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477\n...\n",[1397],{"type":431,"tag":781,"props":1398,"children":1399},{"__ignoreMap":424},[1400,1411,1420,1429,1438,1447],{"type":431,"tag":1401,"props":1402,"children":1405},"span",{"class":1403,"line":1404},"line",1,[1406],{"type":431,"tag":1401,"props":1407,"children":1408},{},[1409],{"type":466,"value":1410},"gene_name ctrl_1\n",{"type":431,"tag":1401,"props":1412,"children":1414},{"class":1403,"line":1413},2,[1415],{"type":431,"tag":1401,"props":1416,"children":1417},{},[1418],{"type":466,"value":1419},"A1BG      10\n",{"type":431,"tag":1401,"props":1421,"children":1423},{"class":1403,"line":1422},3,[1424],{"type":431,"tag":1401,"props":1425,"children":1426},{},[1427],{"type":466,"value":1428},"A1BG-AS1  0\n",{"type":431,"tag":1401,"props":1430,"children":1432},{"class":1403,"line":1431},4,[1433],{"type":431,"tag":1401,"props":1434,"children":1435},{},[1436],{"type":466,"value":1437},"A1CF      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DNAnexus workspace for each workflow run.",{"type":431,"tag":517,"props":1476,"children":1477},{},[1478,1480,1487],{"type":466,"value":1479},"You can navigate to the WARDEN Differential Expression Analysis workflow page ",{"type":431,"tag":504,"props":1481,"children":1484},{"href":1482,"rel":1483},"https://platform.stjude.cloud/workflows/warden",[1380],[1485],{"type":466,"value":1486},"here",{"type":466,"value":925},{"type":431,"tag":511,"props":1489,"children":1491},{"id":1490},"uploading-input-files",[1492],{"type":466,"value":1493},"Uploading Input Files",{"type":431,"tag":517,"props":1495,"children":1496},{},[1497,1499,1505],{"type":466,"value":1498},"The WARDEN Differential Expression analysis pipeline takes FASTQ files, BAM files, or feature count files generated by an RNA-Seq experiment as ",{"type":431,"tag":504,"props":1500,"children":1502},{"href":1501},"#inputs",[1503],{"type":466,"value":1504},"input",{"type":466,"value":925},{"type":431,"tag":517,"props":1507,"children":1508},{},[1509],{"type":466,"value":1510},"Due to the dynamic nature of the WARDEN workflow, we recommend limiting the cohort in a single run to around 60 samples.\nRunning with additional samples may lead to intermittent failures, especially when starting with FASTQs or BAMs.",{"type":431,"tag":517,"props":1512,"children":1513},{},[1514,1516,1520],{"type":466,"value":1515},"Refer to ",{"type":431,"tag":504,"props":1517,"children":1518},{"href":1043},[1519],{"type":466,"value":1472},{"type":466,"value":1521}," to learn how to upload input files to the workspace you just created.",{"type":431,"tag":511,"props":1523,"children":1525},{"id":1524},"running-the-workflow",[1526],{"type":466,"value":1527},"Running the Workflow",{"type":431,"tag":517,"props":1529,"children":1530},{},[1531,1532,1537],{"type":466,"value":1515},{"type":431,"tag":504,"props":1533,"children":1535},{"href":1534},"/genomics-platform/analyzing-data/running-our-workflows#running-the-workflow",[1536],{"type":466,"value":1472},{"type":466,"value":1538}," to learn how to launch the workflow, hook up input files, adjust parameters, start a run, and monitor run progress.",{"type":431,"tag":570,"props":1540,"children":1542},{"id":1541},"hooking-up-inputs",[1543],{"type":466,"value":1544},"Hooking up Inputs",{"type":431,"tag":517,"props":1546,"children":1547},{},[1548,1550,1556,1558,1564],{"type":466,"value":1549},"First, in the ",{"type":431,"tag":781,"props":1551,"children":1553},{"className":1552},[],[1554],{"type":466,"value":1555},"Execution Output Folder",{"type":466,"value":1557}," field, select a folder to output results in.\nYou can structure your experiments however you like (e.g. ",{"type":431,"tag":781,"props":1559,"children":1561},{"className":1560},[],[1562],{"type":466,"value":1563},"/my_outputs",{"type":466,"value":1565},").\nIf left blank, a directory named with the execution ID will be created in order to avoid cluttering your workspace and keep separate runs separate.",{"type":431,"tag":517,"props":1567,"children":1568},{},[1569,1571,1577,1579,1585,1587,1593,1595,1601,1603,1608,1610,1616],{"type":466,"value":1570},"Next, you'll need to hook up the FASTQ files, BAM files, or count files (depending on which entrypoint you wish to use) and sample sheet you uploaded in ",{"type":431,"tag":504,"props":1572,"children":1574},{"href":1573},"#uploading-input-files",[1575],{"type":466,"value":1576},"the upload data section",{"type":466,"value":1578},".\nClick the ",{"type":431,"tag":781,"props":1580,"children":1582},{"className":1581},[],[1583],{"type":466,"value":1584},"FASTQ_FILES",{"type":466,"value":1586},", ",{"type":431,"tag":781,"props":1588,"children":1590},{"className":1589},[],[1591],{"type":466,"value":1592},"BAM_FILES",{"type":466,"value":1594},", or ",{"type":431,"tag":781,"props":1596,"children":1598},{"className":1597},[],[1599],{"type":466,"value":1600},"COUNT_FILES",{"type":466,"value":1602}," input field and select ",{"type":431,"tag":462,"props":1604,"children":1605},{},[1606],{"type":466,"value":1607},"all",{"type":466,"value":1609}," input files listed in your sample sheet.\nNext, click the ",{"type":431,"tag":781,"props":1611,"children":1613},{"className":1612},[],[1614],{"type":466,"value":1615},"sample_list",{"type":466,"value":1617}," input field and select the corresponding sample sheet.",{"type":431,"tag":517,"props":1619,"children":1620},{},[1621,1623,1629],{"type":466,"value":1622},"If running \"WARDEN [FASTQ]\" or \"WARDEN [BAM]\", select the ",{"type":431,"tag":781,"props":1624,"children":1626},{"className":1625},[],[1627],{"type":466,"value":1628},"sequence_strandedness",{"type":466,"value":1630}," drop down menu and choose the appropriate option.\nThis information can be determined from the sequencing or source of the data.\nIf you don't know what to put here, select \"Unstranded\".",{"type":431,"tag":517,"props":1632,"children":1633},{},[1634,1636,1642,1644,1650,1651,1657],{"type":466,"value":1635},"If running \"WARDEN [BAM]\", select the ",{"type":431,"tag":781,"props":1637,"children":1639},{"className":1638},[],[1640],{"type":466,"value":1641},"sort_order",{"type":466,"value":1643}," drop down menu and choose the method with which your BAM files are sorted, either ",{"type":431,"tag":781,"props":1645,"children":1647},{"className":1646},[],[1648],{"type":466,"value":1649},"name",{"type":466,"value":1166},{"type":431,"tag":781,"props":1652,"children":1654},{"className":1653},[],[1655],{"type":466,"value":1656},"position",{"type":466,"value":1658},".\nUnsorted BAM files are not supported by WARDEN.",{"type":431,"tag":517,"props":1660,"children":1661},{},[1662,1664,1670,1672,1677,1679,1684],{"type":466,"value":1663},"Finally, select the ",{"type":431,"tag":781,"props":1665,"children":1667},{"className":1666},[],[1668],{"type":466,"value":1669},"Genome",{"type":466,"value":1671}," dropdown menu, choose the appropriate option, and WARDEN is ready to be run! For \"WARDEN [FASTQ]\", ",{"type":431,"tag":781,"props":1673,"children":1675},{"className":1674},[],[1676],{"type":466,"value":1669},{"type":466,"value":1678}," determines which genome STAR will map your FASTQs to.\nFor \"WARDEN [BAM]\" and \"WARDEN [Counts]\", ",{"type":431,"tag":781,"props":1680,"children":1682},{"className":1681},[],[1683],{"type":466,"value":1669},{"type":466,"value":1685}," should be which genome your BAM files or counts files were previously aligned to.",{"type":431,"tag":517,"props":1687,"children":1688},{},[1689],{"type":466,"value":1690},"Continue reading to learn about the available advanced options.",{"type":431,"tag":517,"props":1692,"children":1693},{},[1694],{"type":431,"tag":1096,"props":1695,"children":1698},{"alt":1696,"src":1697},"warden inputs","/img/genomics-platform/workflow-guides/warden/warden-inputs.gif",[],{"type":431,"tag":570,"props":1700,"children":1702},{"id":1701},"selecting-parameters",[1703],{"type":466,"value":1704},"Selecting Parameters",{"type":431,"tag":517,"props":1706,"children":1707},{},[1708,1710,1715],{"type":466,"value":1709},"For the general workflow instructions on parameter selection, refer ",{"type":431,"tag":504,"props":1711,"children":1713},{"href":1712},"/genomics-platform/analyzing-data/running-our-workflows#selecting-parameters",[1714],{"type":466,"value":1486},{"type":466,"value":925},{"type":431,"tag":1022,"props":1717,"children":1718},{"color":1024,"icon":1115},[1719,1727],{"type":431,"tag":517,"props":1720,"children":1721},{},[1722],{"type":431,"tag":462,"props":1723,"children":1724},{},[1725],{"type":466,"value":1726},"Parameter options",{"type":431,"tag":1126,"props":1728,"children":1729},{},[1730,1776,1802,1901,1928],{"type":431,"tag":581,"props":1731,"children":1732},{},[1733,1735,1744,1745,1754,1755,1763,1765,1774],{"type":466,"value":1734},"Options under \"Advanced: Run Control\" can be enabled or disabled.\n",{"type":431,"tag":504,"props":1736,"children":1737},{"href":714},[1738],{"type":431,"tag":781,"props":1739,"children":1741},{"className":1740},[],[1742],{"type":466,"value":1743},"generate_name_sorted_BAM",{"type":466,"value":1586},{"type":431,"tag":504,"props":1746,"children":1747},{"href":714},[1748],{"type":431,"tag":781,"props":1749,"children":1751},{"className":1750},[],[1752],{"type":466,"value":1753},"generate_transcriptome_BAM",{"type":466,"value":1586},{"type":431,"tag":504,"props":1756,"children":1757},{"href":805},[1758],{"type":431,"tag":781,"props":1759,"children":1761},{"className":1760},[],[1762],{"type":466,"value":825},{"type":466,"value":1764},", and ",{"type":431,"tag":504,"props":1766,"children":1768},{"href":1767},"#bigwig-viewer",[1769],{"type":431,"tag":781,"props":1770,"children":1772},{"className":1771},[],[1773],{"type":466,"value":794},{"type":466,"value":1775}," are disabled by default to reduce run time and costs.",{"type":431,"tag":581,"props":1777,"children":1778},{},[1779,1785,1787,1793,1795,1800],{"type":431,"tag":781,"props":1780,"children":1782},{"className":1781},[],[1783],{"type":466,"value":1784},"STAR_subsample_n_reads",{"type":466,"value":1786}," can be used to reduce runtime and run costs.\nThe default of 100 million reads will map the entirety of most samples and is a sufficient number of reads for differential expression analysis.\nThe reason for enabling it by default is to prevent surprise run-away costs when mapping exceptionally large numbers of reads.\nSetting this value to \"0\" or \"-1\" will disable subsampling, and map the entirety of all input FASTQs.\nWith sufficiently large FASTQs, this can take a long time and cost a significant amount of money.\nLarge FASTQs also occasionally cause errors in the STAR step.\nIf those are encountered, we recommend re-enabling subsampling or increasing the size of the ",{"type":431,"tag":781,"props":1788,"children":1790},{"className":1789},[],[1791],{"type":466,"value":1792},"star_instance",{"type":466,"value":1794},".\n",{"type":431,"tag":462,"props":1796,"children":1797},{},[1798],{"type":466,"value":1799},"Warning:",{"type":466,"value":1801}," a larger STAR instance will incur larger costs.",{"type":431,"tag":581,"props":1803,"children":1804},{},[1805,1807],{"type":466,"value":1806},"The LIMMA parameters can be left at their defaults for most analyses.\nIf you are an advanced LIMMA user, you can change the various settings exposed.\n",{"type":431,"tag":1126,"props":1808,"children":1809},{},[1810,1846],{"type":431,"tag":581,"props":1811,"children":1812},{},[1813,1815,1821,1823,1829,1831,1837,1838,1844],{"type":466,"value":1814},"Descriptions of ",{"type":431,"tag":781,"props":1816,"children":1818},{"className":1817},[],[1819],{"type":466,"value":1820},"calcNormFactors_method",{"type":466,"value":1822}," and ",{"type":431,"tag":781,"props":1824,"children":1826},{"className":1825},[],[1827],{"type":466,"value":1828},"p_value_adjust",{"type":466,"value":1830}," can be found ",{"type":431,"tag":504,"props":1832,"children":1835},{"href":1833,"rel":1834},"https://www.rdocumentation.org/packages/edgeR/versions/3.14.0/topics/calcNormFactors",[1380],[1836],{"type":466,"value":1486},{"type":466,"value":1822},{"type":431,"tag":504,"props":1839,"children":1842},{"href":1840,"rel":1841},"https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/p.adjust",[1380],[1843],{"type":466,"value":1486},{"type":466,"value":1845},", respectively.",{"type":431,"tag":581,"props":1847,"children":1848},{},[1849,1855,1857,1863,1864,1870,1872,1877,1879,1885,1887,1892,1894,1899],{"type":431,"tag":781,"props":1850,"children":1852},{"className":1851},[],[1853],{"type":466,"value":1854},"filter_count_type",{"type":466,"value":1856}," is either ",{"type":431,"tag":781,"props":1858,"children":1860},{"className":1859},[],[1861],{"type":466,"value":1862},"raw_counts",{"type":466,"value":1166},{"type":431,"tag":781,"props":1865,"children":1867},{"className":1866},[],[1868],{"type":466,"value":1869},"CPM",{"type":466,"value":1871}," (Counts Per Million).\nFor ",{"type":431,"tag":781,"props":1873,"children":1875},{"className":1874},[],[1876],{"type":466,"value":1862},{"type":466,"value":1878},", the sum of counts for each feature has to be >= ",{"type":431,"tag":781,"props":1880,"children":1882},{"className":1881},[],[1883],{"type":466,"value":1884},"filter_count",{"type":466,"value":1886},".\nFor ",{"type":431,"tag":781,"props":1888,"children":1890},{"className":1889},[],[1891],{"type":466,"value":1869},{"type":466,"value":1893},", the mean library size of all samples is determined.\nA counts per million cutoff value is determined for the ",{"type":431,"tag":781,"props":1895,"children":1897},{"className":1896},[],[1898],{"type":466,"value":1884},{"type":466,"value":1900}," parameter based on this mean library size.\nA feature can be recovered from this filter if the number of samples with a CPM for the feature greater than the cutoff is greater than or equal to the samples in the sample class with the smallest number of members.",{"type":431,"tag":581,"props":1902,"children":1903},{},[1904,1906,1912,1913,1919,1921,1927],{"type":466,"value":1905},"If you are interested in a feature besides genes, you should change the ",{"type":431,"tag":781,"props":1907,"children":1909},{"className":1908},[],[1910],{"type":466,"value":1911},"feature_type",{"type":466,"value":1822},{"type":431,"tag":781,"props":1914,"children":1916},{"className":1915},[],[1917],{"type":466,"value":1918},"id_attribute",{"type":466,"value":1920}," HTSeq-count parameters.\nNote that changing from the defaults will disable FPKM calculations.\nThe other options can be changed by advanced users of HTSeq-count.\nThe HTSeq documentation can be found ",{"type":431,"tag":504,"props":1922,"children":1925},{"href":1923,"rel":1924},"https://htseq.readthedocs.io",[1380],[1926],{"type":466,"value":1486},{"type":466,"value":925},{"type":431,"tag":581,"props":1929,"children":1930},{},[1931,1933,1939],{"type":466,"value":1932},"Similarly STAR parameters can be be changed by advanced users familiar with the STAR aligner.\nYou can read the STAR v2.5.3a manual ",{"type":431,"tag":504,"props":1934,"children":1937},{"href":1935,"rel":1936},"https://github.com/alexdobin/STAR/blob/2.5.3a/doc/STARmanual.pdf",[1380],[1938],{"type":466,"value":1486},{"type":466,"value":925},{"type":431,"tag":517,"props":1941,"children":1942},{},[1943],{"type":466,"value":1944},"Once all parameters have been adjusted to your needs, you're ready to run WARDEN!",{"type":431,"tag":511,"props":1946,"children":1948},{"id":1947},"summary-of-results",[1949],{"type":466,"value":1950},"Summary of Results",{"type":431,"tag":517,"props":1952,"children":1953},{},[1954],{"type":466,"value":1955},"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":431,"tag":517,"props":1957,"children":1958},{},[1959,1960,1965],{"type":466,"value":1515},{"type":431,"tag":504,"props":1961,"children":1963},{"href":1962},"/genomics-platform/analyzing-data/running-our-workflows#custom-visualizations",[1964],{"type":466,"value":1472},{"type":466,"value":1966}," to learn how to access these visualizations.",{"type":431,"tag":517,"props":1968,"children":1969},{},[1970],{"type":466,"value":1971},"We also include the raw output files for you to dig into if the visualization is not sufficient to answer your research question.",{"type":431,"tag":517,"props":1973,"children":1974},{},[1975,1976,1981],{"type":466,"value":1515},{"type":431,"tag":504,"props":1977,"children":1979},{"href":1978},"/genomics-platform/analyzing-data/running-our-workflows#raw-results-files",[1980],{"type":466,"value":1472},{"type":466,"value":1982}," to learn how to access raw results files.",{"type":431,"tag":570,"props":1984,"children":1986},{"id":1985},"primary-results",[1987],{"type":466,"value":1988},"Primary Results",{"type":431,"tag":1050,"props":1990,"children":1992},{"id":1991},"multidimensional-scaling-mds-plot",[1993],{"type":466,"value":1994},"Multidimensional scaling (MDS) Plot",{"type":431,"tag":517,"props":1996,"children":1997},{},[1998,2005,2007,2013],{"type":431,"tag":504,"props":1999,"children":2002},{"href":2000,"rel":2001},"https://en.wikipedia.org/wiki/Multidimensional_scaling",[1380],[2003],{"type":466,"value":2004},"Multidimensional scaling (MDS) plots",{"type":466,"value":2006}," are created using the plotMDS function within LIMMA.\nSimilar to PCA, these graphs will show how similar samples are to each other.\nThere are different sets of MDS plots.\nFor comparisons where there are 3 or more samples per condition, an MDS plot using Voom (Limma) normalized values are generated.\nAn example can be seen below.\nThese files will be labeled ",{"type":431,"tag":781,"props":2008,"children":2010},{"className":2009},[],[2011],{"type":466,"value":2012},"mds_plot.limma.png",{"type":466,"value":925},{"type":431,"tag":517,"props":2015,"children":2016},{},[2017],{"type":431,"tag":1096,"props":2018,"children":2021},{"alt":2019,"src":2020},"mds plot","/img/genomics-platform/workflow-guides/warden/mdsPlot.png",[],{"type":431,"tag":517,"props":2023,"children":2024},{},[2025,2027,2033],{"type":466,"value":2026},"For all comparisons, regardless of sample size, an MDS plot will also be generated with Counts Per Million (CPM) normalized feature counts.\nThese files will be labeled ",{"type":431,"tag":781,"props":2028,"children":2030},{"className":2029},[],[2031],{"type":466,"value":2032},"mds_plot.norm_cpm.png",{"type":466,"value":925},{"type":431,"tag":517,"props":2035,"children":2036},{},[2037],{"type":431,"tag":1096,"props":2038,"children":2041},{"alt":2039,"src":2040},"mds plot norm cpm","/img/genomics-platform/workflow-guides/warden/mdsPlot.normCPM.png",[],{"type":431,"tag":517,"props":2043,"children":2044},{},[2045],{"type":466,"value":2046},"These files will be in the root of the output directory.",{"type":431,"tag":1050,"props":2048,"children":2050},{"id":2049},"proteinpaint-visualizations",[2051],{"type":466,"value":2052},"ProteinPaint Visualizations",{"type":431,"tag":517,"props":2054,"children":2055},{},[2056],{"type":466,"value":2057},"Several files on DNAnexus allow the data to be viewed in the Protein Paint viewer.\n(Note: We plan to have links downloaded in the future to allow the viewing of these files off of DNAnexus.)",{"type":431,"tag":517,"props":2059,"children":2060},{},[2061],{"type":466,"value":2062},"At the time of writing, there are two user interfaces for DNAnexus you can toggle between, a \"Classic Version\" and a \"New version\".\nOur ProteinPaint \"viewer shortcuts\" don't properly launch from the new user interface.\nTo launch these, please switch to the \"Classic Version\" of the user interface.\nWe are working with DNAnexus on a fix for this.",{"type":431,"tag":1102,"props":2064,"children":2066},{"id":2065},"limma-differential-expression-viewer",[2067],{"type":466,"value":2068},"LIMMA differential expression viewer",{"type":431,"tag":517,"props":2070,"children":2071},{},[2072,2074,2080],{"type":466,"value":2073},"Within the root directory, there will be a viewer file for each valid comparison (",{"type":431,"tag":781,"props":2075,"children":2077},{"className":2076},[],[2078],{"type":466,"value":2079},"limma.\u003Ccontrast>.viewer",{"type":466,"value":2081},").\nSimply select the file and press 'Launch viewer'.\nA viewer will pop up showing both the MA plot and Volcano plot.\nBy moving the mouse over a circle, the circle will highlight, and the corresponding feature on the other graph will also highlight.\nAdditional information about the feature and its expression values will also be shown.\nOne can also type in multiple feature identifiers in the provided text box.\nBy pressing 'Show gene labels' all these features will be highlighted on the plots.",{"type":431,"tag":517,"props":2083,"children":2084},{},[2085],{"type":431,"tag":1096,"props":2086,"children":2089},{"alt":2087,"src":2088},"ma volcano","/img/genomics-platform/workflow-guides/warden/ma_volcano.png",[],{"type":431,"tag":517,"props":2091,"children":2092},{},[2093],{"type":466,"value":2094},"The MA plot shows the average expression of the feature on the X-axis, and Log2 fold change between condition/phenotype is on the Y-axis (if the name is for example MA_plot.condition2-condition1.png then the fold change would represent condition1 minus condition2).\nEach feature is represented by a circle.\nThe top 20 feature (by p-value) are identified on the plot.\nThe features are color coded by the chosen multiple testing correction method (False Discovery Rate (FDR) by default). An example MA plot can be seen below.",{"type":431,"tag":517,"props":2096,"children":2097},{},[2098],{"type":431,"tag":1096,"props":2099,"children":2102},{"alt":2100,"src":2101},"ma plot","/img/genomics-platform/workflow-guides/warden/maPlot.png",[],{"type":431,"tag":517,"props":2104,"children":2105},{},[2106],{"type":466,"value":2107},"The volcano plot shows the Log2Fold change between the conditions on the X-axis, and the -Log10 of the multiple testing corrected P-value on the Y-axis.",{"type":431,"tag":517,"props":2109,"children":2110},{},[2111],{"type":431,"tag":1096,"props":2112,"children":2115},{"alt":2113,"src":2114},"volcano plat","/img/genomics-platform/workflow-guides/warden/volcanoPlot.png",[],{"type":431,"tag":1102,"props":2117,"children":2119},{"id":2118},"simple-differential-expression-viewer",[2120],{"type":466,"value":2121},"Simple differential expression viewer",{"type":431,"tag":517,"props":2123,"children":2124},{},[2125,2127,2133,2135,2141],{"type":466,"value":2126},"There will also be a viewer for the simple differential expression analysis in the root directory (",{"type":431,"tag":781,"props":2128,"children":2130},{"className":2129},[],[2131],{"type":466,"value":2132},"simple_DE.\u003Ccontrast>.viewer",{"type":466,"value":2134},").\nThe P-value for the results have all been set to 1, so the volcano plot will not be relevant.\nStatic versions of the plots will be in the ",{"type":431,"tag":781,"props":2136,"children":2138},{"className":2137},[],[2139],{"type":466,"value":2140},"DIFFERENTIAL_EXPRESSION/",{"type":466,"value":2142}," directory.",{"type":431,"tag":1102,"props":2144,"children":2146},{"id":2145},"bigwig-viewer",[2147],{"type":466,"value":2148},"bigWig viewer",{"type":431,"tag":517,"props":2150,"children":2151},{},[2152,2154,2160,2162,2167,2169,2175],{"type":466,"value":2153},"There will be a ",{"type":431,"tag":781,"props":2155,"children":2157},{"className":2156},[],[2158],{"type":466,"value":2159},"bigwig_viewer",{"type":466,"value":2161}," file if the option ",{"type":431,"tag":781,"props":2163,"children":2165},{"className":2164},[],[2166],{"type":466,"value":794},{"type":466,"value":2168}," was selected in either the FASTQ or BAM app.\nSelect this file and then 'Launch viewer'.\nA graph of coverage for the genome of all samples will be visible.\nThis viewer can be found in the ",{"type":431,"tag":781,"props":2170,"children":2172},{"className":2171},[],[2173],{"type":466,"value":2174},"BIGWIG/",{"type":466,"value":2176}," folder.",{"type":431,"tag":1050,"props":2178,"children":2180},{"id":2179},"combined-count-and-fpkm-files",[2181],{"type":466,"value":2182},"Combined count and FPKM files",{"type":431,"tag":517,"props":2184,"children":2185},{},[2186,2188,2194,2196,2202,2203,2209],{"type":466,"value":2187},"HTSeq-count files are combined into a file called ",{"type":431,"tag":781,"props":2189,"children":2191},{"className":2190},[],[2192],{"type":466,"value":2193},"combined_counts.htseq.txt",{"type":466,"value":2195},".\nIf the counts were generated using \"gene name\" as the feature, then FPKM and FPKMlog2 files will also be combined into ",{"type":431,"tag":781,"props":2197,"children":2199},{"className":2198},[],[2200],{"type":466,"value":2201},"combined_counts.fpkm.txt",{"type":466,"value":1822},{"type":431,"tag":781,"props":2204,"children":2206},{"className":2205},[],[2207],{"type":466,"value":2208},"combined_counts.fpkm.log2.txt",{"type":466,"value":2210}," respectively.\nAll of these will be found at the root of the output folder.",{"type":431,"tag":570,"props":2212,"children":2214},{"id":2213},"secondary-results",[2215],{"type":466,"value":2216},"Secondary Results",{"type":431,"tag":1050,"props":2218,"children":2220},{"id":2219},"alignment-statistics",[2221],{"type":466,"value":2222},"Alignment statistics",{"type":431,"tag":517,"props":2224,"children":2225},{},[2226,2232,2234,2240],{"type":431,"tag":781,"props":2227,"children":2229},{"className":2228},[],[2230],{"type":466,"value":2231},"alignment_statistics.txt",{"type":466,"value":2233}," shows alignment statistics for all samples.\nThis file is a plain text tab-delimited file that can be opened in Excel or a text editor such as Notepad++.\nThis file contains information on the total reads per sample, the percentage of duplicate reads and the percentage of mapped reads.\nAn example of this file is below.\nThis file will be in the ",{"type":431,"tag":781,"props":2235,"children":2237},{"className":2236},[],[2238],{"type":466,"value":2239},"STAR/",{"type":466,"value":2176},{"type":431,"tag":1089,"props":2242,"children":2243},{},[2244],{"type":431,"tag":517,"props":2245,"children":2246},{},[2247],{"type":431,"tag":1096,"props":2248,"children":2251},{"alt":2249,"src":2250},"alignment 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results",{"type":431,"tag":517,"props":2284,"children":2285},{},[2286,2288,2294,2296,2302,2304,2310,2312,2317],{"type":466,"value":2287},"Other useful differential expression results will be created.\nThis includes tabular output from the differential expression analysis.\nFor each comparison with three or more samples per condition, ",{"type":431,"tag":781,"props":2289,"children":2291},{"className":2290},[],[2292],{"type":466,"value":2293},"results.limma.\u003Ccontrast>.txt",{"type":466,"value":2295}," will be produced.\n",{"type":431,"tag":781,"props":2297,"children":2299},{"className":2298},[],[2300],{"type":466,"value":2301},"results.simple_DE.\u003C*contrast>.txt",{"type":466,"value":2303}," will be created for all comparisons, regardless of sample size.\nYou can find a counts matrix normalized by Counts Per Million (CPM) in the file ",{"type":431,"tag":781,"props":2305,"children":2307},{"className":2306},[],[2308],{"type":466,"value":2309},"CPM_normalized_values.txt",{"type":466,"value":2311},".\nAll of these will be located in the ",{"type":431,"tag":781,"props":2313,"children":2315},{"className":2314},[],[2316],{"type":466,"value":2140},{"type":466,"value":2142},{"type":431,"tag":1050,"props":2319,"children":2321},{"id":2320},"gseainputcontrasttxt-and-gseatstatcontrasttxt",[2322,2324,2329,2331,2335],{"type":466,"value":2323},"GSEA.input.\u003C",{"type":431,"tag":694,"props":2325,"children":2326},{},[2327],{"type":466,"value":2328},"contrast",{"type":466,"value":2330},">.txt and GSEA.tStat.\u003C",{"type":431,"tag":694,"props":2332,"children":2333},{},[2334],{"type":466,"value":2328},{"type":466,"value":2336},">.txt",{"type":431,"tag":517,"props":2338,"children":2339},{},[2340,2342,2348],{"type":466,"value":2341},"These are input files that can be used for GSEA analysis.\nThe tStat file is preferred for a more accurate analysis, but will not give a heatmap diagram.\nThese files will be in the ",{"type":431,"tag":781,"props":2343,"children":2345},{"className":2344},[],[2346],{"type":466,"value":2347},"AUXILIARY/",{"type":466,"value":2142},{"type":431,"tag":1050,"props":2350,"children":2352},{"id":2351},"coverage-results",[2353],{"type":466,"value":2354},"Coverage results",{"type":431,"tag":517,"props":2356,"children":2357},{},[2358,2360,2365,2367,2374,2376,2382,2384,2390,2391,2397,2399,2404],{"type":466,"value":2359},"bigWig files will be optionally generated (via the ",{"type":431,"tag":781,"props":2361,"children":2363},{"className":2362},[],[2364],{"type":466,"value":794},{"type":466,"value":2366}," option) for use in genome browsers (such as ",{"type":431,"tag":504,"props":2368,"children":2371},{"href":2369,"rel":2370},"http://software.broadinstitute.org/software/igv/",[1380],[2372],{"type":466,"value":2373},"IGV",{"type":466,"value":2375},").\nFor each sample, multiple bigWig files may be found. For all types of sequencing strandedness, there will be bigWig files labeled, ",{"type":431,"tag":781,"props":2377,"children":2379},{"className":2378},[],[2380],{"type":466,"value":2381},"*.sortedcoverage_file.bed.bw",{"type":466,"value":2383}," where '*' is the sample name.\nFor stranded data there will also be ",{"type":431,"tag":781,"props":2385,"children":2387},{"className":2386},[],[2388],{"type":466,"value":2389},"*.sortedPoscoverage_file.bed.bw",{"type":466,"value":1822},{"type":431,"tag":781,"props":2392,"children":2394},{"className":2393},[],[2395],{"type":466,"value":2396},"*.sortedNegcoverage_file.bed.bw",{"type":466,"value":2398}," which contains coverage information for the positive and negative strand of the genome.\nThese files will be in the ",{"type":431,"tag":781,"props":2400,"children":2402},{"className":2401},[],[2403],{"type":466,"value":2174},{"type":466,"value":2142},{"type":431,"tag":1050,"props":2406,"children":2408},{"id":2407},"quality-control-results-fastqc",[2409],{"type":466,"value":2410},"Quality Control Results (FastQC)",{"type":431,"tag":517,"props":2412,"children":2413},{},[2414,2416,2421,2423,2429,2431,2437,2438,2444],{"type":466,"value":2415},"If FastQC was opted into (via the ",{"type":431,"tag":781,"props":2417,"children":2419},{"className":2418},[],[2420],{"type":466,"value":825},{"type":466,"value":2422}," option), there will be a ",{"type":431,"tag":781,"props":2424,"children":2426},{"className":2425},[],[2427],{"type":466,"value":2428},"FASTQC/",{"type":466,"value":2430}," directory.\nFor each sample and read direction there will be an html file and a zip file (",{"type":431,"tag":781,"props":2432,"children":2434},{"className":2433},[],[2435],{"type":466,"value":2436},"*.fastqc.html",{"type":466,"value":1822},{"type":431,"tag":781,"props":2439,"children":2441},{"className":2440},[],[2442],{"type":466,"value":2443},"*.fastqc.zip",{"type":466,"value":2445},"), containing results from FastQC.\nFor the average user the html file is sufficient.\nThis file can give some basic statistics on the quality of the data.",{"type":431,"tag":1050,"props":2447,"children":2449},{"id":2448},"bam-alignment-files",[2450],{"type":466,"value":2451},"BAM alignment files",{"type":431,"tag":517,"props":2453,"children":2454},{},[2455,2457,2463,2465,2471,2473,2478,2480,2485,2487,2493,2495,2500,2502,2508,2510,2515,2517,2523],{"type":466,"value":2456},"There may be up to 3 BAM files generated per sample that contain mapping information for all reads.\nThe first is labeled ",{"type":431,"tag":781,"props":2458,"children":2460},{"className":2459},[],[2461],{"type":466,"value":2462},"*.Aligned.bam",{"type":466,"value":2464}," where '*' is the sample name.\nThe BAM file is sorted by coordinates and will have duplicates marked (by default, but duplicate marking can be disabled with the ",{"type":431,"tag":781,"props":2466,"children":2468},{"className":2467},[],[2469],{"type":466,"value":2470},"mark_duplicates",{"type":466,"value":2472}," option).\nThis is always generated and can be found in the ",{"type":431,"tag":781,"props":2474,"children":2476},{"className":2475},[],[2477],{"type":466,"value":2239},{"type":466,"value":2479}," directory.\nIf ",{"type":431,"tag":781,"props":2481,"children":2483},{"className":2482},[],[2484],{"type":466,"value":1743},{"type":466,"value":2486}," is enabled, then there will also be ",{"type":431,"tag":781,"props":2488,"children":2490},{"className":2489},[],[2491],{"type":466,"value":2492},"*.Aligned.name_sorted.bam",{"type":466,"value":2494}," in the ",{"type":431,"tag":781,"props":2496,"children":2498},{"className":2497},[],[2499],{"type":466,"value":2239},{"type":466,"value":2501}," directory.\nThe third file is optionally generated, ",{"type":431,"tag":781,"props":2503,"children":2505},{"className":2504},[],[2506],{"type":466,"value":2507},"*.Aligned.to_transcriptome.bam",{"type":466,"value":2509},", and contains reads mapped to transcripts.\nThis can be enabled with the ",{"type":431,"tag":781,"props":2511,"children":2513},{"className":2512},[],[2514],{"type":466,"value":1753},{"type":466,"value":2516}," option.\nIf generated, these BAMs will be in the ",{"type":431,"tag":781,"props":2518,"children":2520},{"className":2519},[],[2521],{"type":466,"value":2522},"STAR/TRANSCRIPTOME/",{"type":466,"value":2142},{"type":431,"tag":1050,"props":2525,"children":2527},{"id":2526},"chimeric-reads-and-junction-files",[2528],{"type":466,"value":2529},"Chimeric reads and junction files",{"type":431,"tag":517,"props":2531,"children":2532},{},[2533,2535,2540,2541,2547,2549,2555,2556,2562],{"type":466,"value":2534},"Additional files created by STAR are provided.\nMore information on these files can be found ",{"type":431,"tag":504,"props":2536,"children":2538},{"href":1935,"rel":2537},[1380],[2539],{"type":466,"value":1486},{"type":466,"value":1794},{"type":431,"tag":781,"props":2542,"children":2544},{"className":2543},[],[2545],{"type":466,"value":2546},"*.SJ.out.tab",{"type":466,"value":2548}," contain splice junction information.\nFusion detection files are labeled ",{"type":431,"tag":781,"props":2550,"children":2552},{"className":2551},[],[2553],{"type":466,"value":2554},"*.Chimeric.out.bam",{"type":466,"value":1822},{"type":431,"tag":781,"props":2557,"children":2559},{"className":2558},[],[2560],{"type":466,"value":2561},"*.Chimeric.out.junction",{"type":466,"value":925},{"type":431,"tag":517,"props":2564,"children":2565},{},[2566,2571,2573,2579,2581,2587],{"type":431,"tag":781,"props":2567,"children":2569},{"className":2568},[],[2570],{"type":466,"value":2546},{"type":466,"value":2572}," files will be in ",{"type":431,"tag":781,"props":2574,"children":2576},{"className":2575},[],[2577],{"type":466,"value":2578},"STAR/TABS",{"type":466,"value":2580}," and chimeric BAMs and chimeric junction files will be in the ",{"type":431,"tag":781,"props":2582,"children":2584},{"className":2583},[],[2585],{"type":466,"value":2586},"STAR/CHIMERIC/",{"type":466,"value":2142},{"type":431,"tag":1050,"props":2589,"children":2591},{"id":2590},"fpkm-and-count-files-per-sample",[2592],{"type":466,"value":2593},"FPKM and count files (per sample)",{"type":431,"tag":517,"props":2595,"children":2596},{},[2597,2599,2605,2606,2612,2613,2619,2621,2627,2629,2635],{"type":466,"value":2598},"Per sample files containing FPKM and raw count values for each feature can be found in ",{"type":431,"tag":781,"props":2600,"children":2602},{"className":2601},[],[2603],{"type":466,"value":2604},"*.fpkm.txt",{"type":466,"value":1586},{"type":431,"tag":781,"props":2607,"children":2609},{"className":2608},[],[2610],{"type":466,"value":2611},"*.fpkm.log2.txt",{"type":466,"value":1764},{"type":431,"tag":781,"props":2614,"children":2616},{"className":2615},[],[2617],{"type":466,"value":2618},"*.htseq_counts.txt",{"type":466,"value":2620}," 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WARDEN.\nIt can be found in the ",{"type":431,"tag":781,"props":2660,"children":2662},{"className":2661},[],[2663],{"type":466,"value":2347},{"type":466,"value":2176},{"type":431,"tag":511,"props":2666,"children":2668},{"id":2667},"rerunning-analysis",[2669],{"type":466,"value":2670},"Rerunning analysis",{"type":431,"tag":517,"props":2672,"children":2673},{},[2674],{"type":466,"value":2675},"If you complete a WARDEN run from FASTQs or BAM files and wish to change some of the final differential expression parameters, we recommend you use the count files already generated as input to the \"WARDEN [Counts]\" app.\nThis should save you significant amounts of time and money.",{"type":431,"tag":517,"props":2677,"children":2678},{},[2679],{"type":466,"value":2680},"Similarly, if you started with FASTQs and wish to rerun with different parameters to HTSeq-Count, we recommend using the previously generated BAM files as input to the \"WARDEN [BAM]\" 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