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

Demo Data Options (Shotgun):

We need to set up our space to do metagenomic work.  That means having a plan for software, and getting hold of some data.

I) Pull from SRA

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: