diff --git a/assets/supported_alleles.json b/assets/supported_alleles.json
new file mode 100644
index 0000000..79416b8
--- /dev/null
+++ b/assets/supported_alleles.json
@@ -0,0 +1 @@
+{"mhcflurry": ["Atbe-B*01:01", "Atbe-E*03:01", "Atbe-G*03:01", "Atbe-G*03:02", "Atbe-G*06:01", "Atfu-B*01:01", "Atfu-B*01:02", "Atfu-B*01:03", "Atfu-B*02:01", "Atfu-B*02:02", "Atfu-B*02:03", "Atfu-B*02:04", "Atfu-B*03:01", "Atfu-B*03:02", "Atfu-B*04:01", "Atfu-B*04:02", "Atfu-B*05:01", "Atfu-E*03:01", "Atfu-E*03:02", "Atfu-G*06:01", "Atfu-G*06:02", "Atfu-G*06:03", "Atfu-G*22:01", "Atfu-G*23:01", "Atfu-G*23:02", "Atfu-G*23:03", "Atfu-G*24:01", "Atfu-G*25:01", "BoLA-1*07:01", "BoLA-1*09:01", "BoLA-1*09:02", "BoLA-1*19:01", "BoLA-1*20:01", "BoLA-1*21:01", "BoLA-1*23:01", "BoLA-1*28:01", "BoLA-1*29:01", "BoLA-1*31:01", "BoLA-1*31:02", "BoLA-1*42:01", "BoLA-1*49:01", "BoLA-1*61:01", "BoLA-1*67:01", "BoLA-1*74:01", "BoLA-1*75:01", "BoLA-2*05:01", "BoLA-2*06:01", "BoLA-2*06:02", "BoLA-2*08:01", "BoLA-2*08:02", "BoLA-2*12:01", "BoLA-2*16:01", "BoLA-2*16:02", "BoLA-2*16:03", "BoLA-2*18:01", "BoLA-2*18:02", "BoLA-2*22:01", "BoLA-2*25:01", "BoLA-2*26:01", "BoLA-2*26:02", "BoLA-2*26:03", "BoLA-2*26:04", "BoLA-2*30:01", "BoLA-2*32:01", "BoLA-2*32:02", "BoLA-2*43:01", "BoLA-2*44:01", "BoLA-2*44:02", "BoLA-2*45:01", "BoLA-2*45:02", "BoLA-2*46:01", "BoLA-2*47:01", "BoLA-2*48:01", "BoLA-2*54:01", "BoLA-2*55:01", "BoLA-2*56:01", "BoLA-2*57:01", "BoLA-2*60:01", "BoLA-2*60:02", "BoLA-2*62:01", "BoLA-2*69:01", "BoLA-2*70:01", "BoLA-2*71:01", "BoLA-2*71:02", "BoLA-2*75:01", "BoLA-2*76:01", "BoLA-2*77:01", "BoLA-2*78:01", "BoLA-2*79:01", "BoLA-3*01:01", "BoLA-3*01:02", "BoLA-3*01:03", "BoLA-3*02:01", "BoLA-3*04:01", "BoLA-3*04:02", "BoLA-3*04:03", "BoLA-3*04:04", "BoLA-3*10:01", "BoLA-3*11:01", "BoLA-3*17:01", "BoLA-3*17:02", "BoLA-3*17:03", "BoLA-3*27:01", "BoLA-3*27:02", "BoLA-3*33:01", "BoLA-3*35:01", "BoLA-3*36:01", "BoLA-3*37:01", "BoLA-3*38:01", "BoLA-3*50:01", "BoLA-3*50:02", "BoLA-3*51:01", "BoLA-3*52:01", "BoLA-3*53:01", "BoLA-3*58:01", "BoLA-3*59:01", "BoLA-3*65:01", "BoLA-3*66:01", "BoLA-3*66:02", "BoLA-3*68:01", "BoLA-3*73:01", "BoLA-3*74:01", "BoLA-3*75:01", 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"HLA-DRB1*12:91", "HLA-DRB1*12:92", "HLA-DRB1*12:93", "HLA-DRB1*12:94", "HLA-DRB1*12:95", "HLA-DRB1*12:96", "HLA-DRB1*12:97", "HLA-DRB1*13:01", "HLA-DRB1*13:02", "HLA-DRB1*13:03", "HLA-DRB1*13:04", "HLA-DRB1*13:05", "HLA-DRB1*13:06", "HLA-DRB1*13:07", "HLA-DRB1*13:08", "HLA-DRB1*13:09", "HLA-DRB1*13:100", "HLA-DRB1*13:101", "HLA-DRB1*13:102", "HLA-DRB1*13:103", "HLA-DRB1*13:104", "HLA-DRB1*13:105", "HLA-DRB1*13:106", "HLA-DRB1*13:107", "HLA-DRB1*13:108", "HLA-DRB1*13:109", "HLA-DRB1*13:10", "HLA-DRB1*13:110", "HLA-DRB1*13:111", "HLA-DRB1*13:112", "HLA-DRB1*13:114", "HLA-DRB1*13:115", "HLA-DRB1*13:116", "HLA-DRB1*13:117", "HLA-DRB1*13:118", "HLA-DRB1*13:119", "HLA-DRB1*13:11", "HLA-DRB1*13:120", "HLA-DRB1*13:121", "HLA-DRB1*13:122", "HLA-DRB1*13:123", "HLA-DRB1*13:124", "HLA-DRB1*13:125", "HLA-DRB1*13:126", "HLA-DRB1*13:127", "HLA-DRB1*13:128", "HLA-DRB1*13:129", "HLA-DRB1*13:12", "HLA-DRB1*13:130", "HLA-DRB1*13:131", "HLA-DRB1*13:132", "HLA-DRB1*13:133", "HLA-DRB1*13:134", "HLA-DRB1*13:135", "HLA-DRB1*13:136", "HLA-DRB1*13:138", "HLA-DRB1*13:139", "HLA-DRB1*13:13", "HLA-DRB1*13:140", "HLA-DRB1*13:141", "HLA-DRB1*13:143", "HLA-DRB1*13:144", "HLA-DRB1*13:145", "HLA-DRB1*13:146", "HLA-DRB1*13:147", "HLA-DRB1*13:148", "HLA-DRB1*13:149", "HLA-DRB1*13:14", "HLA-DRB1*13:150", "HLA-DRB1*13:151", "HLA-DRB1*13:152", "HLA-DRB1*13:153", "HLA-DRB1*13:154", "HLA-DRB1*13:155", "HLA-DRB1*13:156", "HLA-DRB1*13:157", "HLA-DRB1*13:158", "HLA-DRB1*13:159", "HLA-DRB1*13:15", "HLA-DRB1*13:160", "HLA-DRB1*13:161", "HLA-DRB1*13:162", "HLA-DRB1*13:163", "HLA-DRB1*13:164", "HLA-DRB1*13:165", "HLA-DRB1*13:166", "HLA-DRB1*13:167", "HLA-DRB1*13:168", "HLA-DRB1*13:169", "HLA-DRB1*13:16", "HLA-DRB1*13:170", "HLA-DRB1*13:171", "HLA-DRB1*13:172", "HLA-DRB1*13:173", "HLA-DRB1*13:174", "HLA-DRB1*13:175", "HLA-DRB1*13:176", "HLA-DRB1*13:177", "HLA-DRB1*13:178", "HLA-DRB1*13:179", "HLA-DRB1*13:17", "HLA-DRB1*13:180", "HLA-DRB1*13:181", "HLA-DRB1*13:182", "HLA-DRB1*13:183", "HLA-DRB1*13:184", "HLA-DRB1*13:186", "HLA-DRB1*13:187", "HLA-DRB1*13:188", "HLA-DRB1*13:189", "HLA-DRB1*13:18", "HLA-DRB1*13:190", "HLA-DRB1*13:191", "HLA-DRB1*13:192", "HLA-DRB1*13:193", "HLA-DRB1*13:194", "HLA-DRB1*13:195", "HLA-DRB1*13:196", "HLA-DRB1*13:197", "HLA-DRB1*13:198", "HLA-DRB1*13:199", "HLA-DRB1*13:19", "HLA-DRB1*13:201", "HLA-DRB1*13:202", "HLA-DRB1*13:203", "HLA-DRB1*13:204", "HLA-DRB1*13:205", "HLA-DRB1*13:206", "HLA-DRB1*13:207", "HLA-DRB1*13:208", "HLA-DRB1*13:209", "HLA-DRB1*13:20", "HLA-DRB1*13:210", "HLA-DRB1*13:211", "HLA-DRB1*13:212", "HLA-DRB1*13:213", "HLA-DRB1*13:214", "HLA-DRB1*13:215", "HLA-DRB1*13:216", "HLA-DRB1*13:217", "HLA-DRB1*13:218", "HLA-DRB1*13:219", "HLA-DRB1*13:21", "HLA-DRB1*13:220", "HLA-DRB1*13:221", "HLA-DRB1*13:222", "HLA-DRB1*13:223", "HLA-DRB1*13:224", "HLA-DRB1*13:225", "HLA-DRB1*13:226", "HLA-DRB1*13:227", "HLA-DRB1*13:228", "HLA-DRB1*13:229", "HLA-DRB1*13:22", "HLA-DRB1*13:230", "HLA-DRB1*13:231", "HLA-DRB1*13:232", 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"HLA-DRB1*13:284", "HLA-DRB1*13:285", "HLA-DRB1*13:286", "HLA-DRB1*13:287", "HLA-DRB1*13:288", "HLA-DRB1*13:28", "HLA-DRB1*13:290", "HLA-DRB1*13:291", "HLA-DRB1*13:292", "HLA-DRB1*13:293", "HLA-DRB1*13:294", "HLA-DRB1*13:296", "HLA-DRB1*13:297", "HLA-DRB1*13:299", "HLA-DRB1*13:29", "HLA-DRB1*13:300", "HLA-DRB1*13:301", "HLA-DRB1*13:302", "HLA-DRB1*13:303", "HLA-DRB1*13:304", "HLA-DRB1*13:305", "HLA-DRB1*13:306", "HLA-DRB1*13:307", "HLA-DRB1*13:308", "HLA-DRB1*13:309", "HLA-DRB1*13:30", "HLA-DRB1*13:311", "HLA-DRB1*13:312", "HLA-DRB1*13:313", "HLA-DRB1*13:314", "HLA-DRB1*13:315", "HLA-DRB1*13:316", "HLA-DRB1*13:317", "HLA-DRB1*13:318", "HLA-DRB1*13:31", "HLA-DRB1*13:320", "HLA-DRB1*13:321", "HLA-DRB1*13:323", "HLA-DRB1*13:325", "HLA-DRB1*13:326", "HLA-DRB1*13:327", "HLA-DRB1*13:32", "HLA-DRB1*13:33", "HLA-DRB1*13:34", "HLA-DRB1*13:35", "HLA-DRB1*13:36", "HLA-DRB1*13:37", "HLA-DRB1*13:38", "HLA-DRB1*13:39", "HLA-DRB1*13:40", "HLA-DRB1*13:41", "HLA-DRB1*13:42", "HLA-DRB1*13:43", "HLA-DRB1*13:44", "HLA-DRB1*13:45", "HLA-DRB1*13:46", "HLA-DRB1*13:47", "HLA-DRB1*13:48", "HLA-DRB1*13:49", "HLA-DRB1*13:50", "HLA-DRB1*13:51", "HLA-DRB1*13:52", "HLA-DRB1*13:53", "HLA-DRB1*13:54", "HLA-DRB1*13:55", "HLA-DRB1*13:56", "HLA-DRB1*13:57", "HLA-DRB1*13:58", "HLA-DRB1*13:59", "HLA-DRB1*13:60", "HLA-DRB1*13:61", "HLA-DRB1*13:62", "HLA-DRB1*13:63", "HLA-DRB1*13:64", "HLA-DRB1*13:65", "HLA-DRB1*13:66", "HLA-DRB1*13:67", "HLA-DRB1*13:68", "HLA-DRB1*13:69", "HLA-DRB1*13:70", "HLA-DRB1*13:71", "HLA-DRB1*13:72", "HLA-DRB1*13:73", "HLA-DRB1*13:74", "HLA-DRB1*13:75", "HLA-DRB1*13:76", "HLA-DRB1*13:77", "HLA-DRB1*13:78", "HLA-DRB1*13:79", "HLA-DRB1*13:80", "HLA-DRB1*13:81", "HLA-DRB1*13:82", "HLA-DRB1*13:83", "HLA-DRB1*13:84", "HLA-DRB1*13:85", "HLA-DRB1*13:86", "HLA-DRB1*13:87", "HLA-DRB1*13:88", "HLA-DRB1*13:89", "HLA-DRB1*13:90", "HLA-DRB1*13:91", "HLA-DRB1*13:92", "HLA-DRB1*13:93", "HLA-DRB1*13:94", "HLA-DRB1*13:95", "HLA-DRB1*13:96", "HLA-DRB1*13:97", "HLA-DRB1*13:98", "HLA-DRB1*13:99", "HLA-DRB1*14:01", "HLA-DRB1*14:02", "HLA-DRB1*14:03", "HLA-DRB1*14:04", "HLA-DRB1*14:05", "HLA-DRB1*14:06", "HLA-DRB1*14:07", "HLA-DRB1*14:08", "HLA-DRB1*14:09", "HLA-DRB1*14:100", "HLA-DRB1*14:101", "HLA-DRB1*14:102", "HLA-DRB1*14:103", "HLA-DRB1*14:104", "HLA-DRB1*14:105", "HLA-DRB1*14:106", "HLA-DRB1*14:107", "HLA-DRB1*14:108", "HLA-DRB1*14:109", "HLA-DRB1*14:10", "HLA-DRB1*14:110", "HLA-DRB1*14:111", "HLA-DRB1*14:112", "HLA-DRB1*14:113", "HLA-DRB1*14:114", "HLA-DRB1*14:115", "HLA-DRB1*14:116", "HLA-DRB1*14:117", "HLA-DRB1*14:118", "HLA-DRB1*14:119", "HLA-DRB1*14:11", "HLA-DRB1*14:120", "HLA-DRB1*14:121", "HLA-DRB1*14:122", "HLA-DRB1*14:123", "HLA-DRB1*14:124", "HLA-DRB1*14:125", "HLA-DRB1*14:126", "HLA-DRB1*14:127", "HLA-DRB1*14:128", "HLA-DRB1*14:129", "HLA-DRB1*14:12", "HLA-DRB1*14:130", "HLA-DRB1*14:131", "HLA-DRB1*14:132", "HLA-DRB1*14:133", "HLA-DRB1*14:134", "HLA-DRB1*14:135", "HLA-DRB1*14:136", "HLA-DRB1*14:138", "HLA-DRB1*14:139", "HLA-DRB1*14:13", "HLA-DRB1*14:140", "HLA-DRB1*14:141", "HLA-DRB1*14:142", "HLA-DRB1*14:143", "HLA-DRB1*14:144", "HLA-DRB1*14:145", "HLA-DRB1*14:146", "HLA-DRB1*14:147", "HLA-DRB1*14:148", "HLA-DRB1*14:149", "HLA-DRB1*14:14", "HLA-DRB1*14:150", "HLA-DRB1*14:151", "HLA-DRB1*14:153", "HLA-DRB1*14:154", "HLA-DRB1*14:155", "HLA-DRB1*14:156", "HLA-DRB1*14:157", "HLA-DRB1*14:158", "HLA-DRB1*14:159", "HLA-DRB1*14:15", "HLA-DRB1*14:160", "HLA-DRB1*14:161", "HLA-DRB1*14:162", "HLA-DRB1*14:163", "HLA-DRB1*14:164", "HLA-DRB1*14:165", "HLA-DRB1*14:167", "HLA-DRB1*14:168", "HLA-DRB1*14:169", "HLA-DRB1*14:16", "HLA-DRB1*14:170", "HLA-DRB1*14:171", "HLA-DRB1*14:172", "HLA-DRB1*14:173", "HLA-DRB1*14:174", "HLA-DRB1*14:175", "HLA-DRB1*14:176", "HLA-DRB1*14:177", "HLA-DRB1*14:178", "HLA-DRB1*14:179", "HLA-DRB1*14:17", "HLA-DRB1*14:180", "HLA-DRB1*14:181", "HLA-DRB1*14:182", "HLA-DRB1*14:183", "HLA-DRB1*14:184", "HLA-DRB1*14:185", "HLA-DRB1*14:186", "HLA-DRB1*14:187", "HLA-DRB1*14:189", "HLA-DRB1*14:18", "HLA-DRB1*14:190", "HLA-DRB1*14:191", "HLA-DRB1*14:192", "HLA-DRB1*14:193", "HLA-DRB1*14:194", "HLA-DRB1*14:196", "HLA-DRB1*14:198", "HLA-DRB1*14:199", "HLA-DRB1*14:19", "HLA-DRB1*14:200", "HLA-DRB1*14:201", "HLA-DRB1*14:202", "HLA-DRB1*14:203", "HLA-DRB1*14:204", "HLA-DRB1*14:205", "HLA-DRB1*14:206", "HLA-DRB1*14:207", "HLA-DRB1*14:208", "HLA-DRB1*14:209", "HLA-DRB1*14:20", "HLA-DRB1*14:210Q", "HLA-DRB1*14:211", "HLA-DRB1*14:212", "HLA-DRB1*14:213", "HLA-DRB1*14:214", "HLA-DRB1*14:215", "HLA-DRB1*14:216", "HLA-DRB1*14:217", "HLA-DRB1*14:218", "HLA-DRB1*14:219", "HLA-DRB1*14:21", "HLA-DRB1*14:220", "HLA-DRB1*14:221", "HLA-DRB1*14:223", "HLA-DRB1*14:224", "HLA-DRB1*14:225", "HLA-DRB1*14:226", "HLA-DRB1*14:227", "HLA-DRB1*14:228", "HLA-DRB1*14:229", "HLA-DRB1*14:22", "HLA-DRB1*14:230", "HLA-DRB1*14:232", "HLA-DRB1*14:233", "HLA-DRB1*14:234", "HLA-DRB1*14:235", "HLA-DRB1*14:236", "HLA-DRB1*14:237", "HLA-DRB1*14:238", "HLA-DRB1*14:239", "HLA-DRB1*14:23", "HLA-DRB1*14:240", "HLA-DRB1*14:241", "HLA-DRB1*14:242", "HLA-DRB1*14:243", "HLA-DRB1*14:24", "HLA-DRB1*14:25", "HLA-DRB1*14:26", "HLA-DRB1*14:27", "HLA-DRB1*14:28", "HLA-DRB1*14:29", "HLA-DRB1*14:30", "HLA-DRB1*14:31", "HLA-DRB1*14:32", "HLA-DRB1*14:33", "HLA-DRB1*14:34", "HLA-DRB1*14:35", "HLA-DRB1*14:36", "HLA-DRB1*14:37", "HLA-DRB1*14:38", "HLA-DRB1*14:39", "HLA-DRB1*14:40", "HLA-DRB1*14:41", "HLA-DRB1*14:42", "HLA-DRB1*14:43", "HLA-DRB1*14:44", "HLA-DRB1*14:45", "HLA-DRB1*14:46", "HLA-DRB1*14:47", "HLA-DRB1*14:48", "HLA-DRB1*14:49", "HLA-DRB1*14:50", "HLA-DRB1*14:51", "HLA-DRB1*14:52", "HLA-DRB1*14:53", "HLA-DRB1*14:54", "HLA-DRB1*14:55", "HLA-DRB1*14:56", "HLA-DRB1*14:57", "HLA-DRB1*14:58", "HLA-DRB1*14:59", "HLA-DRB1*14:60", "HLA-DRB1*14:61", "HLA-DRB1*14:62", "HLA-DRB1*14:63", "HLA-DRB1*14:64", "HLA-DRB1*14:65", "HLA-DRB1*14:67", "HLA-DRB1*14:68", "HLA-DRB1*14:69", "HLA-DRB1*14:70", "HLA-DRB1*14:71", "HLA-DRB1*14:72", "HLA-DRB1*14:73", "HLA-DRB1*14:74", "HLA-DRB1*14:75", "HLA-DRB1*14:76", 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"HLA-DRB1*15:123", "HLA-DRB1*15:124", "HLA-DRB1*15:125", "HLA-DRB1*15:126", "HLA-DRB1*15:127", "HLA-DRB1*15:128", "HLA-DRB1*15:12", "HLA-DRB1*15:130", "HLA-DRB1*15:131", "HLA-DRB1*15:132", "HLA-DRB1*15:133", "HLA-DRB1*15:135", "HLA-DRB1*15:136", "HLA-DRB1*15:139", "HLA-DRB1*15:13", "HLA-DRB1*15:140", "HLA-DRB1*15:141", "HLA-DRB1*15:142", "HLA-DRB1*15:143", "HLA-DRB1*15:144", "HLA-DRB1*15:145", "HLA-DRB1*15:146", "HLA-DRB1*15:147", "HLA-DRB1*15:149", "HLA-DRB1*15:14", "HLA-DRB1*15:150", "HLA-DRB1*15:151", "HLA-DRB1*15:152", "HLA-DRB1*15:153", "HLA-DRB1*15:155", "HLA-DRB1*15:156", "HLA-DRB1*15:157", "HLA-DRB1*15:158", "HLA-DRB1*15:15", "HLA-DRB1*15:160", "HLA-DRB1*15:161", "HLA-DRB1*15:162", "HLA-DRB1*15:164Q", "HLA-DRB1*15:165", "HLA-DRB1*15:166", "HLA-DRB1*15:167", "HLA-DRB1*15:168", "HLA-DRB1*15:169", "HLA-DRB1*15:16", "HLA-DRB1*15:170", "HLA-DRB1*15:171", "HLA-DRB1*15:172", "HLA-DRB1*15:173", "HLA-DRB1*15:174", "HLA-DRB1*15:175", "HLA-DRB1*15:177", "HLA-DRB1*15:178", "HLA-DRB1*15:179", "HLA-DRB1*15:181", "HLA-DRB1*15:182", "HLA-DRB1*15:184", "HLA-DRB1*15:185", "HLA-DRB1*15:186", "HLA-DRB1*15:187", "HLA-DRB1*15:188", "HLA-DRB1*15:189", "HLA-DRB1*15:18", "HLA-DRB1*15:190", "HLA-DRB1*15:191", "HLA-DRB1*15:192", "HLA-DRB1*15:193", "HLA-DRB1*15:194", "HLA-DRB1*15:195", "HLA-DRB1*15:196", "HLA-DRB1*15:197", "HLA-DRB1*15:198", "HLA-DRB1*15:199", "HLA-DRB1*15:19", "HLA-DRB1*15:201", "HLA-DRB1*15:202", "HLA-DRB1*15:203Q", "HLA-DRB1*15:204", "HLA-DRB1*15:205", "HLA-DRB1*15:206", "HLA-DRB1*15:20", "HLA-DRB1*15:21", "HLA-DRB1*15:22", "HLA-DRB1*15:23", "HLA-DRB1*15:24", "HLA-DRB1*15:25", "HLA-DRB1*15:26", "HLA-DRB1*15:27", "HLA-DRB1*15:28", "HLA-DRB1*15:29", "HLA-DRB1*15:30", "HLA-DRB1*15:31", "HLA-DRB1*15:32", "HLA-DRB1*15:33", "HLA-DRB1*15:34", "HLA-DRB1*15:35", "HLA-DRB1*15:36", "HLA-DRB1*15:37", "HLA-DRB1*15:38", "HLA-DRB1*15:39", "HLA-DRB1*15:40", "HLA-DRB1*15:41", "HLA-DRB1*15:42", "HLA-DRB1*15:43", "HLA-DRB1*15:44", "HLA-DRB1*15:45", "HLA-DRB1*15:46", 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"HLA-DQA1*06:02/DQB1*05:04", "HLA-DQA1*06:02/DQB1*05:05", "HLA-DQA1*06:02/DQB1*05:06", "HLA-DQA1*06:02/DQB1*05:07", "HLA-DQA1*06:02/DQB1*05:08", "HLA-DQA1*06:02/DQB1*05:09", "HLA-DQA1*06:02/DQB1*05:103", "HLA-DQA1*06:02/DQB1*05:10", "HLA-DQA1*06:02/DQB1*05:117", "HLA-DQA1*06:02/DQB1*05:11", "HLA-DQA1*06:02/DQB1*05:12", "HLA-DQA1*06:02/DQB1*05:13", "HLA-DQA1*06:02/DQB1*05:14", "HLA-DQA1*06:02/DQB1*05:18", "HLA-DQA1*06:02/DQB1*05:23", "HLA-DQA1*06:02/DQB1*05:24", "HLA-DQA1*06:02/DQB1*05:28", "HLA-DQA1*06:02/DQB1*05:35", "HLA-DQA1*06:02/DQB1*05:37", "HLA-DQA1*06:02/DQB1*05:42", "HLA-DQA1*06:02/DQB1*05:46", "HLA-DQA1*06:02/DQB1*05:52", "HLA-DQA1*06:02/DQB1*05:83", "HLA-DQA1*06:02/DQB1*06:01", "HLA-DQA1*06:02/DQB1*06:02", "HLA-DQA1*06:02/DQB1*06:03", "HLA-DQA1*06:02/DQB1*06:04", "HLA-DQA1*06:02/DQB1*06:05", "HLA-DQA1*06:02/DQB1*06:07", "HLA-DQA1*06:02/DQB1*06:08", "HLA-DQA1*06:02/DQB1*06:09", "HLA-DQA1*06:02/DQB1*06:108", "HLA-DQA1*06:02/DQB1*06:10", "HLA-DQA1*06:02/DQB1*06:110", "HLA-DQA1*06:02/DQB1*06:11", "HLA-DQA1*06:02/DQB1*06:123", "HLA-DQA1*06:02/DQB1*06:126", "HLA-DQA1*06:02/DQB1*06:12", "HLA-DQA1*06:02/DQB1*06:13", "HLA-DQA1*06:02/DQB1*06:140", "HLA-DQA1*06:02/DQB1*06:14", "HLA-DQA1*06:02/DQB1*06:153", "HLA-DQA1*06:02/DQB1*06:15", "HLA-DQA1*06:02/DQB1*06:16", "HLA-DQA1*06:02/DQB1*06:17", "HLA-DQA1*06:02/DQB1*06:18", "HLA-DQA1*06:02/DQB1*06:19", "HLA-DQA1*06:02/DQB1*06:20", "HLA-DQA1*06:02/DQB1*06:214", "HLA-DQA1*06:02/DQB1*06:21", "HLA-DQA1*06:02/DQB1*06:22", "HLA-DQA1*06:02/DQB1*06:23", "HLA-DQA1*06:02/DQB1*06:24", "HLA-DQA1*06:02/DQB1*06:25", "HLA-DQA1*06:02/DQB1*06:27", "HLA-DQA1*06:02/DQB1*06:28", "HLA-DQA1*06:02/DQB1*06:29", "HLA-DQA1*06:02/DQB1*06:30", "HLA-DQA1*06:02/DQB1*06:31", "HLA-DQA1*06:02/DQB1*06:32", "HLA-DQA1*06:02/DQB1*06:33", "HLA-DQA1*06:02/DQB1*06:34", "HLA-DQA1*06:02/DQB1*06:35", "HLA-DQA1*06:02/DQB1*06:36", "HLA-DQA1*06:02/DQB1*06:37", "HLA-DQA1*06:02/DQB1*06:38", "HLA-DQA1*06:02/DQB1*06:39", "HLA-DQA1*06:02/DQB1*06:40", "HLA-DQA1*06:02/DQB1*06:41", "HLA-DQA1*06:02/DQB1*06:42", "HLA-DQA1*06:02/DQB1*06:43", "HLA-DQA1*06:02/DQB1*06:44", "HLA-DQA1*06:02/DQB1*06:48", "HLA-DQA1*06:02/DQB1*06:52"]}
diff --git a/modules/local/merge_predictions/main.nf b/modules/local/merge_predictions/main.nf
index a08edbb..9a39f4e 100644
--- a/modules/local/merge_predictions/main.nf
+++ b/modules/local/merge_predictions/main.nf
@@ -2,10 +2,10 @@ process MERGE_PREDICTIONS {
     label 'process_single'
     tag "${meta.sample}"
 
-    conda "bioconda::mhcgnomes=1.8.4"
+    conda "bioconda::mhcgnomes=1.8.6"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://depot.galaxyproject.org/singularity/mhcgnomes:1.8.4--pyh7cba7a3_0' :
-        'quay.io/biocontainers/mhcgnomes:1.8.4--pyh7cba7a3_0' }"
+        'https://depot.galaxyproject.org/singularity/mhcgnomes:1.8.6--pyh7cba7a3_0' :
+        'biocontainers/mhcgnomes:1.8.6--pyh7cba7a3_0' }"
 
     input:
     tuple val(meta), path(prediction_files)
diff --git a/modules/local/merge_predictions/templates/merge_predictions.py b/modules/local/merge_predictions/templates/merge_predictions.py
index 37e082c..01e55fe 100644
--- a/modules/local/merge_predictions/templates/merge_predictions.py
+++ b/modules/local/merge_predictions/templates/merge_predictions.py
@@ -8,6 +8,7 @@
 import math
 
 import pandas as pd
+import mhcgnomes
 
 # Create logger object with date and time
 import logging
@@ -124,7 +125,7 @@ def _format_prediction_result(self):
             self.predictor = 'mhcflurry'
             return self._format_mhcflurry_prediction()
         elif 'mhcnuggets' in self.file_path:
-            self.predictor = 'mhcnuggets'
+            self.predictor = 'mhcnuggetsii' if 'mhcnuggetsii' in self.file_path else 'mhcnuggets'
             return self._format_mhcnuggets_prediction()
         elif 'netmhcpan' in self.file_path:
             self.predictor = 'netmhcpan'
@@ -152,7 +153,7 @@ def _format_mhcflurry_prediction(self) -> pd.DataFrame:
         df.rename(columns={"peptide": "sequence", "mhcflurry_presentation_percentile": "rank"}, inplace=True)
         df = df[['sequence', 'allele', 'rank', 'BA']]
         df["binder"] = df["rank"] <= PredictorBindingThreshold.MHCFLURRY.value
-        df["predictor"] = "mhcflurry"
+        df["predictor"] = self.predictor
 
         return df
 
@@ -166,7 +167,7 @@ def _format_mhcnuggets_prediction(self) -> pd.DataFrame:
         df = df[['sequence', 'allele', 'rank', 'BA']]
         # Use IC50 < 500 as threshold since mhcnuggets provides a different ranking compared to other predictors
         df["binder"] = df["BA"] >= PredictorBindingThreshold.MHCNUGGETS.value
-        df["predictor"] = "mhcnuggets"
+        df["predictor"] = self.predictor
 
         return df
 
@@ -195,7 +196,7 @@ def _format_netmhcpan_prediction(self) -> pd.DataFrame:
         df_pivot = df_long.pivot_table(index=["sequence", "allele"], columns="metric", values="value").reset_index()
         df_pivot['allele'] = [alleles_dict[int(index.strip("."))] for index in df_pivot['allele']]
         df_pivot['binder'] = df_pivot['rank'] <= self.threshold
-        df_pivot['predictor'] = 'netmhcpan'
+        df_pivot['predictor'] = self.predictor
         df_pivot.index.name = ''
 
         return df_pivot
@@ -228,7 +229,7 @@ def _format_netmhciipan_prediction(self) -> pd.DataFrame:
         df_pivot = df_long.pivot_table(index=["sequence", "allele"], columns="metric", values="value").reset_index()
         df_pivot['allele'] = [alleles_dict[int(index.strip("."))] for index in df_pivot['allele']]
         df_pivot['binder'] = df_pivot['rank'] <= PredictorBindingThreshold.NETMHCIIPAN.value
-        df_pivot['predictor'] = 'netmhciipan'
+        df_pivot['predictor'] = self.predictor
         df_pivot.index.name = ''
 
         return df_pivot
@@ -245,11 +246,13 @@ def main():
         output_df.append(result.prediction_df)
 
     output_df = pd.concat(output_df)
+    # Normalize allele names and write to file
+    output_df['allele'] = output_df['allele'].apply(lambda x : mhcgnomes.parse(x).to_string())
     output_df.to_csv(f'{args.prefix}_predictions.tsv', sep='\t', index=False)
 
     # Parse versions
     versions_this_module = {}
-    versions_this_module["${task.process}"] = Version.get_versions([argparse, pd])
+    versions_this_module["${task.process}"] = Version.get_versions([argparse, pd, mhcgnomes])
     with open("versions.yml", "w") as f:
         f.write(Version.format_yaml_like(versions_this_module))
 
diff --git a/modules/local/mhcflurry.nf b/modules/local/mhcflurry.nf
index 50f5db9..991d2c4 100644
--- a/modules/local/mhcflurry.nf
+++ b/modules/local/mhcflurry.nf
@@ -15,14 +15,14 @@ process MHCFLURRY {
 
     output:
     tuple val(meta), path("*.csv"), emit: predicted
-    path "versions.yml", emit: versions
+    path "versions.yml"           , emit: versions
 
     script:
     if (meta.mhc_class == "II") {
         error("MHCflurry prediction of ${meta.sample} is not possible with MHC class II!")
     }
     def args   = task.ext.args ?: ''
-    def prefix = task.ext.prefix ?: meta.sample
+    def prefix = task.ext.prefix ?: "${meta.sample}"
 
     """
     mhcflurry-downloads fetch models_class1_presentation
diff --git a/modules/local/mhcnuggets/main.nf b/modules/local/mhcnuggets/main.nf
index 73d0649..f941758 100644
--- a/modules/local/mhcnuggets/main.nf
+++ b/modules/local/mhcnuggets/main.nf
@@ -11,7 +11,7 @@ process MHCNUGGETS {
     tuple val(meta), path(tsv)
 
     output:
-    tuple val(meta), path("*_predicted_mhcnuggets.csv"), emit: predicted
+    tuple val(meta), path("*{_predicted_mhcnuggets.csv,_predicted_mhcnuggetsii.csv}"), emit: predicted
     path "versions.yml"                                , emit: versions
 
     script:
@@ -20,7 +20,7 @@ process MHCNUGGETS {
 
     stub:
     def args       = task.ext.args ?: ''
-    def prefix     = task.ext.prefix ?: meta.sample
+    def prefix     = task.ext.prefix ?: "${meta.sample}"
     """
     touch ${prefix}_predicted_mhcnuggets.tsv
 
diff --git a/modules/local/mhcnuggets/templates/mhcnuggets.py b/modules/local/mhcnuggets/templates/mhcnuggets.py
index ea478ca..1c6b1ce 100644
--- a/modules/local/mhcnuggets/templates/mhcnuggets.py
+++ b/modules/local/mhcnuggets/templates/mhcnuggets.py
@@ -22,7 +22,7 @@ def __init__(self) -> None:
         self.input = "$tsv"
         self.prefix = "$task.ext.prefix" if "$task.ext.prefix" != "null" else "$meta.id"
         self.mhc_class = "$meta.mhc_class"
-        self.alleles = "$meta.alleles".split(";")
+        self.alleles = "$meta.alleles_supported".split(";")
         self.parse_ext_args("$task.ext.args")
 
     def parse_ext_args(self, args_string: str) -> None:
@@ -87,7 +87,7 @@ def main():
     # Predict and load written tsv file
     predicted_df = []
     for allele in args.alleles:
-        mhcnuggets_allele = 'HLA-'+allele.replace('*','')
+        mhcnuggets_allele = allele.replace('*','')
         predict(class_=args.mhc_class, peptides_path = args.input, mhc=mhcnuggets_allele,
                 output=f'{args.prefix}_{allele}.csv', rank_output=True)
 
@@ -97,7 +97,8 @@ def main():
 
     # Append dataframe per allele and write file
     predicted_df = pd.concat(predicted_df)
-    predicted_df.to_csv(f'{args.prefix}_predicted_mhcnuggets.csv', index=False)
+    filename_out = f'{args.prefix}_predicted_mhcnuggets.csv' if args.mhc_class == 'I' else f'{args.prefix}_predicted_mhcnuggetsii.csv'
+    predicted_df.to_csv(filename_out, index=False)
 
     # Parse versions
     versions_this_module = {}
@@ -106,7 +107,7 @@ def main():
         f.write(Version.format_yaml_like(versions_this_module))
         # No __version__ dunder or similar available, need to hardcode version
         f.write('mhcnuggets: 2.4.0')
-        
+
 
 if __name__ == "__main__":
     main()
diff --git a/modules/local/netmhciipan.nf b/modules/local/netmhciipan.nf
index 8cf60c0..98ed50d 100644
--- a/modules/local/netmhciipan.nf
+++ b/modules/local/netmhciipan.nf
@@ -17,12 +17,13 @@ process NETMHCIIPAN {
     }
     def args       = task.ext.args ?: ''
     def prefix     = task.ext.prefix ?: "${meta.id}"
-    // Adjust for netMHCIIpan allele format
-    def alleles = meta.alleles.tokenize(';').collect {
-                    it.contains('DRB') ?
-                        it.replace('*', '_').replace(':', '') :
-                        ('HLA-' + it.replace('*', '').replace(':', ''))
-                }.join(',')
+    // Adjust for netMHCIIpan allele format (e.g. DRB1_0101, HLA-DPA10103-DPB10101)
+    def alleles = meta.alleles_supported.tokenize(';')
+                    .collect {
+                        it.contains('DRB') ?
+                            it.replace('*', '_').replace(':', '').replace('HLA-', '') :
+                            it.replace('*', '').replace(':', '').replace('/','-')
+                    }.join(',')
 
     """
     netmhciipan/netMHCIIpan \
diff --git a/modules/local/netmhcpan.nf b/modules/local/netmhcpan.nf
index 55cbb44..8fa90e1 100644
--- a/modules/local/netmhcpan.nf
+++ b/modules/local/netmhcpan.nf
@@ -17,8 +17,7 @@ process NETMHCPAN {
     }
     def args       = task.ext.args ?: ''
     def prefix     = task.ext.prefix ?: meta.sample
-    // A*01:217 to HLA-A01:217 for meta.alleles: Add HLA- to the allele names and strip the *.
-    def alleles    = meta.alleles.tokenize(';').collect { 'HLA-' + it.replace('*', '') }.join(',')
+    def alleles    = meta.alleles_supported.tokenize(';').collect { it.replace('*', '') }.join(',')
 
     """
     netmhcpan/netMHCpan \
diff --git a/modules/local/prepare_prediction_input/main.nf b/modules/local/prepare_prediction_input/main.nf
index 92e2b60..49531d2 100644
--- a/modules/local/prepare_prediction_input/main.nf
+++ b/modules/local/prepare_prediction_input/main.nf
@@ -2,20 +2,20 @@ process PREPARE_PREDICTION_INPUT {
     label 'process_single'
     tag "${meta.sample}"
 
-    conda "bioconda::mhcgnomes=1.8.4"
+    conda "bioconda::mhcgnomes=1.8.6"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://depot.galaxyproject.org/singularity/mhcgnomes:1.8.4--pyh7cba7a3_0' :
-        'quay.io/biocontainers/mhcgnomes:1.8.4--pyh7cba7a3_0' }"
+        'https://depot.galaxyproject.org/singularity/mhcgnomes:1.8.6--pyh7cba7a3_0' :
+        'biocontainers/mhcgnomes:1.8.6--pyh7cba7a3_0' }"
 
     input:
     tuple val(meta), path(tsv)
+    path(supported_alleles_json)
 
     output:
-    tuple val(meta), path("*.{csv,tsv}"), emit: prepared
-    path "versions.yml", emit: versions
+    tuple val(meta), path("*.json"), path("*.{csv,tsv}"), emit: prepared
+    path "versions.yml"                                 , emit: versions
 
     script:
-
     template "prepare_prediction_input.py"
 
     stub:
diff --git a/modules/local/prepare_prediction_input/templates/prepare_prediction_input.py b/modules/local/prepare_prediction_input/templates/prepare_prediction_input.py
index ebd1144..e9d6d58 100644
--- a/modules/local/prepare_prediction_input/templates/prepare_prediction_input.py
+++ b/modules/local/prepare_prediction_input/templates/prepare_prediction_input.py
@@ -4,8 +4,10 @@
 import shlex
 from enum import Enum
 import logging
+import json
 
 import pandas as pd
+import mhcgnomes
 
 logging.basicConfig(
     level=logging.INFO,
@@ -37,14 +39,16 @@ class Arguments:
 
     def __init__(self) -> None:
         self.input = "$tsv"
+        self.supported_alleles_json = "$supported_alleles_json"
         self.prefix = "$task.ext.prefix" if "$task.ext.prefix" != "null" else "$meta.id"
         self.mhc_class = "$meta.mhc_class"
-        self.alleles = "$meta.alleles".split(";")
-        self.tools = "$params.tools"
+        self.alleles = "$meta.alleles"
+        self.tools = "$params.tools".split(',')
         self.min_peptide_length_classI = int("$params.min_peptide_length_classI")
         self.max_peptide_length_classI = int("$params.max_peptide_length_classI")
         self.min_peptide_length_classII = int("$params.min_peptide_length_classII")
         self.max_peptide_length_classII = int("$params.max_peptide_length_classII")
+        self.peptide_col_name = "sequence"
         self.parse_ext_args("$task.ext.args")
 
     def parse_ext_args(self, args_string: str) -> None:
@@ -100,52 +104,121 @@ def format_yaml_like(data: dict, indent: int = 0) -> str:
                 yaml_str += f"{spaces}{key}: {value}\\n"
         return yaml_str
 
+
+class Utils:
+    def parse_alleles(allele_str: str) -> str:
+        """
+        Function that parses optional txt allele input and
+        normalizes alleles using mhcgnomes
+        Args:
+            alleles (str): File path (one allele per line) or string in required format ("Allele1;Allele2;..")
+
+        Returns:
+            alleles_normalized: mhcgnomes-normalized allele string in format "Allele1;Allele2;.."
+        """
+        if allele_str.endswith(('.txt','.csv','.tsv')):
+            with open(allele_str, 'r') as f:
+                alleles = [line.strip() for line in f.readlines()]
+        else:
+            alleles = allele_str.split(";")
+        alleles_normalized = [mhcgnomes.parse(allele).to_string() for allele in alleles]
+
+        return alleles_normalized
+
+    def keep_supported_alleles(allele_ls: list, tool: str, supported_alleles_tool: dict) -> list:
+        """
+        Function that filters out unsupported alleles of predictor for a given MHC class
+        Args:
+            allele_ls (list): List of alleles in mhcgnomes-normalized format
+            tool (str): Name of predictor tool
+            supported_alleles (dict): Dictionary with supported alleles or predictor
+
+        Returns:
+            tool_allele_input: List of supported alleles for the given tool
+        """
+        tool_allele_input = [allele for allele in allele_ls if allele in supported_alleles_tool]
+        if len(tool_allele_input) == 0:
+            logging.warning(f"No supported alleles for {tool} found. Aborting..")
+        elif len(tool_allele_input) < len(allele_ls):
+            logging.warning(f"Provided alleles {allele_ls}, but only {tool_allele_input} are supported by {tool}. Continuing with supported alleles..")
+
+        return tool_allele_input
+
+
 def main():
     args = Arguments()
 
+    ### DEBUGGING INPUT
+    #args.input = '/mnt/volume/data/epitopeprediction/peptides.tsv'
+    #args.supported_alleles_json = '/mnt/volume/dev/mhcgnomes/supported_alleles.json'
+    #args.mhc_class = 'I'
+    #args.alleles='/mnt/volume/dev/epitopeprediction-2/modules/local/check_supported_alleles/templates/test_alleles.txt'
+    #args.tools = ['mhcflurry', 'mhcnuggets', 'mhcnuggetsii', 'netmhcpan','netmhciipan']
+    #args.prefix = "test"
+    #args.min_peptide_length_classI = 8
+    #args.max_peptide_length_classI = 12
+    #args.min_peptide_length_classII = 12
+    #args.max_peptide_length_classII = 25
+    #args.peptide_col_name = "sequence"
+    ###
+
+    # Parse alleles to uniform format
+    alleles_normalized = Utils.parse_alleles(args.alleles)
+    supported_alleles_dict = json.load(open(args.supported_alleles_json))
+    # Filter alleles based on supported alleles for each tool and write to json
+    tools_allele_input = {}
+    for tool in args.tools:
+        tool_allele_input = Utils.keep_supported_alleles(alleles_normalized, tool, supported_alleles_dict[tool])
+        tools_allele_input[tool] = ';'.join(tool_allele_input)
+    with open(f"{args.prefix}_allele_input.json", "w") as f:
+        json.dump(tools_allele_input, f)
+
+    # Parse input file to desired format of tools
     df_input = pd.read_csv(args.input, sep="\t")
-    logging.info(f"Reading in file with {len(df_input)} peptides..")
+    logging.info(f"Read file with {len(df_input)} peptides.")
 
     # Filter peptides based on user-defined length
     if args.mhc_class == "I":
-        df = df_input[df_input["sequence"].str.len().between(args.min_peptide_length_classI, args.max_peptide_length_classI)]
+        df = df_input[df_input[args.peptide_col_name].str.len().between(args.min_peptide_length_classI, args.max_peptide_length_classI)]
     else:
-        df = df_input[df_input["sequence"].str.len().between(args.min_peptide_length_classII, args.max_peptide_length_classII)]
+        df = df_input[df_input[args.peptide_col_name].str.len().between(args.min_peptide_length_classII, args.max_peptide_length_classII)]
 
     if len(df) == 0:
         raise ValueError("No peptides left after applying length filters! Aborting..")
 
     # Filter peptides based on tool length boundaries and adjust input format
     if "mhcflurry" in args.tools and args.mhc_class == "I":
-        df_mhcflurry = df[df["sequence"].str.len().between(MinLength.MHCFLURRY.value, MaxLength.MHCFLURRY.value)]
-        logging.info(f"Input for NetMHCpan detected. Preparing {len(df_mhcflurry)} peptides for prediction..")
+        df_mhcflurry = df[df[args.peptide_col_name].str.len().between(MinLength.MHCFLURRY.value, MaxLength.MHCFLURRY.value)]
+        logging.info(f"Input for MHCflurry detected. Preparing {len(df_mhcflurry)} peptides for prediction..")
         # Get every combination of sequence and allele and write them to csv with columns sequence and allele
-        df_mhcflurry['allele'] = [args.alleles] * len(df_mhcflurry)
+        df_mhcflurry['allele'] = [tools_allele_input['mhcflurry'].split(';')] * len(df_mhcflurry)
         df_mhcflurry = df_mhcflurry.explode('allele').reset_index(drop=True)
-        df_mhcflurry.rename(columns={"sequence": "peptide"}, inplace=True)
+        df_mhcflurry.rename(columns={args.peptide_col_name: "peptide"}, inplace=True)
         df_mhcflurry[['peptide','allele']].to_csv(f'{args.prefix}_mhcflurry_input.csv', index=False)
 
-    if "mhcnuggets" in args.tools:
-        if args.mhc_class == "I":
-            df_mhcnuggets = df[df["sequence"].str.len().between(MinLength.MHCNUGGETS.value, MaxLength.MHCNUGGETS_CLASSI.value)]
-        else:
-            df_mhcnuggets = df[df["sequence"].str.len().between(MinLength.MHCNUGGETS.value, MaxLength.MHCNUGGETS_CLASSII.value)]
+    if "mhcnuggets" in args.tools and args.mhc_class == "I":
+        df_mhcnuggets = df[df[args.peptide_col_name].str.len().between(MinLength.MHCNUGGETS.value, MaxLength.MHCNUGGETS_CLASSI.value)]
         logging.info(f"Input for MHCnuggets detected. Preparing {len(df_mhcnuggets)} peptides for prediction..")
         df_mhcnuggets[['sequence']].to_csv(f'{args.prefix}_mhcnuggets_input.tsv', sep="\t", header=False, index=False)
 
-    if "netmhcpan" in args.tools and args.mhc_class == "I":
-        df_netmhcpan = df[df["sequence"].str.len().between(MinLength.NETMHCPAN.value, MaxLength.NETMHCPAN.value)]
+    if "mhcnuggetsii" in args.tools and args.mhc_class == "II":
+        df_mhcnuggets_ii = df[df[args.peptide_col_name].str.len().between(MinLength.MHCNUGGETS.value, MaxLength.MHCNUGGETS_CLASSII.value)]
+        logging.info(f"Input for MHCnuggets II detected. Preparing {len(df_mhcnuggets_ii)} peptides for prediction..")
+        df_mhcnuggets_ii[['sequence']].to_csv(f'{args.prefix}_mhcnuggetsii_input.tsv', sep="\t", header=False, index=False)
+
+    if "netmhcpan-4.1" in args.tools and args.mhc_class == "I":
+        df_netmhcpan = df[df[args.peptide_col_name].str.len().between(MinLength.NETMHCPAN.value, MaxLength.NETMHCPAN.value)]
         logging.info(f"Input for NetMHCpan detected. Preparing {len(df_netmhcpan)} peptides for prediction..")
         df_netmhcpan[['sequence']].to_csv(f'{args.prefix}_netmhcpan_input.tsv', sep="\t", header=False, index=False)
 
-    elif "netmhciipan" in args.tools and args.mhc_class == "II":
-        df_netmhciipan = df[df["sequence"].str.len().between(MinLength.NETMHCIIPAN.value, MaxLength.NETMHCIIPAN.value)]
-        logging.info(f"Input for NetMHCpan detected. Preparing {len(df_netmhciipan)} peptides for prediction..")
+    elif "netmhciipan-4.3" in args.tools and args.mhc_class == "II":
+        df_netmhciipan = df[df[args.peptide_col_name].str.len().between(MinLength.NETMHCIIPAN.value, MaxLength.NETMHCIIPAN.value)]
+        logging.info(f"Input for NetMHCIIpan detected. Preparing {len(df_netmhciipan)} peptides for prediction..")
         df_netmhciipan[['sequence']].to_csv(f'{args.prefix}_netmhciipan_input.tsv', sep="\t", header=False, index=False)
 
     # Parse versions
     versions_this_module = {}
-    versions_this_module["${task.process}"] = Version.get_versions([argparse, pd])
+    versions_this_module["${task.process}"] = Version.get_versions([argparse, pd, mhcgnomes])
     with open("versions.yml", "w") as f:
         f.write(Version.format_yaml_like(versions_this_module))
 
diff --git a/subworkflows/local/mhc_binding_prediction.nf b/subworkflows/local/mhc_binding_prediction.nf
index 0eb1ac6..6ce3ea1 100755
--- a/subworkflows/local/mhc_binding_prediction.nf
+++ b/subworkflows/local/mhc_binding_prediction.nf
@@ -1,13 +1,11 @@
-//
-// Check input samplesheet and get read channels
-//
 include { PREPARE_PREDICTION_INPUT } from '../../modules/local/prepare_prediction_input/main'
 include { SYFPEITHI } from '../../modules/local/syfpeithi'
 include { MHCFLURRY } from '../../modules/local/mhcflurry'
-include { MHCNUGGETS } from '../../modules/local/mhcnuggets'
+include { MHCNUGGETS;
+        MHCNUGGETS as MHCNUGGETSII} from '../../modules/local/mhcnuggets'
 include { NETMHCPAN } from '../../modules/local/netmhcpan'
 include { NETMHCIIPAN } from '../../modules/local/netmhciipan'
-include { parse_netmhc_params } from '../../subworkflows/local/utils_nfcore_epitopeprediction_pipeline'
+include { json2map; parse_netmhc_params } from '../../subworkflows/local/utils_nfcore_epitopeprediction_pipeline'
 include { EXTERNAL_TOOLS_IMPORT as NETMHCPAN_IMPORT;
         EXTERNAL_TOOLS_IMPORT as NETMHCIIPAN_IMPORT} from '../../modules/local/external_tools_import'
 include { MERGE_PREDICTIONS } from '../../modules/local/merge_predictions/main'
@@ -15,38 +13,32 @@ include { MERGE_PREDICTIONS } from '../../modules/local/merge_predictions/main'
 workflow MHC_BINDING_PREDICTION {
     take:
         ch_peptides
+        supported_alleles_json
 
     main:
         ch_versions = Channel.empty()
         ch_binding_predictors_out = Channel.empty()
 
-        //TODO: Add //"pattern": "^(syfpeithi|mhcnuggets|mhcflurry|netmhcpan|netmhciipan)(,(syfpeithi|mhcnuggets|mhcflurry|netmhcpan|netmhciipan)){0,4}$",
-        // to nextflow_schema.json once this subworkflow is completed
-        //TODO: Parse input alleles file to meta.alleles
-
         //prepare the input file
-        PREPARE_PREDICTION_INPUT( ch_peptides )
+        PREPARE_PREDICTION_INPUT( ch_peptides, supported_alleles_json)
             .prepared
             .transpose()
             .branch {
-                meta, file ->
-                    syfpeithi : file.name.contains('syfpeithi')
-                        return [meta, file]
-                    mhcflurry : file.name.contains('mhcflurry')
-                        return [meta, file]
-                    mhcnuggets : file.name.contains('mhcnuggets')
-                        return [meta, file]
-                    netmhcpan: file.name.contains('netmhcpan')
-                        return [meta, file]
-                    netmhciipan: file.name.contains('netmhciipan')
-                        return [meta, file]
+                meta, json, file ->
+                    def allele_input_dict = json2map(json)
+                    mhcflurry : (file.name.contains('mhcflurry_input') && allele_input_dict['mhcflurry'])
+                        return [meta + [alleles_supported: allele_input_dict['mhcflurry']], file]
+                    mhcnuggets : (file.name.contains('mhcnuggets_input') && allele_input_dict['mhcnuggets'])
+                        return [meta + [alleles_supported: allele_input_dict['mhcnuggets']], file]
+                    mhcnuggetsii : (file.name.contains('mhcnuggetsii_input') && allele_input_dict['mhcnuggetsii'])
+                        return [meta + [alleles_supported: allele_input_dict['mhcnuggetsii']], file]
+                    netmhcpan: (file.name.contains('netmhcpan_input') && allele_input_dict['netmhcpan-4.1'])
+                        return [meta + [alleles_supported: allele_input_dict['netmhcpan-4.1']], file]
+                    netmhciipan: (file.name.contains('netmhciipan_input') && allele_input_dict['netmhciipan-4.3'])
+                        return [meta + [alleles_supported: allele_input_dict['netmhciipan-4.3']], file]
                     }
             .set{ ch_prediction_input }
 
-        SYFPEITHI ( ch_prediction_input.syfpeithi )
-        ch_versions = ch_versions.mix(SYFPEITHI.out.versions)
-        ch_binding_predictors_out = ch_binding_predictors_out.mix(SYFPEITHI.out.predicted)
-
         MHCFLURRY ( ch_prediction_input.mhcflurry )
         ch_versions = ch_versions.mix(MHCFLURRY.out.versions)
         ch_binding_predictors_out = ch_binding_predictors_out.mix(MHCFLURRY.out.predicted)
@@ -55,6 +47,10 @@ workflow MHC_BINDING_PREDICTION {
         ch_versions = ch_versions.mix(MHCNUGGETS.out.versions)
         ch_binding_predictors_out = ch_binding_predictors_out.mix(MHCNUGGETS.out.predicted)
 
+        MHCNUGGETSII ( ch_prediction_input.mhcnuggetsii )
+        ch_versions = ch_versions.mix(MHCNUGGETSII.out.versions)
+        ch_binding_predictors_out = ch_binding_predictors_out.mix(MHCNUGGETSII.out.predicted)
+
         if ( "netmhcpan-4.1" in params.tools.tokenize(",") )
         {
             //TODO: Refactor to support only one netmhc version
@@ -77,7 +73,7 @@ workflow MHC_BINDING_PREDICTION {
             ch_binding_predictors_out = ch_binding_predictors_out.mix(NETMHCIIPAN.out.predicted)
         }
 
-    MERGE_PREDICTIONS( ch_binding_predictors_out.groupTuple() )
+    MERGE_PREDICTIONS( ch_binding_predictors_out.map {meta, file -> [meta.subMap('sample','alleles','mhc_class','input_type'), file] }.groupTuple())
     ch_versions = ch_versions.mix(MERGE_PREDICTIONS.out.versions)
 
     emit:
diff --git a/subworkflows/local/utils_nfcore_epitopeprediction_pipeline/main.nf b/subworkflows/local/utils_nfcore_epitopeprediction_pipeline/main.nf
index 1062872..49d9a82 100644
--- a/subworkflows/local/utils_nfcore_epitopeprediction_pipeline/main.nf
+++ b/subworkflows/local/utils_nfcore_epitopeprediction_pipeline/main.nf
@@ -152,7 +152,7 @@ def validate_tools_param() {
     //    'netmhc_darwin-4.0', 'netmhcpan_darwin-4.0', 'netmhcpan_darwin-4.1', 'netmhciipan_darwin-4.1'
     //]
     valid_tools = [
-        'syfpeithi', 'mhcnuggets', 'mhcflurry','netmhcpan-4.1', 'netmhciipan-4.3'
+        'syfpeithi', 'mhcnuggets', 'mhcnuggetsii', 'mhcflurry','netmhcpan-4.1', 'netmhciipan-4.3'
     ]
     tools = params.tools.tokenize(',')
 
@@ -198,6 +198,13 @@ def parse_netmhc_params(tool_name, tool_version) {
     }
 }
 
+// Groovy function to parse JSON and return a map
+def json2map(jsonString) {
+    def jsonSlurper = new groovy.json.JsonSlurper()
+    def parsedJson = jsonSlurper.parse(file(jsonString, checkIfExists: true))
+    return parsedJson
+}
+
 //
 // Validate channels from input samplesheet
 //
diff --git a/workflows/epitopeprediction.nf b/workflows/epitopeprediction.nf
index 9486957..83795e2 100644
--- a/workflows/epitopeprediction.nf
+++ b/workflows/epitopeprediction.nf
@@ -106,6 +106,9 @@ workflow EPITOPEPREDICTION {
     ch_multiqc_files = Channel.empty()
     ch_nonfree_paths = Channel.empty()
 
+    // Load supported alleles file
+    supported_alleles_json = file("$projectDir/assets/supported_alleles.json", checkIfExists: true)
+
     // Load samplesheet and branch channels based on input type
     samplesheet
         .branch { meta, filename ->
@@ -343,7 +346,7 @@ workflow EPITOPEPREDICTION {
     //MERGE_JSON_MULTI( ch_json_reports.multi )
     //ch_versions = ch_versions.mix( MERGE_JSON_MULTI.out.versions )
 
-    MHC_BINDING_PREDICTION( ch_samples_uncompressed.peptide )
+    MHC_BINDING_PREDICTION( ch_samples_uncompressed.peptide, supported_alleles_json )
     ch_versions = ch_versions.mix(MHC_BINDING_PREDICTION.out.versions)
 
     ch_predicted_peptides = MHC_BINDING_PREDICTION.out.predicted