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185 Free RNA-seq Tools - Software and Resources

185 Free RNA-seq Tools - Software and Resources

Graph: The words 'RNA-seq' and 'single-cell RNA-seq' occurences in scientific articles stored in PubMed from 2008 to October 2019.
The words 'RNA-seq' and 'single-cell RNA-seq' occurences in scientific articles stored in PubMed from 2008 to October 2019.

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  1. StringTie
    • Description : A tool to assemble RNA-seq sequence alignments into transcripts. The StringTie algorithm can optionally make de novo assemblies and uses a novel network flow algorithm.
  2. EDGE-pro
    • Description : EDGE-pro (Estimated Degree of Gene Expression in PROkaryotes) is a tool to quantify gene expression in prokaryotes and archaea. The EDGE-pro algorithm can align overlapping gene regions.
    • Description : A tool to detect a functional identity of cells from RNA-seq expression data profiles. The ACTION algorithm uses cells' expression profile to classify cells by dominant functions and reconstructs gene regulatory networks involved in mediating identities. NOTE! The source may be available from the Authors. The given GitLab repository does not exist.
  4. ascend
    • Description : ascend (Analysis of Single Cell Expression, Normalisation, and Differential expression) is an R tool for analysis of single-cell RNA-seq data. The ascend tool kit contains functions for data quality control, normalization, reduction of dimensionality, sequence clustering, and differential expression analysis.
  5. BALDR
    • Description : BALDR is a pipeline tool to build immunoglobulin(Ig)/B cell receptor(BCR) sequences from Illumina single-cell RNA-Seq data. The BALDR offers the following reconstruction methods for human and rhesus macaque: 1. Transcript assembly using all reads with a selected Ig model, 2. Read assembly mapped to Ig loci, and alternatively including unmapped reads. For human sequences only: 1. Read assembly mapping to IMGT V(D)J and C sequences, 2. Reas assembly mapping to the Ig recombinome. For rhesus macaque, it can assemble reads filtered not to match Ig genes. The BALDR pipeline requires the following tools: Trimmomatic 0.32, Trinity v2.3.2, bowtie2 2.3.0, STAR v2.5.2b, samtools v1.3.1, IgBLAST v1.6.1, seqtk 1.2, and Perl 5.
  6. mmquant
    • Description : A tool to quantiy gene expression. The mmquant algorithm handles multiply mapping reads, i.e., duplicated genes by constructing merged genes.
  7. DendroSplit
    • Description : A tool for the analysis of single-cell RNA-seq data. The DendroSplit algorithm uses a feature selection approach to cluster and to reveal different population data hierarchies.
    • Description : A tool for the detection of similarities between single-cell scRNA-seq data sets. The RAFSIL algorithm uses the random forest method in which it first detects features and then learns similarities.
    • Description : A tool for the identification of splice variants in RNA-seq data. The ISVASE algorithm uses rule-based filters, identifies splicing junctions, sequence variants, and exon-exon junction shifts.
  10. Cascade
    • Description : A web-based tool for 3D visualization of RNA-seq data sets from cancer genomic investigations. The Cascade can visualize such data, as alternative splicing frequencies, mutations, and gene expressions.
  11. BTR
    • Description : BTR is an R tool for analysis of differential gene expression in single-cell RNA-seq data. The BTR algorithm learns models and expression data using a new Boolean state space scoring function.
  12. HISAT2
    • Description : A tool to map DNA and RNA sequences to one or more genomes. The HISAT2 algorithm uses an extension of the Burrows-Wheeler transform (BWT) to generate graphs, a new graph FM index (GFM), and a Hierarchical Graph FM index (HGFM) to index a whole-genome and population of genomes.
  13. MINTmap
    • Description : MINTmap (MItochondrial and Nuclear TRF mapping) is a tool for identification and determining the abundance of transfer RNA fragments (tRFs) in RNA-seq data. The MINTmap algorithm also classifies fragments as false positives if it finds them to originate outside the tRNA scope.
    • Description : RSEQREP (RNA-Seq Reports) is a tool for complete RNA-seq analysis. The RSEQREP algorithm runs on an RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS, or on a local Linux machine (Ubuntu). The RSEQREP has automated, customizable analysis methods for reference alignment, reference alignment quality control, data normalization, data compression, identification of differentially expressed genes, identification of co-expressed gene clusters, enriched pathways, and heatmaps. The algorithm outputs a PDF report, publication-ready tables, and figures.
  15. DRAP
    • Description : DRAP (de novo RNA-Seq Assembly Pipeline) pipeline tool uses two separate assemblers, Trinity and Oasis, to make de novo RNA-seq assemblies. The DRAP algorithm has three modules: runDrap, runMeta, and runAssessment.
  16. SimBA
    • Description : SimBA consists of a set of tools for benchmarking and evaluation of RNA-seq pipelines. Set contains two tools, SimCT and BenchCT. SimCT simulates RNA-seq data sets, and BenchCT does the benchmarking by comparing output from an RNA-seq pipeline against the simulated data.
  17. SCANPY
    • Description : SCANPY is a Python library to analyze single-cell RNA-seq data. The SCANPY package has functions for preprocessing, clustering, differential expression testing, pseudo-time and trajectory inference, gene regulatory networks simulation, and visualization,
  18. BPSC
    • Description : BPSC (Beta-Poisson model for Single-Cell RNA-seq data analyses) is an R tool for the examination of single-cell RNA-seq data. The BPSC algorithm uses a beta-Poisson mixture model to estimate the bimodality of expression distribution.
  19. zUMIs
    • Description : A pipeline tool for analysis of RNA-seq data comprising sample-specific barcodes and unique molecular identifiers. The zUMIs algorithm can identify cells by a read distribution. Besides, it has a function to downsample varying library sizes.
  20. kissDE
    • Description : kissDE is an R tool for retrieving specific variants from RNA-seq data, such as insertions, deletions (indels), single-nucleotide variation (SNVs), and splicing variants.
  21. omicplotR
    • Description : omicplotR is a Shiny app to visualize RNA-seq, meta-RNA-seq, and 16s rRNA data. The omicplotR package has methods for differential expression analysis using ALDEx2, biplots for principal component analysis (PCA), dendrograms, stacked bar, and effect plots.
  22. scPipe
    • Description : scPipe is a pipeline tool to preprocess single-cell RNA-seq data. The scPipe package has methods for barcode demultiplexing, read alignment, UMI-aware gene-level quantification, and quality control. It can use the following data from CEL-seq, MARS-seq, Drop-seq, Chromium 10x, and SMART-seq protocols.
  23. GDCRNATools
    • Description : An R package for interactive analysis of RNA -seq data, specifically lncRNA, mRNA, related to ceRNA regulatory networks in cancer data sets from the Genomic Data Commons (GDC). GDCRNATools also has functions for downloading and organizing the GDC data.
  24. coseq
    • Description : coseq is an R tool to analyze co-expression in RNA-seq data. The coseq algorithm uses modified transformations and mixture models or a k-means method.
  25. ideal
    • Description : ideal is a tool for differential expression analysis of RNA-seq data. The ideal is a Shiny app.
  26. BASiCS
    • Description : BASiCS (Bayesian Analysis of Single-Cell Sequencing), is a tool for the analysis of single-cell RNA-seq data. The BASiCS algorithm uses a Bayesian hierarchical approach in supervised experiments. It estimates cell-specific normalization parameters from the model. It can quantify technical variability using spiking genes included in a cell's lysate, and it reports the total variability as experimental and biological components. Requires: SingleCellExperiment.
  27. IntEREst
    • Description : IntEREst is an R tool to analyze Intron-Exon Retention in RNA-seq data sets. The IntEREst algorithm can analyze both annotated and non-annotated retention events. The IntEREst algorithm uses statistical methods, modified from DESeq2, edgeR, and DEXSeq R packages and supports single and multiple cores.
  28. SCnorm
    • Description : SCnorm is an R tool to normalize single-cell RNA-seq data. The SCnorm algorithm estimates the sequencing coverage for each gene by quantile regression and uses estimated scaling factors for group-wise calculations for expression levels.
  29. bcSeq (Duke)
    • Description : bcSeq is an R tool to map short hairpin RNA ( shRNA) and CRISPR screens reference barcode library. The bcSeq parallelized algorithm uses the Trie data structure and resolves sequencing errors using Phred quality scores. It also uses a Baye's method to classify read barcode origins. It also supports user-defined probability models and supports multi-threading.
  30. phenopath
    • Description : phenopath is an R tool to estimate pseud-time trajectories in single-cell RNA-seq data. The phenopath algorithm uses a new statistical approach, a mixture regression-latent variable model, which can detect known and unique covariate-pseudo-time interaction influences.
  31. ZINB-WaVE
    • Description : ZINB-WaVE is a tool to produce low-dimensional representations of single-cell RNA-seq data sets. The ZINB-WaVE algorithm uses a zero-inflated negative binomial model that estimates zero inflation (dropouts), over-dispersion, and the counts. It also considers varied library sizes and covariates, that eliminates the need to normalize data.
  32. BEARscc
    • Description : BEARscc (Bayesian ERCC Assessment of Robustness of Single Cell Clusters) is a tool to assess single-cell RNA-seq clusters. The BEARscc algorithm estimates experimental noise using ERCC spike-in controls.
  33. EventPointer
    • Description : It identifies alternative splicing events that involve either simple or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays or sequencing data. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation.
  34. TRAPR
    • Description : A tool for visualization and statistical analysis of RNA-seg data. The TRAPR algorithm can filter low-quality data, normalize, transform, perform statistical analyses, and visualize data. The ability to visualize results supports the creation of customized computing pipelines. It additionally has functions for data management.
  35. CORNAS
    • Description : CORNAS is a tool for differential expression analysis of RNA-seq data. The CORNAS algorithm uses a Bayesian approach to compute the sequence coverage from concentrations of RNA-seq samples. It uses a posterior distribution to estimate the true gene count.
  36. mfa
    • Description : mfa is an R tool for modeling bifurcations in single-cell RNA-seq data. The mfa algorithm uses a Bayesian hierarchical mixture of factor analyzers and Markov-Chain Monte Carlo sampling.
  37. scDD
    • Description : A tool for analysis of single-cell RNA-seq data sets. The scDD algorithm uses Dirichlet Process mixture models to characterize and classify various expression patterns. This tool also has methods to simulate data.
  38. DEsingle
    • Description : DEsingle is an R tool for differential expression analysis in single-cell RNA-seq. The DEsingle algorithm differentiates and evaluates status, abundance, and general differential expressions. The algorithm uses a zero-inflated Negative Binomial method.
  39. TCseq
    • Description : TCseq is an R tool for analysis of quantitative and differential expression of RNA-seq data. The TCseq algorithm can also do cluster analysis and has functions for visualization of time-course data.
  40. Matataki
    • Description : A tool to estimate gene expression levels in RNA-seq data sets. The Matalaki algorithm uses unique k-mers for each gene to quickly map all the fragments to genes. According to the Authors, Matalaki is faster than conventional methods.
  41. OLego
    • Description : A tool for mapping of spliced mRNA-seq reads. The OLego algorithm uses the Burrows-Wheeler transform for the mapping of seeds, splice junctions, and detection of exons. The algorithm also allows multiple threads.
  42. EMASE
    • Description : EMASE (Expectation-Maximization for Allele-Specific Expression) is a tool for estimating total gene expression, isoform usage, and allele-specific expression in RNA-seq data. The EMASE algorithm approaches the problem hierarchically by first resolving uncertainties between genes secondly between isoforms, and finally between alleles. EMASE is a prototype implementation in Python language, and EMASE-Zero is a C++ version.
  43. SpliceDetector
    • Description : SpliceDetector is a tool to detect alternative splicing in RNA-seq. The SpliceDetector algorithm uses the Ensembl transcript IDs to identify splicing events, computes statistical analysis of alternative and differential splicing, and presents the results in a graphical view.
  44. SPAR
    • Description : A web portal for analysis, annotation, and visualization of RNA-seq data sets. The SPAR portal supports data originating from smRNA-seq, short total RNA sequencing, single-cell small RNA-seq, and microRNA-seq. Additionally, SPAR contains sncRNA data sets and data from 185 human tissues from ENCODE.
  45. RED-ML
    • Description : A tool for detection of RNA editing. The RED-ML algorithm is based on machine learning and does not require access to RNA editing databases.
  46. XBSeq
    • Description : XBSeq is an R tool for genome-wide expression analysis of RNA-seq data. The XBSeq algorithm uses a statistical approach in which observed signals are a convolution of real expression signals and sequencing noises. It assumes the reads that map on intergenic regions are distributed according to Poisson and distinguishes signals using the negative binomial distribution.
  47. SparseIso
    • Description : A tool for identification of alternatively spliced transcripts in RNA-seq data. The SparseIso algorithm Gibbs sampling method for simultaneous identification and quantification of transcripts. It also estimates the joint distribution of all transcript candidates to improve the detection of transcripts that are expressed in small quantities and expressed isoforms.
  48. CIDR
    • Description : CIDR (Clustering through Imputation and Dimensionality Reduction) is an R tool to reduce the dimensionality and clustering of single-cell RNA-seq data. The CIDR algorithm uses an implicit imputation method to deal with dropouts.
  49. AltHapAlignR
    • Description : AltHapAlignR is a tool estimate transcript abundace on gene and haplotype levels on genomic regions.
  50. scEpath
    • Description : A tool for analysis of single-cell RNA-seq data. The scEpath (single-cell Energy path) algorithm induces cellular trajectories based on Waddington's landscape metaphor of cell development. The algorithm also identifies significant marker genes and transcription factors. Note, requires the R package "princurve."
  51. BNBR
    • Description : BNBR is an R tool for the analysis of differential expression in RNA-seq data. The BNBR algorithm uses a new Bayesian negative binomial regression technique (BNB-R).
  52. sleuth
    • Description : sleuth, an R tool for differential analysis and benchmarking RNA-seq experiments. The sleuth package analyzes and benchmarks data, quantified by the kallisto tool (see links). sleuth also includes interactive graphical visualization tools.
  53. CPSS
    • Description : CPSS is a web-based tool for the analysis of microRNAs (miRNAs) and non-coding RNAs (ncRNAs) in RNA-seq data. It performs functional annotation, distribution of lengths, mapping, quantification, prediction of novel micro RNAs, identification of piwi-interacting and differentially expressed miRNAs, editing, prediction of signaling pathways, protein-protein interactions of predicted genes, detection of non-coding and differentially expressed RNAs (ncRNAs). CPSS also visualizes results as charts and graphs.
  54. NGScloud
    • Description : A tool for analysis of RNA-seq data at Amazon cloud computing services. The NGScloud has an interface for controlling Amazon's hardware resources and analysis workflow. The NGScloud algorithm can run workflows in numerous different instances.
  55. VDJPuzzle
    • Description : VDJPuzzle is a tool to reconstruct full-length B-cell receptors from single-cell RNA-seq data sets. The VDJPuzzle algorithm can reconstruct both heavy and light chains, and detect mutations in the V(D)J region.
  56. Salmon
    • Description : A tool to quantify transcript expression in RNA-seq data. The Salmon algorithm can correct for GC-bias, and it uses 'selective-alignment' and massively-parallel stochastic collapsed variational inference to achieve high accuracy and speed.
  57. umis
    • Description : A tool for expression estimation in RNA-seq tagged data. The umis package includes tools for read formatting, barcode filtering, pseudo-mapping to cDNAs, and counting molecular identifiers.
  58. CellTrails
    • Description : A tool for expression analysis, de novo chronological ordering, and visualization of single-cell RNA-seq data. The CellTrails algorithm uses a geometric lower-dimensional manifold learning to estimate noise, drop-outs, experimental variance, and redundancy of predictive variables. CellTrails also has functions for branching reconstruction and graphical visualization of expression patterns and branches.
  59. scater
    • Description : An R package for pre-processing, quality control, expression normalization, and visualization of single-cell RNA-seq data. The scater algorithm includes a suite of plotting functions.
  60. IsoEM2
    • Description : A tool to estimate differential expression and confidence intervals in RNA-seq data. The IsoEM2 package integrates both IsoEM2 and IsoDE2. The IsoEM2 algorithm uses bootstrapping to evaluate expression levels and confidence intervals. IsoDE2 uses the data generated by IsoEM2 to analyze differential expression.
  61. expVIP
    • Description : expVIP is a web-based tool for RNA-seq data visualization and analysis of differential expression. The features include a generation of a custom web browser for sorting, filtering, and visualization of RNA-seq data.
  62. RUM
    • Description : RUM (RNA-Seq Unified Mapper) is a pipeline to map Illumina RNA-seq data. The RUM algorithm Bowtie to map the reads onto a genome and to a transcriptome database. It uses BLAT for mapping against the genome and merges all three mappings into one. Note that the usage of BLAT requires a separate license.
  63. ASAP
    • Description : ASAP is a pipeline tool for the analysis of single-cell RNA-seq (scRNA) data. The ASAP tool includes functions for the identification of cell clusters, differentially expressed genes, enrichment analysis, and a variety of visualization tools.
  64. RNAscClust
    • Description : RNAscClust a pipeline tool for clustering RNA sequences. The RNAscClust algorithm uses minimum free energy and a graph kernel-based strategy.
  65. STAR
    • Description : A tool to align RNA-seq data. The STAR algorithm uses suffix arrays, seed clustering, and stitching. It can detect non-canonical splice sites, chimeric sequences, and can also map full-length RNA sequences.
  66. FRAMA
    • Description : A pipeline tool to assemble and annotate transcriptomes for RNA-seq data. The FRAMA algorithm uses a reference transcriptome and makes de novo assemblies, assigns gene symbols, detects fusions and constructs scaffolds, and annotates contigs.
  67. FEELnc
    • Description : FEELnc (FlExible Extraction of LncRNAs) is a tool for annotation of long non-coding RNAs (lncRNAs) in assembled RNA-seq data sets. The FEELnc algorithm uses a Random Forest approach.
  68. TruHmm
    • Description : TruHmm is a tool for assembling prokaryote RNA-seq transcriptomes based on a reference.
  69. TSCAN
    • Description : TSCAN (Tools for Single Cell Analysis) is a tool for differential analysis of single-cell RNA-seq data. The TSCAN algorithm uses a cluster-based minimum spanning tree (MST) method for the pseudo-temporal ordering of cells. TSCAN package has a graphical user interface implemented in Shiny.
  70. ABSSeq
    • Description : ABSSeq is a tool for differential gene expression analysis in RNA-seq data. The ABSSeq algorithm uses a negative binomial distribution approach to infer expression differences.
  71. AllelicImbalance
    • Description : A tool to identify, manage, and visualize allelic imbalances in RNA-seq data.
  72. scran
    • Description : scran is an R tool for single-cell RNA-seq data experiments. The scran algorithm has functions for low-level analyses of single-cell RNA-seq data, including normalization, the designation of the cell-cycle phase, and the determination of notably variable and significantly correlated gene expressions. Requires: SingleCellExperiment tool.
  73. switchde
    • Description : switchde is an R tool to detect switch-like differential expression in single-cell RNA-seq data. The switchde algorithm uses a fast model fitting to produce interpretable parameter estimates for the speed of up- and downregulation and trajectories. It also models zero-inflation data.
  74. SOAPfusion
    • Description : SOAPfusion is a tool to discover fusions in paired-endn RNA-seq data.
  75. AltAnalyze
    • Description : AltAnalyze is a tool for the analysis of differential gene expression and alternative splicing in RNA-seq and Affymetrix (exon, gene, junction) data. The AltAnalyze includes multiple visualization functions, for example, network, pathway, splicing graph, and varying levels of abstraction: isoform, exon, protein, domain, molecular interactions.
  76. Oscope
    • Description : Oscope is a tool for the identification of oscillatory genes single-cell RNA-seq data. The package has functions for searching candidate oscillatory pairs, k-medoids clustering, and to recover the base cycle order for oscillatory groups.
  77. derfinder
    • Description : derfinder is a tool for differential expression analysis of RNA-seq data. The derfinder package provides single base-level F-statistics and identification at differentially expressed region (DER) levels. The derfinder algorithm uses a bump-hunting method for the identification of differentially expressed regions and among others, multi-group and time-course analyses. The derfinder package also contains visualization functions.
  78. MMAPPR
    • Description : MMAPPR (MAPPR2) is a tool for mapping mutations in pooled RNA_seq data, specifically forward screens of F2 data. The MMAPPR2 algorithm is an improved version of MMAPPR, and it distinguishes differences among control and mutant RNA sequences. It also uses Ensembl's Variant Effect Predictor to predict effects and ranks candidate mutations.
  79. NSMAP
    • Description : NSMAP (Nonnegativity and Sparsity constrained Maximum A Posteriori ) is a tool for quantification of expression levels and identification of isoforms in RNA-seq data. The NSMAP algorithm uses A Nonnegativity and Sparsity constrained Maximum APosteriori model, to simultaneous identification of isoform structures and estimation of expression levels.
  80. CEM
    • Description : A tool for assembling transcriptome sequences and estimating expression levels in RNA-seq data. The CEM algorithm uses a quasi-multinomial distribution model to detect RNA-seq biases, such as mappability and positional sequencing biases.
  81. MaLTA
    • Description : A tool to assemble and quantify transcripts in Ion Torrent RNA-seq data sets. The MaLTA uses the IsoEM algorithm for the estimation of expression levels. It also uses a maximum likelihood method in both assembly and quantification steps.
  82. RVboost
    • Description : RVboost is a tool for prioritizing RNA-seq variants. The RVboost algorithm uses characteristics that are uniques to RNA library preparation and sequencing. RVboost utilizes a boosting method to train models using variants stored in HapMap. The RVboost consists of a workflow combining variant calling, filtering, and annotation.
  83. DEBrowser
    • Description : DEBrowser is an R Shiny app for step-wise visualizing expression data from RNA-seq. The debrowser package has methods to plot various types of graphs, such as scatter, box, bar, heatmaps, MA, and volcano.
  84. RPASuite
    • Description : RPASuite (RNA Processing Analysis Suite) is a pipeline tool for the identification of differential expression in RNA-seq data. RPASuite suite processes Differentially Processed Loci (DPL) and Coherently Processed Loci (CPL).
  85. pcaReduce
    • Description : pcaReduce is an R tool to cluster cell expression in RNA-seq data. The pcaReduce algorithm uses a new agglomerative clustering approach to compute the hierarchies of cell states.
  86. GENIE3
    • Description : An R tool for prediction of gene regulatory networks from RNA-seq data. The GENIE3 algorithm uses the random forest or Extra-Trees approach.
  87. NURD
    • Description : NURD is a tool to estimate expression levels of isoforms in RNA-seq data. The NURD algorithm uses a binary interval search method and can correct for experimental sequencing biases both globally and locally. The home page is currently not available. You may email the Authors and request the source code.
  88. RSVP
    • Description : RSVP is a tool to predict protein-coding gene isoforms in RNA-seq data. The RSVP algorithm uses ORF graphs, genomic DNA evidence, and aligned RNA-seq reads for the predictions.
  89. spongeScan
    • Description : spongeScan is a web-based tool for identification and visualization of long non-coding RNAs (lncRNAs) response elements (MREs) that function as miRNA sponges.
  90. WemIQ
    • Description : WemIQ is a tool for quantification of isoform expression and exon splicing ratios in RNA-seq data. The WemIQ algorithm uses the expectation-maximization (EM) approach and a Poisson model.
  91. ORMAN
    • Description : ORMAN (Optimal Resolution of Multimapping Ambiguity of RNA-Seq Reads) is a tool to resolve transcript mappings in RNA-seq data. Th ORMAN algorithm uses combinatorial optimization, integer linear programming, heuristics, and well-know approximation methods.
  92. DSGseq
    • Description : This program aims to identify differentially spliced genes from two groups of RNA-seq samples.
  93. RDDpred
    • Description : RDDpred is a tool to predict RNA editing with specific conditions in RNA-seq data. The RDDpred algorithm uses a Random Forest RDD classifier to find candidate RNA-editing events.
  94. RNA-MATE
    • Description : A recursive mapping strategy for high-throughput RNA-sequencing data. This pipeline described here is written in Perl, and makes use of a PBS queue manager, however it can be configured to use LSF or SGE
  95. Arboreto
    • Description : Arboreto is a tool to infer gene regulatory networks in RNA-seq data. The Arboreto framework uses GRNBoost2 and an improved GENIE3 version. GRNBoost2 uses a gradient boosting approach for gene network inference.
  96. AUCell
    • Description : An R tool for identification of gene signatures and modules in RNA-seq data. The AUCell algorithm detects and ranks enriched gene sets by computing the receiver operator characteristics, i.e., the area under the curve (AUC).
  97. LIONS
    • Description : A pipeline to detect and quantify transposable elements (TEs) initiated transcription in paired-end RNA-seq data. The LIONS pipeline uses the following software tools: Python3, pysam, Bowtie2, Tophat2, Java v8 or higher, Samtools v0.1.18, R v3.5.0 or higher, Bedtools v2.25.0, and Cufflinks v2.2.1.
  98. DREAMSeq
    • Description : DREAMSeq is an R tool for the detection of differentially expressed genes in RNA-seq data. The DREAMSeq algorithm uses a double Poisson model to capture all data properties, such as underdispersion, overdispersion, and equidispersion.
  99. ACTINN
    • Description : ACTINN (Automated Cell Type Identification using Neural Networks) is a tool to identify cell types in sing-cell RNA-seq data. The ACTINN algorithm uses a neural network with three hidden layers. The publication describes the training on mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and the results of prediction of cell types for mouse leukocytes, human PBMCs and human T cell subtypes.
  100. alevinQC
    • Description : alevinQC is an R tool to generate quality control reports that summarize the "alevin" output.
  101. BAMMSC
    • Description : A tool for clustering single-cell RNA-seq data. The BAMMSC algorithm uses a Bayesian hierarchical Dirichlet multinomial mixture model and can characterize variabilities of genes, cell types, and individuals. It also accounts for batch effects, variable read lengths, and technical bias, within multiple individual samples. Besides, it combines DIMMSC in analyses of individuals.
  102. BASIC (uchicago)
    • Description : BASIC is a tool for assembling B-cell receptor (BCR) single-cell RNA-seq data. The BASIC algorithm first identifies known constant and variable regions to anchor sequences and then uses the recognized anchors to guide the assembly of BCR.
  103. bayNorm
    • Description : A tool for normalization, true count recovery, and imputation of single-cell RNA data. The bayNorm algorithm uses a new Bayesian approach for scaling and inference of counts. The likelihood approach uses a binomial model, and it estimates the priors using empirical Bayesian estimates of values across cells.
  104. DECENT
    • Description : DECENT is an R tool for differential expression analysis of single-cell RNA-seq data. The DECENT algorithm requires UMIs and it has a hierarchical approach in modeling the observed and unobserved counts to detect dropouts.
  105. BBKNN
    • Description : A tool for batch alignment of single-cell RNA-seq transcriptome data. The BBKNN algorithm uses graph-based data integration to increase alignment speed. It first identifies k nearest neighbors and transforms candidates to connectivities for subsequent analyses.
  106. BCseq (USC)
    • Description : BCseq (bias-corrected sequencing analysis) is a tool to quantify and correct bias in single-cell RNA-seq data sets. The BCseq algorithm detects dropouts by measurement of outputs from similar cells and assigns a quality score for each gene.
  107. SingleCellExperiment
    • Description : SingleCellExperiment is an R tool for defining an S4 class for data storage in single-cell RNA-seq. The SingleCellExperiment algorithm stores and retrieves spike-in information, dimensionality reduction coordinates, metadata, and size factors for each cell sample.
  108. DomainGraph
    • Description : DomainGraph is a plugin for Cytoscape to visualize molecular interaction networks. DomainGraph facilitates analysis of the effects of alternative splicing on genes, protein isoforms, molecular interactions, pathways, and miRNA binding sites. DomainGraph visualizes results produced by the AltAnalyze tool, including automatic annotation of affected genes, pathways, and miRNA binding sites. Requires: Cytoscape 2.6, and Java SE 8.
  109. kallisto
    • Description : A tool to quantify RNA-seq data. The kallisto algorithm uses a pseudo alignment approach to speed up the alignment procedure. The "pseudo alignment" approach can quantify reads without making actual alignments.
  110. TraCeR
    • Description : An R tool to reconstruct T-cell receptor genes from single-cell RNA-seq data. The TraCeR algorithm can also identify cells containing identical T-cell receptors to determine clonally propagated cells.
  111. LINCS_RNAseq
    • Description : LINCS_RNAseq is a tool for the analysis of RAN-seq data. The LINCS_RNAseq algorithm uses the unique molecular identifier data from the LINCS Drug ToxicitySignature (DToxS) Generation Center at the Icahn School of Medicine at Mount Sinai in New York. The pipeline has three steps: split, align, and merge.
  112. DNABarcodes
    • Description : DNABarcodes is a tool for creating DNA barcodes for the purpose of error correction. The DNABarcodes algorithm can analyze existing barcodes by determining their distances.
  113. pySCENIC
    • Description : A Python pipeline for inference of gene regulatory networks and identification of cell states in RNA-seq data sets. The pySCENIC workflow is a Python implementation of the original SCENIC R package and much faster (see links).
  114. MBASED
    • Description : MBASED is a tool to detect allele-specific gene expression in RNA-seq data. The MBASED algorithm uses simulation to determine the statistical significance and combines single-nucleotide variants (SNVs) into the allele counts.
  115. ScanNeo
    • Description : A pipeline tool for prediction of neoepitopes derived from small to large-sized indels in RNA-seq data. The ScanNeo pipeline comprises three steps: Indel discovery, annotation and filtering, and neoantigen prediction.
  116. ORE
    • Description : ORE (Outlier-RV Enrichment) is a tool for the identification of non-coding rare variants in RNA-seq data. The ORE algorithm detects biologically significant outliers having more or less rare variants than expected by chance. Requires: Python >= 3.5.0, bedtools >=, samtools >= 1.3, and bcftools >=1.6.
  117. TANRIC
    • Description : An interactive open platform to explore the function of lncRNAs in cancer.
  118. miARma-Seq
    • Description : miARma-Seq contains a suite of tools for differential expression analysis of miRNA, mRNA, and circRNA. It can also predict functions and targets and works in any organism.
  119. SCENIC
    • Description : An R pipeline for inference of gene regulatory networks and identification of cell states in RNA-seq data sets. The SCENIC (Single-Cell rEgulatory Network Inference and Clustering) workflow utilizes three separate packages, GENIE3 or GRNBoost2, RcisTarget, and AUCell. A Python implementation of this workflow is faster than this initial R version. See links for pySCENIC. The current version supports human, mouse, and Drosophila melanogaster.
  120. VCNet
    • Description : A tool to construct co-expressed gene networks from RNA-seq data. The VCNet algorithm uses a new statistical test on the correlation of a gene pair using the Frobenius norm (Euclidean norm ).
  121. Necklace
    • Description : A tool to assemble RNA-seq data. The Necklace algorithm can assemble genomes both de novo, and guided by a template. It combines an assembled transcriptome with annotations from a reference.
  122. STARChip
    • Description : STARChip (Star Chimeric Post) is a tool for detect fused sequences and circular RNA from the output of the STAR alignment tool.
  123. RNAseqEval
    • Description : RNAseqEval is a tool collection for evaluation and benchmarking of RNA-seq mapping.
  124. netSmooth
    • Description : netSmooth is an R tool for smoothing noisy single-cell scRNA-seq data. The netSmooth algorithm uses a network-diffusion based approach in which it uses priors to analyse the covariance of expression profiles.
  125. PathwaySplice
    • Description : An R tool for analysis of splicing pathways. The PathwaySplice algorithm adjusts the number of exon junctions, visualizes a selection bias, supports Gene Ontology terms, user-defined gene sets, distinguishes primary genes in a pathway, and arranges pathways into an enrichment map.
  126. QAPA
    • Description : QAPA (Quantification of APA) is a tool that finds alternative polyadenylation (APA) codes from RNA-seq data sets.
  127. Mikado
    • Description : Mikado is a pipeline tool for the selection of useful transcript sequences from transcriptome assemblies. The Mikado algorithm uses several different methods to identify expressed loci, for the assignment of representative transcripts, and the selection gene model sets. The algorithm uses up to 50 different metrics, and can also use BLAST data for the scoring of transcript similarity and identify chimeras.
  128. MAGIC (
    • Description : MAGIC (Markov Affinity-based Graph Imputation of Cells) is a tool for denoising high-dimensional single-cell RNA-seq data. The MAGIC algorithm works by information sharing over alike cells to denoise cell counts and to detect dropouts.
  129. CellFishing
    • Description : CellFishing (cell finder via hashing) is a tool for discovering cells that are alike in large-scale RNA-seq data.
  130. recount (Bioconductor)
    • Description : A tool to obtain and search data from the Recount2 Project at The data consists of the gene, exon or exon-exon junctions, raw counts, the phenotype metadata, the URLs to the sample, or the mean coverage bigWig files.
  131. cellity
    • Description : cellity is a tool to detect and filter cells in single-cell RNA-seq data. The cellity algorithm works based on over 20 curated sets of biological and experimental characteristics. A Python version is also available, see links.
  132. Rail-RNA
    • Description : Rail-RNA is a tool to align spliced sequences from RNA-seq data. Rail-RNA is cloud-enabled and can analyze multiple samples at a time.
  133. BRAPeS
    • Description : BRAPeS (BCR Reconstruction Algorithm for Paired-End Single-cell) is a tool to reconstruct B cell receptors (BCR) paired-end single-cell RNA-seq data. The BRAPeS algorithm can use short sequences, down to 25bp of length.
  134. Rail-dbGaP
    • Description : Rail-dbGaP is a tool to analyze protected data on a commercial cloud, compliant with The National Institutes of Health (NIH) guidelines. The Rail-dbGaP protocol works with all tools that run on Amazon Web Services Elastic MapReduce. See also Rail-RNA in the links.
  135. Snaptron
    • Description : Snaptron is a tool to search compiled RNA-seq data. The Snaptron algorithm uses R-tree, B-tree, and inverted indexing. It can also score splice junctions to estimate tissue specificity, the relative frequency of splicing patterns, and according to various other criteria.
  136. bigSCale
    • Description : A framework tool to analyze and visualize single-cell RNA-seq data. The bigSCale functions support clustering, differential expression analysis, phenotype, pseudo-time analysis, inference of gene regulatory networks (GRNs), dataset minimizing. The algorithm estimates models using large sample sizes using a directed convolution strategy to estimate models and cope with noise. Alternative name: bigSCale2
  137. ASCOT
    • Description : ASCOT is a web-based tool for querying, summarizing, and visualization of alternative splicing models in public human and mouse RNA-Seq data. ASCOT uses Snaptron and Recount2.
  138. CellBench
    • Description : CellBench is an R tool to benchmark single-cell analysis approaches. CellBench has the means to organize and test mixtures of analysis methods. The CellBench also implements methods to build lists of functions with many parameters, sample, and filter single-cell test targets.
  139. SCRL
    • Description : SCRL (Single Cell Representation Learning) is a tool for collecting representations of single-cell RNA-seq information, such as pathways, from multiple sources.
  140. Bisque (ucla)
    • Description : Bisque is an R tool to estimate cell composition from single-cell RNA-seq data. The Bisque algorithm uses a regression approach to compute reference expression profiles and, consequently, to learn gene-specific bulk expression transformations for decomposition.
  141. CellRanger
    • Description : CellRanger is a tool for sequence read alignment, clustering, gene expression analysis, and generation of gene-cell matrices from Chromium single cell 3 RNA-seq data.
  142. ZIFA
    • Description : ZIFA (Zero Inflated Factor Analysis) is a tool to reduce dimensionality in single-cell RNA-seq data. The ZIFA algorithm also has models for characteristics of dropouts.
  143. SLICER
    • Description : SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) is a tool to compute cell trajectories during expression changes in RNA-seq data. The SLICER algorithm detects non-linear expression changes, detect branching, loop components, and their number and locations.
  144. CALISTA
    • Description : CALISTA (Clustering and Lineage Inference in Single-Cell Transcriptional Analysis) is a tool for analysis of single-cell RNA-seq data. The CALISTA algorithm includes functions for analysis of differential expression, single-cell clustering, cell-lineage reconstruction, and pseudo-time ordering.
  145. scTDA
    • Description : scTDA (single-cell topological data analysis) is a Python library for the analysis of single-cell RNA-seq data. The scTDA algorithm uses an unsupervised statistical structure to characterize cellular states. It is non-linear and model-independent.
  146. Cidane
    • Description : A tool to assemble ab initio and quantify transcripts in RNA-seq data. The Cidane algorithm also annotates known splice sites, transcription starts and ends.
  147. CellView
    • Description : CellView is a tool for the analysis of differential and co-expression patterns, and for viewing 3D representations of single-cell RNA-seq data. The CellView has functions for knowledge-based and hypothesis-driven investigations of data.
  148. SigEMD
    • Description : SigEMD is an R tool to identify differential expression in single-cell RNA-seq data. The SigEMD algorithm uses imputation, logistic regression, gene interaction network information, and a nonparametric method based on the Earth Mover's Distance for the identification.
  149. Conos
    • Description : Conos is a tool to analyze heterogeneous single-cell RNA-seq datasets. The Conos algorithm estimates multiple inter-sample mappings to compute a graph over cells that identify cell clusters and information proliferation.
  150. Boiler
    • Description : A tool to compress RNA-seq alignments. The Boiler algorithm extracts summary, coverage, and distribution data from the sequence alignments. By discarding individual sequence data, the resulting data set is small.
  151. scVEGs
    • Description : scVEGs is a tool for analysis of differentially expressed genes in single-cell RNA-seq data. The scVEGs algorithm uses a gene expression variation model (GEVM) and makes use of the relation between the coefficient of variation (CV) and mean expression level to adjust for over-dispersion and compute statistical significance.
  152. CellBender
    • Description : CellBender is a tool to eliminate experimental artifacts in droplet-based single-cell RNA-seq (scRNA-seq) assays.
  153. CellBIC
    • Description : CellBIC is a tool for clustering single-cell RNA-seq data. The CellBIC algorithm does top-down hierarchical clustering.
  154. Wishbone
    • Description : Wishbone is a tool to position cells with accompanying bifurcating developmental trajectories in single-cell RNA-seq or cytometry data.
  155. trendsceek
    • Description : trendsceek is an R tool for the identification of statistically significant spatial expression trends in single-cell RNA-seq data. The trendsceek algorithm uses modified marked point processes and also discovers genes in sequential fluorescence in situ hybridization data. It can unveil significant gene expression gradients and hot spots.
  156. cellAlign
    • Description : cellAlign is an R tool to compare and quantify cell expression and trajectories among and between cells in single-cell RNA-seq data sets.
  157. txburst
    • Description : txburst is a tool to infer transcriptional burst kinetics and analysis in single-cell RNA-seq data.
  158. RCA (GIS)
    • Description : RCA (clustering single-cell RNA-seq data using reference component analysis) is an R tool for clustering single-cell RNA-seq data.
  159. BranchedGP
    • Description : BranchedGP is a tool to detect gene-specific branching dynamics and estimate branching times. The algorithm can also predict likely branching regions. Other names: BGP (Branching Gaussian process).
  160. p-Creode
    • Description : p-Creode is a tool for mapping multi-branched cellular differentiation pathways using single-cell RNA-seq data. The p-Creode algorithm uses an unsupervised method to create, compare multi-branching graphs, and infer developmental trajectories. Alternative name: Creode.
  161. NanoPARE
    • Description : NanoPARE is a set of tools for the analysis of 5' RNA data from nanoPARE sequencing libraries. The NanoPARE package contains (1) EndMap for Aligning 5P and BODY FASTQ files to a reference genome, (2) EndGraph for Identifying 5P features, (3) EndClass for Classifying 5P features as capped or noncapped and label features according to a reference transcriptome, (3) EndMask for Masking genomic regions with capped features and converting genome coordinates to transcriptome coordinates, (4) EndCut for searching evidence of small RNA mediated cleavage in transcript-mapping noncapped 5P reads. Requirements: STAR aligner 2.5+, Python 3.6+, Samtools 1.3+, Bedtools 2.26, and Cutadapt 1.9.
  162. anndata
    • Description : AnnData is a Python class to manage annotated data matrices and developed for use with SCANPY (see links).
  163. BRIE
    • Description : BRIE (Bayesian regression for isoform estimation) is a tool to quantify splicing from RNA-seq data. The BRIE algorithm learns prior distribution isoform proportions from the sequences in samples using a Bayesian hierarchical model.
  164. ouijaflow
    • Description : A tool for estimating pseudo-times in RNA-seq data. The Ouija algorithm uses an orthogonal Bayesian method to learn pseudo-times using marker genes.
  165. CellO
    • Description : CellO (Cell Ontology-based classification) is a tool to classify cell types in single-cell RNA-seq data. The Authors trained the CellO algorithm using curated datasets from Sequence Read Archive at NCBI.
  166. MUSIC (Tongji University)
    • Description : MUSIC is an R tool to analyze single-cell data from a combination of Perturb-Seq, CRISP-seq, CROP-seq, and single-cell RNA-seq. The MUSIC algorithm analysis step consists of preprocessing, model construction, and perturbation impact prioritizing.
  167. BackSPIN
    • Description : BackSPIN is a tool for clustering RNA-seq data. The BackSPIN algorithm, based on SPIN, is a divisive biclustering method.
  168. powsimR
    • Description : powsimR is a tool to simulate differential expression RNA-seq data. The powsimR package can simulate read counts, model the mean, dispersion, dropout distributions, and compute the power of sample sizes.
  169. Cell BLAST
    • Description : Cell BLAST is a tool for searching single-cell RNA-seq databases and annotation of RNA-seq data. Apart from the standalone version, there is also a web server (see links).
  170. Expedition
    • Description : Expedition is a suite of tools for visualization and analysis of single-cell alternative splicing RNA-seq data, consisting of anchor, bonvoyage, and outrigger packages.
  171. Outrigger
    • Description : Outrigger is a tool for the creation of de novo alternative splicing annotation for RNA-seq data. The Outrigger uses junction reads, a graph database, and quantifies spliced-in (Psi) events.
  172. bonvoyage
    • Description : bonvoyage is a tool for the detection of alternative splicing in RNA-seq data. The bonvoyage algorithm uses the outrigger de novo splice graph method and a Bayesian approach for modality assignment. It can also show changes in modalities using non-negative matrix factorization.
  173. anchor
    • Description : Anchor is a tool for finding unimodal, bimodal, and multimodal features in any data normalized between 0 and 1, such as alternative splicing.
  174. singlecell_pnm
    • Description : singlecell_pnm is the Jupyter notebook code for single-cell alternative splicing. This is the code for the paper by Song and Botvinnik, et al., Molecular Cell (2017), investigating alternative splicing in single cells in neurodegenerative disease.
  175. CellRouter
    • Description : CellRouter is a tool for the identification of cell-state transition trajectories in single-cell RNA-seq data. THe CellRouter algorithm uses flow networks to identify subpopulations.
  176. Palantir
    • Description : A tool for computation of cellular differentiation trajectories in single-cell RNA-seq and Mass cytometry data. The Palantir algorithm models differentiation as a stochastic process to estimate cell fates.
  177. BraCeR
    • Description : An R tool to reconstruct full-length B-cell-receptor (BCR) sequences from paired-end or single-end single-cell RNA-seq data. BraCeR is an extension of the TraCeR pipeline and can also determine clonality, estimate somatic hypermutations (SHMs), isotype switching, and create resource files for analyses of additional species. The default version is human and mice.
  178. Alevin
    • Description : Alevin is a tool for estimating gene abundances in droplet-based single-cell dscRNA-seq data. The Alevin tool is included in the Salmon package (see links).
  179. Cardelino
    • Description : Cardelino is an R tool to identify clones in single-cell RNA-seq data. The Cardelino algorithm has methods for inferring clonal structures in a cell population and analyses of differential gene expression.
  180. SCmut
    • Description : SCmut is a tool for the detection of cells that contain mutations in RNA-seq and bulk-cell DNA-sequencing data. The SCmut algorithm can use a list of somatic mutations in the absence of DNA-sequencing data.
  181. batchelor
    • Description : batchelor is an R tool for batch correction in single-cell RNA-seq data. The batchelor algorithm uses mutual nearest neighbors (MNNs) method in the high-dimensional expression space for the detection of batch effects. The algorithm does not depend on predefined population compositions. Requires: SingleCellExperiment.
  182. Wx
    • Description : Wx is a tool to select the optimal set of genes for gene expression. The Wx uses Keras (Python Deep Learning library) neural network to learn features and to generate a discriminative index.
  183. CNNC
    • Description : CNNC is a tool for co-expression analysis of single-cell RNA-seq data. The CNNC algorithm implements a Convolutional neural network for the purpose.
  184. scvis
    • Description : scvis is a tool to reduce dimensionality in single-cell RNA-seq data. The scvis algorithm learns a probabilistic parametric mapping function for adding new data.
  185. BISCUIT
    • Description : BISCUIT is an R tool to normalize and cluster single-cell RNA-seq data. The BISCUIT algorithm uses a hierarchical Bayesian mixture model that scales related to each cell, facilitating iterative normalization and clustering of cells, and reduces experimental variation. The algorithm also applies a scalable Gibbs inference algorithm to enhance the deduction of cluster building.

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