High-throughput data types mainly include gene chips and gene sequencing, and I'm assuming that you want to know the specifics.

The specifics actually refer to the application of high-throughput sequencing technologies, such as microarray, RNA-Seq, Exome-Seq, Target-Seq, Whole-genome-sequencing, macro-genome, 16S RNA, microRNA, and lncRNA sequencing.

The research issues are even more varied, like now the concept of precision medicine is very hot, mainly with gene sequencing as the entrance, the latter application, such as prenatal diagnosis, pre-pregnancy diagnosis, etc., even like paternity testing, tumor targeting, etc. can be handled by means of bioinformatics analysis.

Bioinformatics analysis is divided into several levels, the first level is basically to use others to do a good job of mature software, directly analyze the results you want, and then in-depth is that you will be based on the problem to find more suitable software or modules, to set up some of their own analytical processes, including their own auxiliary programs to write some scripts, more in-depth level is the market does not meet your requirements for software or statistical algorithms, you rely on their own analysis of the software. Statistical algorithms, you according to their own needs, customize their own analysis process, write their own basic program from scratch, write statistical algorithms, write models and so on. At this point, there are not so many restrictions, the main ratio is the individual's thinking and ideas and the degree of eye-opening.

Now there are a lot of bioinformatics analytics applied in various fields of big data. The essence is the realization of a variety of statistical thinking methods to find out the results of a particular pattern.