Zseq

Zseq, an approach for filtering the reads produced by high throughput sequencing technologies. It’s a linear method that identifies the most informative genomic sequences and reduces the number of biased sequences, low complex regions sequences and ambiguous nucleotides. Filtering reads produced by sequencing technologies is necessary before secondary level data processing takes place. It is a usual task in bioinformatics. There are a lot of known sources of bias that come from library preparation, the chosen sequencing technology, or possible contamination. Filtering the reads helps improve the mapping rate for mapper/aligners by removing confusing low information reads that can be mapped to different locations, and also helps improve the performance of the alignment process and the quality of the assembled transcripts.

Zseq takes the reads in .fastq file format as an input. It is freely available at the following link: http://sourceforge.net/p/zseq/wiki/Home/ . When using these tools, please cite A. Alkhateeb, S. Reddy, I. Rezaeian, L. Rueda, "Zseq: an approach for filtering low complex and biased sequences in next generation sequencing data", Advanced in Bioinformatics and Artificial Intelligence: Bridging the Gap (IJCAI-BAI 2015), Buenos Aires, Argentina, 2015.

 

CMT

Constrained Multi-level Thresholding is another tool that we have delveloped for finding enriched regions and binding sites in ChIP-Seq data. The software tool can be donwloaded from this link. When using these tools, please cite: CMT: A Constrained Multi-level Thresholding Approach for ChIP-Seq Data Analysis", PLOS ONE, 2014. Accepted - In Press. 

 

OMTG

Optimal Multi-level Thresholding Gridding. A gridding method for gridding cDNA microarray images based on the principles of segmentation techniques for image processing. This method allows finding sub-grids in full cDNA microarray images, and the location of individual spots in each sub-grid. It works automatically and free of user-defined parameters. It has been found to outperform state-of-the-art methods. The tools can be downloaded from this link. When using these tools, please cite: L. Rueda, I. Rezaeian, A Fully Automatic Gridding Method for cDNA Microarray Images, BMC Bioinformatics, 2011, 12:113.

 

PPIPAW

Protein-protein Interaction Prediction @ Windsor. A server for prediction of types of protein-protein interactions, namely obligate and non-obligate complexes. It uses solvent accessible area and desolvation energy as properties. It allows to predict complexes from PDB, binary or multi-chain, and to compute the underlying physicochemical properties. It can also select the relevant properties in the prediction. The server can be accessed through this link. When using this server, please cite: Md. Aziz, M. Maleki, L. Rueda, M. Raza, S. Banerjee. Prediction of Biological Protein-protein Interactions using Atom-type and Amino Acid Properties. Proteomics, 2011, pp. 3802–3810, DOI: 10.1002/pmic.201100186.