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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<p><a href="https://travis-ci.org/alyssafrazee/ballgown"><img src="data:image/svg+xml;base64,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" alt="Build Status" /></a></p>
<div id="introduction-and-preprocessing" class="section level1">
<h1>Introduction and Preprocessing</h1>
<p>Ballgown is a software package designed to facilitate flexible differential expression analysis of RNA-Seq data. It also provides functions to organize, visualize, and analyze the expression measurements for your transcriptome assembly.</p>
<p>Before using the Ballgown R package, a few preprocessing steps are necessary:</p>
<ol style="list-style-type: decimal">
<li>RNA-Seq reads should be aligned to a reference genome.</li>
<li>A transcriptome should be assembled, or a reference transcriptome should be downloaded.</li>
<li>Expression for the features (transcript, exon, and intron junctions) in the transcriptome should be estimated in a Ballgown readable format.</li>
</ol>
<p>Two sample pipelines for preprocessing are as follows:</p>
<ol style="list-style-type: decimal">
<li><strong>Pipeline 1:</strong> <em>TopHat2</em> (1) + <em>Stringtie</em> (2,3)</li>
<li><em>TopHat2</em> [<a href="http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract">Trapnell et al. (2009)</a>] is built on the ultrafast short read mapping program <em>Bowtie</em> and aligns RNA-Seq reads to a genome while identifying exonic splice junctions. Sample command: | <code>tophat2  -G reference.gff -o outputDirectory -p 6 referenceIndex reads</code></li>
<li><em>Stringtie</em> [<a href="http://www.nature.com/nbt/journal/v33/n3/full/nbt.3122.html">M. Pertea et al. (2015)</a>] is a highly efficient assembler for RNA-Seq alignments using a novel network flow algorithm. It simultaneously assembles and quantifies expression levels for the features of the transcriptome in a Ballgown readable format (by using the option -B). One command to <em>Stringtie</em> satisfies steps 2 and 3 above. Sample command: | <code>stringtie -B -G reference.gff -p 6 accepted_hits.bam -o stringtie.gff</code></li>
<li><strong>Pipeline 2:</strong> <em>TopHat2</em> (1) + <em>Cufflinks</em> (2) + <em>Tablemaker</em> (3)</li>
<li><em>Tophat2</em> produces alignments as noted above.</li>
<li><em>Cufflinks</em> [<a href="http://dx.doi.org/10.1038/nbt.1621">Trapnell et al. (2010)</a>] also assembles transcriptomes from RNA-Seq data and quantifies their expression.</li>
<li><em>Tablemaker</em> calls <em>Cufflinks</em> to estimate feature expressions in a Ballgown readable format.</li>
</ol>
</div>
<div id="installation" class="section level1">
<h1>Installation</h1>
<p>Start R and run:</p>
<pre class="r"><code>source(&quot;http://bioconductor.org/biocLite.R&quot;)
biocLite(&quot;ballgown&quot;)</code></pre>
</div>
<div id="ballgown-readable-expression-output" class="section level1">
<h1>Ballgown readable expression output</h1>
<p>The files that <em>Stringtie</em> and <em>Tablemaker</em> produce for Ballgown to load is as follows:</p>
<ul>
<li><code>e_data.ctab</code>: exon-level expression measurements. One row per exon. Columns are <code>e_id</code> (numeric exon id), <code>chr</code>, <code>strand</code>, <code>start</code>, <code>end</code> (genomic location of the exon), and the following expression measurements for each sample:
<ul>
<li><code>rcount</code>: reads overlapping the exon</li>
<li><code>ucount</code>: uniquely mapped reads overlapping the exon</li>
<li><code>mrcount</code>: multi-map-corrected number of reads overlapping the exon</li>
<li><code>cov</code> average per-base read coverage</li>
<li><code>cov_sd</code>: standard deviation of per-base read coverage</li>
<li><code>mcov</code>: multi-map-corrected average per-base read coverage</li>
<li><code>mcov_sd</code>: standard deviation of multi-map-corrected per-base coverage</li>
</ul></li>
<li><code>i_data.ctab</code>: intron- (i.e., junction-) level expression measurements. One row per intron. Columns are <code>i_id</code> (numeric intron id), <code>chr</code>, <code>strand</code>, <code>start</code>, <code>end</code> (genomic location of the intron), and the following expression measurements for each sample:
<ul>
<li><code>rcount</code>: number of reads supporting the intron</li>
<li><code>ucount</code>: number of uniquely mapped reads supporting the intron</li>
<li><code>mrcount</code>: multi-map-corrected number of reads supporting the intron</li>
</ul></li>
<li><code>t_data.ctab</code>: transcript-level expression measurements. One row per transcript. Columns are:
<ul>
<li><code>t_id</code>: numeric transcript id</li>
<li><code>chr</code>, <code>strand</code>, <code>start</code>, <code>end</code>: genomic location of the transcript</li>
<li><code>t_name</code>: Cufflinks-generated transcript id</li>
<li><code>num_exons</code>: number of exons comprising the transcript</li>
<li><code>length</code>: transcript length, including both exons and introns</li>
<li><code>gene_id</code>: gene the transcript belongs to</li>
<li><code>gene_name</code>: HUGO gene name for the transcript, if known</li>
<li><code>cov</code>: per-base coverage for the transcript (available for each sample)</li>
<li><code>FPKM</code>: Cufflinks-estimated FPKM for the transcript (available for each sample)</li>
</ul></li>
<li><code>e2t.ctab</code>: table with two columns, <code>e_id</code> and <code>t_id</code>, denoting which exons belong to which transcripts. These ids match the ids in the <code>e_data</code> and <code>t_data</code> tables.</li>
<li><code>i2t.ctab</code>: table with two columns, <code>i_id</code> and <code>t_id</code>, denoting which introns belong to which transcripts. These ids match the ids in the <code>i_data</code> and <code>t_data</code> tables.</li>
</ul>
</div>
<div id="loading-data-into-r" class="section level1">
<h1>Loading data into R</h1>
<p>The default directory structure produced by <em>Stringtie</em> and <em>Tablemaker</em> should mirror the <code>extdata</code> folder in the Ballgown pacakge:</p>
<pre><code>extdata/
    sample01/
        e2t.ctab
        e_data.ctab
        i2t.ctab
        i_data.ctab
        t_data.ctab
    sample02/
        e2t.ctab
        e_data.ctab
        i2t.ctab
        i_data.ctab
        t_data.ctab
    ...
    sample20/
        e2t.ctab
        e_data.ctab
        i2t.ctab
        i_data.ctab
        t_data.ctab</code></pre>
<p>Data is loaded using the <code>ballgown</code> function.</p>
<p>If your data is stored in directories matching the above structure (one root folder, subfolders named by sample, and <code>.ctab</code> files in the subfolders), you can use the <code>dataDir</code> and <code>samplePattern</code> arguments to load the data. <code>samplePattern</code> takes a regular expressions specifying the subfolders that should be included in the ballgown object:</p>
<pre class="r"><code>library(ballgown)
data_directory = system.file('extdata', package='ballgown') # automatically finds ballgown's installation directory
# examine data_directory:
data_directory</code></pre>
<pre><code>## [1] &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata&quot;</code></pre>
<pre class="r"><code># make the ballgown object:
bg = ballgown(dataDir=data_directory, samplePattern='sample', meas='all')
bg</code></pre>
<pre><code>## ballgown instance with 100 transcripts and 20 samples</code></pre>
<p>If your data is stored in a directory structure other than the one specified above, you can use the <code>samples</code> argument in the <code>ballgown</code> function: <code>samples</code> should be a vector (1-d array) with one entry per sample, where the entry gives the path to the folder containing that sample’s <code>.ctab</code> files.</p>
<!-- If you choose the `samples` option, you will also need to provide a vector called `sampleNames` that corresponds to `samples` and gives each sample a unique ID. Some example code to do this is:

```R
sample_IDs = c('oneSample', 'anotherSample', 'aThirdSample')
sample_paths = paste0('/home/', sample_IDs, '/ballgown')
sample_paths

## [1] "/home/oneSample/ballgown"
## [2] "/home/anotherSample/ballgown"
## [3] "/home/aThirdSample/ballgown"

bg = ballgown(samples=sample_paths, sampleNames=sample_IDs, meas='all')
```
-->

<p>The result from either of these approaches is an object of class <code>ballgown</code> (named <code>bg</code> in these examples).</p>
<p>In the rest of this document, we use <code>bg</code> to refer to the first example, where samples are named <code>sample01</code> through <code>sample20</code>.</p>
<p>A note for large experiments (with many samples or with large genomes): loading the data might require a lot of time and memory. In these cases, it’s often useful to do the data loading in non-interactive mode. More specifically, you could create a script called <code>load.R</code> that contains these lines:</p>
<pre class="r"><code>library(ballgown)
data_directory = system.file('extdata', package='ballgown') 
bg = ballgown(dataDir=data_directory, samplePattern='sample', meas='all')
save(bg, file='bg.rda')</code></pre>
<p>You could then run this script non-interactively using <code>R CMD BATCH</code>: from the command line, run:</p>
<pre><code>R CMD BATCH load.R</code></pre>
<p>This may take some time, but when it finishes, the file <code>bg.rda</code> will be saved in the current directory, and you can read it back into R using the <code>load()</code> function. Rda files are usually only a few Gb on disk, even for large experiments. It is also possible to load only a subset of all the expression measurements by changing the <code>meas</code> argument to the <code>ballgown</code> function. For example, to only load transcript-level FPKMs, set <code>meas = 'FPKM'</code> and to load average coverage values and read counts, set <code>meas=c('cov', 'rcount').</code></p>
<p>See <code>?ballgown</code> for detailed information on creating Ballgown objects.</p>
</div>
<div id="accessing-assembly-data" class="section level1">
<h1>Accessing assembly data</h1>
<p>A <code>ballgown</code> object has six slots: <code>structure</code>, <code>expr</code>, <code>indexes</code>, <code>dirs</code>, <code>mergedDate</code>, and <code>meas</code>.</p>
<div id="structure" class="section level4">
<h4>structure</h4>
<p>The <code>structure</code> slot depends heavily on the <code>GenomicRanges</code> Bioconductor package (<a href="http://dx.doi.org/10.1371/journal.pcbi.1003118">Lawrence et al. (2013)</a>). The slot specifies the structure, i.e., genomic locations and relationships between exons, introns, and transcripts, of the transcriptome assembly. It is convenient to represent exons and introns as intervals and to represent transcripts as a set of intervals (exons), so assembled exons and introns are available as <code>GRanges</code> objects, and the assembled transcripts are available as a <code>GRangesList</code> object. This means that useful range operations, such as <code>findOverlaps</code> and <code>reduce</code>, are readily available for assembled features.</p>
<p>Exon, intron, and transcript structures are easily extracted from the main <code>ballgown</code> object:</p>
<pre class="r"><code>structure(bg)$exon</code></pre>
<pre><code>## GRanges object with 633 ranges and 2 metadata columns:
##         seqnames               ranges strand   |        id transcripts
##            &lt;Rle&gt;            &lt;IRanges&gt;  &lt;Rle&gt;   | &lt;integer&gt; &lt;character&gt;
##     [1]       18 [24412069, 24412331]      *   |        12          10
##     [2]       22 [17308271, 17308950]      +   |        55          25
##     [3]       22 [17309432, 17310226]      +   |        56          25
##     [4]       22 [18121428, 18121652]      +   |        88          35
##     [5]       22 [18138428, 18138598]      +   |        89          35
##     ...      ...                  ...    ... ...       ...         ...
##   [629]       22 [51221929, 51222113]      -   |      3777        1294
##   [630]       22 [51221319, 51221473]      -   |      3782        1297
##   [631]       22 [51221929, 51222162]      -   |      3783        1297
##   [632]       22 [51221929, 51222168]      -   |      3784        1301
##   [633]        6 [31248149, 31248334]      *   |      3794        1312
##   -------
##   seqinfo: 3 sequences from an unspecified genome; no seqlengths</code></pre>
<pre class="r"><code>structure(bg)$intron</code></pre>
<pre><code>## GRanges object with 536 ranges and 2 metadata columns:
##         seqnames               ranges strand   |        id
##            &lt;Rle&gt;            &lt;IRanges&gt;  &lt;Rle&gt;   | &lt;integer&gt;
##     [1]       22 [17308951, 17309431]      +   |        33
##     [2]       22 [18121653, 18138427]      +   |        57
##     [3]       22 [18138599, 18185008]      +   |        58
##     [4]       22 [18185153, 18209442]      +   |        59
##     [5]       22 [18385514, 18387397]      -   |        72
##     ...      ...                  ...    ... ...       ...
##   [532]       22 [51216410, 51220615]      -   |      2750
##   [533]       22 [51220776, 51221928]      -   |      2756
##   [534]       22 [51220780, 51221318]      -   |      2757
##   [535]       22 [51221474, 51221928]      -   |      2758
##   [536]       22 [51220780, 51221928]      -   |      2759
##                 transcripts
##                 &lt;character&gt;
##     [1]                  25
##     [2]                  35
##     [3]                  35
##     [4]                  35
##     [5]                  41
##     ...                 ...
##   [532] c(1294, 1297, 1301)
##   [533]                1294
##   [534]                1297
##   [535]                1297
##   [536]                1301
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths</code></pre>
<pre class="r"><code>structure(bg)$trans</code></pre>
<pre><code>## GRangesList object of length 100:
## $10 
## GRanges object with 1 range and 2 metadata columns:
##       seqnames               ranges strand |        id transcripts
##          &lt;Rle&gt;            &lt;IRanges&gt;  &lt;Rle&gt; | &lt;integer&gt; &lt;character&gt;
##   [1]       18 [24412069, 24412331]      * |        12          10
## 
## $25 
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames               ranges strand | id transcripts
##   [1]       22 [17308271, 17308950]      + | 55          25
##   [2]       22 [17309432, 17310226]      + | 56          25
## 
## $35 
## GRanges object with 4 ranges and 2 metadata columns:
##       seqnames               ranges strand | id transcripts
##   [1]       22 [18121428, 18121652]      + | 88          35
##   [2]       22 [18138428, 18138598]      + | 89          35
##   [3]       22 [18185009, 18185152]      + | 90          35
##   [4]       22 [18209443, 18212080]      + | 91          35
## 
## ...
## &lt;97 more elements&gt;
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengths</code></pre>
</div>
<div id="expr" class="section level4">
<h4>expr</h4>
<p>The <code>expr</code> slot is a list that contains tables of expression data for the genomic features. These tables are very similar to the <code>*_data.ctab</code> <em>Tablemaker</em> output files. Ballgown implements the following syntax to access components of the <code>expr</code> slot:</p>
<pre class="r"><code>*expr(ballgown_object_name, &lt;EXPRESSION_MEASUREMENT&gt;)</code></pre>
<p>where <code>*</code> is either e for exon, i for intron, t for transcript, or g for gene, and <EXPRESSION MEASUREMENT> is an expression-measurement column name from the appropriate <code>.ctab</code> file. Gene-level measurements are calculated by aggregating the transcript-level measurements for that gene. All of the following are valid ways to extract expression data from the <code>bg</code> ballgown object:</p>
<pre class="r"><code>transcript_fpkm = texpr(bg, 'FPKM')
transcript_cov = texpr(bg, 'cov')
whole_tx_table = texpr(bg, 'all')
exon_mcov = eexpr(bg, 'mcov')
junction_rcount = iexpr(bg)
whole_intron_table = iexpr(bg, 'all')
gene_expression = gexpr(bg)</code></pre>
<p>Calculating the gene-level expression measurements can be slow for large experiments.</p>
<p>The <code>*expr</code> functions return matrices unless <code>meas = 'all'</code>, in which case some additional feature metadata is returned and the result is a <code>data.frame</code>.</p>
</div>
<div id="indexes" class="section level4">
<h4>indexes</h4>
<p>The <code>indexes</code> slot of a ballgown object connects the pieces of the assembly and provides other experimental information. <code>indexes(bg)</code> is a list with several components that can be extracted with the <code>$</code> operator.</p>
<p>Perhaps most importantly, there is a component called <code>pData</code> that should hold a data frame of phenotype information for the samples in the experiment. This must be created manually. It is <strong>very important</strong> that the rows of pData are in the correct order. Each row corresponds to a sample, and the rows of pData should be ordered the same as the tables in the <code>expr</code> slot. You can check that order by running <code>sampleNames(bg)</code>. The <code>pData</code> component can be added during construction (you can pass a data frame to the <code>ballgown</code> function), or you can add it later:</p>
<pre class="r"><code>pData(bg) = data.frame(id=sampleNames(bg), group=rep(c(1,0), each=10))</code></pre>
<p>The other components of <code>indexes</code> are the <code>e2t</code> and <code>i2t</code> tables described in the <em>Tablemaker</em> section, as well as a <code>t2g</code> table denoting which transcripts belong to which genes. There is also a <code>bamfiles</code> component, designed to hold paths to the read alignment files for each sample. The <code>bamfiles</code> component isn’t currently used by any ballgown functions, but it could come in handy for users of <code>RSamtools</code> or similar packages. Here are some examples of how to extract <code>indexes</code> components from ballgown objects:</p>
<pre class="r"><code>exon_transcript_table = indexes(bg)$e2t
transcript_gene_table = indexes(bg)$t2g
head(transcript_gene_table)</code></pre>
<pre><code>##   t_id        g_id
## 1   10 XLOC_000010
## 2   25 XLOC_000014
## 3   35 XLOC_000017
## 4   41 XLOC_000246
## 5   45 XLOC_000019
## 6   67 XLOC_000255</code></pre>
<pre class="r"><code>phenotype_table = pData(bg)</code></pre>
</div>
<div id="other-slots" class="section level4">
<h4>other slots</h4>
<p>The <code>dirs</code> slot gives full filepaths to <em>Tablemaker</em> output:</p>
<pre class="r"><code>head(bg@dirs)</code></pre>
<pre><code>##                                                                                   sample01 
## &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata/sample01&quot; 
##                                                                                   sample02 
## &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata/sample02&quot; 
##                                                                                   sample03 
## &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata/sample03&quot; 
##                                                                                   sample04 
## &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata/sample04&quot; 
##                                                                                   sample05 
## &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata/sample05&quot; 
##                                                                                   sample06 
## &quot;/Library/Frameworks/R.framework/Versions/3.1/Resources/library/ballgown/extdata/sample06&quot;</code></pre>
<p>The <code>mergedDate</code> slot indicates when the <code>ballgown</code> object was created:</p>
<pre class="r"><code>bg@mergedDate</code></pre>
<pre><code>## [1] &quot;Sun Feb 22 23:51:45 2015&quot;</code></pre>
<p>And the <code>meas</code> slot gives the expression measurements present in the object:</p>
<pre class="r"><code>bg@meas</code></pre>
<pre><code>## [1] &quot;all&quot;</code></pre>
</div>
</div>
<div id="plotting-transcript-structures" class="section level1">
<h1>Plotting transcript structures</h1>
<p>Visualization of the assembled transcripts is done with the <code>plotTranscripts</code> function. Transcripts or exons can be colored by expression level. This plot colors transcripts by expression level:</p>
<pre class="r"><code>plotTranscripts(gene='XLOC_000454', gown=bg, samples='sample12', 
    meas='FPKM', colorby='transcript', 
    main='transcripts from gene XLOC_000454: sample 12, FPKM')</code></pre>
<p><img 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" /></p>
<p>It is also possible to plot several samples at once:</p>
<pre class="r"><code>plotTranscripts('XLOC_000454', bg, 
    samples=c('sample01', 'sample06', 'sample12', 'sample19'), 
    meas='FPKM', colorby='transcript')</code></pre>
<p><img 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" /></p>
<p>You can also make side-by-side plots comparing mean abundances between groups (here, 0 and 1):</p>
<pre class="r"><code>plotMeans('XLOC_000454', bg, groupvar='group', meas='FPKM', colorby='transcript')</code></pre>
<p><img src="data:image/png;base64,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" /></p>
</div>
<div id="differential-expression-analysis" class="section level1">
<h1>Differential expression analysis</h1>
<p>Ballgown provides a wide selection of simple, fast statistical methods for testing whether transcripts are differentially expressed between experimental conditions or across a continuous covariate (such as time).</p>
<p>The default statistical test in ballgown is a parametric F-test comparing nested linear models; details are available in the Ballgown manuscript (<a href="http://biorxiv.org/content/early/2014/03/30/003665">Frazee et al. (2014)</a>). These models are conceptually simialar to the models used by <a href="http://link.springer.com/chapter/10.1007%2F0-387-29362-0_23">Smyth (2005)</a> in the <code>limma</code> package. In <code>limma</code>, more sophisticated empirical Bayes shrinkage methods are used, and generally a single linear model is fit per feature instead of doing a nested model comparison, but the flavor is similar (and in fact, <code>limma</code> can easily be run on any of the data matrices in a <code>ballgown</code> object).</p>
<p>Ballgown’s statistical models are implemented with the <code>stattest</code> function. Two models are fit to each feature, using expression as the outcome: one including the covariate of interest (e.g., case/control status or time) and one not including that covariate. An F statistic and p-value are calculated using the fits of the two models. A significant p-value means the model including the covariate of interest fits significantly better than the model without that covariate, indicating differential expression. We adjust for multiple testing by reporting q-values (<a href="http://dx.doi.org/10.1073/pnas.1530509100">Storey &amp; Tibshirani (2003)</a>) for each transcript in addition to p-values: reporting features with, say, q &lt; 0.05 means the false discovery rate should be controlled at about 5%.</p>
<p><code>stattest</code> automatically handles two-group (e.g. case/control) comparisons, multi-group comparisons (e.g. comparison of several tissue types), and “timecourse” comparisons (with the scare quotes meaning that these comparisons are also applicable to continuous covariates that aren’t time). For two- and multi-group comparisons, a significant result indicates that the feature is differentially expressed in at least one of the groups. For timecourse comparisons, significant results mean the feature has an expression profile that varies significantly over time (i.e., values of the continuous covariate) as opposed to being flat over time.</p>
<p>The example dataset <code>bg</code> contains two group labels, 0 and 1. We can test each transcript for differential expression with <code>stattest</code>:</p>
<pre class="r"><code>stat_results = stattest(bg, feature='transcript', meas='FPKM', covariate='group')
head(stat_results)</code></pre>
<pre><code>##      feature id       pval       qval
## 1 transcript 10 0.01381576 0.10521233
## 2 transcript 25 0.26773622 0.79114975
## 3 transcript 35 0.01085070 0.08951825
## 4 transcript 41 0.47108019 0.90253747
## 5 transcript 45 0.08402948 0.48934813
## 6 transcript 67 0.27317385 0.79114975</code></pre>
<p>The result is a data frame containing the feature tested, feature ids, and corresponding p- and q-values. See <code>?stattest</code> for further usage details.</p>
<div id="timecourse-experiments" class="section level4">
<h4>timecourse experiments</h4>
<p>For timecourse experiments, a smooth curve is fit to time (or the continuous covariate) using <a href="http://en.wikipedia.org/wiki/Spline_interpolation">natural splines</a>. The default degrees of freedom used for the spline model is 4, but this can be adjusted with the <code>df</code> option. The model for expression including these spline terms is compared to a model without any spline terms for the F-test. The results indicate which features’ expression levels change significantly over time. For our example, we can define a “time” covariate and then demonstrate a typical call to <code>stattest</code> for a timecourse experiment:</p>
<pre class="r"><code>pData(bg) = data.frame(pData(bg), time=rep(1:10, 2)) #dummy time covariate
timecourse_results = stattest(bg, feature='transcript', meas='FPKM', covariate='time', timecourse=TRUE)</code></pre>
<p>The timecourse option assumes that “time” in your study is truly continuous, i.e., that it takes several values along a time scale. If you have very few timepoints (e.g., fewer than 5), we recommend treating time as a categorical variable, since having very few values does not give much granularity for fitting a smooth curve using splines. You can do this by setting covariate equal to ‘time’ (or whatever your time variable is named) and simply leaving timecourse as FALSE, its default. If you don’t have more timepoints than degrees of freedom in the spline model, a warning will be printed and time will be coerced to categorical.</p>
</div>
<div id="adjusting-for-confounders" class="section level4">
<h4>adjusting for confounders</h4>
<p>You can adjust for any or all variables in <code>pData</code> when testing for differential expression. Ballgown automatically adjusts for library size using the sum of all logged nonzero expression measurements below the 75th percentile of those measurements, for each sample. If you would like to adjust for other variables, just provide those confounders as the <code>adjustvars</code> argument to <code>stattest</code>:</p>
<pre class="r"><code>group_adj_timecourse_results = stattest(bg, feature='transcript', meas='FPKM', covariate='time', 
    timecourse=TRUE, adjustvars='group')</code></pre>
</div>
<div id="defining-custom-models" class="section level4">
<h4>defining custom models</h4>
<p>It is also possible to explicitly provide the design matrices for the models to be compared. For example, suppose we had sex and age information available, in addition to group and time, and we wanted to compare a model including all information (sex, age, group, time) to a model including only group and time. Code to do this with <code>ballgown</code> is:</p>
<pre class="r"><code># create example data:
set.seed(43)
sex = sample(c('M','F'), size=nrow(pData(bg)), replace=TRUE)
age = sample(21:52, size=nrow(pData(bg)), replace=TRUE)

# create design matrices:
mod = model.matrix(~ sex + age + pData(bg)$group + pData(bg)$time)
mod0 = model.matrix(~ pData(bg)$group + pData(bg)$time)

# run differential expression tests:
adjusted_results = stattest(bg, feature='transcript', meas='FPKM', mod0=mod0, mod=mod)
head(adjusted_results)</code></pre>
<pre><code>##      feature id      pval      qval
## 1 transcript 10 0.8242859 0.9755925
## 2 transcript 25 0.1985488 0.9725569
## 3 transcript 35 0.8114314 0.9755925
## 4 transcript 41 0.7647284 0.9755925
## 5 transcript 45 0.6497589 0.9755925
## 6 transcript 67 0.9775119 0.9874866</code></pre>
</div>
</div>
<div id="using-alternative-statistical-methods" class="section level1">
<h1>Using alternative statistical methods</h1>
<p>Ballgown’s statistical methods for differential expression testing are straightforward and accurate (<a href="http://biorxiv.org/content/early/2014/03/30/003665">Frazee et al. (2014)</a>), but users may wish to use one of the many existing packages for differential expression. Ballgown’s data structures make it easy to use table-based packages like limma (<a href>Smyth (2005)</a>), limma Voom (<a href="http://dx.doi.org/10.1186/gb-2014-15-2-r29">Law et al. (2014)</a>), DESeq (<a href="http://dx.doi.org/10.1186/gb-2010-11-10-r106">Anders &amp; Huber (2010)</a>), DEXSeq (<a href="http://dx.doi.org/10.1101/gr.133744.111">Anders et al. (2012)</a>), or EdgeR (<a href="http://dx.doi.org/10.1093/bioinformatics/btp616">Robinson et al. (2010)</a>) for differential expression analysis. A feature-by-sample expression table can be easily created with a <code>*expr</code> function and used directly as input to these or other differential expression packages.</p>
</div>
<div id="simple-transcript-clustering" class="section level1">
<h1>Simple transcript clustering</h1>
<p>Sometimes several very similar transcripts are assembled for the same gene, which might cause expression estimates for those transcripts to be unreliable: statistically, it can very difficult or impossible to tell which of two very similar transcript a read came from. This means differential expression results might also be unreliable.</p>
<p>As a preliminary attempt at addressing this issue, Ballgown provides some simple transcript clustering functions. The idea is that similar assembled transcripts can be grouped together in clusters, and differential expression analysis could be performed on the cluster, whose expression measurement aggregates the expression estimates of the transcripts that compose it.</p>
<p>These functions measure the distance between transcripts using <a href="http://en.wikipedia.org/wiki/Jaccard_index">Jaccard distance</a>, where each transcript’s “set” is the nucleotides included in its exons. Transcripts can be clustered using either <em>k</em>-means clustering or hierarchical clustering.</p>
<pre class="r"><code>clusterTranscripts(gene='XLOC_000454', gown=bg, k=2, method='kmeans')</code></pre>
<pre><code>## $clusters
##   cluster t_id
## 1       2 1294
## 2       1 1297
## 3       2 1301
## 
## $pctvar
## [1] 0.9117737</code></pre>
<p>You can also visualize the transcript clusters:</p>
<pre class="r"><code>plotLatentTranscripts(gene='XLOC_000454', gown=bg, k=2, method='kmeans', returncluster=FALSE)</code></pre>
<p><img 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" /></p>
<p>And you can calculate aggregate cluster expression measurements for some gene using <code>collapseTranscripts</code>. The <code>tab</code> result of <code>collapseTranscripts</code> can be passed to <code>stattest</code> as the <code>gowntable</code> argument, for differential expression analysis of the clusters:</p>
<pre class="r"><code>agg = collapseTranscripts(gene='XLOC_000454', gown=bg, k=2, method='kmeans')
stattest(gowntable=agg$tab, pData=pData(bg), feature='transcript_cluster', 
    covariate='group', libadjust=FALSE)</code></pre>
<pre><code>##              feature id      pval      qval
## 1 transcript_cluster  1 0.3332238 0.6664477
## 2 transcript_cluster  2 0.6882119 0.6882119</code></pre>
<p>This example clustered only three transcripts, but we imagine clustering could be useful when many more than three transcripts have been assembled for a single gene.</p>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<ul>
<li>Simon Anders, Wolfgang Huber (2010). Differential Expression Analysis For Sequence Count Data. <em>Genome Biology</em> <strong>11</strong> R106-NA <a href="http://dx.doi.org/10.1186/gb-2010-11-10-r106">10.1186/gb-2010-11-10-r106</a></li>
<li>Simon Anders, Alejandro Reyes, Wolfgang Huber (2012). Detecting Differential Usage of Exons From RNA-Seq Data. <em>Genome Research</em> <strong>22</strong> 2008-2017 <a href="http://dx.doi.org/10.1101/gr.133744.111">10.1101/gr.133744.111</a></li>
<li>Alyssa C. Frazee, Geo Pertea, Andrew E. Jaffe, Ben Langmead, Steven L. Salzberg, Jeffrey T. Leek (2014). Flexible isoform-level differential expression analysis with Ballgown. <em>bioRxiv</em> <a href="http://biorxiv.org/content/early/2014/03/30/003665"><a href="http://biorxiv.org/content/early/2014/03/30/003665">http://biorxiv.org/content/early/2014/03/30/003665</a></a></li>
<li>Charity W Law, Yunshun Chen, Wei Shi, Gordon K Smyth, (2014) Voom: Precision Weights Unlock Linear Model Analysis Tools For RNA-Seq Read Counts. <em>Genome Biology</em> <strong>15</strong> R29-NA <a href="http://dx.doi.org/10.1186/gb-2014-15-2-r29">10.1186/gb-2014-15-2-r29</a></li>
<li>Michael Lawrence, Wolfgang Huber, Herve Pages, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan, Vincent J. Carey, Andreas Prlic (2013). Software For Computing And Annotating Genomic Ranges. <em>Plos Computational Biology</em> <strong>9</strong> e1003118-NA <a href="http://dx.doi.org/10.1371/journal.pcbi.1003118">10.1371/journal.pcbi.1003118</a></li>
<li>Mihaela Pertea, Geo M Pertea, Corina M Antonescu, Tsung-Cheng Chang, Joshua T Mendell, Steven L Salzberg (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. <em>Nature Biotechnology</em> <a href="http://www.nature.com/nbt/journal/v33/n3/full/nbt.3122.html"><a href="http://www.nature.com/nbt/journal/v33/n3/full/nbt.3122.html">http://www.nature.com/nbt/journal/v33/n3/full/nbt.3122.html</a></a></li>
<li>Mark D. Robinson, Davis J. McCarthy and Gordon K. Smyth (2009) “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data”. <em>Bioinformatics</em> <strong>26.1</strong> pp. 139-140. <a href="http://dx.doi.org/10.1093/bioinformatics/btp616.">10.1093/bioinformatics/btp616</a></li>
<li>Gordon Smyth, (2005) Limma: linear models for microarray data. 397-420</li>
<li>John D. Storey, Robert Tibshirani (2003). Statistical Significance For Genomewide Studies. <em>Proceedings of The National Academy of Sciences</em> <strong>100</strong> 9440-9445 <a href="http://dx.doi.org/10.1073/pnas.1530509100">10.1073/pnas.1530509100</a></li>
<li>Cole Trapnell, Lior Pachter, Steven Salzberg (2009) TopHatL discovering splice junctions with RNA-Seq. <em>Bioinformatics</em>. [<a href="http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract">http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract</a>] (<a href="http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract">http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract</a>)</li>
<li>Cole Trapnell, Brian A Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Marijke J van Baren, Steven L Salzberg, Barbara J Wold, Lior Pachter (2010) Transcript Assembly And Quantification by Rna-Seq Reveals Unannotated Transcripts And Isoform Switching During Cell Differentiation. <em>Nature Biotechnology</em> <strong>28</strong> 511-515 <a href="http://dx.doi.org/10.1038/nbt.1621">10.1038/nbt.1621</a></li>
</ul>
</div>
<div id="session-information" class="section level1">
<h1>Session Information</h1>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>## R version 3.1.1 (2014-07-10)
## Platform: x86_64-apple-darwin13.1.0 (64-bit)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ballgown_1.0.1 knitr_1.8.2    colorout_1.0-3 devtools_1.6.1
## 
## loaded via a namespace (and not attached):
##  [1] annotate_1.44.0         AnnotationDbi_1.28.1   
##  [3] base64enc_0.1-2         BatchJobs_1.5          
##  [5] BBmisc_1.8              Biobase_2.26.0         
##  [7] BiocGenerics_0.12.1     BiocParallel_1.0.0     
##  [9] Biostrings_2.34.0       bitops_1.0-6           
## [11] brew_1.0-6              checkmate_1.5.0        
## [13] codetools_0.2-9         DBI_0.3.1              
## [15] digest_0.6.4            evaluate_0.5.5         
## [17] fail_1.2                foreach_1.4.2          
## [19] formatR_1.0             genefilter_1.48.1      
## [21] GenomeInfoDb_1.2.3      GenomicAlignments_1.2.1
## [23] GenomicRanges_1.18.3    grid_3.1.1             
## [25] IRanges_2.0.0           iterators_1.0.7        
## [27] lattice_0.20-29         limma_3.22.1           
## [29] Matrix_1.1-4            mgcv_1.8-4             
## [31] nlme_3.1-118            parallel_3.1.1         
## [33] RColorBrewer_1.1-2      RCurl_1.95-4.5         
## [35] Rsamtools_1.18.2        RSQLite_1.0.0          
## [37] rtracklayer_1.26.2      S4Vectors_0.4.0        
## [39] sendmailR_1.2-1         splines_3.1.1          
## [41] stats4_3.1.1            stringr_0.6.2          
## [43] survival_2.37-7         sva_3.12.0             
## [45] tools_3.1.1             XML_3.98-1.1           
## [47] xtable_1.7-4            XVector_0.6.0          
## [49] zlibbioc_1.12.0</code></pre>
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