crop5 miniproject template

• Five miniprojects for DBT/KARYA interns 2021-09.
• Duration 2 months
• Each intern chooses a project from a list of 7 crops (see TIGR2ESS 2019 workshop)
• project is phased , some being iterative

We take Maize (Zea mays, Zm) as a typical project. Each intern will substitute their crop.

Communal resources

• terpene synthase dictionary (Sagar)
• terpenes (eo_compound)
• eo_plants (useful to see which plants are co-studied)
• eo_plant_parts

goals of each miniproject

• manually assess rapidly (hours) whether the literature on Zm + TPS is large enough to be useful. If not, select another plant. This may need communal discussion.

• each intern builds separate mini-dictionaries for:

  • Zm genes or enzymes keyed on enzyme name. Start with Sagar's dictionary. We want to find what is mentioned in the literature.

  • Zm enzyme products (mainly terpenes)

• search EPMC using mini-dictionaries to assess scope/feasibility

• increase size or precision of dictionaries by snowballing (particularly important for abbreviations - if they are common).

• refine minicorpus to contain high precision content on Zm enzymes. At this stage the minicorpus will be a collection of papers which are primarily about terpene synthases and their products in Zm.

• communally compare dictionaries and corpus (mainly by term frequency) to decide:

  • which TPS are most important in each plant
  • which terpenes are most important in each plant

project architecture

• each intern has their own wiki (e.g. Zea_mays)

• they record everything daily on the wiki. For large data they create a subdirectory (see TIGR2ESS projects https://github.com/petermr/tigr2ess/tree/master/crops )

• a daily standup report with links to wiki

• create an initial minicorpus (Zm100), main purpose to snowball terms, abbreviations, etc.

• create skeleton dictionaries by searching Zm100. The goal is to find out what genes or compounds are most frequently reported, what syntaxes are used

  • TPSgene => Zm100gene. Initially a list of enzyme names. Gradually add synonyms, new enzymes and abbreviations. NOTE: The genes may or may not have the form ZmTPS or ZmHMGR , etc. Abbreviations may be standard or highly variable. This will be messy, but valuable.
  • eo_compounds => Zm100terp . A list of compounds created by terpene synthases. There will be many synonyms and possibly some abbreviation.

• Each intern has a major project that they are responsible for, and a minor project that they help with.

Crop specific TPS dictionary (KARYA Interns)

• Generation of Hand created terms in text file

• Installation of pygetpapers and (ami) https://github.com/petermr/pygetpapers/blob/main/README.md

• Pygetpaper query

  pygetpapers -q "terpene synthase volatile Camellia AND (((SRC:MED OR SRC:PMC OR SRC:AGR OR SRC:CBA) NOT (PUB_TYPE:"Review")))" -o CamelliaTPS -x -p -s

  It will create a folder "CamelliaTPS" containing papers

  Interns can also use the following queries.

  "terpene synthase volatile Mentha"

  "terpene synthase volatile Citrus sinensis"

  "terpene synthase volatile Zea mays"

  "terpene synthase volatile Vitis vinifera"

• Focus only research articles

• Go through each paper with control F function scoping for TPS.

• Collect gene (names) terms such as CsTPS, MonoTPS and so on. Put those terms into excel file as a list and save excel file as gene.txt file

• Use this command to create a dictionary

  amidict -v --dictionary eo_Gene --directory gene --input gene.txt create --informat list --outformats xml

• Create corpus using this command pygetpapers -q "terpene synthase TPS plant volatile" -o TPSvolatile -x -p -k <number of papers>

• Testing dictionary ami -p "TPSvolatile" section

  ami -p "TPSvolatile" search --dictionary eo_Gene.xml