BOL

bioinformatics.sdstate.edu - 2/3/2020: Now published by Bioinformatics. 11/3/2019: V 0.61, Improve graphical visualization (thanks to reviewers). Interactive networks and much more. 5/20/2019: V.0.60, Annotation database updated to Ensembl 96. New bacterial and fungal...

advaitabio.com - The confusion about gene ontology and gene ontology analysis can start right from the term itself. There are actually two different entities that are commonly referred to as gene ontology or “GO”: the ontology itself, which is a...

diytranscriptomics.com - A semester-long course covering best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies, with a primary focus on empowering students to be independent in the use of lightweight and open-source...

A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since Measures the average expression level for each gene across a large population of input cells Useful for comparative transcriptomics,...

One of the technical advantages of ONT data is the read length, which offers great prospects for genome assembly.

github.com - With the EGAD (Extending ‘Guilt-by-Association’ by Degree) package, we present a series of highly efficient tools to calculate functional properties in networks based on the guilt-by-association principle. These allow rapid controlled...

The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods...

github.com - ProteoClade is a Python library for taxonomic-based annotation and quantification of bottom-up proteomics data. It is designed to be user-friendly, and has been optimized for speed and storage requirements. ProteoClade helps you analyze two...

master.bioconductor.org - Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were quantified to the reference transcripts, and prepare gene-level count...

the sequenced reads can be mapped to the organism’s genes to assess how differently the genes are expressed under the experimental circumstances as opposed to the control scenario. This is known as differential expression (DE) analysis