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MISO / Mixture of Isoforms
Offers a model for alternative splicing (AS) at exon or isoform level. MISO is a program that uses information in single-end or paired-end RNA-seq data and a Bayesian inference to estimate the probability for a read to be issued from a particular isoform. The program is available through two packages: in C language (fastmiso) or in Python language (misopy). The application supplies confidence intervals (CIs) for: (i) estimating of exon and isoform abundance, (ii) identifying differential expression. It can be applied for analyzing isoform regulation.
Analyzes parallel RNA sequence data to catalog transcripts and assess differential and alternative expression of known and predicted mRNA isoforms in cells and tissues. ALEXA-Seq comprises several functions: (1) creation of a database of expression and alternative expression sequence ‘features’, (2) mapping of short paired-end sequence reads to these features, (3) identification of features that are expressed above background noise while taking into account locus-by-locus noise, or (4) identification of features that are differentially expressed in samples.
rMATS / replicate Multivariate Analysis of Transcript Splicing
A statistical model and computer program designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The old version of rMATS was called MATS.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
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.
Allows user-friendly automated stage-wise analysis of high-throughput genomic data. stageR implements two-stage testing as a general paradigm for assessing high throughput experiments involving multiple hypotheses that can be aggregated. The procedure was optimized towards RNA-seq applications: differential transcript expression, differential transcript usage and differential gene expression analysis with simple and complex experimental designs. stageR achieves an optimal middle ground between biological resolution and statistical power while providing gene-level false discovery rate (FDR) control, which is beneficial for downstream biological interpretation and validation.
Detects and quantify alternative splicing (AS) events in RNA-Seq experiments. Solas is an R package that is composed of three different functions: (i) DASI (Differential Alternative Splicing Index), for the detection of alternative splicing events differentiating two conditions; (ii) CASI (Cell Alternative Splicing Index), for the identification of genes and exons which are part of an AS event; and (iii) POEM (Proportion Estimation) for the quantification of the relative proportion of different isoforms.
An R/Bioconductor package that builds on the statistical techniques used by the well-established DEXSeq package to detect differential usage of both exonic regions and splice junctions. In particular, JunctionSeq is capable of detecting differentials in novel splice junctions without the need for an additional isoform assembly step, greatly improving performance when the available transcript annotation is flawed or incomplete. JunctionSeq also provides a powerful and streamlined visualization toolset that allows bioinformaticians to quickly and intuitively interpret their results.
SEVA / Splice Expression Variation Analysis
An algorithm to simultaneously account for tumor heterogeneity and mitigate confounding of alternative splicing events (ASEs) with differentially expressed genes. SEVA compares the degree of variability of junction expression within a population of normal samples relative to that in tumor samples. SEVA identified differential gene isoform usage robust in cross-study validation in head and neck tumors. Therefore, SEVA can identify alternative gene isoform usage in heterogeneous tumor samples.
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A junction-centric method that enables de novo extraction of alternative splicing events from RNA-sequencing data with high accuracy, reliability, and speed. Unlike Spanki and rMATS, which rely on databases of splicing events, jSplice performs a de novo reconstruction of all possible alternative splicing events based on junction positions, allowing thus detection of simple and complex alternative splicing events even in poorly annotated genomes. Application to clear cell renal carcinoma (ccRCC) cell lines and 65 ccRCC patients revealed experimentally validatable alternative splicing changes and signatures able to prognosticate ccRCC outcome. In the aggregate, our results propose jSplice as a key analytic tool for the derivation of cell context-dependent alternative splicing patterns from large-scale RNA-sequencing datasets.
Predicts the two functional conformations of riboswitches, using merely as input the plain RNA nucleotide sequence. The procedure consists in three major steps, including: i) prediction of the switching Sequence (SwSeq), ii) prediction of the alternate secondary structure conformations, and iii) scoring, and consequent classification, of the sequence as a putative riboswitch. SwiSpot is able to model the switching behavior of riboswitches whose generated ensemble covers both alternate configurations. Beyond structural predictions, SwisSpot can also be paired to homology based riboswitch searches.
MAJIQ / Modeling Alternative Junction Inclusion Quantification
Provides a method to detect, quantify and visualize differential splicing between groups of experiments. Two key features distinguish MAJIQ from other algorithms. First, MAJIQ does not quantify whole gene isoforms. Instead, MAJIQ defines a more general concept of “local splicing variations”, or LSVs. Briefly, LSVs are defined as splits in a gene splice graph where a reference exon is spliced together with other segments downstream (single source LSV) or upstream of it (single target LSV). The second important distinguishing element of MAJIQ is that it allows users to supplement previous transcriptome annotation with reliably-detected de-novo junctions from RNA-Seq experiments.
CASH / Comprehensive alternative splicing Hunting
Aims to self-construct alternative splicing (AS) sites and detect differential AS events between samples of RNA-Seq data. CASH (Comprehensive alternative splicing hunting) consists of two major stages: SpliceCons (Splice site Construction) and SpliceDiff (differential AS detection). This tool constructs comprehensive splice sites including known and novel AS sites in cells, and identifies differentially AS events between cells. It can improve the detection of AS events and work on the study of AS regulation and function in cells.
A generalized framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model.
RATs / Relative Abundance of Transcripts
Supplies a way to detect changes in the abundance ratios of transcript isoforms of a gene, this method is named Differential Transcript Usage (DTU). The RATs method serves for identification and visualization of differential transcript usage and recommend that caution and scrutiny must be exercised in the interpretation of quantifications. This tool reaches to take advantage of the bootstrapped quantifications coming from the alignment-free tools.
Detects and quantifies novel and existing alternative splicing (AS) events by focusing on intron excisions. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds AS events of high complexity. It obviates the need for transcript annotations and overcomes the challenges in determining relative isoform or exon usage in complex splicing events. This tool can be used to discover differential splicing between sample groups, and to map splicing quantitative trait loci (sQTLs).
Allows users to analyze alternative splicing using RNA-Seq. Quantas is a toolkit that is composed of two main elements: (i) gapless which is a package that uses paired-end RNA-seq data to deduce transcript structure; and (ii) countit, that numbers RNA-seq reads for each alternative splicing isoform and is also able to quantify gene expression. The application permits users to summarize alternative splicing results and to perform statistical tests before generating files that can be visualized through the UCSC genome browser.
Identifies and analysis the potential consequences of isoform switches from RNA-seq derived quantification of both novel and annotated full-length isoforms. IsoformSwitchAnalyzeR is an easy to use R package that facilitates integration of many sources of annotation including features such as Open Reading Frame (ORF), protein domains, signal peptides, coding potential. Finally, this module also offers article ready visualization of isoform switches as well as multiple layers summary statistics describing the global amount and consequences of isoform switching.
FDM / flow difference metric
Detects differential transcription between pairs of samples or between groups of replicates. FDM is based on a statistical method for performing a permutation test on ACT-Graphs that does not depend on annotations or an underlying transcripts inference. The application first align RNA-seq reads to a reference genome and determines the regions of differential RNA transcript expression between pairs of splice graphs for finally assess the significance of differential transcription.
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