as BUSCO, can be used. For eukaryotes, CEGMA may also be used, although it is officially no longer supported since 2015. Assemblers The following is a partial compendium of assembly software that has been used to generate transcriptomes, and has also been cited in scientific literature. SeqMan NGen SeqMan NGen, part of DNASTAR's software pipeline, includes a de novo transcriptome assembler for small or large transcriptome data sets. SeqMan NGen uses a patented algorithm which utilizes RefSeq to identify and merge transcripts, and automatically annotates assembled transcripts using DNASTAR's proprietary transcript annotation tool to identify and highlight known and novel genes. SOAPdenovo-Trans SOAPdenovo-Trans is a de novo transcriptome assembler inherited from the SOAPdenovo2 framework, designed for assembling transcriptome with alternative splicing and different expression level. The assembler provides a more comprehensive way to construct the full-length transcript sets compare to SOAPdenovo2. Velvet/Oases The Velvet algorithm uses de Bruijn graphs to assemble transcripts. In simulations, Velvet can produce contigs up to 50-kb N50 length using prokaryotic data and 3-kb N50 in mammalian bacterial artificial chromosomes (BACs). These preliminary transcripts are transferred to Oases, which uses paired end read and long read information to build transcript isoforms. Trans-ABySS ABySS is a parallel, paired-end sequence assembler. Trans-ABySS (Assembly By Short Sequences) is a software pipeline written in Python and Perl for analyzing ABySS-assembled transcriptome contigs. This pipeline can be applied to assemblies generated across a wide range of k values. It first reduces the dataset into smaller sets of non-redundant contigs, and identifies splicing events including exon-skipping, novel exons, retained introns, novel introns, and alternative splicing. The Trans-ABySS algorithms are also able to estimate gene expression levels, identify potential polyadenylation sites, as well as candidate gene-fusion events. Trinity Trinity first divides the sequence data into a number of de Bruijn graphs, each representing transcriptional variations at a single gene or locus. It then extracts full-length splicing isoforms and distinguishes transcripts derived from paralogous genes from each graph separately. Trinity consists of three independent software modules, which are used sequentially to produce transcripts: Inchworm assembles the RNA-Seq data into transcript sequences, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts. Chrysalis clusters the Inchworm contigs and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptional complexity for a given gene (or a family or set of genes that share