compcodeR is an R package for comparing the results of multiple differential expression analysis methods applied to a common RNAseq data set. It also contains functionalities for simulating realistic count matrices and interfaces to several of the most widely used differential expression analysis methods for RNAseq data.
Charlotte Soneson <Charlotte.Soneson at isb-sib.ch>
The purpose of CADBURE (Comparing Alignment results of user Data Based on the relative reliability and advantage of Uniquely aligned REads) is to evaluate spliced aligner performance on user’s RNA-Seq data by comparing a pair of alignment results obtained either from two different aligners with the similar parameter set or from two different parameter sets with the same aligner.
Implemented in C++, FindTail first detects all perfect poly(A) tracts (or homopolymers)in a sequence. Starting with the first tract, FindTail then determines whether the downstream tracts can be merged to form a longer poly(A) fragment using an adjustable gap. After the merging step, it calculates the identity for all resultant poly(A) fragments and remaining tracts, and filter them using an adjustable minimum identity. Finally, the poly(A) length is calculated and filtered by an adjustable minimum length.
The eSNV-Detect is a method to detect expressed single nucleotide variants (eSNVs) with high specificity and sensitivity from the high throughput transcriptome sequencing data. Alignments from multiple aligers are used to cover the aligner bias and multiple genomic features are used to improve the specificity. For the expressed SNVs detected, it can also identify the amino acid change and classify the protein domains.
OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis.
CRAC is designed to find splice junctions, fusion junctions, SNVs, indels in reads. It focuses on the unique location of a read. It performs particularly well on long reads. It is designed for resequencing projects and is therefore able to map reads coming from the same species or a close one.
CellR is a single cell-based data-driven method to recover and quantify the cellular composition of bulk transcriptional data. It is a fully unsupervised approach based on clustering the reference single-cell RNA-Seq (scRNA-seq) followed by extracting the unique marker genes defining each cell cluster.