RNA-seq aligner accuracy tested with simulated reads
In the previous post we looked at how choice of RNA-seq aligner influenced results. In this post, we'll use simulated reads to empirically determine the accuracy of OLego, STAR, SubJunc and SubRead.
I made a pretty basic bash script (see below) to generate fasta format reads at uniform intervals along the length of transcripts without errors. The user can readily change the read length and the interval between read initiation. The script created 11.3 million 100 bp reads in 9 minutes so it is OK in terms of speed. It is designed to take Ensembl cDNA files as an input and has been tested on human and Arabidopsis. Here is what the generated reads look like
>AT2G01210.1:gene:AT2G01210_197863
CGTCAGCTTTCGTTCTGGGGAAGAGCGGAATCGGAATTGTCTACAAAGTG
>AT2G01210.1:gene:AT2G01210_197864
GTTCTAGAGAACGGGCTCACACTGGCCGTACGGAGATTGGGTGAAGGAGG
>AT2G01210.1:gene:AT2G01210_197865
GTCTCAGAGATTCAAGGAGTTTCAGACAGAAGTTGAAGCCATAGGGAAAC
>AT2G01210.1:gene:AT2G01210_197866
TAAAACATCCTAACATTGCTAGTCTTC…
I made a pretty basic bash script (see below) to generate fasta format reads at uniform intervals along the length of transcripts without errors. The user can readily change the read length and the interval between read initiation. The script created 11.3 million 100 bp reads in 9 minutes so it is OK in terms of speed. It is designed to take Ensembl cDNA files as an input and has been tested on human and Arabidopsis. Here is what the generated reads look like
>AT2G01210.1:gene:AT2G01210_197863
CGTCAGCTTTCGTTCTGGGGAAGAGCGGAATCGGAATTGTCTACAAAGTG
>AT2G01210.1:gene:AT2G01210_197864
GTTCTAGAGAACGGGCTCACACTGGCCGTACGGAGATTGGGTGAAGGAGG
>AT2G01210.1:gene:AT2G01210_197865
GTCTCAGAGATTCAAGGAGTTTCAGACAGAAGTTGAAGCCATAGGGAAAC
>AT2G01210.1:gene:AT2G01210_197866
TAAAACATCCTAACATTGCTAGTCTTC…