The endless enhancement and decreasing charges of a complete human genome have given rise to fast acceptance of genetic and genomic information at both research institutions and clinics. Biologists are enchanting the primary steps in the direction of knowing the locations and functions of all the genes and controlling sites in the genomes of various organisms. As these researchers govern the nucleotide arrangement of large stretches of the human genome, they are constructing excessive volumes of sequence data. Direct research laboratory investigation of this data is expensive and tough, creating computational techniques vital. The arena of pattern analysis, which intends to build computer algorithms that enhance with knowledge, embraces the capacity to empower computers to support humans in the analysis of complex, large genetic and genomic data sets. Here, an overview of pattern analysis techniques for the study of genome sequencing datasets, as well as the proteomics, epigenetic and metabolomic data is delivered. These techniques employ data pre-processing, feature extraction and selection, classification and clustering. The aim of this survey is to present deliberations and recurring challenges in the application of pattern analysis methods, as well as of discriminative and reproductive modeling approaches and discuss the future research directions of these methods for the analysis of genomic and genetic data sets. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.