysis task I'm asked to perform on RNAseq data is to generate the venerable "Volcano Plot." These are kind of the bioinformatics equivalent of saying "Hey! Look how much data I have!" Regardless, they are a pretty good way to quickly …
Posted on Thu 22 August 2019 in commentary • Tagged with bioinformatics, python, workflows
In contrast to the zen of python there are actually many ways to handle sequence data in Python. There are several packages on PyPI that provide parsers for sequence formats like .fastq and .fasta. I've never bothered with these, including the oft-used Biopython. I vaguely remembered Biopython being slower than …
Posted on Mon 19 August 2019 in how-to • Tagged with bioinformatics, python, workflows, snakemake
Over the past few years the use of workflow managers in genomics and bioinformatics has grown greatly. This is a great thing for the field and adds to our ability to perform reproducible analyses, especially for pipelines with many steps. These are common in bioinformatics, but prior to the use …
Posted on Fri 09 August 2019 in commentary • Tagged with bioinformatics, thoughts, workflows
Bioinformatic analyses often require lengthy workflows or pipleines, where the output of program A feeds into program B, and so on. These programs may also not output their results in a format which is convenient to use in the subsequent steps, requiring writing a conversion script, or piping its output …
Posted on Wed 17 July 2019 in how-to • Tagged with bioinformatics, data-science, r, python, big-data
Working with large amounts of tabular data is a daily occurance for both bioinformaticians and data scientists. There's a lot the two groups can learn from each other (great future post material). However, I recently ran into a situation that I was sure had to be relatively common. Apparently it …
Posted on Tue 09 July 2019 in commentary • Tagged with bioinformatics, thoughts, rnaseq, workflows
When analyzing RNAseq you're faced with many possible analysis pipelines. The biggest decision you need to make is what the purpose of your experiment is. I will make the assumption that most of the time people want to determine which genes are differentially expressed between two samples, genotypes, conditions, etc …
Posted on Fri 28 June 2019 in how-to • Tagged with bioinformatics, data-visualization
Last time we prepared our data using Deeptools.
Now we're going to do something kind of scandalous. R and python, living together in peace. What is this madness? I like R's ecosystem for manipulating data and plotting with the tidyverse. It still requires some tweaking, but with a bit of …
Posted on Thu 27 June 2019 in how-to • Tagged with bioinformatics, data-visualization
Commonly, in bioinformatics we're in the business of determining whether something, be it gene expression, or DNA methylation, or splicing, etc. is different between multiple conditions. Typically this would be done by comparing those data and using some kind of statistical test. However, with the continued advances in sequencing technologies …