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",{"type":429,"tag":460,"props":798,"children":799},{},[800],{"type":464,"value":801},"CICERO",{"type":464,"value":787},{"type":429,"tag":502,"props":804,"children":807},{"href":805,"rel":806},"https://pubmed.ncbi.nlm.nih.gov/32466770/",[633],[808],{"type":464,"value":809},"Li et al.",{"type":464,"value":811},") to detect putative gene fusions.",{"type":429,"tag":746,"props":813,"children":814},{},[815],{"type":464,"value":816},"Custom visualizations for putative gene fusions and genome coverage are produced by ProteinPaint.",{"type":429,"tag":818,"props":819,"children":821},"h3",{"id":820},"mapping",[822],{"type":464,"value":823},"Mapping",{"type":429,"tag":515,"props":825,"children":826},{},[827,829,836],{"type":464,"value":828},"We use the ",{"type":429,"tag":502,"props":830,"children":833},{"href":831,"rel":832},"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3530905/",[633],[834],{"type":464,"value":835},"STAR aligner",{"type":464,"value":837},"  to rapidly map reads to the GRCh37 human reference genome.\nThis step generally takes around one hour to complete assuming approximately 55-75 million paired reads are supplied.",{"type":429,"tag":818,"props":839,"children":841},{"id":840},"coverage",[842],{"type":464,"value":843},"Coverage",{"type":429,"tag":515,"props":845,"children":846},{},[847],{"type":464,"value":848},"Internally developed scripts calculate the coverage of mapped reads genome wide.\nThe resulting bigWig file can be viewed in ProteinPaint or used for quality control.",{"type":429,"tag":818,"props":850,"children":852},{"id":851},"splice-junction-read-quantification",[853],{"type":464,"value":854},"Splice junction read quantification",{"type":429,"tag":515,"props":856,"children":857},{},[858],{"type":464,"value":859},"We use our RNAPEG software to quantify reads spanning known and novel splice junctions.\nRNAPEG also corrects improper mappings at splice junction boundaries for more accurate definition of novel splice junctions.\nThe resulting junctions file can be viewed along with the coverage bigWig file to gain insights into gene expression and splicing patterns.",{"type":429,"tag":818,"props":861,"children":863},{"id":862},"genome-wide-fusion-prediction",[864],{"type":464,"value":865},"Genome-wide fusion prediction",{"type":429,"tag":515,"props":867,"children":868},{},[869,871,878],{"type":464,"value":870},"We developed an assembly-based algorithm CICERO (Clipped-reads Extended for RNA Optimization) that is able to extend the read-length spanning fusion junctions for detecting complex fusions.\nCICERO finds clipped reads and junction spanning reads, assembles them into a contig and maps the contig back to the reference genome.\nMapped contigs are then annotated and filtered.\nThose with potential genic effects including gene fusion, ITD, readthrough or circular RNA are reported in the ",{"type":429,"tag":872,"props":873,"children":875},"code",{"className":874},[],[876],{"type":464,"value":877},"final_fusions.txt",{"type":464,"value":879}," file.\nAn interactive version of this file with predictions sorted by quality can be inspected with the ProteinPaint interactive fusion viewer.",{"type":429,"tag":515,"props":881,"children":882},{},[883],{"type":429,"tag":502,"props":884,"children":887},{"href":885,"rel":886},"https://www.ashg.org/wp-content/uploads/2019/10/2014-platform-abstracts.pdf",[633],[888],{"type":464,"value":889},"An abstract describing CICERO was presented at ASHG, 2014",{"type":429,"tag":818,"props":891,"children":893},{"id":892},"low-stringency-fusion-gene-hotpot-search",[894],{"type":464,"value":895},"Low stringency fusion gene \"Hotpot\" search",{"type":429,"tag":515,"props":897,"children":898},{},[899,901,907],{"type":464,"value":900},"We have observed that certain fusions such as KIAA1549-BRAF in low-grade glioma have apparently limited read support in the bam file — either due to low expression or low tumor purity.\nIn these cases, we use a secondary tool, FUZZION, that performs fuzzy matching for known fusion gene junctions for every read in the bam file (both mapped and unmapped).\nFUZZION can recover even a single low quality read potentially supporting a known fusion gene junction.\nThe FUZZION output is a simple text file with read IDs and sequences supporting a particular gene fusion.\nThe fusion point is indicated with square brackets ",{"type":429,"tag":872,"props":902,"children":904},{"className":903},[],[905],{"type":464,"value":906},"[]",{"type":464,"value":908},".",{"type":429,"tag":509,"props":910,"children":912},{"id":911},"creating-a-workspace",[913],{"type":464,"value":914},"Creating a workspace",{"type":429,"tag":515,"props":916,"children":917},{},[918,920,926],{"type":464,"value":919},"Before you can run one of our workflows, you must first create a workspace in DNAnexus for the run.\nRefer to ",{"type":429,"tag":502,"props":921,"children":923},{"href":922},"/genomics-platform/analyzing-data/running-our-workflows#getting-started",[924],{"type":464,"value":925},"the general workflow guide",{"type":464,"value":927}," to learn how to create a DNAnexus workspace for each workflow run.",{"type":429,"tag":515,"props":929,"children":930},{},[931,933,940],{"type":464,"value":932},"You can navigate to the Rapid RNA-Seq workflow page ",{"type":429,"tag":502,"props":934,"children":937},{"href":935,"rel":936},"https://platform.stjude.cloud/workflows/rapid_rna-seq",[633],[938],{"type":464,"value":939},"here",{"type":464,"value":908},{"type":429,"tag":509,"props":942,"children":944},{"id":943},"uploading-input-files",[945],{"type":464,"value":946},"Uploading Input Files",{"type":429,"tag":595,"props":948,"children":949},{"color":597,"icon":598},[950],{"type":429,"tag":515,"props":951,"children":952},{},[953,957,960,962,966,968,973],{"type":429,"tag":460,"props":954,"children":955},{},[956],{"type":464,"value":607},{"type":429,"tag":609,"props":958,"children":959},{},[],{"type":464,"value":961},"\nThis pipeline assumes GRCh37-lite coordinates.\nIf your BAM is  ",{"type":429,"tag":622,"props":963,"children":964},{},[965],{"type":464,"value":626},{"type":464,"value":967}," aligned to this genome build, we recommend converting the BAM  back to FASTQ files using ",{"type":429,"tag":502,"props":969,"children":971},{"href":631,"rel":970},[633],[972],{"type":464,"value":636},{"type":464,"value":974}," functionality.",{"type":429,"tag":515,"props":976,"children":977},{},[978,980,986],{"type":464,"value":979},"The Rapid RNA-Seq pipeline takes as ",{"type":429,"tag":502,"props":981,"children":983},{"href":982},"#inputs",[984],{"type":464,"value":985},"input",{"type":464,"value":987}," either a paired set of Gzipped FASTQ files or a GRCh37-lite aligned BAM from human RNA-Seq.",{"type":429,"tag":515,"props":989,"children":990},{},[991,993,998],{"type":464,"value":992},"Refer to ",{"type":429,"tag":502,"props":994,"children":996},{"href":995},"/genomics-platform/analyzing-data/running-our-workflows#uploading-files",[997],{"type":464,"value":925},{"type":464,"value":999}," to learn how to upload input files to the workspace you just created.",{"type":429,"tag":509,"props":1001,"children":1003},{"id":1002},"running-the-workflow",[1004],{"type":464,"value":1005},"Running the Workflow",{"type":429,"tag":515,"props":1007,"children":1008},{},[1009,1010,1015],{"type":464,"value":992},{"type":429,"tag":502,"props":1011,"children":1013},{"href":1012},"/genomics-platform/analyzing-data/running-our-workflows#running-the-workflow",[1014],{"type":464,"value":925},{"type":464,"value":1016}," to learn how to launch the workflow, hook up input files, adjust parameters, start a run, and monitor run progress.",{"type":429,"tag":595,"props":1018,"children":1021},{"color":1019,"icon":1020},"blue","i-heroicons-document-text-solid",[1022],{"type":429,"tag":515,"props":1023,"children":1024},{},[1025,1030,1033],{"type":429,"tag":460,"props":1026,"children":1027},{},[1028],{"type":464,"value":1029},"Note",{"type":429,"tag":609,"props":1031,"children":1032},{},[],{"type":464,"value":1034},"\nSeveral of the apps that make up the Rapid RNA-seq workflow have an automatic timeout value included to prevent possible runaway costs, if you need to adjust or remove any of these, currently you must do so via the DNAnexus CLI (though a method to do so from the UI should be available in the future).\nSee the section below for more information about these timeouts and how to adjust or remove.",{"type":429,"tag":515,"props":1036,"children":1037},{},[1038],{"type":464,"value":1039},"The Rapid RNA-seq pipeline contains several different apps and each of their timeout values are currently set as:",{"type":429,"tag":762,"props":1041,"children":1042},{},[1043,1048,1053,1058,1063],{"type":429,"tag":746,"props":1044,"children":1045},{},[1046],{"type":464,"value":1047},"STAR - 24.5 hour timeout",{"type":429,"tag":746,"props":1049,"children":1050},{},[1051],{"type":464,"value":1052},"RNApeg - 15 hour timeout",{"type":429,"tag":746,"props":1054,"children":1055},{},[1056],{"type":464,"value":1057},"CICERO - 30 hour timeout",{"type":429,"tag":746,"props":1059,"children":1060},{},[1061],{"type":464,"value":1062},"Coverage_bw - no timeout",{"type":429,"tag":746,"props":1064,"children":1065},{},[1066],{"type":464,"value":1067},"Fuzzion - no timeout",{"type":429,"tag":515,"props":1069,"children":1070},{},[1071],{"type":464,"value":1072},"You can use the following command to change or remove a timeout for one of the steps in the workflow, but will need the information for the specific apps within your version of the workflow.\nTo do this, here is an example where the CICERO timeout is removed entirely:",{"type":429,"tag":515,"props":1074,"children":1075},{},[1076],{"type":429,"tag":872,"props":1077,"children":1079},{"className":1078},[],[1080],{"type":464,"value":1081},"dx run \"Rapid RNA-Seq (BAM)\" --extra-args '{\"timeoutPolicyByExecutable\": {\"app-GQB77jQ078YQk83ZYF5X7fyq\":{\"*\": {\"hours\": 0}}}}'",{"type":429,"tag":515,"props":1083,"children":1084},{},[1085,1087,1093],{"type":464,"value":1086},"You will need to specify the workflow you are trying to run, either \"Rapid RNA-Seq (BAM)\" or \"Rapid RNA-Seq (FastQ)\".\nYou will also need to update with the appropriate DNAnexus app ID (replace ",{"type":429,"tag":872,"props":1088,"children":1090},{"className":1089},[],[1091],{"type":464,"value":1092},"app-GQB77jQ078YQk83ZYF5X7fyq",{"type":464,"value":1094}," above).",{"type":429,"tag":515,"props":1096,"children":1097},{},[1098,1100,1106],{"type":464,"value":1099},"To find the information required in that command, if you have ",{"type":429,"tag":872,"props":1101,"children":1103},{"className":1102},[],[1104],{"type":464,"value":1105},"jq",{"type":464,"value":1107}," installed, you can find the app name from your copy of the workflow with:",{"type":429,"tag":515,"props":1109,"children":1110},{},[1111],{"type":429,"tag":872,"props":1112,"children":1114},{"className":1113},[],[1115],{"type":464,"value":1116},"dx describe --json \"Rapid RNA-Seq (BAM)\" | jq -r '.stages | .[] | select(.id == \"cicero\").executable'",{"type":429,"tag":515,"props":1118,"children":1119},{},[1120,1122,1128],{"type":464,"value":1121},"And that can be combined with a second ",{"type":429,"tag":872,"props":1123,"children":1125},{"className":1124},[],[1126],{"type":464,"value":1127},"dx describe",{"type":464,"value":1129}," to get the app ID that's needed for the run command:",{"type":429,"tag":515,"props":1131,"children":1132},{},[1133],{"type":429,"tag":872,"props":1134,"children":1136},{"className":1135},[],[1137],{"type":464,"value":1138},"dx describe --json $(dx describe --json \"Rapid RNA-Seq (BAM)\" | jq -r '.stages | .[] | select(.id == \"cicero\").executable') | jq -r .id",{"type":429,"tag":515,"props":1140,"children":1141},{},[1142,1144,1149],{"type":464,"value":1143},"The values can also be manually found in the output of ",{"type":429,"tag":872,"props":1145,"children":1147},{"className":1146},[],[1148],{"type":464,"value":1127},{"type":464,"value":908},{"type":429,"tag":515,"props":1151,"children":1152},{},[1153,1155,1162],{"type":464,"value":1154},"Detailed information on timeout values can be found in the ",{"type":429,"tag":502,"props":1156,"children":1159},{"href":1157,"rel":1158},"https://documentation.dnanexus.com/developer/api/running-analyses/applets-and-entry-points#specifying-job-timeouts",[633],[1160],{"type":464,"value":1161},"DNAnexus documentation",{"type":464,"value":908},{"type":429,"tag":515,"props":1164,"children":1165},{},[1166,1168,1174,1176,1181],{"type":464,"value":1167},"Please reach out to ",{"type":429,"tag":502,"props":1169,"children":1171},{"href":1170},"mailto:support@dnanexus.com",[1172],{"type":464,"value":1173},"support@dnanexus.com",{"type":464,"value":1175}," and ",{"type":429,"tag":502,"props":1177,"children":1178},{"href":504},[1179],{"type":464,"value":1180},"support@stjude.cloud",{"type":464,"value":1182}," with any questions about doing this.",{"type":429,"tag":509,"props":1184,"children":1186},{"id":1185},"analysis-of-results",[1187],{"type":464,"value":1188},"Analysis of Results",{"type":429,"tag":515,"props":1190,"children":1191},{},[1192],{"type":464,"value":1193},"Each tool in St. Jude Cloud produces a visualization that makes understanding results more accessible than working with excel spreadsheet or tab delimited files.\nThis is the primary way we recommend you work with your results.",{"type":429,"tag":515,"props":1195,"children":1196},{},[1197,1198,1203],{"type":464,"value":992},{"type":429,"tag":502,"props":1199,"children":1201},{"href":1200},"/genomics-platform/analyzing-data/running-our-workflows#custom-visualizations",[1202],{"type":464,"value":925},{"type":464,"value":1204}," to learn how to access these visualizations.",{"type":429,"tag":515,"props":1206,"children":1207},{},[1208],{"type":464,"value":1209},"We also include the raw output files for you to dig into if the visualization is not sufficient to answer your research question.",{"type":429,"tag":515,"props":1211,"children":1212},{},[1213,1214,1219],{"type":464,"value":992},{"type":429,"tag":502,"props":1215,"children":1217},{"href":1216},"/genomics-platform/analyzing-data/running-our-workflows#raw-results-files",[1218],{"type":464,"value":925},{"type":464,"value":1220}," to learn how to access raw results files.",{"type":429,"tag":818,"props":1222,"children":1224},{"id":1223},"interpreting-results",[1225],{"type":464,"value":1226},"Interpreting results",{"type":429,"tag":515,"props":1228,"children":1229},{},[1230,1232,1238],{"type":464,"value":1231},"The complete output file specification is listed in the ",{"type":429,"tag":502,"props":1233,"children":1235},{"href":1234},"#overview",[1236],{"type":464,"value":1237},"overview section",{"type":464,"value":1239}," of this guide.\nHere, we will discuss each of the different output files in more detail.",{"type":429,"tag":762,"props":1241,"children":1242},{},[1243],{"type":429,"tag":746,"props":1244,"children":1245},{},[1246,1251,1253,1259],{"type":429,"tag":460,"props":1247,"children":1248},{},[1249],{"type":464,"value":1250},"Predicted gene fusions",{"type":464,"value":1252},":\nThe putative gene fusions will be contained in the file ",{"type":429,"tag":872,"props":1254,"children":1256},{"className":1255},[],[1257],{"type":464,"value":1258},"[SAMPLE].final_fusions.txt",{"type":464,"value":1260},".\nThis file is a tab-delimited file containing many fields for each of the predicted SV.\nThe most important columns are the following.",{"type":429,"tag":430,"props":1262,"children":1263},{},[1264,1279],{"type":429,"tag":434,"props":1265,"children":1266},{},[1267],{"type":429,"tag":438,"props":1268,"children":1269},{},[1270,1275],{"type":429,"tag":442,"props":1271,"children":1272},{},[1273],{"type":464,"value":1274},"Field Name",{"type":429,"tag":442,"props":1276,"children":1277},{},[1278],{"type":464,"value":549},{"type":429,"tag":449,"props":1280,"children":1281},{},[1282,1299,1316,1333,1350,1367,1384],{"type":429,"tag":438,"props":1283,"children":1284},{},[1285,1294],{"type":429,"tag":456,"props":1286,"children":1287},{},[1288],{"type":429,"tag":872,"props":1289,"children":1291},{"className":1290},[],[1292],{"type":464,"value":1293},"sample",{"type":429,"tag":456,"props":1295,"children":1296},{},[1297],{"type":464,"value":1298},"Sample 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file.",{"type":429,"tag":430,"props":1434,"children":1435},{},[1436,1450],{"type":429,"tag":434,"props":1437,"children":1438},{},[1439],{"type":429,"tag":438,"props":1440,"children":1441},{},[1442,1446],{"type":429,"tag":442,"props":1443,"children":1444},{},[1445],{"type":464,"value":1274},{"type":429,"tag":442,"props":1447,"children":1448},{},[1449],{"type":464,"value":549},{"type":429,"tag":449,"props":1451,"children":1452},{},[1453,1478,1495,1512,1529,1546,1563,1580,1597,1614],{"type":429,"tag":438,"props":1454,"children":1455},{},[1456,1465],{"type":429,"tag":456,"props":1457,"children":1458},{},[1459],{"type":429,"tag":872,"props":1460,"children":1462},{"className":1461},[],[1463],{"type":464,"value":1464},"junction",{"type":429,"tag":456,"props":1466,"children":1467},{},[1468,1470,1476],{"type":464,"value":1469},"Splice junction in the TCGA format \"chrX:start:+,chrX:end,+\". \"start\" and \"end\" are the 1-based position of the last mapped nucleotide before the skip and the first mapped nucleotide after the skip (i.e. the last base of the previous exon and the first base of the next exon). Note that in ",{"type":429,"tag":872,"props":1471,"children":1473},{"className":1472},[],[1474],{"type":464,"value":1475},".bed",{"type":464,"value":1477}," output these coordinates will be different, see the .bed output section below. The \"+\" is currently hardcoded, though this may change in the future.",{"type":429,"tag":438,"props":1479,"children":1480},{},[1481,1490],{"type":429,"tag":456,"props":1482,"children":1483},{},[1484],{"type":429,"tag":872,"props":1485,"children":1487},{"className":1486},[],[1488],{"type":464,"value":1489},"count",{"type":429,"tag":456,"props":1491,"children":1492},{},[1493],{"type":464,"value":1494},"Raw count of reads supporting the junction. During correction counts for ambiguous junctions can be combined, though obviously these additional reads will not be visible in the raw BAM file.",{"type":429,"tag":438,"props":1496,"children":1497},{},[1498,1507],{"type":429,"tag":456,"props":1499,"children":1500},{},[1501],{"type":429,"tag":872,"props":1502,"children":1504},{"className":1503},[],[1505],{"type":464,"value":1506},"type",{"type":429,"tag":456,"props":1508,"children":1509},{},[1510],{"type":464,"value":1511},"Either \"known\" (matching a reference junction) or \"novel\" (not observed in the reference junction collection).",{"type":429,"tag":438,"props":1513,"children":1514},{},[1515,1524],{"type":429,"tag":456,"props":1516,"children":1517},{},[1518],{"type":429,"tag":872,"props":1519,"children":1521},{"className":1520},[],[1522],{"type":464,"value":1523},"genes",{"type":429,"tag":456,"props":1525,"children":1526},{},[1527],{"type":464,"value":1528},"Gene symbols from the junction calling process. These still need work in the raw junction calling process, it's recommended to use the \"annotated\" output files instead which assign matching HUGO gene symbols based on the UCSC refGene table.",{"type":429,"tag":438,"props":1530,"children":1531},{},[1532,1541],{"type":429,"tag":456,"props":1533,"children":1534},{},[1535],{"type":429,"tag":872,"props":1536,"children":1538},{"className":1537},[],[1539],{"type":464,"value":1540},"transcripts",{"type":429,"tag":456,"props":1542,"children":1543},{},[1544],{"type":464,"value":1545},"List of known transcript IDs matching the junction.",{"type":429,"tag":438,"props":1547,"children":1548},{},[1549,1558],{"type":429,"tag":456,"props":1550,"children":1551},{},[1552],{"type":429,"tag":872,"props":1553,"children":1555},{"className":1554},[],[1556],{"type":464,"value":1557},"qc_flanking",{"type":429,"tag":456,"props":1559,"children":1560},{},[1561],{"type":464,"value":1562},"Count of supporting reads passing flanking sequence checks (junctions observed adjacent to read ends require 18+ nt of flanking sequence, otherwise 10+ nt).",{"type":429,"tag":438,"props":1564,"children":1565},{},[1566,1575],{"type":429,"tag":456,"props":1567,"children":1568},{},[1569],{"type":429,"tag":872,"props":1570,"children":1572},{"className":1571},[],[1573],{"type":464,"value":1574},"qc_plus",{"type":429,"tag":456,"props":1576,"children":1577},{},[1578],{"type":464,"value":1579},"Count of supporting reads aligned to the + strand.",{"type":429,"tag":438,"props":1581,"children":1582},{},[1583,1592],{"type":429,"tag":456,"props":1584,"children":1585},{},[1586],{"type":429,"tag":872,"props":1587,"children":1589},{"className":1588},[],[1590],{"type":464,"value":1591},"qc_minus",{"type":429,"tag":456,"props":1593,"children":1594},{},[1595],{"type":464,"value":1596},"Count of supporting reads aligned to the - strand.",{"type":429,"tag":438,"props":1598,"children":1599},{},[1600,1609],{"type":429,"tag":456,"props":1601,"children":1602},{},[1603],{"type":429,"tag":872,"props":1604,"children":1606},{"className":1605},[],[1607],{"type":464,"value":1608},"qc_perfect_reads",{"type":429,"tag":456,"props":1610,"children":1611},{},[1612],{"type":464,"value":1613},"Count of supporting reads with perfect alignments (no reference mismatches of quality 15+, indels, or soft clips).",{"type":429,"tag":438,"props":1615,"children":1616},{},[1617,1626],{"type":429,"tag":456,"props":1618,"children":1619},{},[1620],{"type":429,"tag":872,"props":1621,"children":1623},{"className":1622},[],[1624],{"type":464,"value":1625},"qc_clean_reads",{"type":429,"tag":456,"props":1627,"children":1628},{},[1629],{"type":464,"value":1630},"Count of supporting reads whose alignments are not perfect but which have a ratio of \u003C= 5% of reference mismatches of quality 15+, indels, or soft clips relative to the count of aligned bases on both the left and right flanking sequence. Note: qc_clean_reads does NOT include qc_perfect_reads: to get a count of \"perfect plus pretty good\" reads the two values must be added together.",{"type":429,"tag":509,"props":1632,"children":1634},{"id":1633},"known-issues",[1635],{"type":464,"value":1636},"Known issues",{"type":429,"tag":595,"props":1638,"children":1639},{"color":597,"icon":598},[1640],{"type":429,"tag":515,"props":1641,"children":1642},{},[1643,1648,1651],{"type":429,"tag":460,"props":1644,"children":1645},{},[1646],{"type":464,"value":1647},"Adapter Contamination",{"type":429,"tag":609,"props":1649,"children":1650},{},[],{"type":464,"value":1652},"\nThis pipeline does not, at present, remove adapter sequences.\nIf the sequencing library is contaminated with adapters, CICERO runtimes can increase exponentially.\nWe recommend running FASTQ files through a QC pipeline such as FastQC and trimming adapters with tools such as Trimmomatic if adapters are found.",{"type":429,"tag":595,"props":1654,"children":1655},{"color":597,"icon":598},[1656],{"type":429,"tag":515,"props":1657,"children":1658},{},[1659,1664,1667],{"type":429,"tag":460,"props":1660,"children":1661},{},[1662],{"type":464,"value":1663},"Higher Coverage Regions",{"type":429,"tag":609,"props":1665,"children":1666},{},[],{"type":464,"value":1668},"\nCertain cell types show very high transcription of certain loci, for example, the immunoglobulin heavy chain locus in plasma cells.\nThe presence of very highly covered regions (typically 100,000-1,000,000+ X) has an adverse effect on CICERO runtimes.\nPresently, we have no good solution to this problem as strategies such as down-sampling may reduce sensitivity over important regions of the genome.",{"type":429,"tag":595,"props":1670,"children":1673},{"color":1671,"icon":1672},"red","i-heroicons-bug-ant",[1674],{"type":429,"tag":515,"props":1675,"children":1676},{},[1677,1682,1685],{"type":429,"tag":460,"props":1678,"children":1679},{},[1680],{"type":464,"value":1681},"Interactive Visualizations Exon vs Intron Nomenclature",{"type":429,"tag":609,"props":1683,"children":1684},{},[],{"type":464,"value":1686},"\nWhen a codon is split over a fusion gene junction, the annotation software marks the event as intronic when really, the event should be exonic.\nWe are working to fix this bug.\nIn the mean time, if a fusion is predicted to be in frame but the interactive plot shows \"intronic\", we suggest the user blat the contig shown just below to clarify if the true junction is either in the intron or exon.",{"type":429,"tag":509,"props":1688,"children":1690},{"id":1689},"frequently-asked-questions",[1691],{"type":464,"value":1692},"Frequently asked questions",{"type":429,"tag":515,"props":1694,"children":1695},{},[1696,1698,1702],{"type":464,"value":1697},"If you have any questions not covered here, feel free to email us at ",{"type":429,"tag":502,"props":1699,"children":1700},{"href":504},[1701],{"type":464,"value":1180},{"type":464,"value":908},{"type":429,"tag":509,"props":1704,"children":1706},{"id":1705},"submit-batch-jobs-on-command-line",[1707],{"type":464,"value":1708},"Submit batch jobs on command line",{"type":429,"tag":515,"props":1710,"children":1711},{},[1712,1714],{"type":464,"value":1713},"See ",{"type":429,"tag":502,"props":1715,"children":1717},{"href":1716},"/genomics-platform/getting-started/faq#how-can-i-run-an-analysis-workflow-on-multiple-sample-files-at-the-same-time",[1718],{"type":464,"value":1719},"How can I run an analysis workflow on multiple sample files at the same time?",{"type":429,"tag":509,"props":1721,"children":1723},{"id":1722},"similar-topics",[1724],{"type":464,"value":1725},"Similar Topics",{"type":429,"tag":515,"props":1727,"children":1728},{},[1729,1734,1737,1742,1745],{"type":429,"tag":502,"props":1730,"children":1731},{"href":89},[1732],{"type":464,"value":1733},"Running our Workflows",{"type":429,"tag":609,"props":1735,"children":1736},{},[],{"type":429,"tag":502,"props":1738,"children":1739},{"href":67},[1740],{"type":464,"value":1741},"Working with our Data Overview",{"type":429,"tag":609,"props":1743,"children":1744},{},[],{"type":429,"tag":502,"props":1746,"children":1747},{"href":70},[1748],{"type":464,"value":1749},"Upload/Download Data 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