Capturing mutation patterns of each individual influenza virus sequence is often challenging; in this paper, we demon- strated that using a binary encoding scheme coupled with dimension reduction technique, we were able to capture the intrinsic mutation pattern of the virus. Our approach looks at the variance between sequences instead of the commonly used p-distance or Hamming distance. We first convert the influenza genetic sequence to a binary string and then ap- ply Principal Component Analysis (PCA) to the converted sequence. PCA also provides a prediction capability for de- tecting reassortant virus by using data projection technique. Due to the sparsity of the binary string, we were able to analyze large volume of influenza sequence data in a very short time. For protein sequences, our scheme also allows the incorporation of biophysical properties of each amino acid. Here, we present various results from analyzing in- fluenza nucleotide, protein and genome sequences using the proposed approach. With the Next-Generation Sequencing (NGS) promises of sequencing DNA at unprecedented speed and production of massive quantity of data, it is imperative that new technique needs to be developed to provide quick and reliable analysis of any sequence data. Here, we believe our approach can be used at the upstream stage of sequence data analysis pipeline to gain insight as to which direction should be continued on in analyzing the available data.