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1 - 33 of 33 results

1 - 33 of 33 results

Detects functional signals in tag profiles from different assays such as histone ChIP-seq, TF ChiP-seq, DNase-seq and FAIRE-seq. DFilter is based on a single receiver operating characteristic – area under the curve (ROC-AUC) optimizing algorithm. This software suits for genomic signals of individual cell types diluted in cellular mixture because the proportion of marginal signals can be mistaken for noise by suboptimal algorithms.

PIQ / Protein Interaction Quantification

A computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq).

HOMER / Hypergeometric Optimization of Motif EnRichment

Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.

cERMIT / conserved Evidence-Ranked Motif Identification Tool

Allows motif identification. cERMIT is designed to analyze current large genomic regulatory datasets such as those from ChIPchip or ChIP-seq experiments. The software makes use of the complete data without the need to pre-define or infer thresholds. It can take different data as evidence for regulatory interactions, and can optionally utilize orthologous sequences from related species to restrict the search to co-occurring motifs.

An efficient footprint detection program. DNase2TF searches for relatively protected regions within DNase I hypersensitive sites and generates a set of footprint candidates at a preset FDR threshold. Starting with an empirical set of candidate regions based on raw cut counts, the algorithm proceeds by iterating two basic steps: i) assessing the significance of cut depletion for all regions in the current set and ii) deciding whether to merge two closest neighboring regions for improved significance of depletion. DNase2TF provides a marked gain in computational speed, scanning DHSs in a mammalian genome for footprint candidates in minutes.

Applies a hierarchical Bayesian mixture model to infer regions of the genome that are bound by particular transcription factors (TFs). CENTIPEDE starts by identifying a set of candidate binding sites (e.g., sites that match a certain position weight matrix (PWM)), and then aims to classify the sites according to whether each site is bound or not bound by a TF. It is an unsupervised learning algorithm that discriminates between two different types of motif instances using as much relevant information as possible.

Allows users to analyse DNase-seq data. pyDNase is a suite of tools that provide several algorithms, including Wellington, a method for pairwise analysis of DNase-seq data sets, Wellington 1D, and Wellington-boostrap foot-printing. It also offers an integrated API to provide an interface with a sorted and indexed BAM file from a DNase-seq experiment. This allows an efficient and easy random access to DNase-seq cutting data from any genomic location.

Identifies and quantifies footprints of the effects of noncoding variants on transcription factor (TF) binding. Sasquatch provides a relatively simple and yet informative approach, requiring only a single DNase-seq data set from the appropriate cell type. It can use data from any genotype to assess variants that are appropriate to that cell type. It can employ publicly available data of any reasonable depth and quality, generated by any of the existing DNase-seq protocols, including low-input DNase-seq protocols.

SSA / Submodular Selection of Assays

Chooses a diverse panel of genomic assays that leverages methods from submodular optimization. SSA serves as a model for how submodular optimization can be applied to other discrete problems in biology. This method is computationally efficient, results in high-quality panels according to several quality measures, and is mathematically optimal under some assumptions. It can be used partway through the investigation of a cell type, when several assays are already available. The tool can determine the most informative next experiments to perform.

Predicts transcription factors binding sites and epigenetic data by combining sets of open-chromatin regions with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. It computes transcription factors affinities and uses open-chromatin / Histone-Mark signal intensity as quantitative measures of transcription factor (TF) binding strength. The tool outperforms several competitive approaches.

Octopus-toolkit

Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.

FootprintMixture

A mixture modeling framework to train multinomial based footprint models and assign footprint likelihood scores to each candidate binding site. The modeling approach was also able to detect variation in the consensus motifs that transcription factors (TFs) bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

Integrates chromatin accessibility, motif scores, TF footprints, CpG/GC content, evolutionary conservation and other factors in an ensemble of TFCT-specific classifiers. Mocap uses sequence-derived genomic features and one chromatin accessibility experiment per cell type to profile TFCT-specific binding activities. The tool aims to help reveal the mechanistic complexity of mammalian gene regulation and chart the mammalian regulatory landscape spanning multi-lineage differentiation.

MuSERA / Multiple Sample Enriched Region Assessment

A broadly useful standalone tool for both interactive and batch analysis of combined evidence from enriched regions (ERs) in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser.

HINT / Hmm-based IdeNtification of Tf footprints

A method based on hidden Markov models to integrate DNase I hypersensitivity and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such as de novo motif finding.

A peak-calling algorithm written in Perl, mainly intended for use in the identification of peaks in mapped DNaseI-seq data. PeaKDEck also includes a set of utilities for processing and manipulation of this data. It selects a threshold read density for peak calling by constructing a probability distribution of background read density scores using kernel density estimation. PeaKDEck can be used for similar methods such as chromatin immunoprecipitation sequencing and FAIRE-seq, by applying a user-defined offset to calculated genomic positions. It is especially useful compared with other peak callers where signal-to-noise ratio is low.

A computational method for identifying individual transcription factor (TF) binding sites from genome sequence information and cell-type–specific experimental data, such as DNase-seq. Romulus combines the strengths of previous approaches, and improves robustness by reducing the number of free parameters in the model by an order of magnitude. We show that Romulus significantly outperforms existing methods across three sources of DNase-seq data, by assessing the performance of these tools against ChIP-seq profiles. The difference was particularly significant when applied to binding site prediction for low-information-content motifs.

ALTRE / ALTered Regulatory Elements

An R package and associated R Shiny web app for differential analysis of regulatory elements genome-wide using chromatin accessibility data, such as DNase-seq or ATAC-seq. Users can run the workflow either in the R console or by launching the embedded web application. User-friendly way of analysis includes guiding chromatin accessibility alignment and peak data to interpretable results of Response Elements (REs) and pathways of interest.

DeFCoM / Detecting Footprints Containing Motifs

A supervised learning based footprint prediction framework. DeFCoM was designed to capture variation in DNaseI signal within active footprints and unbound motif sites to enhance footprint classification accuracy, a consideration unaccounted for in previous footprinters. From a set of motif sites labeled as active or inactive for a given transcription factor in a cell experimental condition, the Support Vector Machine (SVM) classifier is trained on features that are derived from DNase-seq data from the same cell type for each motif site. This allows DeFCoM to capture the complexity of the data when necessary with the Radial Basis Function (RBF) kernel, while avoiding over-fitting, a common problem in supervised learning, by choosing the linear kernel when that complexity is lacking.

Deopen / Deep openness prediction network

Learns regulatory sequence code and predicts chromatin accessibility at the whole genome level. Deopen is able to achieve state-of-the-art performance in the chromatin accessibility classification problem. It recovers continuous degree of chromatin accessibility for an input sequence, and fills the gap of predicting DNA accessibility signals in continuous values. This tool is based on a deep convolutional neural network (CNN) and a typical three-layer feed forward network.

Enables the identification of protein binding footprints in DNase I hypersensitive sites sequencing (DNase-seq) data. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalized count differences of opposed sign at each DNA strand. This is done in order to determine the protein-binding footprint flanks. Preprocessing of the mapped reads is recommended before running DNaseR (e.g., quality checking and removal of sequence-specific bias).

An algorithm for accurately inferring transcription factor binding sites using chromatin accessibility data (DNase-seq, ATAC-seq). The hierarchical multiscale model underlying msCentipede identifies factor-bound genomic sites by using patterns in DNA cleavage resulting from the action of nucleases in open chromatin regions (regions typically bound by transcription factors). msCentipede, a generalization of the CENTIPEDE model, accounts for heterogeneity in the DNA cleavage patterns around sites bound by transcription factors.

Automates quality controls and data analyses on ChIP-seq and DNase-seq data. ChiLin generates comprehensive quality control reports that include comparisons with historical data derived from over 23,677 public ChIP-seq and DNase-seq samples (11,265 datasets) from eight literature-based classified categories. Therefore, ChiLin can be an attractive solution to rapidly process batches of ChIP-seq datasets in an automated manner with detailed QC reports.

Automates the processing and analysis of several commonly used Next Generation Sequencing (NGS) datasets including: ChIP-seq, RNA-seq, Global Run On sequencing (GRO-seq), micrococcal nuclease footprint sequencing (MNase-seq), DNase hypersensitivity sequencing (DNase-seq), and transposase-accessible chromatin using sequencing ATAC-seq datasets. CIPHER provides an analysis mode that accomplishes complex bioinformatics tasks such as enhancer prediction. It supplies functions to integrate various NGS datasets together.

Visualizes next-generation sequencing (NGS) signals and sequence motif densities along genomic features using average plots and heatmaps. It can also calculate sequence motif density profiles from reference genome. SeqPlots is useful both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features.

Combines DNase data with information on transcription factor (TF) binding specificity to identify TF binding sites. MILLIPEDE is based on a logistic regression method. It focuses on the large-scale differences between bound and unbound sites that makes it robust to biases that might arise at the single nucleotide level. The tool is available through a supervised mode that permits to identify TF binding sites from DNase data alone.

Determines functional effect of genetic variants on local chromatin accessibility. circuitSNPs computes the log-odds difference between a “reference prediction” and an “alternate prediction”. It takes into account specific regulatory context considering all the footprints in the region when assessing the neural network output. This tool can be useful for tissue-specific tasks. It enables users to capture biologically relevant information.

MARGE / Mutation Analysis for Regulatory Genomic Elements

Investigates ChIP-seq, ATAC-seq, DNase I Hypersensitivity or other next generation sequencing (NGS) assays. MARGE recognizes DNA binding motifs that potentially affect transcription factor (TF) binding using traditional de-novo motif analysis on genomic sequence for each polymorphic allele. It serves to find combinations of collaborating transcription factors. This tool contains visualization software to interpret the results.

LR-DNase / logistic regression DNase

A logistic regression model. LR-DNase predicts binding sites for a specific transcription factor (TF) using seven features derived from DNase-seq and genomic sequence. We calculate the area under the precision-recall curve at a false discovery rate cutoff of 0.5 for the LR-DNase model, a number of logistic regression models with fewer features, and several existing state-of-the-art TF binding prediction methods. The LR-DNase model outperforms existing unsupervised and supervised methods. Additionally, for many TFs, a model that uses only two features, DNase-seq reads and motif score, is sufficient to match the performance of the best existing methods.

BiFET / Bias-free Footprint Enrichment Test

Consists of an enrichment test for transcription factor (TF) footprinting data studies. BiFET can mimic true TF binding events and is able to recognize TFs whose footprints are over-represented in target regions compared to background regions. It employs DNase-seq or ATAC-seq data to make shape detection and motif-driven. This tool enables users to differentiate the most probable regulators of cell- or disease-specific functions from potentially spurious ones.

Corrects batch effects (from multiple confounding variables) and library depth. ImpulseDE2 is a differential expression algorithm for longitudinal count data sets which arise in sequencing experiments such as RNA-seq, ChIP-seq, ATAC-seq and DNaseI-seq. This method is based on a negative binomial noise model with dispersion trend smoothing by DESeq2 and uses the impulse model to constrain the mean expression trajectory of each gene.

Produces genome-wide raw or normalized signal tracks from aligned sequencing reads. Wiggler is a program that enables production of genome-wide signal coverage tracks for ChIP-seq, DNase-seq, FAIRE-seq and MNase-seq datasets. To perform, this tool takes into account of different details: depth of sequencing, the mappabilty of the genome, different tag shifts for the different datasets being combined, or differentiates between positions.

A discriminative algorithm for predicting TF-DNA interaction sites using DNase-seq data. BinDNase implements an efficient method for selecting and extracting informative features from DNase I signal for each TF, either at single nucleotide resolution or for larger regions.

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1 - 11 of 11 results

1 - 11 of 11 results

Bioinformatician OmicX

PhD ès Neurosciences, I worked 8 years on the brain and its diseases. I then specialized in bioinformatics (NGS, epigenetics) and worked in CEA and GENETHON before to join OMICX and help OMICtools community.

aCNViewer MEDOC Bowtie

Big data scientist National Institute of Plant Genome Research

Research Interests Gene fusion detection in Plants Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions are well known in case of human. But, in plants, this phenomenon is not yet explored. We are planning to discover the fusion transcripts/gene fusions in different type of plants by using RNA-Seq datasets. Further, we are planning to understand the mechanism of gene fusion formation and significance of fusions in plants. Whole genome and transcriptome sequencing data analysis of Plants In this era of Next Generation Sequencing (NGS), there is huge amount of sequencing data available in the public domain. Any novel finding from these available datasets is major challenge for a computational biologist. We are interested in the analysis of whole genome and transcriptome sequencing data of different plants to fetch out the useful information from those datasets, with the help of bioinformatics tools. Currently, we are planning to study the gene clusters of secondary metabolite pathways in different plants. Development of webservers, databases and computational pipelines for plant research Development of database is necessary to compile and share the information with scientific community. We are dedicated to develop useful databases and webserver for plant research. Another area of interest is to develop automated pipelines and tools for the analysis of high throughput genomics data, generated by NGS technologies. Professional & Academic Background Staff Scientist II (May 2017- present): National Institute of Plant Genome Research (NIPGR), New Delhi, India Postdoctoral Research Associate (2015-2017): University Of Virginia, Charlottesville, VA, USA Research Scientist (2014-2015): Sir Ganga Ram Hospital, New Delhi, India PhD Bioinformatics (2009-2014): Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh under Jawaharlal Nehru University (JNU), New Delhi, India M.Sc. Life Sciences (2007-2009): Jawaharlal Nehru University (JNU), New Delhi, India B.Sc. Biotechnology (2004-2007): Jamia Millia Islamia (JMI), New Delhi, India Awards and Fellowships Junior and Senior Research Fellowship (2009-2014): Council of Scientific and Industrial Research (CSIR), New Delhi, India GATE (Graduate Aptitude Test in Engineering): Qualified in years 2008 and 2009 Scientific Contributions/ Recognitions Associate editor: Journal of Translational Medicine. Editorial Board Member of Journal: Theoretical Biology and Medical Modelling. Reviewer: PloS One, BMC Genomics, BMC Bioinformatics, BMC Biology, BMC Biotechnology, Frontiers in Physiology and several other journals. Web Resources/ Databases (Developed/ Contributed) A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer (http://www.imtech.res.in/raghava/cancertope/) GenomeABC: A webserver for benchmarking of genome assemblers. (http://crdd.osdd.net/raghava/genomeabc/). Genomics web portal page. (http://crdd.osdd.net/raghava/genomesrs/). Map/Alignment module of CancerDr: Cancer Drug Resistance Database. (http://crdd.osdd.net/raghava/cancerdr/). Short reads and contigs alignment module of PCMDB: Pancreatic cancer methylation database. (http://crdd.osdd.net/raghava/pcmdb/). Burkholderia sp. SJ98 database. (http://crdd.osdd.net/raghava/genomesrs/burkholderia/). Rhodococcus imtechensis RKJ300 database. (http://crdd.osdd.net/raghava/genomesrs/rkj300/). Genotrick: A pipeline for whole genome assembly and annotation of Genomes (http://crdd.osdd.net/raghava/genomesrs/genotrick/) Development of Debian packages in OSDDlinux: A Customized Operating System for Drug Discovery. (http://osddlinux.osdd.net/). A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. (http://crdd.osdd.net/raghava/mtbveb/).

FusionFinder comrad

Mahantesh Biradar

‎Graduate PhD researcher focusing on Cardiovascular Epidemiology & Social Media Enthusiast Research Stash

I'm a Graduate PhD researcher in the area of Biomedical Science and focussing on the Pharmacogenomics/Pharmacogenomics of Diabetes and related complications

PhD Student Institute of Life Sciences

Working on Immunology and Systems Biology.

EuMicrobedbLite Bowtie GATK

Sangram keshari sahu

Computational Biology and Bioinformatics Indian Institute of Science Education and Research Mohali

Bowtie GATK SAMtools

Brennan Gerlach

PhD Candidate in Molecular Cellular Physiology Albany Medical College

Bowtie miRnalyze SAMtools

Bioinformatician in training University of Guelph

DFilter

Bioinfomagician Dankook University

Octopus-toolkit

I am an Electrical Engineer and Developer University of Engineering and Technology Lahore

SNPSVM GATK SAMtools

Rinaldo Catta-Preta

Graduate Student Researcher University of California Davis Medical Center

DeepEnhancer Spectacle BWA

Przemyslaw Stempor

Genomics, Genetics, Machine learning, Visualisation University of Cambridge

SeqPlots

DNase-seq data analysis software tools

Sequencing of DNase I hypersensitive sites (DNase-seq) is a powerful technique for identifying cis-regulatory elements across the genome. Many people currently analyzing DNase-seq data are using tools designed for ChIP-seq work, but may be inappropriate for DNase-seq data.

Parent category

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Benchmarking

(Gusmao et al., 2016) Analysis of computational footprinting methods for DNase sequencing experiments. Nature methods.
PMID: 26901649

(Gusmao et al., 2016) Analysis of computational footprinting methods for DNase sequencing experiments. Nature methods.
PMID: 26901649