Similar protocols

Protocol publication

[…] Pre-filtering the reads for quality is critical to obtaining a high quality assembly and produce accurate RNA-Seq expression data. Reads were filtered that contained a Phred score below 20 across more than 20% of the bases using the fastx-toolkit fastq_quality_filter script (http://hannonlab.cshl.edu/fastx_toolkit/commandline.html). These quality-filtered reads were then normalized to reduce redundant read data and discard read errors using Trinity's normalize_by_kmer_coverage.pl script with a kmer size of 25 and maximum read coverage of 30. The resulting normalized reads were used to create a de novo transcriptome assembly using the Trinity de novo transcriptome assembly pipeline (r2012-10-05) [, ]. The Trinity pipeline (Inchworm, Chrysalis, and Butterfly) was executed using default parameters, implementing the --REDUCE flag in Butterfly and utilizing the Jellyfish k-mer counting approach []. Assembly completed in 3 hours and 13 minutes on a compute node consisting of 32 Xeon 3.1 GHz cpus and 256 Gb of RAM available to the software on the USDA-ARS Pacific Basin Agricultural Research Center Moana computer cluster (http://moana.dnsalias.org). […]

Pipeline specifications

Software tools FASTX-Toolkit, Trinity, Jellyfish
Application RNA-seq analysis
Organisms Drosophila melanogaster, Bactrocera dorsalis