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RNA sequencing libraries are typically processed before sequencing. We compared the performance of automated mRNA enrichment (autoE) and manual mRNA enrichment (manE) with globin depletion, and automated rRNA depletion (autoD) with globin depletion prior to sequencing. PAXgene tubes from 21 people living with HIV (PLWH) and 7 people without HIV were used, with RT-qPCR data from unprocessed RNA as an orthogonal comparator. Duplicate reads were higher for manE (58–61%) and autoE (59–70%) than autoD (29–33%). AutoE had the highest mapping to exonic regions (72–78% vs. 50–74% manE and 30–39% autoD). Globin transcripts were more abundant for autoD than autoE and manE, but remained low (median 10% vs. 0.1 and 1.3%). 94% of protein-coding genes were detected by all methods vs. 63–66% for other RNA-biotypes. All methods correlated strongly with qPCR data for highly expressed genes but not lowly expressed genes; concordance was lowest for autoE. When comparing PLWH with controls, manE and autoD identified more differentially expressed genes than autoE. While all methods exceeded the minimum threshold required for downstream analysis, autoD provided the most stable sequencing quality. In conclusion, manE and autoD yielded similar results with autoD providing advantages of robotic liquid handling for large-scale studies.

Background: Coverage uniformity is pivotal in whole genome sequencing (WGS), as uneven read distributions can obscure clinically relevant variants and compromise downstream analyses. While enzyme-based fragmentation methods for WGS library preparation are widely used, they can introduce sequence-specific biases that disproportionately affect high-GC or low-GC regions. Here, we compare four PCR-free WGS library preparation workflows—one employing mechanical fragmentation and three based on enzymatic fragmentation—to assess their impact on coverage uniformity and variant detection. Results: Libraries were generated with Coriell NA12878 and DNA isolated from DNA blood, saliva, and formalin-fixed paraffin-embedded (FFPE) samples. Sequencing was performed on an Illumina NovaSeq 6000, followed by alignment to the human reference genome (GRCh38/hg38) and local realignment. We assessed coverage at both chromosomal and gene levels, including 504 clinically relevant genes detected in the TruSight™ Oncology 500 (TSO500) panel. Additionally, we examined the relationship between GC content and normalized coverage, as well as variant detection across high- and low-GC regions. Conclusions: Our findings show that mechanical fragmentation yields a more uniform coverage profile across different sample types and across the GC spectrum. Enzymatic workflows, on the other hand, demonstrated more pronounced coverage imbalances, particularly in high-GC regions, potentially affecting the sensitivity of variant detection. This effect was evident in analyses focusing on the TSO500 gene set, where uniform coverage is critical for accurate identification of disease-associated variants and for minimizing false negatives. Downsampling experiments further revealed that mechanical fragmentation maintained lower Single Nucleotide Polymorphism (SNPs) false-negative and false-positive rates at reduced sequencing depths, thereby highlighting the advantages of consistent coverage for resource-efficient WGS. This study introduces a novel framework for evaluating WGS coverage uniformity, providing guidance for optimizing library preparation protocols in clinical and translational research. By quantifying how fragmentation strategies influence coverage depth and variant calling accuracy, laboratories can refine their sequencing workflows to ensure more reliable detection of clinically actionable variants—especially in high-GC regions often implicated in hereditary disease and oncology.

The challenge of sequencing ancient DNA has led to the development of specialized laboratory protocols that have focused on reducing contamination and maximizing the number of molecules that are extracted from ancient remains. Despite the fact that success in ancient DNA studies is typically obtained by screening many samples to identify a promising subset, ancient DNA protocols have not, in general, focused on reducing the time required to screen samples. We present an adaptation of a popular ancient library preparation method that makes screening more efficient. First, the DNA extract is treated using a protocol that causes characteristic ancient DNA damage to be restricted to the terminal nucleotides, while nearly eliminating it in the interior of the DNA molecules, allowing a single library to be used both to test for ancient DNA authenticity and to carry out population genetic analysis. Second, the DNA molecules are ligated to a unique pair of barcodes, which eliminates undetected cross-contamination from this step onwards. Third, the barcoded library molecules include incomplete adapters of short length that can increase the specificity of hybridization-based genomic target enrichment. The adapters are completed just before sequencing, so the same DNA library can be used in multiple experiments, and the sequences distinguished. We demonstrate this protocol on 60 ancient human samples.

Background Circulating microRNAs (miRNAs) are attractive non-invasive biomarkers for a variety of conditions due to their stability and altered pathophysiological expression levels. Reliable detection of global expression profiles is required to maximise miRNA biomarker discovery. Although developments in small RNA-Seq technology have improved detection of plasma-based miRNAs, the low RNA content and sequencing bias introduced during library preparation remain challenging. In this study we compare commercially available RNA extraction methods using MagnaZol (Bioo Scientific) or miRNeasy (QIAGEN) and three library preparation methods - CleanTag (TriLink), NEXTflex (Bioo Scientific) and QIAseq (QIAGEN) - which aim to address one or both of these issues. Results Different RNA extractions and library preparation protocols result in differential detection of miRNAs. A greater proportion of reads mapped to miRNAs in libraries prepared with MagnaZol RNA than with miRNeasy RNA. Libraries prepared using QIAseq demonstrated the greatest miRNA diversity with many more very low abundance miRNAs detected (~ 2–3 fold more with < 10 reads), whilst CleanTag detected the fewest individual miRNAs and considerably over-represented miR-486-5p. Libraries prepared with QIAseq had the strongest correlation with RT-qPCR quantification. Analysis of unique molecular indices (UMIs) incorporated in the QIAseq protocol indicate that little PCR bias is introduced during small RNA library preparation. Conclusions Small RNAs were consistently detected using all RNA extraction and library preparation protocols tested, but with some miRNAs at significantly different levels. Choice of the most suitable protocol should be informed by the relative importance of minimising the total sequencing required, detection of rare miRNAs or absolute quantification.

Background Next-generation sequencing technologies have revolutionized the study of small RNAs (sRNAs) on a genome-wide scale. However, classical sRNA library preparation methods introduce serious bias, mainly during adapter ligation steps. Several types of sRNA including plant microRNAs (miRNA), piwi-interacting RNAs (piRNA) in insects, nematodes and mammals, and small interfering RNAs (siRNA) in insects and plants contain a 2’-O-methyl (2’-OMe) modification at their 3′ terminal nucleotide. This inhibits 3′ adapter ligation and makes library preparation particularly challenging. To reduce bias, the NEBNext kit (New England Biolabs) uses polyethylene glycol (PEG), the NEXTflex V2 kit (BIOO Scientific) uses both randomised adapters and PEG, and the novel SMARTer (Clontech) and CATS (Diagenode) kits avoid ligation altogether. Here we compared these methods with Illumina’s classical TruSeq protocol regarding the detection of normal and 2’ OMe RNAs. In addition, we modified the TruSeq and NEXTflex protocols to identify conditions that improve performance. Results Among the five kits tested with their respective standard protocols, the SMARTer and CATS kits had the lowest levels of bias but also had a strong formation of side products, and as a result performed relatively poorly with biological samples; NEXTflex detected the largest numbers of different miRNAs. The use of a novel type of randomised adapters called MidRand-Like (MRL) adapters and PEG improved the detection of 2’ OMe RNAs both in the TruSeq as well as in the NEXTflex protocol. Conclusions While it is commonly accepted that biases in sRNA library preparation protocols are mainly due to adapter ligation steps, the ligation-free protocols were not the best performing methods. Our modified versions of the TruSeq and NEXTflex protocols provide an improved tool for the study of 2’ OMe RNAs.

Background RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases. Results All methods resulted in false isomiR detection; however, the adapter-free method tested was especially prone to false isomiR detection. We demonstrate that using UMIs improves accuracy and we provide a guide for input amounts to improve consistency. Conclusions Our data show differences and limitations of current methods, thus raising concerns about the validity of quantification of miRNA and isomiRs across studies. We advocate for the use of UMIs to improve accuracy and reliability of miRNA quantifications.

Background Novel commercial kits for whole genome library preparation for next-generation sequencing on Illumina platforms promise shorter workflows, lower inputs and cost savings. Time savings are achieved by employing enzymatic DNA fragmentation and by combining end-repair and tailing reactions. Fewer cleanup steps also allow greater DNA input flexibility (1 ng-1 μg), PCR-free options from 100 ng DNA, and lower price as compared to the well-established sonication and tagmentation-based DNA library preparation kits. Results We compared the performance of four enzymatic fragmentation-based DNA library preparation kits (from New England Biolabs, Roche, Swift Biosciences and Quantabio) to a tagmentation-based kit (Illumina) using low input DNA amounts (10 ng) and PCR-free reactions with 100 ng DNA. With four technical replicates of each input amount and kit, we compared the kits’ fragmentation sequence-bias as well as performance parameters such as sequence coverage and the clinically relevant detection of single nucleotide and indel variants. While all kits produced high quality sequence data and demonstrated similar performance, several enzymatic fragmentation methods produced library insert sizes which deviated from those intended. Libraries with longer insert lengths performed better in terms of coverage, SNV and indel detection. Lower performance of shorter-insert libraries could be explained by loss of sequence coverage to overlapping paired-end reads, exacerbated by the preferential sequencing of shorter fragments on Illumina sequencers. We also observed that libraries prepared with minimal or no PCR performed best with regard to indel detection. Conclusions The enzymatic fragmentation-based DNA library preparation kits from NEB, Roche, Swift and Quantabio are good alternatives to the tagmentation based Nextera DNA flex kit from Illumina, offering reproducible results using flexible DNA inputs, quick workflows and lower prices. Libraries with insert DNA fragments longer than the cumulative sum of both read lengths avoid read overlap, thus produce more informative data that leads to strongly improved genome coverage and consequently also increased sensitivity and precision of SNP and indel detection. In order to best utilize such enzymatic fragmentation reagents, researchers should be prepared to invest time to optimize fragmentation conditions for their particular samples.

Background Cell type-specific ribosome-pulldown has become an increasingly popular method for analysis of gene expression. It allows for expression analysis from intact tissues and monitoring of protein synthesis in vivo. However, while its utility has been assessed, technical aspects related to sequencing of these samples, often starting with a smaller amount of RNA, have not been reported. In this study, we evaluated the performance of five library prep protocols for ribosome-associated mRNAs when only 250 pg-4 ng of total RNA are used. Results We obtained total and RiboTag-IP RNA, in three biological replicates. We compared 5 methods of library preparation for Illumina Next Generation sequencing: NuGEN Ovation RNA-Seq system V2 Kit, TaKaRa SMARTer Stranded Total RNA-Seq Kit, TaKaRa SMART-Seq v4 Ultra Low Input RNA Kit, Illumina TruSeq RNA Library Prep Kit v2 and NEBNext® Ultra™ Directional RNA Library Prep Kit using slightly modified protocols each with 4 ng of total RNA. An additional set of samples was processed using the TruSeq kit with 70 ng, as a ‘gold standard’ control and the SMART-Seq v4 with 250 pg of total RNA. TruSeq-processed samples had the best metrics overall, with similar results for the 4 ng and 70 ng samples. The results of the SMART-Seq v4 processed samples were similar to TruSeq (Spearman correlation > 0.8) despite using lower amount of input RNA. All RiboTag-IP samples had an increase in the intronic reads compared with the corresponding whole tissue, suggesting that the IP captures some immature mRNAs. The SMARTer-processed samples had a higher representation of ribosomal and non-coding RNAs leading to lower representation of protein coding mRNA. The enrichment or depletion of IP samples compared to corresponding input RNA was similar across all kits except for SMARTer kit. Conclusion RiboTag-seq can be performed successfully with as little as 250 pg of total RNA when using the SMART-Seq v4 kit and 4 ng when using the modified protocols of other library preparation kits. The SMART-Seq v4 and TruSeq kits resulted in the highest quality libraries. RiboTag IP RNA contains some immature transcripts.

Background Oxford Nanopore sequencing is a long-read sequencing technology that does not rely on a polymerase to generate sequence data. Sequencing library preparation methods used in Oxford Nanopore sequencing rely on the addition of a motor protein bound to an adapter sequence, which is added either using ligation-based methods (ligation sequencing kit), or transposase-based methods (rapid sequencing kit). However, these methods have enzymatic steps that may be susceptible to motif bias, including the underrepresentation of adenine-thymine (AT) sequences due to ligation and biases from transposases. This study aimed to compare the recognition motif and relative interaction frequencies of these library preparation methods and assess their effects on relative sequencing coverage, microbiome, and methylation profiles. The impacts of DNA extraction kits and basecalling models on microbiome analysis were also investigated. Results By using sequencing data generated by the ligation and rapid library kits, we identified the recognition motif (5’-TATGA-3’) consistent with MuA transposase in the rapid kit and low frequencies of AT in the sequence terminus of the ligation kit. The rapid kit showed reduced yield in regions with 40–70% guanine-cytosine (GC) contents, while the ligation kit showed relatively even coverage distribution in areas with various GC contents. Due to longer reads, ligation kits showed increased taxonomic classification efficiency compared to the rapid protocols. Rumen microbial profile at different taxonomic levels and mock community profile showed significant variation due to the library preparation method used. The ligation kit outperformed the rapid kit in subsequent bacterial DNA methylation statistics, although there were no significant differences. Conclusions Our findings indicated that careful and consistent library preparation method selection is essential for quantitative methods such as bovine-related microbiome analysis due to the systematic bias induced by the enzymatic reactions in Oxford Nanopore library preparation.

Whole genome sequencing (WGS) via next-generation sequencing (NGS) platforms offers a powerful approach for investigating viral genetic diversity. Orbiviruses are an economically important group of arboviruses with a double-stranded RNA (dsRNA) genome consisting of 10 segments. Traditional approaches to virus sequencing relied primarily on processing with various treatments such as lithium chloride to enrich dsRNAs and then sequencing on Illumina platforms due to its lower error rate. To reduce the time and cost associated with orbivirus sequencing, we simplified the sample preparation, sequencing, and analysis protocols. Our optimized sample and library preparation protocols achieved comparable results to established methods while benefiting from simpler sample preparation. The optimized protocols for Illumina and Nanopore platforms (which produces longer reads) resulted in both platforms producing high quality whole genome sequences. To streamline the data analysis, we developed OrbiSeq, a reproducible Nextflow workflow for analysis and consensus sequence generation of orbivirus genomes from Illumina and Nanopore sequence data. While the OrbiSeq pipeline was optimized with orbivirus sequences, it can be used to recover consensus sequences for any segmented or non-segmented viral genome, provided that the sequence is sufficiently similar to existing reference sequences. The optimized protocols for producing viral sequences enhance opportunities for extensive genomic surveillance and in turn deeper evolutionary insights.