# ChIP-Seq pipeline Here we provide the tools to perform paired end or single read ChIP-Seq analysis including raw data quality control, read mapping, peak calling, differential binding analysis and functional annotation. As input files you may use either zipped fastq-files (.fastq.gz) or mapped read data (.bam files). In case of paired end reads, corresponding fastq files should be named using *.R1.fastq.gz* and *.R2.fastq.gz* suffixes. ## Pipeline Workflow All analysis steps are illustrated in the pipeline [flowchart](https://www.draw.io/?lightbox=1&highlight=0000ff&edit=_blank&layers=1&nav=1&title=NGSpipe2go_ChIPseq_pipeline.html#R7R1Zc6M489e4KvNgF4fPx0kyzmSPTCbJfLOzLykMsq0NBgLYOX79p5NTYLC5kkyyNRuEAKnV6m712VPPNs8Xruas%2F7YNYPYUyXjuqec9RZGHitLD%2F0nGC22ZDGe0YeVCg3UKG27hK2CNEmvdQgN4sY6%2BbZs%2BdOKNum1ZQPdjbZrr2k%2FxbkvbjH%2FV0VYg1XCra2a69Sc0%2FDVrlcez8MZXAFdr9umpMqE3Fpr%2BsHLtrcW%2BZ9kWoHc2Gn8Nm6O31gz7KdKkfumpZ65t%2B%2FSvzfMZMDFYOcToc%2FOMu8GQXWD5RR44%2F%2B%2FFO%2F%2Fxv8eH1%2FXZ5Hopg0tf6geD8184LICBQMMubddf2yvb0swvYespmS%2FAr5XQ1X%2FbjcP7rzQHtYRP%2FWXbqOFcxt2A77%2Bwlde2vo2a1v7GZHfBM%2FT%2FwS8cjNjVr8id82f2LXLxwi8s332JPIQvf0XvhY%2BRK%2F6c52uuz0Yyxtf21tXBNXDhBvjAvXU0HVqr4FEj0hU9uQJ%2BRlfPd%2B2HAH%2FQ%2Bp6ml4itGv0kbdrpV99vf27%2F2J6%2F%2Fnt3eTf%2F5e3%2B7asM8egHczoO2QLiVYt8gqHABbDRQN0X1MEFpubDXRzhNbZvVkG%2FEIHQHwyHxPj0Vfpjub6a%2BuZufbO4%2Bno7v3%2F92ucbeqeZW%2FapnjI20QxODbjDXzThyiI3xo9bjPqnLtlWwSX6a8X%2BTx5buMkWNDDyLt6awN8IVpnaApinwRY9s03bJZ3UOfkRLtrStjh2yHjNDc1bE2SXY6iPr5bQNKMvlfBv8FJ%2Bh9AE9XTlagZEiJBo1u0N1BkGrUzN8zg2cXIhkc%2F6aPFsDLj%2BTMrDrB1wffCciwns7nSsDpQRfYqRbbR806DtKUIIJ2xZ1xEaOJSGg9HxaCTEavlD49GXMf6tA4%2FgBjGyz55DeahcDSIpUzmGRfJISmHQUJqlMWjM0eoY%2FHn96Z%2FfSf%2F8%2B8f85ou7kY27i5fLvno0V%2Bs8D6uQ1xRkNeqwalZDHv3sutpLpINjQ8v3Im%2B%2Bxg0hug2nihDd5hn91Uluf%2FQHHUGIbcFUCiHg%2FGasX%2Fxzvr4Bfz3qy18vd9%2FOXvvy6L1jYIyctYKOajfRkZ2FstAx1X%2BYj76jmZTX%2F2j0zVuDLO6b4KzJBsz5YrjPeTK%2B0fcI9n5GHeSh8yxg2GdK73OUSdPXpXh31Z9NzuZsfXndvwWP6Ilr6AATIjaLhnaaOwahlJExAQEoqWxz5AyOg0OfbCnoawv0NNrBfOrocA%2BW0IJEDEXog%2F%2FZM8dqFsRzNKtIm4alCxcsY19a%2Bz5WIXzG%2B0WZr6CPRLmBazoDC9EWZQ43i6WN6Az68%2BriFs9VWdnooo%2FX1rQXeJXADis75hwQiBTMMWp4GDPm%2Bho66K8BvztAEqK9e0nPJrsjh6AmQBHRPLObG8azTqOwaWuGhxU0GOBYHWMbWxMvnqRZBkZxz0OcCWom5VIaOc9XtIsr2IDPQN%2F6ZLiRDYhQFb8WWiuyNU888An9jzQ6GnQBmReZ3YlDbhnAw6ek4rPKPgJVck5IcDI1fUyQBQdNmZ8dKj9mDgVsLgECdHxy8J%2FoeOWggxaePBXCuLAz5Q2BfnAoBFcuny0MQzkFMkUAMd5WTvJJiR79hHpgEn%2BBvVx6mIomFqEC8aNd6Zn%2F%2FSsmSe%2BTnmOycyhKN3B%2Bk5WiEnPVAnPG5vvhAffb4j%2BsZECEGOtOBDKkgDwu7GdMHYlGFdPHhe0awO2jZixrYBBxxCfUU0JYPgruRDXxn4lsooIh%2Fg16OJphBO9W8thPK6IEXT5v4D8LtFjxm3kig4AfcVkuDfFscCZZFdhEByXpaLG9QPsd3pJSvQKoR3op2aD3F7bxkmpMcS7f4C2X17n8LeyYfpS3wOi7ECJt8PwhAZmFBIPI22ChL%2BDG9Ihz5xD9TkXz8LSNk5qD8N11zerq%2BsddhZOynK2ftz6Nz63iRWPza33dMBV1Dp0Fe5gNXrKXkRlVMXDcKCIRhIASMheVZfOlDCXC%2FphYEpE4sFAGdc38zCwNPhYjTrndwQRL%2FBkb9VqaRL%2BOlfi9mEBRhcycUK3LM4EAKJKZuVBQgm2jywjnLiGuKdPf4lpxcY0fMt6MuOYIRYcNGj60qHQgOaGERtv7ZLfge%2BPIPYT0fp%2FtoM9EPrDoqXufLu46PASnDuuF1XBOsm2d6nXQVCkpwDenYqHyNKIq64zmKlCADHaa62XqrbK6pUVQJnXnLECG2iq1LvWsQoJROa69gwbYpwoScavo5E4D3dIVejAGpBhABN%2Blj%2FWxNRYuoR7%2FunRiO%2F7gU9FhiIC5lwEq%2BQywDKt7WkMfYHcY%2FOyTS9yAqmd%2Fw6FI%2FyFgf7PyjgmHcz%2BRsaRRLRL%2FYGfVSMP4KtagRhI6t8lyaiHenVzSlCtbzNx7uPCjFrbujjsi%2FQSyxVzz%2FO9nhKoXETwet4h%2B%2BniEuo3WzDb5acjVnsiwiX0CDxbx5oce07cneP56uxggmpHD7xmv36DBYbYxD%2BwcuBvW76Mbj%2FqANgfsey9roOuUyRpE9CzBLph26C9yda6OM5gE8ZJiL5YqPz6NuOmAn5%2FG0xTlk8dymvSNy3smFWEgQmclfqbLIFPAXNhPrVOo%2BijCsCBF4F5lrft7xK1YQ3b6rcofQ4gie9y0f6MIs%2BfNOoki6rQJFMkXdj4IilS88mlvLDlxIpETRlEmVtGnwjU%2FGoVG1aLQXOS02KoaT4qh0eQgcVndKy5X6KZYmChVjppFfR6Eiyz%2F9o0%2BcpWFYG3quFJskdXjF7nSsK44j5h0X0lfeHerHRE5xnF%2BobCDT6YXcrI%2FO2%2FXyl%2F4mb6bWHkYUu5jOfW5yRelTB1B0KTz32yYj6CJ%2Fspo1gCC5p%2FEfyNoPQLS5F1g6LhiRYAQQ4f5ioC3gaFyHEOVfYy9AyhauXjXEooqx6BoYVX5wn7yIVAK68p5S8R38QZoRg8bdx0HmyX2WJaz4hBo04lHXJGwVdWhgSzYLf5T1vN7dPIH2%2BFD1XxohWeQKq%2BfH0awpE79fMXuTDMljsBjtgNj%2BniBKbImfXyu6UkQ%2FbbfM4GEdDD%2FBOkA15dvW5%2B6AeIcJqhDuIUi%2B0d61PM2Vnu%2BLhSN8WYLpgFp1A2NXQnHXcR54XhnvGgMPo%2FJn2sbaGL8%2BwrMHcBv7TXhyjAZTmOor0rjNOrz7XEk6hfE80nH8FxKoboLvK2JOWIXkf0EIRmaGzA%2BEcaiPYTo3qxv0zlcLoEb%2BjDRoZjQ8xsfyjX9tmZZPGNJx3Z3DTt5OBZkTBHtZB75Xf1OnnZ%2BJ5v2qqPbGI2sz7mUbpsmkhZIJCXlWEDT1z3uq%2FcBkHmsFkTmKtjSn7uLe%2Bfs7od%2FtQHj6832yr%2B%2Fajd3SxNOW4efV%2Fnpf%2B95ddaaOSJ33J2hT2mJ2gUOQpuOkqi4C3nN7uI48SI%2BpAYQmXtraL3cswwG97QdHR3vcU92mqWNg5uNkR596n5m%2BGLSsbrLMx6QPujWYA1MB7je4EY8dXHHvBDOdhzi8RohmhTsBbxjXOx7GEtBEJVvCweltTMhxLPh8oWwMh%2FzcH3r%2BXQ%2B3M0%2BTAfhI%2FTy1ohA4CaI3TQtADBHKTyxd39gncgFJQNZqiAt4Fyo%2FT7eobt97XdJaaE25ffw7bpiX1qoJ9snBVXMeIo4axF%2BEEMHP8sJ3FjbYESwFh7dOvEQ49TXaXIV0UeZmFOl8jjTsZtOBc%2BjtPJ42JRzd2TLJFJcAvLTS6a4RHfUsTpTjV4kcWVNvuAqv47Ss5EoTWU9umdxyMrwvZ9%2BEvSs%2B6mYh6M3SyedtWYhkYcoBIsSyu9nWOJCI4C6R6kfpY70X0wcoWVQk5y%2BRoKUjoAPPT%2FonaCVXhat3EMoj4l8Cc1rEQCUp5Kj31QS%2B64LLHQNUkmhHMj1nd2UAw90b5y0JgiOCxK46h11jiVw4NkHLOFTEdr2hXan0Xf41cBakaMhWlg0wN2KiITaagOsiJDYDaJGpT464fK0bNw8LYtRpvKErQJalvQ2GEuTNC1r0NtAHLfzFqL%2FqnPJrijeh5OivTSL56Zv2XNroiY9sY7K4lzcE0vbnGEtE6l%2FU5BK6og6%2BiQBle1uEBnwiDlsAVdPEBNIH8l8D02ELqOx62zs5QnepHmCV4P0Neb5UFuiWD8t9eHp9PHu35%2FX9zfWn456ubv%2FXVyhFKGapgmVEKqV06mjNK0812s3JewDBewmA4hE657rhdW2a3EiIGi4z7U40X%2FEMKZuhoaP8vcR96LCTA1Res2FHkleT20%2BO%2BjhHB2vzE%2BpGYVEfPjl%2Bdq0W4L8coR%2FRYL8mPz0qAkrKuCTn3oE%2FGHL7sRila7SJr%2F8rdLNyRPxFlW6C70wzbs%2Bu0FvO7V93wQW0LFj5pkNlkuoQ8DSEzRA8Rblsw%2FR9fnoqtfhSGBwb91A9e5T8785ajYq6hXYPWoGHQQIqoQtStQc08ZvpSnO%2BcPNkLJwtKUp2kjqNU7RaiBJI255aYkkiYs9vvtae5XKPoVjXqcNUYuCGolO2%2FzegkaicEqTjmok5HIpTZrSSGw03Sse6syCs3TNNIkTRSOMiwyxvBT%2BPmKM1anAz6vtGGNulupMRETays3tPF0MiFjA1c%2BYMYpFazGVnhcLPexuqHEF2J700h6NBTbuuuK3FpOtcWFf%2FeOATf%2F7zcyf6Rfj%2Frs%2FIFbJlKeFmXJbwljusA8iX7VXC7kBJBxaxzbsC2DZsRJeOby5fbpGw55Ov%2F28u%2FxC%2BDPaFc%2FE%2FL6KzSMZKMXvSjjdN46WQnx%2FSyHQdBAOmQC0cTmLaBQ42h36A%2FZCoKWh8mOI2q4Iwjz%2FTVsnoycxQXgO%2B4qhHFSio2MVN%2BLC00SYpUJw4Jfl2mjNSEBrEoCrt75GQO0%2Bbn0NYaR2q7EKcb1LwNxL6V2CRGQNcPqZwMtWqOjgWv%2BOsPpZdjakztdxDXh9JASZoFL%2BoeajlVClIInnbEL%2FO0FIbgBaVpxFAidDuXlo7YMFffdTeL2EFklFE%2BTvoaVPNIJuosoo1CNDQ0%2B9eNAjgAaON4gsSpEqlAWLcPbKVsQs%2Bf4PMrBeqVKclVbWrEn4mfFSMLkhz8IS9XWFPKdlndZO028gbbcsFw7W6xSTDcbdij6QVx7OT5CiPRkaPlt2UR9Iqlz1wmyDJ6%2FQcUhitpbPlOisy2IlbWuHz47kLEyVlY4GKXNNhTB1J4cGLd0sD27kAYHxYPXa7XEqB4zzTSuAlTjLkkVHT1GFzFlNpIznUO%2BwZQMnFY6ldukQJQtTTxRWkb1p9E3aLxBYBCKXwH4xqk3iOt6A0cHg4ialsJlcUAjjeeG645BGcnXdsDxgRW36DCCYyVP1MWHvaL8%2FGPaT1QsTauFRV2Hsz8sozrON4RmUNvnPovB%2Fuyb%2FybBdk%2F9X6Y%2Fl%2Bmrqm7v1zeLq6%2B38%2FvUrD%2FAS8MUUamHaK%2BQtSZXSUMRC%2FgIrEuUeoC59XYqFLDJZCGa38UWKe0FbaPF7cZdp1sSxgTFo9TTJjTbQMAjFFGFFnIpWwV24byyXjtQUXgxF0pFSHi%2BKIUG6VvgC7%2B5eJMNsT1GfP%2FdoemmTnA9IPjrbwYcHzfyUWq3DdmUV0E0WKRTsuqEAusO6oHuMVXqcFD0jSlgOzbCRwFV0LImlEoskTgwXp3UR80C5sox%2BrgrkmsRdj7nabS9yVSAXCpGrSTNkLnZ%2FXCukECxv0D7F1ONzamjJ2Y0fzRKVC4wPa2jp7MBatABVwGFmk8PEF3WYTRuP4jBt1mOB1AYwtxMatE46yX0AvVgSOQuLP%2BOakDO7xEigXb%2F0ETT1qMt4qHgny7n1MEZJDNeCFNvv9vQ5ies25aIUprbjZ1o3f06jXYleClBHRwNBjOR%2FEjlqEL%2BM97xkcSlWFmiSFOGSVWBPEbrTv%2FuEwAdqnoXAKqp4bsr6v0%2Fv3AFn%2BeuojsJawtXW5U7PXbSPxQuDoFOLlfxqzcVCKJBwO%2B42oJehK3jSnCfsxGEoKv4hmlKz3vtNA9JxgbZBpwUvE4qCHnkgbMcFIQ48kddBqqFmCHP1Y9xARKrdZII6cTcPzN3x%2Fkg1GGBJSdqT7T6YNislbGkrIn%2FumcP%2BdSvR4ALKtLwS3z2pboAIENrWJKUv2AgYLD1z68a0QDjtOy18Y0dlc8nb%2FxFSWgM8Ax3JEW5snXEEjRlbwELzwp7E1CNYdDeM0wnKzQYEIniQVmqKhfCcELPsAwAYJ0PAeEgSgtbK%2BzTYM7zE0TJXasu3o3bA1TXhNjQapuN8hH5D8kgpLQUVMZkKoagIzpx1qdzz5MkPrHKfC%2Bsw5eesfpO5OJo8phTODVp5CpaDMnEkk1dPWMadrEwcqWTX41ECC%2BvJxMGqtxKz61%2B43mzx5KDOC1kPjxROJqVqaU2AQB3KGrlWVOnVlrZjAz1satEsYG89IieTWZGTRODRowh9erKR7c259IzkOIOaSIJQjAZdesR0MM2PWqWD0qAUJUQXQXq6JHWU26OOohgOIfh5cvSOZKjiBfXePzYobaNDrttpV9ChYwnLKkSHhOiktowNeTJW88iQO%2BxCR5mta76c4sRGeIL7eG2IMfSK50U%2F789Kwr44cx5ORoNR%2FOgxkwaiWMk0f1YntWUIfIenkrr89vNKULzVBIGjSaymTmcSBBpwucRFEwsfR%2FADOIERzjTTixZcDOPj95w%2FqigzG2qM%2BQTywgmyEarZo0d19QmqTu%2BdDFIYqTMRyRSeacqTTMGZZh%2FCdtZdsmZzSYD7xPYRwXfixoZpoXcfbAH%2FWeD3ndErz3jy0Tw7b%2BEGmppLyNYDCLiMR0GFjQ8LomdhCUTwaGi8A8aQMGcq8x9aYt3MSWCjoAFXGPoZ5FKyLfMFK%2FilNLWNLEqF%2FpiBAeEaDZ7iJ8uG0gsSrLCkKonp5YxPZJWoa8xndDhk4DhjW4%2FlcGlktL1SjqX5tWJzeVBnU5EocWahKoqAWYgtNNJkUD4G4je%2FqIRfEEc5dH8fx8js95tn5NClvXT%2BjJB65stEAEwoFmU7uB13QALsgE79z2877%2BcdGv%2FJn9%2F6P%2B%2F2WoDroLUXCNmdXpAVE%2B9Zlm0yxiE7RlXza6O%2FFaqqDAtmy5g0aFRot15Yh8tZCKFVNKtiU6XLC6qq5KPXuHOqqpo0VUVXPTcZTcc0Vcq4XCkLhVVuqltThejwwn7yIVC2FnzcgsIaKxdsEDlHb9%2BgAw7s4wQ7lDW7QDNqUFflJcBITiJPa5WNXM1qrfJKbBaoRFeDdkoYDde2vV0%2Bnmx%2BINY4KUgkeWGgrvDG4%2BWfD80biy57R3mjOixXePpI3lgMJysoMde9nGD7zPaJQpgtEKbKw7QOw9BkmpvRHukt2b8hO%2BPN5nzrFHd6DGQ2Y%2BuYJOTTqE9gE9gXUVf0r7tBn89NVJaNQs3KaOhlUOf0unPymiq3W%2FpczMqlNuW1dhh3jGoeTiNnRbl452r6nmqb72eFqZAoxpzYmcJEqHup0THe1pQKoY895pYpz16jt%2BdZnajpK1KCytzjoC3KMTqectQocCndIiYVy0dpgSaRRXE8m8Rfwaqi06dyJKMJjzLjTEtNZCthNdOTLyodsCJNhQPOHFeiv9qQvu1vrC0rQSy97WajuRhXFen7WS%2FIE12%2Feo2SSaLdO4BQUkf9N0cpkzLWeKp0T8YaZVeOoAb6dPJKl4y0XF6lmO05QZkjUCfYfhrY8CPC75z8CKlcNMXSOEn14v7NCVn7yxj%2FpmRtlltm5WoGBKF3H2uOCvIrU%2FO8pFAvVYM%2Bs6RmQ1SIWFRNRpUqSOIkRpZ9yRJLZDolT23NZIsZhr5rT%2Fh0SYqGCHMJOabtR9OMmCnbduRlgAXxc8uChx3dPT%2BebyrvDZhAhpWCf5zdYgLKy4NKCM%2BeaHaqoKQwCQBkFYUJyWVj0LHlmpSyzNkveSOBFvoSCT2n701PpfQrNV0HnkchY8KFq7kU1LgqxjOF%2BwL%2Fc312g%2F%2B0fd8EFtCJJ5cNlkuow3hu2ryPYZHDcqG%2B3pBn%2BDoyyEaHcnlNpoc20Qq79RV6u%2B7ankfG5VK5hZYPZd9ILYqz1iw%2F8KMrigzEfc0IH5K4Iw3NHvA%2FYOGPGlBDNGQTzo2WGcFHFIQCpuYUXZ1YIdQYtJZA87cuUcMgFHDhYsu63c17UR8X09ZhMDzug5dAxD2jwL13EDylxlDA37wUWqLGNF3Yw0OOzyDGJIUj8ofFtDpRLlEFM%2BCnrCDZpCDhgixgBsqs%2FFkd69Vs24%2FKq2i%2B679tA4uiX%2F4P). Specify the desired analysis details for your data in the *essential.vars.groovy* file (see below) and run the pipeline *chipseq.pipeline.groovy* as described [here](https://gitlab.rlp.net/imbforge/NGSpipe2go/-/blob/master/README.md). A markdown file *ChIPreport.Rmd* will be generated in the output reports folder after running the pipeline. Subsequently, the *ChIPreport.Rmd* file can be converted to a final html report using the *knitr* R-package. ### The pipelines includes - raw data quality control with FastQC, BamQC and MultiQC - mapping reads or read pairs to the reference genome using bowtie2 (default) or bowtie1 - filter out multimapping reads from bowtie2 output with samtools (optional) - identify and remove duplicate reads with Picard MarkDuplicates (optional) - generation of bigWig tracks for visualisation of alignment with deeptools bamCoverage. For single end design, reads are extended to the average fragment size - characterization of insert size using Picard CollectInsertSizeMetrics (for paired end libraries only) - characterize library complexity by PCR Bottleneck Coefficient using the GenomicAlignments R-package (for single read libraries only) - characterize phantom peaks by cross correlation analysis using the spp R-package (for single read libraries only) - peak calling of IP samples vs. corresponding input controls using MACS2 - peak annotation using the ChIPseeker R-package (optional) - differential binding analysis using the diffbind R-package (optional). For this, input peak files must be given in *NGSpipe2go/tools/diffbind/targets_diffbind.txt* and contrasts of interest in *NGSpipe2go/tools/diffbind/contrasts_diffbind.txt* (see below) ### Pipeline-specific parameter settings - targets.txt: tab-separated txt-file giving information about the analysed samples. The following columns are required: - IP: bam file name of IP sample - IPname: IP sample name to be used in plots and tables - INPUT: bam file name of corresponding input control sample - INPUTname: input sample name to be used in plots and tables - group: variable for sample grouping (e.g. by condition) - essential.vars.groovy: essential parameter describing the experiment including: - ESSENTIAL_PROJECT: your project folder name - ESSENTIAL_BOWTIE_REF: full path to bowtie2 indexed reference genome (bowtie1 indexed reference genome if bowtie1 is selected as mapper) - ESSENTIAL_BOWTIE_GENOME: full path to the reference genome FASTA file - ESSENTIAL_BSGENOME: Bioconductor genome sequence annotation package - ESSENTIAL_TXDB: Bioconductor transcript-related annotation package - ESSENTIAL_ANNODB: Bioconductor genome annotation package - ESSENTIAL_BLACKLIST: files with problematic 'blacklist regions' to be excluded from analysis (optional) - ESSENTIAL_PAIRED: either paired end ("yes") or single read ("no") design - ESSENTIAL_READLEN: read length of library - ESSENTIAL_FRAGLEN: mean length of library inserts and also minimum peak size called by MACS2 - ESSENTIAL_THREADS: number of threads for parallel tasks - ESSENTIAL_USE_BOWTIE1: if true use bowtie1 for read mapping, otherwise bowtie2 by default - additional (more specialized) parameter can be given in the var.groovy-files of the individual pipeline modules If differential binding analysis is selected it is required additionally: - contrasts_diffbind.txt: indicate intended group comparisions for differential binding analysis, e.g. *KOvsWT=(KO-WT)* if targets.txt contains the groups *KO* and *WT*. Give 1 contrast per line. - targets_diffbind.txt: - SampleID: IP sample name (as IPname in targets.txt) - Condition: variable for sample grouping (as group in targets.txt) - Replicate: number of replicate - bamReads: bam file name of IP sample (as IP in targets.txt but with path relative to project directory) - ControlID: input sample name (as INPUTname in targets.txt) - bamControl: bam file name of corresponding input control sample (as INPUT in targets.txt but with path relative to project directory) - Peaks: peak file name opbatined from peak caller (path relative to project directory) - PeakCaller: name of peak caller (e.g. macs) ## Programs required - Bedtools - Bowtie2 - deepTools - encodeChIPqc (provided by another project from imbforge) - FastQC - MACS2 - MultiQC - Picard - R with packages ChIPSeeker, diffbind, GenomicAlignments, spp and genome annotation packages - Samtools - UCSC utilities