ArticleBMC genomics2019
Impact of sequencing depth and technology on de novo RNA-Seq assembly.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Comprehensive assessment of transcriptome assembly quality using CATS.Nature communications · 2026Article
- Energy metabolism alteration and gene expression reprogramming in a cell model of high fat load non-alcoholic fatty liver disease.BMC gastroenterology · 2026Article
- Chromatin-mediated mechanisms of hybrid fertility in FBMC biology · 2026Article
- Iron Deficiency Impairs Dendritic Cell Development and Function, Compromising Host Anti-Infection Capacity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Variation and Interaction of Distinct Subgenomes Contribute to Growth Diversity in Intergeneric Hybrid Fish.Genomics, proteomics & bioinformatics · 2025Article
- Article
- Lung megakaryocytes engulf inhaled airborne particles to promote intrapulmonary inflammation and extrapulmonary distribution.Nature communications · 2024Article
- RNA-Seq Analysis Reveals Altered Expression of Cell Adhesion-Related Genes Following PZR Knockout in Lung Cancer Cells.Applied biochemistry and biotechnology · 2024Article
- Genome-Wide Transcriptome Profiling Reveals the Mechanisms Underlying Hepatic Metabolism under Different Raising Systems in Yak.Animals : an open access journal from MDPI · 2024Article
- Comparison of the DNBSEQ platform and Illumina HiSeq 2000 for bacterial genome assembly.Scientific reports · 2024Article
- Cross-platform comparisons for targeted bisulfite sequencing of MGISEQ-2000 and NovaSeq6000.Clinical epigenetics · 2023Article
- Identification of the Efficacy of Ex Situ Conservation ofPlants (Basel, Switzerland) · 2023Article
- Article
- In-depth Temporal Transcriptome Profiling of Monkeypox and Host Cells using Nanopore Sequencing.Scientific data · 2023Article
- Comprehensive analysis identifies novel targets of gemcitabine to improve chemotherapy treatment strategies for colorectal cancer.Frontiers in endocrinology · 2023Article
- Comparative transcriptomic analysis of Illumina and MGI next-generation sequencing platforms using RUNX3- and ZBTB46-instructed embryonic stem cells.Frontiers in genetics · 2023Article
- Transcriptome Comparison Reveals the Difference in Liver Fat Metabolism between Different Sheep Breeds.Animals : an open access journal from MDPI · 2022Article
- Assembly-free rapid differential gene expression analysis in non-model organisms using DNA-protein alignment.BMC genomics · 2022Article
- Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics.Frontiers in plant science · 2022Review
- Transcriptome analysis identifies putative genes involved in triterpenoid biosynthesis in Platycodon grandiflorus.Planta · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundRNA-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.
resultsOn 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.
conclusionsIncreasing 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.