Open Source Perl Data Analytics Tools

* Introduction: - This Module is a Library based Perl code. - The library provide: - Convert INFIX expressions to POSTFIX; - Convert POSTFIX expressions to INFIX and; - Perform POSTFIX context validations. - Context validation can be implemented in item selection routines or data context validation, when it is possible to identify data to be selected or ignored in some data analysis process. * NOTE: - Before any implementation, we recommend details in WIKI (https://sourceforge.net/p/csi-math-notation-postfixinfix/wiki/) or the CPAN Perl Modules, see: https://metacpan.org/pod/Math::Notation::PostfixInfix * Support: The Support Service is FREE. Do you need support? Open a ticket and I will get back to you as soon as possible. * Professional Services: Do you need any FREE professional services to use? Open a ticket as "FAQ request" and I will get back to you as soon as possible. i.e: subject:

PINCIS.pl (PIcs N-/C-terminal Inferred Substrates perl script) is a small, command line tool to designate and analyze PICS (Schilling et al., Nat. Protocols, 2011) data to gain the prime and non-prime site specificity of proteases. Thus, the script filters given peptide lists for library peptides (generated by the digestion protease in the proteomics workflow rather then the protease of interest) and prints out lists of inferred N- and C-terminal cleavage window extensions which can be concurrently used to generate cleavage specificity visualizations like the iceLogo (https://iomics.ugent.be/icelogoserver/create).

Interactive Perl environment for data analysis. It consists of several integrated tools such as a Plotting tool, Interactive Shell, and more. Written entirely in Perl using Tk.

The q utility is a platform for creating and managing data analysis pipelines. It expands the value of your existing job scheduler - either Grid Engine or TORQUE PBS - through numerous functions that help you organize, submit, monitor, manage and share your informatics work. Data processing pipelines require high-level organization and parallelization of work to optimize resource utilization and decrease the time to results. q (from queue) allows complex job sequences to be efficiently assembled and managed, including dependency tracking, parallelization, and pipeline-level monitoring, error recovery, and data protection. Pipelines are constructed from modular script files, with job definitions and results stored in easily retrieved job files. A web interface facilitates job submission and monitoring, with the complete pipeline exportable for full transparency.