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A comprehensive and user-friendly system for computational analysis of bacterial RNA-seq data. As input, Rockhopper takes RNA sequencing reads output by high-throughput sequencing technology (FASTQ, QSEQ, FASTA, SAM, or BAM files). Rockhopper supports the following tasks: reference based transcript assembly; de novo transcript assembly; normalizing data from different experiments; quantifying transcript abundance; testing for differential gene expression; characterizing operon structures; visualizing results in a genome browser.
G-Mo.R-Se / Gene MOdeling using RNA-Seq
A method aimed at using RNA-Seq short reads to build de novo gene models. First, candidate exons are built directly from the positions of the reads mapped on the genome (without any ab initio assembly of the reads), and all the possible splice junctions between those exons are tested against unmapped reads. The testing of junctions is directed by the information available in the RNA-Seq dataset rather than a priori knowledge about the genome. Exons can thus be chained into stranded gene models.
A genome-guided transcriptome assembler for RNA-seq data. TransComb can assemble all transcripts from short paired-end reads using a reference genome and analyze their abundances. It was developed based on a junction graph, weighted by a bin-packing strategy and paired-end information. A designed extension method based on weighted junction graphs can accurately extract paths representing expressed transcripts, whether they have low or high expression levels. Tested on both simulated and real datasets, TransComb demonstrates significant improvements in both recall and precision over leading assemblers, including StringTie, Cufflinks, Bayesembler, and Traph.
CAFE / Co-Assembly of stranded and unstranded RNA-seq data Followed by End-correction
Improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data. CAFE is a high-performing transcriptome assembly pipeline that enables to predict the directions of about 220 billion unstranded reads, which led to the construction of more accurate transcriptome maps, comparable to the manually curated map. It should not only help to build comprehensive, precise transcriptome maps from complex genomes but also to expand the universe of non-coding genomes.
CIDANE / Comprehensive Isoform Discovery and AbuNdance Estimation
A framework for genome-based transcript reconstruction and quantification. CIDANE is engineered to not only assembly RNA-seq reads ab initio, but to also make use of the growing annotation of known splice sites, transcription start and end sites, or even full-length transcripts, available for most model organisms. To some extent, CIDANE is able to recover splice junctions that are invisible to existing bioinformatics tools.
Allows isoform discovery and abundance estimation. SLIDE is a sparse linear model approachthat uses RNA-Seq data to discover mRNA isoforms given an extant annotation of gene and exon boundaries, and to estimate the abundance of the discovered or other specified mRNA isoforms. The software can be used as a downstream isoform discovery tool of de novo gene and exon assembly algorithms. It can be extended to incorporate mRNA isoform information from EST (21), CAGE (19), and RACE (18) data.
Traph / Transcrips in gRAPHs
Allows to identify and quantify transcript. Traph is a de novo genome-based tool with two-fold advantage: (i) it translates a problem as an established one in the field of network flows, which can be solved in polynomial time, with different existing solvers and (ii) it is general enough to encompass many of the previous proposals under the least sum of squares model. This method is based on network flows for a multi-assembly problem arising from isoform identification and quantification with RNA-Seq.
Oqtans / Online quantitative transcriptome analysis
Provides a Galaxy interface to RNA-seq analysis tools. Oqtans is the online platform for quantitative RNA-seq data analysis. Its integration into the Galaxy framework ensures transparent and reproducible computational analyses. This application is available in five incarnations: (i) as a cloud machine image, (ii) as a public Galaxy instance, (iii) as a git repository, (iv) the Galaxy Toolshed, and (v) a preconfigured share string to launch Galaxy CloudMan using sharing instance functionality.
A reads mapping algorithm for mapping of short reads onto a de Bruijn graph of assemblies. A hash table of junction k-mers (k-mers spanning branching structures in the de Bruijn graph) is used to facilitate fast mapping of reads to the graph. We developed an application of this mapping algorithm: a reference based approach to metatranscriptome assembly using graphs of metagenome assembly as the reference. This new approach (called TAG) helps to assemble substantially more transcripts that otherwise would have been missed or truncated because of the fragmented nature of the reference metagenome.
TRAM / Transcriptome Mapper
Allows creation and analysis of transcriptome maps. TRAM is a map-centred transcriptome analysis tool that integrates original methods for parsing, normalizing, mapping and statistically analyzing expression data. The software can identify chromosomal segments and gene clusters which are biologically relevant for the cell differentiation toward the megakaryocyte phenotype. It can also summarize and allow the analysis of gene expression data of unmapped genes.
A workflow system for laboratories with the need to analyze data from multiple NGS projects at a time. QuickNGS takes advantage of parallel computing resources, a comprehensive back-end database, and a careful selection of previously published algorithmic approaches to build fully automated data analysis workflows. QuickNGS considerably reduces the barriers that still limit the usability of the powerful NGS technology and finally decreases the time to be spent before proceeding to further downstream analysis and interpretation of the data.
A method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data. This approach explores transcriptome structure and incorporates a maximum likelihood model into the assembly and quantification procedure. A new version of the IsoEM algorithm suitable for Ion Torrent RNA-Seq reads is used to accurately estimate transcript expression levels. Experimental results suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM solution compared to existing state-of-the-art approaches.
RNA-seq portal
Includes three types of workflows for different tasks. RNA-seq portal permits users to perform computing and analysis, including sequence quality control, read-mapping, transcriptome assembly, reconstruction and differential analysis. All these workflows support multiple samples and multiples groups of samples and perform differential analysis between groups in a single workflow job submission. This web portal offers bioinformatics software, workflows, computation and reference data and a platform to study complex RNA-seq data analysis for agricultural animal species.
Infers isoforms from short RNA-Seq reads and information concerning exon-intron boundaries and transcription start site (TSS) - poly-A site (PAS) pairs. IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known. It can also infer isoforms from scratch when they are sufficiently expressed, by trying to find a minimum set of isoforms to explain the read data. The method works for single-end data and data with both single-end and paired-end read. The software was tested on mouse genes.
DRUT / Discovery and Reconstruction of Unannotated Transcripts
Allows transcriptome discovery, reconstruction and quantification in partially annotated genomes. DRUT is an annotation-guided general framework. The software incorporates an enhancement of EM algorithm, VTEM, to detect overexpressed reads and/or exons corresponding to the unannotated transcripts and to estimate annotated transcript frequencies. It was validated using three experiments over human RNA-seq data, two experiments on transcriptome quantification and one experiment on transcriptome discovery and reconstruction.
Automatically combines transcriptomes from difference sources, such as assembly and annotation, into a compact and unified reference. necklace is applicable to any species with an incomplete reference genome. It aligns and counts reads in preparation for testing for differential gene expression and differential transcript usage analysis. This tool provides the following steps: initial assembly, clustering transcripts into gene groupings, reassembly to build the superTranscriptome and finally alignment and counting of mapped reads in preparation for differential expression testing.
A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. You write a high level configuration file specifying your inputs and analysis parameters. This input drives a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps. The goal is to provide a shared community resource that handles the data processing component of sequencing analysis, providing researchers with more time to focus on the downstream biology.
A free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations, and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB, and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides, and a user interface designed to enable both novice and experienced users of RNA-Seq data.
TAP / Transcript Assembly Program
Assembles the joint gene structure of the entire genomic region from individual splice junction pairs. TAP is a software tool developed to delineate gene structures using genomically aligned expressed sequence tags (EST) sequences. It using a novel algorithm that uses the EST-encoded connectivity and redundancy information to sort out the complex alternative splicing patterns. To identify single-exon genes, TAP searches for poly-A sites within coverage gaps, extends them toward upstream to define exon segments, and inserts them into the joint gene structure.
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