QtIPy is a simple GUI-based automator for IPython notebooks. It allows you to attach triggers to files, folders, or timers to automatically run notebooks. IPython notebooks are great for interactively working through analysis problems, so why would you want to automatically run them? To get a record of how you ran your analysis! By running a notebook through QtIPy you get the output, figures, and a step by step log of how the analysis was performed all in the same folder.
Wunderbar is a program which is able to identify mislabeled samples in genotype data when a few extra independent genotypes are available ("genetic barcoding"). Wunderbar calculates the likelihood that genotype mismatches have occurred by chance. It is capable of reliable and sensitive detection of sample mismatches and swaps even in the presence of numerous genotyping errors and in the presence of linkage disequibrilium between the individual genotypes. It only requires a few SNPs to work well.
Pathomx is a workflow-based tool for the analysis of metabolomic and other omics datasets. It is interactive, visual, extensible, intelligent, and free for any use. It lets you dynamically build analysis workflows using the interactive editor. Drag and drop connections between plugin tools to create a complete workflow through which to run your analysis. Data can be loaded and processed automatically, and new approaches tested simply by connecting tools.
Fqutils provides a basic set of bioinformatics command line tools for working with sequence data in FASTQ format. It complements Greg Hannon's fine Fastx Toolkit suite. One characteristic of Fqutils is that it correctly handles the full FASTQ format as described by the published standard, which specifically allows multi-line sequence and quality score information per record. Fqutils is intended to be useful as part of the early portions of post-sequencing pipelines and quality assessment processes.
Visualization of Protein Ligand Graphs (VPLG) uses a graph-based model to describe the structure of proteins on the super-secondary structure level. A protein-ligand graph is computed from the atomic coordinates in a PDB file and the secondary structure assignments of the DSSP algorithm. In this graph, vertices represent secondary structure elements (SSEs, usually alpha helices and beta strands) or ligand molecules, while the edges model contacts and relative orientations between them. The graphs can be visualized, written to a database, and saved in a text-based file format.