Comparability of reference-based and reference-free transcriptome analysis approaches at the gene expression level

Background Lately, high-throughput RNA sequencing has been extensively used to elucidate the transcriptome landscape and dynamics of cell types of different species. In particular, for most non-model organisms lacking complete reference genomes with high-quality annotation of genetic information, reference-free (RF) de novo transcriptome analyses, rather than reference-based (RB) approaches, are widely used, and RF analyses have substantially contributed toward understanding the mechanisms regulating key biological processes and functions. To date, numerous bioinformatics studies have been conducted for assessing the workflow, production rate, and completeness of transcriptome assemblies within and between RF and RB datasets. However, the degree of consistency and variability of results obtained by analyzing gene expression levels through these two different approaches have not been adequately documented. Results In the present study, we evaluated the differences in expression profiles obtained with RF and RB approaches and revealed that the former tends to be satisfactorily replaced by the latter with respect to transcriptome repertoires, as well as from a gene expression quantification perspective. In addition, we urge cautious interpretation of these findings. Several genes that are lowly expressed, have long coding sequences, or belong to large gene families must be validated carefully, whenever gene expression levels are calculated using the RF method. Conclusions Our empirical results indicate important contributions toward addressing transcriptome-related biological questions in non-model organisms.

Internal oligo(dT) priming in bulk and single cell RNA sequencing

Significant advances in RNA sequencing have been recently made possible by the use of oligo(dT) primers for simultaneous mRNA enrichment and reverse transcription priming. The associated increase in efficiency has enabled more economical bulk RNA sequencing methods as well as the advent of high throughput single cell RNA sequencing, now already one of the most widely adopted new methods in the study of transcriptomics. However, the effects of off-target oligo(dT) priming on gene expression quantification have not been fully appreciated. In the present study, we describe the extent, the possible causes, and the consequences of internal oligo(dT) priming across multiple publicly available datasets obtained from a variety of bulk and single cell RNA sequencing platforms. In order to explore and address this issue, we developed a computational algorithm for identification of sequencing read alignments that likely resulted from internal oligo(dT) priming and their subsequent removal from the data. Directly comparing filtered datasets to those obtained by an alternative method reveals significant improvements in gene expression measurement. Finally, we infer a list of genes whose expression quantification is most likely to be affected by internal oligo(dT) priming.

Stability analysis of reference genes for RT-qPCR assays involving compatible and incompatible Ralstonia solanacearum-tomato ‘Hawaii 7996’ interactions

Reverse transcription-quantitative PCR (RT-qPCR) is an analytical tool for gene expression quantification. Reference genes are not yet available for gene expression analysis during interactions of Ralstonia solanacearum with ‘Hawaii 7996’ (the most stable source of resistance in tomato). Here, we carried out a multi-algorithm stability analysis of eight candidate reference genes during interactions of ‘Hawaii 7996’ with one incompatible/avirulent and two compatible/virulent (= resistance-breaking) bacterial isolates. Samples were taken at 24- and 96-h post-inoculation (HPI). Analyses were performed using the ∆∆Ct method and expression stability was estimated using BestKeeper, NormFinder, and geNorm algorithms. TIP41 and EF1α (with geNorm), TIP41 and ACT (with NormFinder), and UBI3 and TIP41 (with BestKeeper), were the best combinations for mRNA normalization in incompatible interactions at 24 HPI and 96 HPI. The most stable genes in global compatible and incompatible interactions at 24 HPI and 96 HPI were PDS and TIP41 (with geNorm), TIP41 and ACT (with NormFinder), and UBI3 and PDS/EXP (with BestKeeper). Global analyses on the basis of the three algorithms across 20 R. solanacearum-tomato experimental conditions identified UBI3, TIP41 and ACT as the best choices as reference tomato genes in this important pathosystem.

Flowcytometric and ImageStream RNA-FISH gene expression, quantification and phenotypic characterization of blood and liver stages from human malaria species

We adapted the RNA FISH stellaris® method to specifically detect the expression of Plasmodium genes by flowcytometry and ImageStream (FlowFISH). This new method accurately quantified the erythrocytic forms of P. falciparum and vivax, and the sexual stages of P. vivax from patient isolates. In addition, ImageStream analysis of liver stage sporozoites using a combination of surface CSP (Circumsporozoite Protein), DNA and 18S RNA labelling proved that the new FlowFISH is suitable for gene expression studies of transmission stages. This powerful multiparametric single-cell method offers a platform of choice for both applied and fundamental research on the biology of malaria parasites.

Evolution and Functional Characteristics of the Novel Elovl8 That Play Pivotal Roles in Fatty Acid Biosynthesis

Elongation of very long-chain fatty acid (Elovl) proteins are key enzymes that catalyze the rate-limiting step in the fatty acid elongation pathway. The most recently discovered member of the Elovl family, Elovl8, has been proposed to be a fish-specific elongase with two gene paralogs described in teleosts. However, the biological functions of Elovl8 are still to be elucidated. In this study, we showed that in contrast to previous findings, elovl8 is not unique to teleosts, but displays a rather unique and ample phylogenetic distribution. For functional determination, we generated elovl8a (elovl8a−/−) and elovl8b (elovl8b−/−) zebrafish using CRISPR/Cas9 technology. Fatty acid composition in vivo and zebrafish liver cell experiments suggest that the substrate preference of Elovl8 overlapped with other existing Elovl enzymes. Zebrafish Elovl8a could elongate the polyunsaturated fatty acids (PUFAs) C18:2n-6 and C18:3n-3 to C20:2n-6 and C20:3n-3, respectively. Along with PUFA, zebrafish Elovl8b also showed the capacity to elongate C18:0 and C20:1. Gene expression quantification suggests that Elovl8a and Elovl8b may play a potentially important role in fatty acid biosynthesis. Overall, our results provide novel insights into the function of Elovl8a and Elovl8b, representing additional fatty acid elongases not previously described in chordates.

Nanopore long-read RNA-seq and absolute quantification delineate transcription dynamics in early embryo development of an insect pest

The Oxford Nanopore Technologies’ long-read RNA sequencing (RNAseq) platform can yield full length transcripts that can be very instrumental in improving the characterization of non-model organisms. The resolution of RNAseq can be increased by addition of external RNA molecules of known concentration such as ERCC in order to obtain absolute gene expression quantification. This protocol details the procedure to use ONT long-read RNAseq with addition of ERCC external RNAs. This protocol can be used with total RNA extracted from any source for which transcripts with a poly(A) tail at the 3’ end are the target. The protocol should be possible to complete in a single day (12 hours) followed by sequencing which takes another 48 hours.

Impact of Gene Annotation Choice on the Quantification of RNA-Seq Data

Background: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.Results: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.Conclusion: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

Background The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis. Findings In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1–77% of all reads (median [IQR], 3% [3–6%]); duplicate reads constitute 3–100% of mapped reads (median [IQR], 27% [13–43%]); and non-exonic reads constitute 4–97% of mapped, non-duplicate reads (median [IQR], 25% [16–37%]). MEND reads constitute 0–79% of total reads (median [IQR], 50% [30–61%]). Conclusions Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.

Impact of gene annotation choice on the quantification of RNA-seq data

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from $>$800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

L-carnitine suppresses cisplatin-induced renal injury in rats: impact on cytoskeleton proteins expression

We designed this work to examine the curative role of L-carnitine (LCAR) in a rat model of cisplatin (CDDP)-induced kidney injury. We induced kidney injury in rats by a single intraperitoneal injection of 5 mg/kg of CDDP. Fifteen days post injection, rats were orally supplemented with 354 mg/kg of LCAR for another 15 days. Kidney tissues were subjected to histo-biochemical analysis along with mRNA gene expression quantification for cytoskeleton proteins encoding genes (vimentin, nestin, and connexin 43) by real-time reverse transcription polymerase chain reaction. LCAR reversed CDDP-induced renal structural and functional impairments. LCAR significantly declined serum urea and creatinine concentrations, restored oxidant/antioxidant balance, reversed inflammation, and antagonized caspase 3-mediated apoptotic cell death in renal tissues. Moreover, LCAR effectively down-regulated cytoskeleton proteins mRNA levels, reflecting amelioration of CDDP-provoked podocyte injury. We concluded that LCAR has a favorable therapeutic utility against CDDP-induced kidney injury.