Current Projects
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PhenoTips

PhenoTips is a software tool providing an easy-to-use Web interface and standardized database back-end for collecting clinical symptoms and physical findings observed in patients with genetic disorders. PhenoTips uses the Human Phenotype Ontology (HPO) to express clinical phenotypes, and provides a friendly interface with error-tolerant, predictive search of phenotypic descriptions. PhenoTips closely mirrors clinician workflows: observations can be recorded directly during the patient encounter, and the interface is compatible with any device that runs a modern Web browser.

Developed by: Michael Brudno’s Lab

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PhenomeCentral

PhenomeCentral is a repository for clinicians and scientists working in the rare disorder community. PhenomeCentral encourages global scientific collaboration while respecting the privacy of patients profiled in this centralized database. Once users enter their patients’ data, they are connected to other patient profiles within PhenomeCentral that share similar phenotypes and genotypes.

Developed by: Michael Brudno’s lab

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GeneMANIA

GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input.

Past Projects
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Medsavant

MedSavant is a software platform for accelerating the identification of disease-causing genetic variants found in population sequencing studies by enabling complex and dynamic querying of patient data. The platform is comprised of two parts: a graphical interface and a backend database. The database is designed to securely store patient data across three main axes: (1) basic patient data: e.g. age, sex, and pedigree (2) phenotype data: e.g. disease, signs, and symptoms (3) genotype data: e.g. candidate variants, their types, and genomic locations. The client-side interface enables users to dynamically visualize global trends in the data, construct complex queries, and analyze the results. MedSavant also supports filters that are generated from external datasources, such as whether or not the variation has been discovered before (using dbSNP data), is predicted to be damaging (using SIFT or Polyphen annotations), is found in genes having a pertinent function (using the GO ontology), or has been associated with a related disease (using OMIM data). Furthermore, MedSavant can be integrated with the Savant Genome Browser, for manual inspection of the read alignment data supporting the most likely causal variants found in the filtration process.

Developed by: Michael Brudno’s Lab

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Savant

The Savant Genome Browser is a desktop visualization tool for genomic data. It was primarily developed for visualizing high throughput (aka next generation) sequencing data, although it can be used to visualize virtually any genome-based sequence, point, interval, or continuous dataset.

Developed by: Michael Brudno’s Lab

SilVA: Silent Variant Analysis using Random Forests

SilVA (Latin for “forest”) is a tool for the automated harmfulness prediction of synonymous (silent) mutations within the human genome. SilVA bases its predictions on a number of features, including CpG, codon usage, splice sites, splicing enhancers and suppressors, and mRNA folding free energy. Given variants in a VCF file, SilVA will rank the rare synonymous variants according to their predicted harmfulness. On a single machine with multiple CPUs, it takes us about 10 minutes to run SilVA (start to finish) on the ~10,000 synonymous SNVs in a typical exome sequencing experiment.

Developed by: Michael Brudno’s Lab

SHRiMP

SHRiMP is a NGS read mapper that achieves high sensitivity at a reasonable speed. SHRiMP supports both letter space and color space reads, enables for direct alignment of paired reads and uses parallel computation to fully utilize multi-core architectures.

Developed by: Michael Brudno’s Lab

CNVer

CNVer is a method for CNV detection that supplements the depth-of-coverage with paired-end mapping information, where matepairs mapping discordantly to the reference serve to indicate the presence of variation. CNVer combines this information within a unified computational framework called the donor graph, allowing it to better mitigate the sequencing biases that cause uneven local coverage. CNVer can also reconstruct the absolute copy counts of segments of the donor genome, and work with low coverage datasets. The method is fully described in Medvedev, P., Fiume, M., Smith, T., Brudno, M., Detecting copy number variation with mated short reads, Genome Research, 20:1613-1622, 2010.

Developed by: Michael Brudno’s Lab

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Enrichment Map

Enrichment Map is a Cytoscape plugin for functional enrichment visualization. Enrichment results have to be generated outside Enrichment Map, using any of the available methods. Gene-sets, such as pathways and Gene Ontology terms, are organized into a network (i.e. the “enrichment map”). In this way, mutually overlapping gene-sets cluster together, making interpretation easier. Enrichment Map also enables the comparison of two different enrichment results in the same map.

Developed by: Gary Bader’s Lab