SCATA - Sequence Clustering and Analysis of Tagged Amplicons

SCATA provids an analysis framework for the analysis of sequenced tagged amplicons, typically derived from high throughput sequencing of microbial communities. It is optimised for target sequences which cannot readily be aligned across wide phylogenies, e.g. the ITS region. For multiple alignable target sequences, such as 16S rRNA, we recommend the use of purposebuilt systems, eg. tools provided by the Ribosomal Database Project.

Please note that the Scata service is offered freely to the non-commercial scientific community, and as such is run on otherwise unused computer time. This implies that at times, analyses will take longer (up to several days) to finish depending on other requirments of other projects for computational resources.

News

• Service disruption contd.
  We are still having some problems with the systems after the server move. Most notably mail notifications are not working as expected. Please check job status on the web pages. (2011-06-30)
• Service disruption
  The servers running Scata have been moved to a new and better server room. Unfortunately we have had some problems in association with this. This may result in longer running times for analyses the coming days. We are sorry for the inconvenience. (2011-06-29)
• Wiki with documentation
  Scata documentation is growing within the Scata Wiki. Not all documentation is up to date, but we are working on it. If you find Scata a useful service, and use it in a way that can be useful for other people, please feel free to contribute your usage case to the wiki. (2011-06-20)
• SCATA updated!
  We have updated several aspects of SCATA during the last few months. These changes affects several aspects of data handling under the hood as well as a number of things affecting you as users. Please se below for some details of what has changed. The old version of SCATA is no longer available (2011-03-23)
• Improved dataset upload
  We have moved the quality screening and filtering of datasets to the import screen. This implies that you only have to wait for this process once per dataset, instead of every time you run the clustering. Due to this, all dataset which have previously been uploaded have to be uploaded again. Another new feature for the filtering is the ability to extract long high quality regions from the reads, instead of basing the filtering on the complete read. This can be good for datasets which are not trimmed by the Roche software. SCATA is now also able to detect primer sequences in either end of amplicons in order reverse complement sequences as necessary. We have also added support for tags in both ends. (2011-03-23)
• Change of homopolymer handling
  Scata now gives the option to collapse homopolymers over a given length before clustering. This only affects the sequences in the search and clustering process. Sequences in the report files are not homopolymer collapsed and thus fully comparable to references in other databases. (2011-03-23)
• Change of default parameters
  Many parameters in parameter sets have got new default values. Most notably, with the new homopolymer handling, we recommend the use of gap open and extension penalties to avoid over clustering. (2011-03-23)

Disclaimer

We provide SCATA as a free service to the scientific community. Please make sure to download your results when done, as we cannot take any long-term responsibility for data storage (datasets are large and use hard disk real estate!). We have tested the analysis pipeline throroughly and use it regularly for our own projects. However, we cannot guarantee that it is error-free; the final responsibility for ensuring that your results are correct rest with you.

We make no warranty (expressed, implied, or statutory) regarding any data stored whithin this service or any results obtained through using it, including without limitation implied warranties of merchantability, fitness for use, or fitness for a particular purpose.

Citing SCATA

A paper describing SCATA is now submitted. The current citation if you use this service is:

Mikael Brandström Durling, Karina E Clemmensen, Jan Stenlid and Björn
Lindahl (2011): SCATA - An efficient bioinformatic pipeline for species identification and quantification after high-throughput sequencing of tagged amplicons (submitted).

This service is sponsored by the Department of Forest Mycology and Plant Pathology at the Swedish University of Agricultural Sciences. Please direct any questions regarding the system to Mikael Brandström Durling using the email address mikael::durling@slu:;se (replacing the colons with dots).