Electric Genetics

StackPACK™, a robust management system for expression variation analysis and transcript reconstruction, provides a comprehensive pipeline for EST and mRNA data processing. The reconstructed, annotated stackPACK transcripts are increasingly being used for the generation of unique microarray probes and improve our understanding of disease leading to quicker gene discovery, gene function analysis and drug development for the biotechnology and life sciences industries.

The peer-reviewed system is scaleable and highly modular. Flexible parameter settings and customizable cluster results help users fine-tune stackPACK to their specific organism or data, while powerful data management and visualization tools link various consensus sequences, alignments and analyses. StackPACK further simplifies your in-house analysis pipeline by including several data export functions - accessed either from a web-based interface or from the command line - enabling smooth data exchange with third-party programs and easy integration with a broad range of bioinformatics tools.

Version 2.2

Incremental addition of new sequence data to existing clusters retaining the history of the cluster updates.
Inclusion of quality scores.

StackPACK™ Key Features

Holistic approach and a complete solution including processing, management, viewing and output.
Configurable command line or one-step web-based data processing.
Distributed and multi-CPU processing.
Alternate expression forms capture. Each alternate expression form has its own consensus sequences and alignment.
Smooth integration with in-house systems.
Relational database management.

StackPACK has enhanced ability to detect cluster variation due to its maximum length transcript reconstruction and increased cluster membership. StackPACK's superior ability to elucidate alternative transcripts is fundamental in the selection of high quality unique clones for developing microarrays and the generation of gene expression profiles.

StackPACK™ Pipeline

Multiple steps are involved in the clustering pipeline. No data is lost during processing and data from each of the following steps can be viewed and output:

Data is imported in GenBank and/or a range of FastA formats. Phred quality data can also be imported.
Poor quality sequences, such as repetitive or vector sequences, are masked with either RepeatMasker or CrossMatch.
Clustering is performed with the d2_cluster.
Clusters are assembled and consensus sequences are generated with phrap and CRAW.
Alignments are analyzed and alternate alignments and consensus sequences are recorded for sub-consensus sequences and gene variants.
Non-overlapping clusters are linked by virtue of clone ID.
Project data, such as primary and alternate consensus sequences and multiple alignment views, can be viewed and output for further evaluation.

StackPACK™ Output:

Each cluster is represented by the following:

Primary and alternate consensus sequences.
Phrap and final alignments for primary and alternate consensus sequences.
Alignment analyses highlighting variation within the clusters.
Clonelink consensus sequences

StackPACK is able to output alignments in MSF, ClustalW and ACE formats allowing you to view alignments in the program of your choice without concerns for reformatting or parsing. In addition there is a list of pre-defined output reports, including a non-redundant output report, which can be selected and downloaded for further data evaluation or to create a searchable database of your clustering results. These reports can be generated from both the command line and the web-based interface.