Hi all, I am actually a bachelor in theoretical mathematics and am now planning to do a master's by research in bioinformatics/biostatistics (in particular statistical genetics). My ultimate target is to possibly publish a paper during my master's study (due to requirement and also it's important nowadays to have publication for Ph.D.). Since I am still 3 months away prior the master program, I have compiled a reading list, i.e

1) Bioinformatics Algorithm: An Active Learning Approach, Vol1&2 (Together with Rosalind)
2) Statistics by David Freedman
3) Statistical Inference by Casella Berger
4) Introduction to Statistical Learning with Application in R.

Are they enough for me to be able to start reading papers and do research(and publish maybe) if I do all of the exercises from these books?

Yes, you are on right track.
One recent publication I read WTDBG2 they used statistical aspect with computational approaches to improve speed of assembly by using their knowledge and implementation skills and also presented one current challenge which need to be address to speed up the entire process. Similarly, there are immense opportunity if you have strong foundation of such concepts.
I want to add one thing that helped me to optimize one currently existed algorithm to predict certain phenomena (I am still evaluating the accuracy). Gain in-depth knowledge of molecular biology and events that occurs in between. You will be able to design and mathematically model problems or able to improve existing algorithm that will help you and science.

Is the molecular biology in the Rosalind website and Bioinformatics Algorithm: An Active Learning Approach sufficient? Certainly, read more is better, but I only have 1-2 years of master's by research. (which I had to try to publish if possible).

Is the molecular biology in the Rosalind website and Bioinformatics Algorithm: An Active Learning Approach sufficient? Certainly, read more is better, but I only have 1-2 years of master's by research. (which I had to try to publish if possible).