Locating　the　transmembrane　regions　of　outer　membrane　proteins　(OMPs)　is　highly　important　for　deciphering　their　biological　functions　at　both　molecular　and　cellular　levels.　Here,　we　propose　a　novel　method　to　predict　the　transmembrane　regions　of　OMPs　by　employing　the　position-and　composition-based　features　of　sequence　profiles.　Furthermore,　a　simple　probability-based　prediction　model,　which　is　estimated　by　the　secondary　structures　of　structurally　known　OMPs,　is　also　developed.　Considering　that　these　two　methods　are　both　effective　and　well　complementary,　we　integrate　them　into　a　method　called　TransOMP,　which　is　also　capable　of　identifying　OMPs.　Furthermore,　we　develop　an　OMP　identification　measure　I_CScore　by　considering　transmembrane　regions　by　TransOMP　and　secondary　structural　topology　by　SSEA-OMP.　Our　methods　were　benchmarked　against　state-of-the-art　methods　and　assessed　in　the　genome　of　Escherichia　coli.　Benchmark　results　confirmed　that　our　methods　were　reliable　and　useful.　Meanwhile,　we　constructed　an　OMP　prediction　web　server,　which　can　be　used　for　OMP　identification,　transmembrane　region　location,　and　3D　model　building.