About the Team
Jan 20th, 2017
Organisation of CASMI 2017 is underway, stay tuned! Dec 4th, 2016
The MS1 peak lists for Category 2+3 have been added for completeness. May 6th, 2016
The winners and full results are available. April 25th, 2016
The solutions are public now. April 18th, 2016
The contest is closed now, the results are fantastic and will be opened soon! April 9th, 2016
All teams who submit before the deadline April 11th will be allowed to update the submission until Friday 15th. February 12th, 2016
New categories 2 and 3 and data for automatic methods released. 10 new challenges in category 1. January 25th, 2016
E. Schymanski and S. Neumann joined the organising team, additional contest data coming soon. January 11th, 2016
New CASMI 2016 raw data files are available.
Category 1: Best Structure Identification on Natural Products
The challenges for Category 1 are natural products from several organisms of different possible origin (plants, fungi, marine sponges, algae or micro-algae), acquired on QToF instruments from Waters and Agilent.
Please note that a third data set for Category 1 will be available in early February.
Based on the MS and MS/MS and other data, the goal is to determine the correct molecular structure at the given retention time using the spectral data and the additional information provided. The additional information may include sample background, experimental factors and retention information in some cases. Whether you use this information or not is up to you. The data is available here.
All kinds of approaches, manual and automatic, are allowed and encouraged!
Category 2: Best Automatic Structural Identification - In Silico Fragmentation Only
The Category 2 files consist of several mixes measured together in one run on an Orbitrap Q Exactive. Each mix contains compounds that are "known" to the contestants, as well as several unknowns, which must be identified by the contestants. The data will be available soon.
Based on the MS/MS data, the goal is to determine the correct molecular structure by the first block of the InChIKey from among the candidates provided for that challenge, using in silico fragmentation techniques alone (i.e. neither retention time, nor spectral library lookup nor additional metadata should be considered). The "knowns" can be used to determine appropriate parameters. Mass spectral libraries can only be used for training of prediction models, but not to solve the challenge by querying with the peaklist against the library.
The aim of this category is to compare different fragmentation approaches, ranging from combinatorial, to rule-based, to simulations; the number of challenges means this is aimed specifically at automatic approaches. The abstract should describe the computational method, including the parameters used for the submission.
Category 3: Best Automatic Structural Identification - Full Information
This category uses the same data files and candidate lists as for Category 2, but in Category 3 any form of additional information can be used (retention time information, mass spectral libraries, patents, reference count, …). This allows to demonstrate whether/how much additional information can improve the results of the unknown annotation. The approach(es) used should be well documented in the abstract submitted with the results file.