Read Length Research Articles

Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads.

Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments. As a result, the splicing status of many reads in scRNA-seq is ambiguous because of a lack of definitive evidence. We are therefore in need of methods that can recover the splicing status of ambiguous reads which, in turn, can lead to more accuracy and confidence in downstream analyses. We develop Forseti, a predictive model to probabilistically assign a splicing status to scRNA-seq reads. Our model has two key components. First, we train a binding affinity model to assign a probability that a given transcriptomic site is used in fragment generation. Second, we fit a robust fragment length distribution model that generalizes well across datasets deriving from different species and tissue types. Forseti combines these two trained models to predict the splicing status of the molecule of origin of reads by scoring putative fragments that associate each alignment of sequenced reads with proximate potential priming sites. Using both simulated and experimental data, we show that our model can precisely predict the splicing status of many reads and identify the true gene origin of multi-gene mapped reads. Forseti and the code used for producing the results are available at https://github.com/COMBINE-lab/forseti under a BSD 3-clause license.

The effects of sequencing strategies on Metagenomic pathogen detection using bronchoalveolar lavage fluid samples

ObjectivesMetagenomic next-generation sequencing (mNGS) is a powerful tool for pathogen detection. The accuracy depends on both wet lab and dry lab procedures. The objective of our study was to assess the influence of read length and dataset size on pathogen detection. MethodsIn this study, 43 clinical BALF samples, which tested positive via clinical mNGS and were consistent with the diagnosis, were subjected to re-sequencing on the Illumina NovaSeq 6000 platform. The raw re-sequencing data, consisting of 100 million (M) paired-end 150 bp (PE150) reads, were divided into simulated datasets with eight different data sizes (5 M, 10 M, 15 M, 20 M, 30 M, 50 M, 75 M, 100 M) and five different read lengths (single-end 50 bp (SE50), SE75, SE100, PE100, and PE150). Both Kraken2 and IDseq bioinformatics pipelines were employed to analyze the previously diagnosed pathogens in the simulated data. Detection of pathogens was based on read counts ranging from 1 to 10 and RPM values ranging from 0.2 to 2. ResultsOur results revealed that increasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection. However, a larger data sizes for mNGS require higher economic costs and longer turnaround time for data analysis. Our findings indicate 20 M reads being sufficient for SE75 mode to achieve high recall rates. Additionally, high nucleic acid loads in samples can lead to increased stability in pathogen detection efficiency, reducing the impact of sequencing strategies. The choice of bioinformatics pipelines had a significant impact on recall rates achieved in pathogen detection. ConclusionsIncreasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection but increase the economic and time costs of sequencing and data analysis. Currently, the 20 M reads in SE75 mode may be the best sequencing option.

MethPhaser: methylation-based long-read haplotype phasing of human genomes

The assignment of variants across haplotypes, phasing, is crucial for predicting the consequences, interaction, and inheritance of mutations and is a key step in improving our understanding of phenotype and disease. However, phasing is limited by read length and stretches of homozygosity along the genome. To overcome this limitation, we designed MethPhaser, a method that utilizes methylation signals from Oxford Nanopore Technologies to extend Single Nucleotide Variation (SNV)-based phasing. We demonstrate that haplotype-specific methylations extensively exist in Human genomes and the advent of long-read technologies enabled direct report of methylation signals. For ONT R9 and R10 cell line data, we increase the phase length N50 by 78%-151% at a phasing accuracy of 83.4-98.7% To assess the impact of tissue purity and random methylation signals due to inactivation, we also applied MethPhaser on blood samples from 4 patients, still showing improvements over SNV-only phasing. MethPhaser further improves phasing across HLA and multiple other medically relevant genes, improving our understanding of how mutations interact across multiple phenotypes. The concept of MethPhaser can also be extended to non-human diploid genomes. MethPhaser is available at https://github.com/treangenlab/methphaser.

Haplotype Inference Using Long-Read Nanopore Sequencing: Application to GSTA1 Promoter.

Recovering true haplotypes can have important clinical consequences. The laboratory process is difficult and is, therefore, most often done through inference. In this paper, we show that when using the Oxford nanopore sequencing technology, we could recover the true haplotypes of the GSTA1 promoter region. Eight LCL cell lines with potentially ambiguous haplotypes were used to characterize the efficacy of Oxford nanopore sequencing to phase the correct GSTA1 promoter haplotypes. The results were compared to Sanger sequencing and inferred haplotypes in the 1000 genomes project. The average read length was 813 bp out of a total PCR length of 1336 bp. The best coverage of sequencing was in the middle of the PCR product and decreased to 50% at the PCR ends. SNPs separated by less than 200 bp showed > 90% of correct haplotypes, while at the distance of 1089 bp, this proportion still exceeded 58%. The number of cycles influences the generation of hybrid haplotypes but not extension or annealing time. The results demonstrate that this long sequencing reads methodology, can accurately determine the haplotypes without the need for inference. The technology proved to be robust but the success of phasing nonetheless depends on the distances and frequencies of SNPs.

Long-Read Structural and Epigenetic Profiling of a Kidney Tumor-Matched Sample with Nanopore Sequencing and Optical Genome Mapping.

Carcinogenesis often involves significant alterations in the cancer genome architecture, marked by large structural and copy number variations (SVs and CNVs) that are difficult to capture with short-read sequencing. Traditionally, cytogenetic techniques are applied to detect such aberrations, but they are limited in resolution and do not cover features smaller than several hundred kilobases. Optical genome mapping and nanopore sequencing are attractive technologies that bridge this resolution gap and offer enhanced performance for cytogenetic applications. These methods profile native, individual DNA molecules, thus capturing epigenetic information. We applied both techniques to characterize a clear cell renal cell carcinoma (ccRCC) tumor's structural and copy number landscape, highlighting the relative strengths of each method in the context of variant size and average read length. Additionally, we assessed their utility for methylome and hydroxymethylome profiling, emphasizing differences in epigenetic analysis applicability.

RiboGraph: an interactive visualization system for ribosome profiling data at read length resolution.

Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses. In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with .ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data. Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph.

LINCATRA: Two-cycle method to amplify RNA for transcriptome analysis from formalin-fixed paraffin-embedded tissue

Whole transcriptome analysis (WTA) using RNA extracted from Formalin Fixed Paraffin Embedded (FFPE) tissue is an invaluable tool to understand the molecular pathology of disease. RNA extracted from FFPE tissue is either degraded and/or in very low quantities hampering gene expression analysis. Earlier studies described protocols applied for cellular RNA using poly-A primer-based linear amplification. The current study describes a method, LINCATRA (LINear amplifiCAtion of RNA for whole TRAnscriptome analysis). It employs random nonamer primer based method which can amplify short, fragmented RNA with high fidelity from as low as 5 ng to obtain enough material for WTA. The two-cycle method significantly amplified RNA at ∼1000 folds (p < 0.0001) improving the mean read lengths (p < 0.05) in WTA. Overall, increased mean read length positively correlated with on-target reads (Pearson's r = 0.71, p < 0.0001) in both amplified and unamplified RNA-seq analysis. Gene expression analysis compared between unamplified and amplified group displayed substantial overlap of the differentially expressed genes (DEGs) (log2 fold change cut-off < −2 and >2, p < 0.05) identified between lung cancer and asthma cohorts validating the method developed. This method is applicable in clinical molecular pathology field for both diagnostics and elucidation of disease mechanisms.

Establishing an optimized ATAC-seq protocol for the maize.

The advent of next-generation sequencing in crop improvement offers unprecedented insights into the chromatin landscape closely linked to gene activity governing key traits in plant development and adaptation. Particularly in maize, its dynamic chromatin structure is found to collaborate with massive transcriptional variations across tissues and developmental stages, implying intricate regulatory mechanisms, which highlights the importance of integrating chromatin information into breeding strategies for precise gene controls. The depiction of maize chromatin architecture using Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) provides great opportunities to investigate cis-regulatory elements, which is crucial for crop improvement. In this context, we developed an easy-to-implement ATAC-seq protocol for maize with fewer nuclei and simple equipment. We demonstrate a streamlined ATAC-seq protocol with four key steps for maize in which nuclei purification can be achieved without cell sorting and using only a standard bench-top centrifuge. Our protocol, coupled with the bioinformatic analysis, including validation by read length periodicity, key metrics, and correlation with transcript abundance, provides a precise and efficient assessment of the maize chromatin landscape. Beyond its application to maize, our testing design holds the potential to be applied to other crops or other tissues, especially for those with limited size and amount, establishing a robust foundation for chromatin structure studies in diverse crop species.

Readon: a novel algorithm to identify read-through transcripts with long-read sequencing data.

There are many clustered transcriptionally active regions in the human genome, in which the transcription complex cannot immediately terminate transcription at the upstream gene termination site, but instead continues to transcribe intergenic regions and downstream genes, resulting in read-through transcripts. Several studies have demonstrated the regulatory roles of read-through transcripts in tumorigenesis and development. However, limited by the read length of next-generation sequencing, discovery of read-through transcripts has been slow. For long but also erroneous third-generation sequencing data, this study developed a novel minimizer sketch algorithm to accurately and quickly identify read-through transcripts. Readon initially splits the reference sequence into distinct active regions. It employs a sliding window approach within each region, calculates minimizers, and constructs the specialized structured arrays for query indexing. Following initial alignment anchor screening of candidate read-through transcripts, further confirmation steps are executed. Comparative assessments against existing software reveal Readon's superior performance on both simulated and validated real data. Additionally, two downstream tools are provided: one for predicting whether a read-through transcript is likely to undergo nonsense-mediated decay or encodes a protein, and another for visualizing splicing patterns. Readon is freely available on GitHub (https://github.com/Bulabula45/Readon).

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data.

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

Whole-genome sequencing of marine water-derived Curvularia verruculosa KHW-7: a pioneering study

Marine microorganisms are renowned for being a rich source of new secondary metabolites that are significant to humans. The fungi strain KHW-7 was isolated from the seawater collected from the Gulf of Khambhat, India, and identified as Curvularia verruculosa KHW-7. On a next-generation sequencing platform, C. verruculosa KHW-7’s whole-genome sequencing (WGS) and gene annotation were carried out using several bioinformatic methods. The 31.59 MB genome size, 52.3% GC, and 158 bp mean read length were discovered using WGS. This genome also contained 9,745 protein-coding genes, including 852 secreted proteins and 2048 transmembrane proteins. The antiSMASH algorithm used to analyze genomes found 25 secondary metabolite biosynthetic gene clusters (BGCs) that are abundant in terpene, non-ribosomal peptide synthetase (NRPS), and polyketides type 1 (T1PKS). To our knowledge, this is the first whole-genome sequence report of C. verruculosa. The WGS analysis of C. verruculosa KHW-7 indicated that this marine-derived fungus could be an efficient generator of bioactive secondary metabolites and an important industrial enzyme, both of which demand further investigation and development.

NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach.

Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms

IntroductionThe rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules. ObjectivesBoth ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated. MethodsTo address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells. ResultsUsing NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies. ConclusionBeyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.

A Full-Length Transcriptome and Analysis of the NHL-1 Gene Family in Neocaridina denticulata sinensis.

Neocaridina denticulata sinensis has emerged as a promising model organism for basic studies in Decapod. However, the current transcriptome information on this species is based on next-generation sequencing technologies, which are limited by a short read length. Therefore, the present study aimed to generate a full-length transcriptome assembly of N. denticulata sinensis utilizing the PacBio Sequel Ⅱ platform. The resulting transcriptome assembly comprised 5831 transcripts with an N50 value of 3697 bp. Remarkably, 90.5% of these transcripts represented novel isoforms of known genes. The transcripts were further searched against the NR, SwissProt, KEGG, KOG, GO, NT, and Pfam databases. A total of 24.8% of the transcripts can be annotated across all seven databases. Additionally, 1236 alternative splicing events, 344 transcription factors, and 124 long non-coding RNAs (LncRNAs) were predicted. Based on the alternative splicing annotation results, a RING finger protein NHL-1 gene from N. denticulata sinensis (NdNHL-1) was identified. There are 15 transcripts in NdNHL-1. The longest transcript is 4995 bp in length and encodes a putative protein of 1665 amino acids. A phylogenetic analysis showed its close relationship with NHL-1 from other crustacean species. This report represents the full-length transcriptome of N. denticulata sinensis and will facilitate research on functional genomics and environmental adaptation in this species.

Haplotype determination of the Bombyx mori nucleopolyhedrovirus by Nanopore sequencing and linkage of single nucleotide variants.

Naturally occurring isolates of baculoviruses, such as the Bombyx mori nucleopolyhedrovirus (BmNPV), usually consist of numerous genetically different haplotypes. Deciphering the different haplotypes of such isolates is hampered by the large size of the dsDNA genome, as well as the short read length of next generation sequencing (NGS) techniques that are widely applied for baculovirus isolate characterization. In this study, we addressed this challenge by combining the accuracy of NGS to determine single nucleotide variants (SNVs) as genetic markers with the long read length of Nanopore sequencing technique. This hybrid approach allowed the comprehensive analysis of genetically homogeneous and heterogeneous isolates of BmNPV. Specifically, this allowed the identification of two putative major haplotypes in the heterogeneous isolate BmNPV-Ja by SNV position linkage. SNV positions, which were determined based on NGS data, were linked by the long Nanopore reads in a Position Weight Matrix. Using a modified Expectation-Maximization algorithm, the Nanopore reads were assigned according to the occurrence of variable SNV positions by machine learning. The cohorts of reads were de novo assembled, which led to the identification of BmNPV haplotypes. The method demonstrated the strength of the combined approach of short- and long-read sequencing techniques to decipher the genetic diversity of baculovirus isolates.

The Third-Generation Sequencing Challenge: Novel Insights for the Omic Sciences.

The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.

PanDepth, an ultrafast and efficient genomic tool for coverage calculation.

Coverage quantification is required in many sequencing datasets within the field of genomics research. However, most existing tools fail to provide comprehensive statistical results and exhibit limited performance gains from multithreading. Here, we present PanDepth, an ultra-fast and efficient tool for calculating coverage and depth from sequencing alignments. PanDepth outperforms other tools in computation time and memory efficiency for both BAM and CRAM-format alignment files from sequencing data, regardless of read length. It employs chromosome parallel computation and optimized data structures, resulting in ultrafast computation speeds and memory efficiency. It accepts sorted or unsorted BAM and CRAM-format alignment files as well as GTF, GFF and BED-formatted interval files or a specific window size. When provided with a reference genome sequence and the option to enable GC content calculation, PanDepth includes GC content statistics, enhancing the accuracy and reliability of copy number variation analysis. Overall, PanDepth is a powerful tool that accelerates scientific discovery in genomics research.

Assembly collapsing versus heterozygosity oversizing: detection of homokaryotic and heterokaryotic Laccaria trichodermophora strains by hybrid genome assembly.

Genome assembly and annotation using short-paired reads is challenging for eukaryotic organisms due to their large size, variable ploidy and large number of repetitive elements. However, the use of single-molecule long reads improves assembly quality (completeness and contiguity), but haplotype duplications still pose assembly challenges. To address the effect of read length on genome assembly quality, gene prediction and annotation, we compared genome assemblers and sequencing technologies with four strains of the ectomycorrhizal fungus Laccaria trichodermophora. By analysing the predicted repertoire of carbohydrate enzymes, we investigated the effects of assembly quality on functional inferences. Libraries were generated using three different sequencing platforms (Illumina Next-Seq, Mi-Seq and PacBio Sequel), and genomes were assembled using single and hybrid assemblies/libraries. Long reads or hybrid assemby resolved the collapsing of repeated regions, but the nuclear heterozygous versions remained unresolved. In dikaryotic fungi, each cell includes two nuclei and each nucleus has differences not only in allelic gene version but also in gene composition and synteny. These heterokaryotic cells produce fragmentation and size overestimation of the genome assembly of each nucleus. Hybrid assembly revealed a wider functional diversity of genomes. Here, several predicted oxidizing activities on glycosyl residues of oligosaccharides and several chitooligosaccharide acetylase activities would have passed unnoticed in short-read assemblies. Also, the size and fragmentation of the genome assembly, in combination with heterozygosity analysis, allowed us to distinguish homokaryotic and heterokaryotic strains isolated from L. trichodermophora fruit bodies.

The Application of Long-Read Sequencing to Cancer.

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

Abstract 6251: Validation and implementation of whole-transcriptome sequencing assay into cancer care: An experience from a cancer center

Abstract Introduction Transcriptome sequencing is a well-established diagnostic tool to characterize and quantify gene expression profiles and to detect fusion transcripts. The utilization of whole-transcriptome sequencing (WTS), in clinical workflows can tremendously help improve diagnosis, risk stratification, and therapy selection. However, there are multiple challenges to widespread clinical implementation of WTS including nucleic acid input, defining appropriate quality control (QC) criteria, tumor specific baseline controls and depth of sequencing. Here, we present a validation study to deploy routine whole transcriptome sequencing with automated analysis in a mid-size academic center. Methods Nucleic acid material was obtained through clinical residual samples in the Center for Genomics and Advanced Technology. RNA samples were extracted using the Purigen Ionic® FFPE to Pure RNA Kit. RNA input sample quantities ranged from 34-100ng. WTS libraries were prepared using the Illumina® RNA Prep with Enrichment (L) Tagmentation kit with the Illumina Exome Panel for FFPE tumor samples. The validation was performed on a set of patient samples with known clinically reported variants from the Illumina TruSight® Tumor 170 assay. A total of 84 true positives with reportable fusions, 5 splicing events, 16 negative samples, and 15 limit of detection (LOD) samples were used. Libraries were prepared in a three-plex format. Sequencing was performed on the Novaseq-6000 system using S2 and S4 flow cells with a target read count of 100 million. All data was processed through the AUGMET informatics platform for demultiplexing, quality control, fusion detection, transcript quantification and determination of alternatively spliced transcript. Results The WTS validation assay demonstrated 98% (n=49/50) sensitivity, with only one false negative, and 100% (n=68/68) specificity. A final review of our data was determined to develop stable QC standards for RNA sequenced libraries to be &amp;gt; 60M reads and &amp;gt; 130bp read length. When analyzing (LOD) for WTS, interestingly, read length was inversely proportional to input and as a result detection of the clinically relevant fusion. Based on our LOD experiments, a minimum input threshold of 20ng of RNA was determined to maintain the sensitivity and specificity. An LOD study for tumor purity was not performed due to the known lack of correlation between tumor purity and fusion transcript quantity. Discussion WTS can detect all clinically reported fusions and splicing events with comparable sensitivity to panel assays. Moreover, simultaneously available transcript quantification and variant calling provide the ability to implement several disease classifiers. This work lays the foundation for integration with our clinically available tumor exome sequencing assay to allow joint exome transcriptome analysis in a single informatics platform. Citation Format: Jillian A. Moran, Edward G. Hughes, Donald C. Green, Shrey Sukhadia, Gregory J. Tsongalis, Samantha F. Allen, Laura Tafe, Brianna Houde, Nevena B. Ognjenovic, Jennifer N. Barbuto, Ivy Riano, Parth Shah. Validation and implementation of whole-transcriptome sequencing assay into cancer care: An experience from a cancer center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6251.