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TRUP / Tumor-specimen suited RNA-seq Unified Pipeline
A pipeline designed for analyzing RNA-seq data from tumor samples. As an unified pipeline, TRUP is designed to sensitively and accurately dissect the complexity of the cancer transcriptome by analyzing RNA-seq data obtained from tumour tissues. The current functionalities of TRUP include: 1) identification of fusion transcripts; 3) RNA-seq quality assessment; 2) Gene-read counting. The fusion detection module in TRUP combines split-read/read-pair mapping with regional de-novo assembly to achieve a balance between sensitivity and precision.
SnowShoes-FTD / SnowShoes-Fusion Transcript Detection
Uses paired-end transcriptome sequencing data to recognize fusion transcripts. SnowsShoes-FTD integrates several subroutines to build template regions for polymerase chain reaction (PCR) primer design. It offers a way to simplify quick PCR validations. This tool is also able to construct template regions for acid sequences of the putative in-frame fusion gene products, to ease predictions concerning the functional significance of the fusion events.
FusionMetaCaller
A meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation. Top performing methods likely had complementary advantages to accurately detect different types of fusion events. First of all, we selected fusion events detected by at least a certain number of tools. We next ranked the detected fusion events from each method by the number of supporting reads. Rank sums of the selected fusion events were calculated and the fusion events were reprioritized accordingly.
NCLscan
A high accurate method for detecting intragenic and intergenic non-co-linear (NCL) transcripts. NCLscan utilizes a stepwise alignment strategy to almost completely eliminate false calls (>98% precision) without sacrificing true positives. NCLscan outperforms 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. NCLscan promises to facilitate the comprehensive characterization of various types of NCL transcripts on a transcriptome-wide scale.
ChimeRScope
Inspects the k-mer contents of the RNA-Seq paired-end reads for fusion transcript detection. ChimeRScope is an alignment-free method which uses large-scale fusion transcript data analysis. It could lead to the discovery of potentially novel and physiologically relevant drug targets for cancer treatment, or biomarkers for effective diagnosis and prognosis in precision medicine. This tool can either be set up as a standalone software or installed on genomic research platforms such as Galaxy server.
chimerascan
Allows users to detect gene fusions in human cancers in paired-end RNA sequencing (RNA-Seq) datasets. chimerascan is an open source software, developed in Python, with the aim of providing a way of investigating various RNA-Seq data collection including those containing long paired-end reads. The application includes functionalities permitting to process ambiguously mapping reads or to identify reads spanning a fusion junction. Moreover, results can be summarized through an HTML report.
COPA / Cancer Outlier Profile Analysis
Finds genes undergoing recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. COPA is intended to find pairs of genes that may be involved in recurrent gene fusion with a third (unknown) gene. The underlying idea here is that in certain cancers it may be common for the promoter region of one gene to become fused to certain oncogenes. The copa package has a function that will repeatedly permute the data and then count the number of gene pairs that had 14 or more mutually exclusive outliers.
PRADA
Processes large numbers of raw RNA-sequencing datasets. PRADA works on paired-end sequencing data and is based on: (1) its mapping to both transcriptomic and genome; or (2) its comprehensive repertoire of output information from the incorporated modules. It enables users to compute multiple analytical metrics. It provides different types of information from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification.
wapRNA
Allows users simultaneously perform mRNA and miRNA expression analysis. wapRNA is a web application that includes major processes for the next-generation mRNA or miRNA data analysis, including preprocessing raw sequenced reads, mapping tags to reference sequences, gene expression annotation, and other downstream functional analysis such as detecting differentially expressed genes, Gene Ontology and KEGG pathway analysis for RNA, novel miRNA predication and miRNA target prediction. Executable packages are available for users to build their pipeline locally.
SOAPfusion
Allows fusion discovery with paired-end RNA-Seq reads. SOAPfusion identifies fusion transcripts with RNA-Seq reads. The software integrates a specially designed SOAPfusion-aligner to perform both intact alignment and two-segmental alignment and enables discovery of fusion transcripts at single-base resolution. With a masking strategy on the reference genome and retention of reliable multiple mappings to report fusions, it provides a proper to make better use of multiple mapping results.
R453Plus1Toolbox
Provides an interface to access 454 Sequencing data processed with Roche GS FLX Software from within R and offers many tools for quality reports, annotation and advanced analyses. Users can add customized methods using the R/Bioconductor infrastructure. Hence, the R453Plus1Toolbox is useful for custom analyses of 454 Sequencing data and may support a broad application of amplicon deep sequencing in a diagnostic laboratory, in particular, for the analyses of tumor specimens.
Dissect / DIScovery of Structural alteration Event Containing Transcripts
Detects and characterizes novel structural alterations in RNA-Seq data. Dissect is a computational tool suitable for high-throughput transcriptome studies. This method is capable of direct global alignment of long transcript sequences to a genome. It achieves high sensitivity and specificity in identifying structural alterations in simulated datasets, as well as in uncovering gene fusions in a prostate cancer cell line.
aRNApipe / automated RNA-seq pipeline
Analyzes single-end and stranded or unstranded paired-end RNA-seq data. aRNApipe focuses on high performance computing (HPC) environments and the independent designation of computational resources at each stage allowing optimization of HPC resources. It is highly flexible because its project configuration and management options. This tool can be adapted to changes in the current applications and the addition of new functionalities. It allows users to complete primary RNA-seq analysis.
FuMa / FusionMatcher
Reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools.
hppRNA
Converts the raw fastq files into gene/isoform expression matrix and differentially expressed genes or isoforms. hppRNA is a one-in-all solution composed of four scenarios such as pre-mapping, core-workflow, post-mapping and sequence variation detection. It also turns the identification of fusion genes, single nucleotide polymorphisms (SNP), long noncoding RNAs and circular RNAs. Finally, this pipeline is specifically designed for performing the systematic analysis on a huge set of samples in one go, ideally for the researchers who intend to deploy the pipeline on their local servers.
Fusion-sense
Uses to calculate the estimated sensitivity of fusion finding for an RNA-seq experiment. Fusion-sense plots the estimated sensitivity as a function of the distance to the 3’ end and calculates the decay rate for the sample. Fusion-sense is provided to minimize the influence of the combination of aligner and fusion detection tool parameters. The fusion detection sensitivity can be affected by the choice of the aligner and the number of minimum supporting reads used to call a fusion event.
FuGePrior
Prioritizes of gene fusions from paired-end RNA-Seq data. FuGePrior combines state of the art tools for chimeric transcript discovery and prioritization, a series of filtering and processing steps designed by considering modern literature on gene fusions and an analysis on functional reliability of gene fusion structure. It has been tested on two paired-end RNA-Seq publicly available datasets from breast cancer cell lines and prostate primary tumors. Results accounted for a reduction in the number of fusions output of chimeric transcript discovery tools that ranges from 64.4 to 75% in breast cancer dataset and from 37 to 65% in the prostate one.
Pegasus
A pipeline for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation.
SQUID
Predicts transcriptomic structural variants (TSVs) from RNA-seq data. SQUID is a computational tool that divides the reference genome into segments and builds a genome segment graph from both concordant and discordant RNA-seq read alignments. It can detect both fusion-gene events and TSVs incorporating previously non-transcribed regions into transcripts. Using an integer linear program rearranges the segments of the reference genome so that as many read alignments as possible are concordant with the rearranged sequence.
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