Benchmarked Software

Genome guided transcriptome assembly approaches

Name Pubmed Version
AUGUSTUS Using native and syntenically mapped cDNA alignments to improve de novo gene finding. (Stanke 2008) augustus-3.0.3
CLASS CLASS: constrained transcript assembly of RNA-seq reads. (Song 2013) CLASS_2.00
Cufflinks Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. (Trapnell 2010) cufflinks-2.2.1.Linux_x86_64
FlipFlop Efficient RNA isoform identification and quantification from RNA-Seq data with network flows. (Bernard 2014) flipflop_1.4.1
iReckon iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data. (Mezlini 2013) IReckon-1.0.8.jar
IsoLasso IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly. (Li 2011) isolasso-2.6.1
MITIE MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples. (Behr 2013) 10/27/2014
StringTie StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. (Pertea 2015) v1.0.0

De novo transcriptome assembly approaches

Name Pubmed Version
SOAPdenovo-Trans SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. (Xie 2014) v1.03
Trans-ABySS De novo assembly and analysis of RNA-seq data. (Robertson 2010) 1.5.2
Trinity Full-length transcriptome assembly from RNA-Seq data without a reference genome. (Grabherr 2011) trinityrnaseq_r20140717

Other tools used in this project

Name Pubmed Version
Bowtie 2 Fast gapped-read alignment with Bowtie 2. (Langmead 2012) bowtie2-2.2.3
GMAP GMAP: a genomic mapping and alignment program for mRNA and EST sequences. (Wu 2005) gmap-gsnap-2014-10-22
SAMtools The Sequence Alignment/Map format and SAMtools. (Li 2009) samtools-0.1.19
STAR STAR: ultrafast universal RNA-seq aligner. (Dobin 2013) STAR_2.4.0d
TopHat2 TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. (Kim 2013) TopHat 2.0.13

Our Code to evaluate the presented algorithms

The benchmarking scripts can be found on github.