Targeted reduction of highly abundant transcripts with pseudo-random primers

Abstract

Transcriptome studies based on quantitative sequencing estimate gene expression levels by measuring the abundance of target RNAs in libraries of sequence reads. The sequencing cost is proportional to the total number of sequenced reads. Therefore, in order to cover rare RNAs, considerable quantities of abundant and identical reads have to be sequenced. This major limitation can be lifted by strategies used to deplete the library from some of the most abundant sequences. However, these strategies involve either an extra handling of the input RNA sample, or the use of a large number of reverse-transcription primers (termed \"not-so-random primers\"), which are costly to synthetize and customize. Here, we demonstrate that with a precise selection of only 40 \"pseudo-random\" reverse-transcription primers, it is possible to decrease the rate of undesirable abundant sequences within a library without affecting the transcriptome diversity. \"Pseudo-random\" primers are simple to design, and therefore are a flexible tool for enriching transcriptome libraries in rare transcripts sequences.