Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.
Simplifies quantitative investigation of comparative RNA-seq data. DESeq2 employs shrinkage estimators for dispersion and fold change. It counts the total number of reads that can be uniquely assigned to a gene. It serves for improved gene ranking and visualization, hypothesis tests above and below a threshold, and the regularized logarithm transformation for quality evaluation and clustering of over-dispersed count data. This version of DESeq uses shrinkage estimators for dispersion and fold change to ease quantitative analysis of comparative RNA-seq data.
Tests for differential usage of exons and hence of isoforms in RNA-seq samples. DEXSeq uses generalized linear models and offers reliable control of false discoveries by taking biological variation into account. It also detects with high sensitivity genes, and in many cases exons, that are subject to differential exon usage. DEXSeq achieves reliable control of false discovery rates by estimating variability for each exon or counting bin and good power by sharing dispersion estimation across features.
Identifies differentially expressed genes or isoforms for RNA-seq data from different samples. DEGseq encourages users to export gene expression values in a table format which could be directly processed by edgeR. It is based on the random sampling model which fits well the random sampling model. This tool can be applied to recognize differential expression of exons or pieces of transcripts.
Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.
Serves for degenerating nucleotides to IUPAC nomenclature ambiguity codes. Degen processes by reading individual DNA sequences as strings of codons with three sequential nucleotides within. It then changes every codon with a fully degenerated codon by using IUPAC nomenclature of polymorphic nucleotides for the ones that can be variable. This program is available as a download desktop and a web application.
Consists of a SAM and BAM file compression tool. DeeZ can encode the positional information of each read within only the relevant contig. This program uses a unique compression method for each field of the SAM record to exploit its specific properties.
Determines the dynamics of the splicing isoform proportions from time series RNA-seq data. DICEseq is able to explicitly model correlations between different RNA-seq experiments. It aims to assist the quantification of isoforms across experiments. This tool allows quantification of trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments.
Identifies differentially expressed (DE) genes for syndrome by accounting for the change in the mean and dispersion when comparing normal and disease groups. DESyn is based on an empirical Bayes method to borrow information across genes to enhance dispersion estimation for each gene and treatment group combination. It provides a pooled permutation test to detect significant DE genes. It can also find the unique combination of genes underlying disease for each afflicted replicate.
Enables the identification of subgraphs from organismal networks with density greater than a given threshold and enriched with proteins from a given query set. DENSE is a program that can serve for detecting phenotype-related functional modules. It proceeds to the enumeration of the “dense and enriched” subgraphs in genome-scale networks of functionally associated or interacting proteins for identifying genes that are functionally associated to a set of known phenotype-related proteins.
Detects candidate regions and evaluates statistical significance at the region level. dmrseq is a two-stage approach that calculates a statistic for each candidate differentially methylated region (DMR) that takes into account variability between biological replicates and spatial correlation among neighboring loci. With this tool, significance of each region is assessed via a permutation procedure which uses a pooled null distribution that can be generated from as few as two biological replicates.
Takes sequence of a protein as input and accurately predicts its functions without relying on any supporting information. DeepSeq is a novel deep learning architecture which is able to automatically extract representations from the input protein sequence without the need for a human expert. It can extract meaningful information from the input sequence that can then be used to solve a myriad of complex problems without human intervention.
Recognizes differentially spliced genes from two groups of RNA-seq samples. DSGseq is based on a negative binomial (NB)-statistic method. It does not require a prior knowledge on the annotation of alternative splicing (AS). This tool is able to design sequencing reads on exons by making comparison between read counts on all exons. It does not need to deduce isoform structure or to determine isoform expression.
Furnishes a solution for predicting enhancers. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties. These components were trained on data with diverse properties that describe enhancer’s activity under different cellular conditions. It utilizes features derived from histone modification marks or attributes coming from sequence characteristics.
Allows classification of metagenomic reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI permits researchers to manage the reads with two key techniques: indexing and classification. Furthermore, this tool is suitable and can be integrated into metagenomics pipelines to handle large number of reads.
Predicts motifs and identifies transcription factor binding sites (TFBSs) in base pair and regional DNA shape features. DESSO is a deep learning (DL)-based motif finding framework containing a convolutional neural network (CNN) model for motif patterns learning and a statistical model for motif instances identification. It was able to detect several previously unidentified motifs and shape factors that contribute to transcription factor (TF)-DNA binding mechanisms. Moreover, it can serve for inferring the indirect regulation mechanisms through tethering binding activities and co-factor motifs predictions.
Allows extraction of differences in gene expression from public next-generation sequencing (NGS) datasets. deSRA enables users to interrogate Sequence Read Archive (SRA) datasets using a dockerized pipeline. The software can be useful for studying the impact of genes of interest on health and disease.
Gives accurate toxicity predictions quickly. Derek Nexus is a knowledge-based expert systems that predicts the toxicity and metabolism of a chemical, respectively. It offers an effective mechanism for the sharing of data and knowledge on chemical toxicity and metabolism. It also provides a more direct assessment of predictive performance, avoiding the inherent difficulties of reference to published studies, by allowing the user to access information directly on predictive performance for an alert within the version of the software.
Provides semantic similarity computations among Disease Ontology (DO) terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. Comparison among gene clusters is also supported. DOSE provides several DO-specific visualization functions to produce highly customizable, publication-quality figures of similarity and enrichment analyses that are not available elsewhere. With these visualization tools, the results obtained by DOSE are more interpretable.
Implements the same mathematics used in the Broad Institute’s BWA-GATK HaplotypeCaller 3.x Best Practice Workflow pipeline. Sentieon DNAseq is a pipeline includes a computing efficiency enhancement to BWA-MEM. For aligned-BAM-to-VCF processing, Sentieon DNAseq is 20X-50X faster than GATK. The software is also able to do joint call on over 100K samples together without intermediate file merging, saving time and effort.
Consists of a dense and consistent map of 358 cortical landmarks. Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. These 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes.
Provides several types of information on farm animals. SIGENAE focuses on 6 species: cattle, chicken, pig, rabbit, sheep, trout. Furthermore, it supplies several tools for: sequence cleaning, sequence clustering, library statistics, redundancy calculation, microArray data storage and processing, and specific data processing.
Compiles results from a framework based on a predictive model for four epigenetic assays. DeepFIGV relies DNA sequence to quantitative variation in epigenetic signal and evaluates the predicted functional impact of genetic variants on multiple assays. This repository can be used to understand the molecular mechanism of a causal variant and prioritize downstream experiments or, in conjunction with large-scale genome-wide association studies.
A platform for discovery, analysis and exploration of information from a number of topic-specific microbial knowledgebases. The knowledgebases include integrated information from published scientific literature and a number of major databases from biology fields. These include PubMed, ChEBI, Entrez Gene, GO, KOBAS, KEGG, REACTOME, UniPathways, PANTHER, BioGrid, etc.). Information exploration is enabled through a number of different ontologies, controlled vocabularies, taxonomy, and integrated information. Most of the concepts contained are normalized. DESM provides users with a number of tools to explore, filter and visualize enriched concepts and their associations.
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.
M.Sc Bioinformatics from Pondicherry University. Currently working as a JRF at Indian Institute of Science Education and Research (IISER) Mohali, with the research focus on Plant Genomics and Systems Biology.
I am Dr. Madhu Sudhana Saddala working as a Postdoctoral Fellow in Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, USA. Currently working on NGS data analysis of retinal and macular disease of eye.