Recent advances in calcium imaging acquisition techniques are creating datasets of the order of Terabytes/week. Memory and computationally efficient algorithms are required to analyze in reasonable amount of time terabytes of data. This project implements a set of essential methods required in the calcium imaging movies analysis pipeline. Fast and scalable algorithms are implemented for motion correction, movie manipulation, and source and spike extraction. CaImAn also contains some routines for the analyisis of behavior from video cameras. In summary, CaImAn provides a general purpose tool to handle large movies, with special emphasis on tools for two-photon and one-photon calcium imaging and behavioral datasets.
A Computational toolbox for large scale Calcium Imaging data Analysis. The code implements the CNMF algorithm for simultaneous source extraction and spike inference from large scale calcium imaging movies. Many more features are included. The code is suitable for the analysis of somatic imaging data. Improved implementation for the analysis of dendritic/axonal imaging data will be added in the future.
DeepSEA is a deep learning-based algorithmic framework for predicting the chromatin effects of sequence alterations with single nucleotide sensitivity. DeepSEA can accurately predict the epigenetic state of a sequence, including transcription factors binding, DNase I sensitivities and histone marks in multiple cell types, and further utilize this capability to predict the chromatin effects of sequence variants and prioritize regulatory variants.
Tissue-specific Interactions: FNTM leverages a tissue-specific gold standard to automatically up-weight datasets relevant to a tissue from a large data compendium of diverse tissues and cell-types. The resulting functional networks accurately capture tissue-specific functional interactions.
Multi-tissue Analysis: Beyond questions pertaining to the role of single genes in single tissues, FNTM also enables examination of changes in gene function across tissues on a broad scale. Users can compare a gene’s functional interaction in different tissues by selecting the relevant tissues in the dropdown menu.
Tissue-specific Interactions: GIANT leverages a tissue-specific gold standard to automatically up-weight datasets relevant to a tissue from a large data compendium of diverse tissues and cell-types. The resulting functional networks accurately capture tissue-specific functional interactions.
Multi-tissue Analysis: Beyond questions pertaining to the role of single genes in single tissues, GIANT also enables examination of changes in gene function across tissues on a broad scale. Users can compare a gene’s functional interaction in different tissues by selecting the relevant tissues in the dropdown menu.
NetWAS Analysis: GIANT can effectively reprioritize functional associations from a genome-wide association study (GWAS) and potentially identify additional disease-associated genes. The approach, named NetWAS, can be applied to any GWAS study, and does not require that the phenotype or disease have any known associated genes.
HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing “biological knowledge” represented in the literature to identify novel, data-driven associations.
Analyze your experimental results in the functional context of gene-gene networks from multiple organisms. Use IMP to direct additional functional experiments by identifying novel gene participants in a pathway or additional processes that a gene of interest participates in.
KNNimpute is an implementation of the k-nearest neighbors algorithm for estimation of missing values in microarray data. In our comparative study of several different methods used for missing value estimation we determined that KNNimpute provides superior performance in a variety of situations.
This server performs in silico nano-dissection, an approach we developed to identify genes with novel cell-lineage specific expression. This method leverages high-throughput functional genomics data from tissue homogenates to accurately predict genes enriched in specific cell types.
SEEK is a computational gene co-expression search engine. SEEK provides biologists with a way to navigate the massive human expression compendium that now contains thousands of expression datasets. SEEK returns a robust ranking of co-expressed genes in the biological area of interest defined by the user’s query genes. In the meantime, it also prioritizes thousands of expression datasets according to the user’s query of interest. The unique strengths of SEEK include its support for multi-gene query and cross-platform analysis, as well as its rich visualization features.
Sleipnir is a C++ library enabling efficient analysis, integration, mining, and machine learning over genomic data. This includes a particular focus on microarrays, since they make up the bulk of available data for many organisms, but Sleipnir can also integrate a wide variety of other data types, from pairwise physical interactions to sequence similarity or shared transcription factor binding sites. All analysis is done with attention to speed and memory usage, enabling the integration of hundreds of datasets covering tens of thousands of genes. In addition to the core library, Sleipnir comes with a variety of pre-made tools, providing solutions to common data processing tasks and examples to help you use Sleipnir in your own programs. Sleipnir is free, open source, fully documented, and ready to be used by itself or as a component in your computational biology analyses.
This is now the home of URSA and URSA(HD). Leveraging gene expression profiles of thousands of tissue and disease samples, URSA and URSA(HD) identify distinct molecular signatures of individual tissues and diseases. Submit your gene expression profile to use these molecular signatures and ascertain the tissue and disease signal in your data.