Impact of sequencing depth and technology on de novo RNA-Seq assembly

Abstract

Background RNA-Seq data is inherently nonuniform for different transcripts because of differences in gene expression. This makes it challenging to decide how much data should be generated from each sample. How much should one spend to recover the less expressed transcripts? The sequencing technology used is another consideration, as there are inevitably always biases against certain sequences. To investigate these effects, we first looked at high-depth libraries from a set of well-annotated organisms to ascertain the impact of sequencing depth on de novo assembly. We then looked at libraries sequenced from the Universal Human Reference RNA (UHRR) to compare the performance of Illumina HiSeq and MGI DNBseq™ technologies. Results On the issue of sequencing depth, the amount of exomic sequence assembled plateaued using data sets of approximately 2 to 8 Gbp. However, the amount of genomic sequence assembled did not plateau for many of the analyzed organisms. Most of the unannotated genomic sequences are single-exon transcripts whose biological significance will be questionable for some users. On the issue of sequencing technology, both of the analyzed platforms recovered a similar number of full-length transcripts. The missing “gap” regions in the HiSeq assemblies were often attributed to higher GC contents, but this may be an artefact of library preparation and not of sequencing technology. Conclusions Increasing sequencing depth beyond modest data sets of less than 10 Gbp recovers a plethora of single-exon transcripts undocumented in genome annotations. DNBseq™ is a viable alternative to HiSeq for de novo RNA-Seq assembly.

An improved de novo assembling and polishing of Solea senegalensis transcriptome shed light on retinoic acid signalling in larvae

“…De novo assembling for new "raw" transcriptomes. It is well established that more input reads do not produce better assemblies 31,32 . According to this observation, 30 Illumina and 1 GS-FLX libraries were adopted as the most informative ones from the original dataset of 111 libraries 7 following criteria detailed in "Methods" section.…”

“…These new pipelines have to deal with errors due to the high number of reads, weakly expressed or 'uncommon' transcripts, circular RNAs, gene fusions, and isoform discrimination. It was well established that assembly quality improves with longer paired-end reads 28 but not increasing the input sequencing dataset 31 due to, for example, the increase in low abundance transcripts and a plethora of unannotated transcripts, most of them containing intronic regions 32 . Hence, small sequencing datasets are desirable to decrease computational requirements and produce robust transcriptomes, since manual curation is excluded, even though it has been proved to render the best results 37 .…”

Progress Towards Plant Community Transcriptomics: Pilot RNA-Seq Data from 24 Species of Vascular Plants at Harvard Forest

“…This is a similar distribution of BUSCO scores to those in the recently published 1KP project One Thousand Plant Transcriptomes Initiative, 2019) and other studies (Blande et al, 2017;Evkaikina et al, 2017;Pokorn et al, 2017;Weisberg et al, 2017) . Like many transcriptome assemblies (Johnson et al, 2012;Carpenter et al, 2019;Patterson et al, 2019) , these assemblies also contain a large number of small scaffolds (<300 bp). Small scaffolds are likely artifacts of library amplification and sequencing, considering that most did not translate to a known plant protein sequence.…”

Comparison of the MGISEQ-2000 and Illumina HiSeq 4000 sequencing platforms for RNA sequencing

“…For example, the MGISEQ-2000 currently generates 1.44 TB of sequence data in a single run with a running cost of 10 USD/GB. Several recent studies have compared the performance of BGI sequencers with Illumina’s sequencers and showed that the BGI sequencers produced high-quality sequence data at lower or similar prices in studies of whole-exome [2,3], whole-genome [4][1], transcriptome [5,6], single-cell transcriptome [2,78], metagenome [9], and small RNA sequencing [10].…”