Please, click on shapes for more information on each step. OTU content is currently under construction, my apologies.
NOTE: A lot of the basic, standard method tutorials (e.g. how to run Kraken2) were generated with ChatGPT in ~2023. Everything was proofread, editted, and tested by humans.
https://viewer.diagrams.net/?tags={}&highlight=0000ff&edit=_blank&layers=1&nav=1&title=workflow.drawio#R7Z1Zc9s4EoB%2FjasyD0zhJvjoI8nMzmZyOMfO0xYAghY3FOlQlGPPr1%2BAh8TLsiySihyrJlMWQaohdn9sNBoHT%2FD5%2FPZNKq5nbxNfRycI%2BLcn%2BOIEIcgBMn9syV1RwjEsCq7S0C8vWhdchv%2FoshCUpcvQ14vGhVmSRFl43SxUSRxrlTXKRJomP5qXBUnUrPVaXOlOwaUSUbf0a%2Bhns%2FIukLsu%2F12HV7OqZsi84sxcVBeXd7KYCT%2F5USvCr07weZokWfFpfnuuI6u8Si9f%2F7j7Gv37G3vzrw%2BL7%2BLz2Z%2Bf%2FvriFMJeP%2BYrq1tIdZztLPrj5cX32y%2FkA%2F0nnUevL9%2Bkf9x9c3Ah%2BkZEy1Jf5b1md5UC02QZ%2B9oKASf47McszPTltVD27A%2BDjCmbZfPIHEHzcctfWt7RjU4zfVuzU%2FnL3%2BhkrrP0zlxSniWlDUoIWWWTH2uTuqwsm9XMiXBZKEqMrlai16oyH0ptPUJzLhlZdSu8ptQj5U09YtrRIyS4q0fI0VR6RE9Rj4yyliJZV5G4B0jIyVSKhE9RkZjiBxWJvL0qcmw97sknQtb1iZj3aI6Qx%2FvEzwudvpP%2Fsw00ApGQOqprJgrjb8XxLMtsu35q5aDXgZgny4Uj5nIZiVhpxw%2FIyzjJwiR%2BuTA6M5e81Zn4%2BvH0vQMDYIwfUFcKRTzscYl9l0LpAcIlIHJd0yeRXunyXv4rjeBvxbnNDZ1X02VJwSgPRhBG0XkSJWkuCL8G9r8JrQ4hbJrd7Zod9j0wBD7e7OawZvlHPEPgaT5DZMuwAoKpwoqxW8PJFIe38D7uxN7ndH4dhabjYErN84fA5SzJrpbxCX492C9Vop0kdUqxDqAUmv%2BZgNAngkOPiMBDTDKqlXSr9mhnF8U2e6hZYn7u4mGLN%2FFo%2BKpcdWdCfbvKL6rcVpzE2l6apcm3VScp921JnL0W8zCyRv6iU1%2FEoiwuO3rQ%2FOIzEYVXRukXyjCm065PDALNlFrVUDvju54EU3pL7DbDC0h7vCWgXUy9UZzlveAuFnoujVoRi8ylZ354Yz5e2Y8vTDXi8v2p7TSjc9sP1VfCWPy36lpTTe3yURpgR5S%2Fx0EauwqpQHAOiecTT1HoUw9KRTDTwB%2BIuLsR8ScHtja60W4f2B5zsWBThgGs2ZODrtsFG%2FX4XzYp2GdhHIfxVcWqTFdYvxW35iQqmLbQmaPiwHhZlSgL%2BECUy8rNGaw0kZoB7RKkqBAuBNi0WMwDyrjqgRDzI8QTQYx6goifA7Ep%2BKiDMNZze8tdmm9EGhocR2HWWdfkCM24EIgoIlwiMeQaAR9obXg2TVIQDER3cyfoiO5j0PUa6GKCDgHdj1qsQouBZK5FOYQEHhMAS%2BArgojkrmQEKyQIdiWCQ6mE4IjlWFi6zeyAsdYhYPlJ3CZxMrc%2F1IS%2BRnuFWwUv%2FkzFN22jgtdnqbGTjod71KouZ12T8yPMZk5Rl1NW5CgFIGQ%2BA8D3iARaKJe7mmEaAABcBYZCDY9QjwY1akBNQU%2Byff9Qn%2Fp%2BaMET1rQfbCT7whe%2BsKK%2FL8NwroezfHr5xflw7ngQQxB4nvI4JtyDgiLgUWXiAUYkw4P9LzqiOlZajDXTYiYwOARU32qDhSl6L8LUmMoGt8JfmGhWzK01Y7m4HieXYKpxikqcvArHEuwLToOAUuBzlxA%2F4Ma7%2BlRySgNzAuKh%2BJIjvlPh6%2FWMD%2B8f3%2FNIi3h53ZMtO58ZP5uKMlWmM%2FVyojRZ%2BROcqsIcbM4k9DVlTFJJIAskVBRIKCUDXmDsOBRsegR7IrAxO4gQohEX18LiPI6YJhS23ALPdwXDUCsoiau1pARgjgNFCBcBGZrlhZtHMo7c7s4tBQeR5r0QmZBikYcUqb4eDGolz7HSckQ9RijzJfQkE8RzA0EY9gFTBElXcjJ0rA0eRyKmQpTgg0DUQHRSDgu%2F%2B%2FR5lCFhg%2BXNwjHSHN%2BnQEmPI0J9AgJplExNV0y6vg6U9L2hdG4eYjgOBe809ZGhl7RB6p4Hg7ecfVbCU5sB0pNomCfZbJm2Mw1HD7bjZM7mJIEpO%2B1bzgLqzp96TBf%2ByMGOHJAmBxP2frfkoDsXday%2B8JGRUXzFlB3JLRlBHUZ6w%2FOjwUdxClOGt1savLto5748QhEmHKODMZ%2F4Kbvg93Vm%2FkxN9De4A5NLcZDyFA7MPwkU0b4rXIBcxWiAsKSQD03%2BoF8sG%2B8LzYPeHgtTXMtgSvLcBniMbgkeYmN0V%2B5j8b24CUVchRaLa%2Fu5ZmH2fWkXRJ6pQlGGTpBeSfECFGFJ689vufqANYoTlMayX5kbd7YoKFmdX%2BRWs2fB9W1RbhSZOaUFT%2FOfIdKsdqoAzJ6JkxK29S9cx0O0vf6VGg3Y0nxx5%2Bqo4pnmj4ApubCfrQWo1Sy1z%2BED18LVtRUvO4lBazFFK7A6U3qK6mxhl9XZ6vGyBba%2BRgbBFuZKsuX5M2lLYH5Yq6%2FAd5s7Wp1a3cyacWopX11Jazd0ty5meK2Ukvq18JrCKvpXJ0ntnA2Iq%2FKrWv1tVeeHK33XC5sUlNd1cFmF2sUjMdK4U%2FG0OYF0FZI84NSDhDMgJPK58glFQcB9zoY67V8sY39ATttjL7fsRSI6pds%2BE%2Bl1lGSLwURWghzGFHMBd11fMqKR9iiTHvIZg5wjTw%2BdZo1%2BsRT94TDp4i0nBY4TSGy5E0E35WX6rouXNvEu7Z8il38qjcksmauM%2FjExPpAOBFtLsHmPwxopLX7vaE10PTNhC7gIze82jsb%2BvBdpqGaxXowwyz4X76yEO5B7WBEPSQwB8aQS1AtEgFyPBdi4taFzP%2FEvNvfzJ7ouTHlr1IaxLdvTHRaaPqY91VkL2KHtql2It0aUcA9IYkcTBScBBYIh4CMlMeYKcDE04sPoiOhYA4vwIAHd6FKNHH2j46fpXfER3bG8K4Mt7%2Br2jYnvH96P5lYDoSxlg%2FGsyXJ8D2AtuWQB1wQTKRUjEnnCZ9A3n4fOpMfH5OdUfRZ3yzH5asHTsdXv4%2FMXm1F8QK2%2BS7xDcJzvX318e%2FrXuy%2Bng8lcSXJ8EshA%2B0QK1yW%2BQjJgEHCGROBSDOHQuZj4mHwczWky2u4t9XXmYc8Of9NieW4MYHOP4Ews7PjDQDYLcU4uzJHSBUJxoaiGBAPJzbPom9Ycc7uCfni4ecxDToen27dv4v7xfHddzK%2B0y%2BbB7yL2o3yKnbSH1Q51pVIiY%2BlZEvmmwa7dMgKxmJdEfBFpKKQddnuQLbI5UWTHMJs4NW1WolI3cFnUgcAaKVQiOi1PzEPft9X0ZkoTc3UQ5aDOzHU6zkHORB6O4wvHm3SDu9ZKnZ797VY0NAK%2FSQmpxvfs03bSN97dGqKGqByjbo825%2FN3DWE2ihR5MHmexIvQ8JSrd1EbSCzqGmkg8cO5Y2p1mpU5WDMUuBB4AbDxJpUB9LnyGPJNX12Dof0hsjnLtPuui%2BP7Tr6N66Sa%2B6TPdXIk7UzAKXvqrXZ9rzvlbDeEQ7oT0yztqNepbevJagTUrIVIdVwKhpt8XMeuU1nJRc1dYVabdTaGUsbpFGxpk%2B421R%2BWIs7CwGhqnV55JvZhrQ2naN%2Fmqfu1D%2B3Y59QEIneLcPG8LOM1nxzes8P7ni3DOpbpWETH%2Fql9R4HVUCQWi1Btiv71bZj9x342QXBx9Hd5nf18cVu77OKuOojNvdS%2BZA%2F%2Frp9bfy0%2Fqr73ODstkmWq9AZllMm4rBYE3L83HgLab7yUoWv1mlVpj1GrslRHxknd1GX1W7qs4X0S5uFZ1Wh6zViS0BYsxX2X31rz0hFEOvsEtwQViukIysFb3fYAFvnzYfFA2EGtDY5xu3%2BxLTsdQVWYtC92ugsKj%2Bzslx0C4DjskPboxsTs0C3ehXBkZ2R2UNPk7fdcbM9OU9Bq3cq%2B2EGjs%2FOcOKDVV6pmg9LdOOgIqvpA%2B%2BJgi3dMHTnYwEGzCUAc78pBS5DH9svBFi%2FMOnKwgYNWu9BeLrA9By1BeM%2F%2BoJvxOHIwgIP2Y7wrBxR4%2B%2BVg%2FPzKc%2BLAbc0%2F2Tk%2B6Ajad3zgHjkYxEFr05Jd44OOoH3HB%2BPnuJ4XB803Uu4cH3QE7Ts%2BGD9f9aw52DU%2BaAvad3xQjVxPkHuqZZ7WeagHc0%2Be16TnJWDwAYLyo%2Fc6DY0y7PDZFEMzpZUeHJrB3kFx2h6bwe2BvF3HZjrLEqfmdLIcKdyJ0yajdFQfNxqM8LAHCp8ujGgyp7lLwh7%2BVBgryB6mER1pnITG8VO%2Fg4aPngiNx4Z6GhrHT0Af7GDmgzRWr6t7kEZ20DCi9kScXWFc7QU9GMb7ZpC%2Fur2OkrQ253LcpQRs89K%2BQ1pK8DiUt59DyFtbL%2BHu0ie%2B94UD58l8voyL5feXWbpU2TLVkwCweRndcwCAoNZONn2ri6q5dPtDIJ9wbXcwugiDQJubVnoxCQGbV6o9BwJYe5lE3269Vbg%2FPgFbBiKTzeYcI2OA9hwXV1PcHoxE3MNKtGLecjbtcHbrUAS3BLUD7Knj4skmiI6Bo%2BuRA0iyHiH9yZC63Vkjl7Mku1reu4CpRuew9qvJ9lQtFwZe%2B%2FU0bnebRtaDCn5807WlzrszNE7n11Go7l819sSUTnjnnUA%2FXend6RD5q6x%2BDX13uu8%2FW9vdUMy%2BLezX0Darpo5Pr%2B2Tcpfsms9f74%2BNX%2F0f
We need to set up our space to do metagenomic work. That means having a plan for software, and getting hold of some data.
Try grabbing some public data used by the carpentries: PRJE22811
Go to https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB22811Links to an external site.
Send Results to RunSelector
Select All
Total – AccessionList
Total - Metadata
On command line:
create a folder
which prefetch
while IFS= read -r line; do prefetch $line; done < SRR_Acc_List.txt
for f in */*.sra; do fasterq-dump $f; done
mkdir save
mv *fastq save
rm -R ERR*
mv save/* .
rmdir save
Now that you have SRA data, you can make some toy data. Toy data will allow you to quickly test your installation and get used to the workflow. However, most small datasets won’t bin, so you will get to a point where you will need larger datasets.
for f in *.fastq; do
head -n 1600 $f > ${f%.fq}.short.fq
done
Run fastqc on your toy files, outputting to a folder called fastqc: