RNAseq is a whole world. There are quite a few analysis you can make, but with which analysis to start with depends on the scientific questions you have. One short answer would be, check the book: RNAseq Data Analysis - A practical approach, by Korpelainen, Tuimala, Somervuo, Huss and Wong.

https://www.amazon.com/RNA-seq-Data-Analysis-Mathematical-Computational/dp/1466595000

It has it all: from a short introduction for each analysis, with recent references, including bioinformatic pipelines and even detailed tutorials of the programs that can be used for each task. I use this book as one of the main references to teach the last of three courses in bioinformatics.

What is your background? The book mentioned by Gonalo Riadi is very good for a beginner. You could also look into the Galaxy Project (https://galaxyproject.org/) and use one of the public servers to get into the analysis pipeline. Or even check out some of coursera courses (genomic data science) to get into that topic.

Hi, you need to choose a platform to analyse your data depending on the questions you already have. Then you sort the most significant genes that appear in your groups and try to make sense of what and why these genes are more abundant in a group than the other. Then you run your genes through gene ontology pipelines to check if there are for example interesting pathways that the groups share or differ.

Good luck  

I agree with Dr. Rana Al-Baghdadi

Since you are interested in understanding the differential gene expression at 4 different conditions, it would be a good idea to align the four samples to the reference genome (if available) by using an aligner  (if you have the computational resources)  and then quantify the  read counts  using HTseq,  followed by Differential Expression Analysis by any of the statistical packages in R (edgeR or DESeq). 

If Reference Genome is not available, Incase if you have sequence data at normal condition then you can assemble those reads and use assembled draft genome  as reference, or you can use one of your four samples (assembled) as  reference. ( But keep in mind,  your reference sample, is what you are using as a base to estimate the expression of gene or transcript ). 

For assembly you can use AbySS Transcript  assembler ( recommended as per literature).

To get a better understanding on how to analyze the RNA seq. data, refer the following papers. 

First, you should align the clean reads to the reference genome if possible. the tools you can use are tophat, mapsplice, or splicemap.

Then, you should calculate the expression level (FPKM or RPKM) of each gene by cufflinks or other available tools.

Then, you can use DEG analysis tools to perform the DEG analysis and selcet DEGs with pre-defined threshold (fold change and fdr), the tools you can use are DESeq, edgeR and so on.

The attachments are tow protocols to do the analysis. I wish this could help you!

Gonzalo Riadi, Richard A. Schäfer,Rana Al-Baghdadi,Jinan A Sabeeh,Yesesri Cherukuri,Dong Chen thank you all for your kind response.