Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized
to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and
environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD.
Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many
genes and many other factors are associated with ASD, various bioinformatics methods have also been developed
for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus
on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants
and gene expression data, and (ii) methods using biological networks, which include gene co-expression
networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate
on the computational techniques used. We also briefly review other approaches and discuss possible future
directions and strategies for developing bioinformatics approaches to analyze ASD.