Correlated　mutation　is　regarded　as　a　phenomenon　induced　by　the　demand　of　maintaining　the　structure　and/or　function　of　a　protein　during　its　biological　evolution.　Since　it　is　closely　related　to　the　underlying　mechanism　of　protein　structure　and　function,　tremendous　efforts　have　been　made　to　reveal　the　relationship　between　correlated　mutations　and　the　structure　and　function　of　the　protein.　In　the　past　few　decades,　different　coevolutionary　analysis　algorithms　have　been　developed.　They　have　been　applied　to　study　various　aspects　of　protein　structure　and　function,　such　as　prediction　of　disulfide　bonds,　functionally　important　residues,　residue-residue　contacts　and　protein-protein　interaction.　Although　considerable　progress　has　been　achieved　so　far,　obstacles　exist　in　many　aspects　such　as　identification,　evaluation　and　interpretation　of　correlated　mutations.　In　this　review,　we　discuss　several　essential　issues　related　to　the　overcoming　of　these　obstacles　in　coevolution　analysis,　including　the　alignment　size　bias,　phylogenetic　bias,　algorithm　evaluation　and　coevolution　interpretation.　In　particular,　we　focus　on　the　inconsistent　results　generated　by　different　algorithms　and　discuss　possible　reasons　accounting　for　this　discrepancy.　We　also　discuss　future　challenges　and　research　directions　in　coevolution　analysis.