A computational framework to predict the belongingness of coding and non-coding genes to biologically related gene-sets using the shared features of the genes like transcription factors and epigenome marks.
Studying single cells using epigenome helps in highlighting active and poised regulatory regions and mechanisms in cells. With rise in number of single cell epigenome profiles from several organisations, there is potential to use it for many biomedical applications. Hence, ScEpiSearch aims to provide a platform to facilitate a primary step in single cell epigenome analysis. ScEpiSearch allows to search matching cells using single-cell epigenome profile. In current version user can submit single cell open chromatin signal to search existing single-cell RNAseq and single-cell ATACseq profiles and perform some task of analysis.
scEChIA (Single Cell Epigenome Chromatin Interaction Analysis) is a computational method that can predict chromatin interactions among distal sites with high accuracy. scEChIA can also make UCSC track on predicted interactions that may be useful for genomics studies based on chromatin interactions using the UCSC Genome Browser.
Studying single cells using epigCellAtlasSearch, for the first time, allows user query single-cell expression profiles to retrieve matching single cell or bulk expression data from over 2000 different studies. CellAtlasSearch Pipeline. The entire web-server is based on GPU framework. Expression profiles are stored as hash codes obtained through LSH. Like-samples are archived in the same bucket. Query expression data is first converted into hash code and then mapped to one of the buckets. User can query one or more single cell transcriptomes.enome helps in highlighting active and poised regulatory regions and mechanisms in cells. With rise in number of single cell epigenome profiles from several organisations, there is potential to use it for many biomedical applications. Hence, ScEpiSearch aims to provide a platform to facilitate a primary step in single cell epigenome analysis. ScEpiSearch allows to search matching cells using single-cell epigenome profile. In current version user can submit single cell open chromatin signal to search existing single-cell RNAseq and single-cell ATACseq profiles and perform some task of analysis.
UniPath provides robust statistical methods to represent every single cell using pathway and gene-set enrichment scores. It can be used with both single cell RNA-seq and single cell ATAC-seq profile with scalability for atlas scale data-sets. UniPath comes with several features like pseudo-temporal ordering using pathway scores and unconventional way of enumerating differences between two cell populations.
GWNet is graph-wavelet filtering method for single-cell gene expression profiles which denoises the expression data. This method has three modules : 1. Denoising gene expression python module takes gene expression profiles as input and returns filtered/denoised expression data. 2. Differential centrality python module takes ageing data (young and old) as input and gives differential pagerank and degree after building network for both datasets separately. User also gets individual centrality for both input datasets. 3. AUC for overlap between ground truth (Golden set of interaction between genes) and unfiltered, filtered graphs.
Forest of Imputation Trees (FITs) is a method to impute highly sparse and noisy data-sets from single cell epigenome profiling. FITs work in two phases. It has been made so to handle very large read-count matrixes and to get better imputation. In phase-1 it builds multiple imputation trees and from every tree it extracts 1 or 2 imputed version of original read-count matrix. One can run phase-1 of FITs in parallel processing mode also, where multiple trees can be build using several processors.After phase-1, the phase-2 part of FITs is used to accumulate the most relevant imputed version for every cell
Dfilter is a generalized signal detection tool for analyzing next-gen massively-parallel sequencing data by using ROC-AUC maximizing linear filter. Hence it is an ideal tool for detecting peaks in tag-profile of ChIP-seq, DNase-seq, FAIRE-seq, ATAC-seq, MNase-seq, RIP-seq, CLIP-seq, ChIP-exo, Sono-seq etc.