People are getting crazy with Pokemon Go recently. I wonder what the result will be like to do an emotional analysis on the topic of #Pokemon Go# on Twitter.

People have been utilize deep learning widely in health, wearable electronics, household appliance, cars, social networks and industry, but in the field of genomics, the magic has just emerged, despite the massive big data generated in this field.

Which genes are dominating when bacteria are trying to form a film? In this blog, I will build a gene regulatory network from E.Coli gene expression data during biofilm formation, using a method based on Random Forests, and perform a functional analysis for that network.

Grape berry development is a dynamic process that involves a complex series of molecular genetic and biochemical changes. In this blog, we will do an unsupervised clustering using expression profile of grapes while ripening.

Differential Gene Expression, also known as DEG (Differentially expressed genes), is becoming a hot topic in fundenmental science research now, with the advancement of Next Generation Sequencing and single-cell isolation technology. In this blog, I will talk about the following topics:

Due to the complexity of genomic datasets, the best approach to analyze data is often not clear from the beginning.

BLAST is one of the most widely used bioinformatics algorithms for sequence searching. This emphasis on speed is vital to making the algorithm practical on the huge genome databases currently available.

In Genomic data analysis, new algorithms are coming out every day. We often hear about new topics on mapping algorithms such as TopHat, Cufflink, FASTAQ, and so on. However, for researchers, the problem is where to access these new algorithms and how to process their own datasets with them conveniently.