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A program for viewing and editing assemblies prepared by the Phrap assembly program. To allow full-feature editing of large datasets while keeping memory requirements low, we developed a viewer, bamScape, that reads billion-read BAM files, identifies and displays problem areas for user review and launches the consed graphical editor on user-selected regions, allowing, in addition to longstanding consed capabilities such as assembly editing, a variety of new features including direct editing of the reference sequence, variant and error detection, display of annotation tracks and the ability to simultaneously process a group of reads.


Allows to compare, align, and assemble large sets of DNA sequences. PHRAP uses a banded version of the Smith-Waterman-Gotoh algorithm to do pairwise comparisons of the sequences. It compares sequences by searching for pairs of perfectly matching “words” or sequence regions that meet criteria, tries to extend the alignment if a match of the designated word size is found and then scores it. The software uses quality values produced by the PHRED basecaller. Cross match/Swat is included in the PHRAP package.

ODS / Online Diagnostic System

Analyses the Sanger sequencing data and provides an easy-to-use one-step solution for genetic testing data analysis. ODS seamlessly integrates base calling, single nucleotide variation (SNV) identification, and SNV annotation into one single platform. It also allows laboratorians to manually inspect the quality of the identified SNVs in the final report. ODS can significantly reduce the data analysis time therefore allows Sanger sequencing-based genetic testing to be finished in a timely manner.

PineSAP / Pine Alignment and SNP Identification Pipeline

Provides a high-throughput solution to single nucleotide polymorphism (SNP) prediction using multiple sequence alignments from re-sequencing data. This pipeline integrates a hybrid of customized scripting, existing utilities and machine learning in order to increase the speed and accuracy of SNP calls. The implementation of this pipeline results in significantly improved multiple sequence alignments and SNP identifications when compared with existing solutions. The use of machine learning in the SNP identifications extends the pipeline's application to any eukaryotic species where full genome sequence information is unavailable.


Efficiently aligns DNA sequencing reads with a reference genome. SMALT employs a hash index of short words (< 21 nucleotides long), sampled at equidistant steps along the genomic reference sequences. For each read, potentially matching segments in the reference are identified from seed matches in the index and subsequently aligned with the read using a banded Smith-Waterman algorithm. The best gapped alignments of each read is reported including a score for the reliability of the best mapping. The user can adjust the trade-off between sensitivity and speed by tuning the length and spacing of the hashed words. A mode for the detection of split (chimeric) reads is provided. Multi-threaded program execution is supported. Mapping with SMALT involves two steps: First, a hash index has to be generated for the genomic reference sequences. Then the sequencing reads are mapped onto the reference using the index.