Next Generation Sequencing Output Research Articles

LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies.

Antibody-based therapeutics must not undergo chemical modifications that would impair their efficacy or hinder their developability. A commonly used technique to de-risk lead biotherapeutic candidates annotates chemical liability motifs on their sequence. By analyzing sequences from all major sources of data (therapeutics, patents, GenBank, literature, and next-generation sequencing outputs), we find that almost all antibodies contain an average of 3-4 such liability motifs in their paratopes, irrespective of the source dataset. This is in line with the common wisdom that liability motif annotation is over-predictive. Therefore, we have compiled three computational flags to prioritize liability motifs for removal from lead drug candidates: 1. germline, to reflect naturally occurring motifs, 2. therapeutic, reflecting chemical liability motifs found in therapeutic antibodies, and 3. surface, indicative of structural accessibility for chemical modification. We show that these flags annotate approximately 60% of liability motifs as benign, that is, the flagged liabilities have a smaller probability of undergoing degradation as benchmarked on two experimental datasets covering deamidation, isomerization, and oxidation. We combined the liability detection and flags into a tool called Liability Antibody Profiler (LAP), publicly available at lap.naturalantibody.com. We anticipate that LAP will save time and effort in de-risking therapeutic molecules.

Virus sequencing performance during the SARS-CoV-2 pandemic: a retrospective analysis of data from multiple rounds of external quality assessment in Austria.

Introduction: A notable feature of the 2019 coronavirus disease (COVID-19) pandemic was the widespread use of whole genome sequencing (WGS) to monitor severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Countries around the world relied on sequencing and other forms of variant detection to perform contact tracing and monitor changes in the virus genome, in the hopes that epidemic waves caused by variants would be detected and managed earlier. As sequencing was encouraged and rewarded by the government in Austria, but represented a new technicque for many laboratories, we designed an external quality assessment (EQA) scheme to monitor the accuracy of WGS and assist laboratories in validating their methods. Methods: We implemented SARS-CoV-2 WGS EQAs in Austria and report the results from 7 participants over 5 rounds from February 2021 until June 2023. The participants received sample material, sequenced genomes with routine methods, and provided the sequences as well as information about mutations and lineages. Participants were evaluated on the completeness and accuracy of the submitted sequence and the ability to analyze and interpret sequencing data. Results: The results indicate that performance was excellent with few exceptions, and these exceptions showed improvement over time. We extend our findings to infer that most publicly available sequences are accurate within ≤1 nucleotide, somewhat randomly distributed through the genome. Conclusion: WGS continues to be used for SARS-CoV-2 surveillance, and will likely be instrumental in future outbreak scenarios. We identified hurdles in building next-generation sequencing capacity in diagnostic laboratories. EQAs will help individual laboratories maintain high quality next-generation sequencing output, and strengthen variant monitoring and molecular epidemiology efforts.

Characterizing Hereditary Hemolytic Anemias in a Hispanic Cohort Using Next Generation Sequencing

Background: Hereditary hemolytic anemias (HHA) are a heterogeneous group of nonimmune disorders characterized by increased red blood cell (RBC) destruction due to abnormalities of hemoglobin stability, defects of RBC metabolism, and disorders of RBC hydration. Although uncommon, they comprise important causes of anemia in children and adults by causing chronic or episodic hemolysis. Clinical, laboratory, and genetic heterogeneity characterize this group of disorders and make recognition challenging. The advent of next generation sequencing (NGS) has made accurate genetic diagnoses attainable, with an increasing number of studies reporting clinical utility in HHA. For patients with suspected HHA, multigene testing is available incorporating an NGS panel associated genes. However, there is limited data on the use of multigene NGS to characterize HHA in the Hispanic population. Herein, we describe results from NGS testing of a pilot cohort referred for HHA investigation to hematology clinics along the US/Mexico border. Methods: Pediatric and adult hematology clinics at Texas Tech University in El Paso, TX collaborated for testing on HHA referral and clinical case review. The diagnostic panel (AnemiaID/PerkinElmer/Revvity®) included 51 genes encoding RBC cytoskeletal proteins, membrane transporter, RBC enzymes, and certain bilirubin metabolism genes. The complete coding region, splice site junctions, and, where appropriate, deep intronic or regulatory regions were covered. Targeted gene capture and library construction for NGS were performed using a Whole Blood and Saliva kit and 150 base pair paired-end sequencing was done on Illumina® NGS systems at target average coverage of 80×. NGS output data were summarized descriptively for all patients and clinically correlated for diagnosis. Results: In this pilot cohort, 11 patients were tested during September 2022 to June 2023, age ranging 7-75 years, with females accounting for 64% of the group. Findings are reported in the Table below; Pathogenic variants leading to a definitive diagnosis were identified in 8 of 11 cases (73%). The most frequently mutated genes were SPTA1 followed by PIEZO1, SPTB, RHAG, GSR, SLC4A1 and TPI1. Approximately 58% of the mutations were reported as novel. Diagnoses included, most commonly, Hereditary Spherocytosis followed by Hereditary Pyropoikilocytosis and Congenital Dyserythropoietic Anemia. Compound heterozygosity leading to complex interactions was noted in 4 cases (36%), involving SPTA1 low expression alleles in trans (alpha-LELY and alpha-LEPRA) in 2 cases. Conclusion: Hereditary hemolytic anemia in the Hispanic population is characterized by complex molecular interactions and a high prevalence of novel mutations in RBC cytoskeleton/enzyme genes. NGS has clinical utility and should be considered for accurate diagnosis. Further research into the molecular basis and genetic spectrum of HHA in the Hispanic population is needed.

P652: MEASURING MINIMAL RESIDUAL DISEASE BEYOND 10-4 THROUGH IGHV LEADER-BASED NEXT GENERATION SEQUENCING IMPROVES PROGNOSTIC STRATIFICATION IN CHRONIC LYMPHOCYTIC LEUKEMIA

Background: The sensitivity of conventional techniques for reliable quantification of minimal/measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is limited to MRD 10-4. Measuring MRD <10-4 could help to further distinguish between CLL patients with durable remission and those at risk of early relapse. Aims: To develop an academically sourced IGHV leader-based next-generation sequencing (NGS) assay for the quantification of MRD in CLL. Methods: The IGHV leader primer set developed by the EuroClonality-NGS working group was used to amplify all IGH rearrangements present in an end of treatment (EOT) DNA pool. The IGH clonal target was amplified in a single-step PCR and subsequent sequencing was performed on the Illumina MiSeq platform. To calculate MRD depth, NGS output was annotated through in the ARResT/Interrogate immunoprofiler. Technical validation of the IGHV leader-based assay was performed on contrived MRD samples, created by serial dilution of pretreatment CLL DNA pooled PBMC DNA from healthy donors. Clinical validation of the IGHV leader-based assay was performed on EOT samples from the CLL11 trial (NCT01010061). Results: In 176/183 (96%) measurements on contrived samples, the CLL-specific rearrangement was detected. The limit of detection and limit of quantitation were estimated at 3.4 [95% CI 1.9-16.0] and 3.8 [95% CI 1.7-8.2] malignant cells per assay, respectivelly. The limit of blank was found to be 0. Linearity was established in the MRD 10-2-10-5 range (r=0.94 [95%CI 0.91-0.96]). Of note, when provided with sufficient DNA input, MRD could even be detected down to MRD 10-6. There was high inter-assay concordance between measurements of the IGHV leader-based NGS assay and allele-specific oligonucleotide quantitative PCR (r=0.92, [95%CI 0.86-0.96]) and droplet digital PCR (r=0.93, [95%CI 0.88-0.96]). In a cohort of 67 patients from the CLL11 trial, using 5μg DNA input and MRD 10-5 as a cut-off, undetectable MRD (uMRD) was associated with superior progression-free survival (PFS) and time to next treatment. Importantly, deeper MRD measurement allowed for additional stratification of patients with MRD <10-4 but ≥10-5. Patients with MRD in this range had a significantly shorter PFS, compared to patients with uMRD <10-5, but significantly longer, compared to patients with MRD ≥10-4 (median PFS: ≥10-4, 10.4 months; <10-4 but ≥10-5, 27.5 months; uMRD <10-5, NR, 4-year PFS rate: ≥10-4, 66.7%; <10-4 but ≥10-5, 25.0%; uMRD <10-5, 7.2%, P<0.001) (Figure 1). Image:Summary/Conclusion: We here present an academically-developed, IGHV leader-based NGS assay for the detection and quantification of MRD in CLL beyond MRD 10-4. The assay has high sensitivity and is quantitative and linear up to MRD 10-5 when using 5μg DNA input. Measurement to MRD 10-6 is feasible, conditional on sufficient DNA input. The deeper MRD measurements enabled by the IGHV leader-based NGS assay resulted in improved stratification of CLL patients following treatment.

Improvement of Diagnostic Yield by an Additional Amplicon Module to Hybridization-Based Next-Generation Sequencing Panels

Many approaches aimed at improving next-generation sequencing output for clinical purposes exist. However, sequencing gaps or misalignments for regions that are difficult to cover because of their low complexity or high homology still exist. Our aim was to improve the yield of sequencing data. A hybridization-based next-generation sequencing library was pooled with custom add-on amplicon-based libraries processed by the same commercial test and run in parallel and sequenced simultaneously. Formulas and steps for proper amplicon pooling (250 to 7000 bp) and final library merging are presented. The novel strategy was tested on selected archetypal situations: diagnostics of a gene with many pseudogenes, a genomic region surrounded by Alu repeats, simple one-time addition of an extra gene, and mosaicism detection. The sequence of all supplemented genomic regions was traced with reasonable coverage at the background of a hybridization captured library. The flexible add-on module expands the possibilities of routine diagnostics. The technical solution makes it possible to mix amplicons that differ significantly in size and process them in one tube simultaneously with samples of the hybridization-based panel. The proposed approach reduces turnaround time and increases diagnostic yield.

Implementing a user-friendly format to analyze PRRSV next-generation sequencing results and associating breeding herd production performance with number of PRRSV strains and recombination events.

The open reading frames (ORF)5 represents approximately 4% of the porcine reproductive and respiratory syndrome virus (PRRSV)-2 genome (whole-PRRSV) and is often determined by the Sanger technique, which rarely detects>1 PRRSV strain if present in the sample. Next-generation sequencing (NGS) may provide a more appropriate method of detecting multiple PRRSV strains in one sample. This work assessed the effect of PRRSV genetic variability and recombination events, using NGS, on the time-to-low prevalence (TTLP) and total losses in breeding herds (n 20) that detected a PRRSV outbreak and adopted measures to eliminate PRRSV. Serum, lung or live virus inoculation material collected within 3-weeks of outbreak, and subsequently, processing fluids (PFs) were tested for PRRSV by RT-qPCR and NGS. Recovered whole-PRRSV or partial sequences were used to characterize within and between herd PRRSV genetic variability. Whole-PRRSV was recovered in five out of six (83.3%) lung, 16 out of 22 (72.73%) serum and in five out of 95 (5.26%) PF. Whole-PRRSV recovered from serum or lung were used as farm referent strains in 16 out of 20 (80%) farms. In four farms, only partial genome sequences were recovered and used as farm referent strains. At least two wild-type PRRSV strains (wt-PRRSV) were circulating simultaneously in 18 out of 20 (90%) and at least one vaccine-like strain co-circulating in eight out of 20 (40%) farms. PRRSV recombination events were detected in 12 farms (59%), been 10 out of 12 between wt-PRRSV and two out of 12 between wt-PRRSV and vaccine-like strains. Farms having ≥3 strains had a 12-week increase TTLP versus herds ≤2 strains detected. Farms with ≤2 strains (n 10) had 1837 and farms with no recombination events detected (n 8) had 1827 fewer piglet losses per 1000 sows versus farms with ≥3 PRRSV strains (n 8) or detected recombination (n 10), respectively. NGS outcomes and novel visualization methods provided more thorough insight into PRRSV dynamics, genetic variability, detection of multiple strains co-circulating in breeding herds and helped establish practical guidelines for using PRRSV NGS outputs.

Successful Genetic Screening and Creating Awareness of Familial Hypercholesterolemia and Other Heritable Dyslipidemias in the Netherlands.

The genetic screening program for familial hypercholesterolemia (FH) in the Netherlands, which was embraced by the Dutch Ministry of Health from 1994 to 2014, has led to twenty years of identification of at least 1500 FH cases per year. Although funding by the government was terminated in 2014, the approach had proven its effectiveness and had built the foundation for the development of more sophisticated diagnostic tools, clinical collaborations, and new molecular-based treatments for FH patients. As such, the community was driven to continue the program, insurance companies were convinced to collaborate, and multiple approaches were launched to find new index cases with FH. Additionally, the screening was extended, now also including other heritable dyslipidemias. For this purpose, a diagnostic next-generation sequencing (NGS) panel was developed, which not only comprised the culprit LDLR, APOB, and PCSK9 genes, but also 24 other genes that are causally associated with genetic dyslipidemias. Moreover, the NGS technique enabled further optimization by including pharmacogenomic genes in the panel. Using such a panel, more patients that are prone to cardiovascular diseases are being identified nowadays and receive more personalized treatment. Moreover, the NGS output teaches us more and more about the dyslipidemic landscape that is less straightforward than we originally thought. Still, continuous progress is being made that underlines the strength of genetics in dyslipidemia, such as discovery of alternative genomic pathogenic mechanisms of disease development and polygenic contribution.

Comparative Assessment of FLT3 Variant Allele Frequency By Capillary Electrophoresis and Next-Generation Sequencing in FLT3mut+ patients with Relapsed/Refractory (R/R) Acute Myeloid Leukemia (AML) Who Received Gilteritinib Therapy

Comparative Assessment of FLT3 Variant Allele Frequency By Capillary Electrophoresis and Next-Generation Sequencing in FLT3mut+ patients with Relapsed/Refractory (R/R) Acute Myeloid Leukemia (AML) Who Received Gilteritinib Therapy

27-OR: Simplifying Detection of Large Scale Deletions Causing MODY5

Maturity Onset Diabetes of the Young (MODY) 5, also known as renal cysts and diabetes syndrome, is a rare autosomal dominant form of monogenic diabetes caused by mutations in the HNF1B gene encoding hepatocyte nuclear factor 1 homeobox beta. Whole gene deletions of one copy of HNF1B are the most common mutation underlying MODY5, with more extensive deletions encompassing HNF1B and several surrounding genes, known as 17q12 deletion syndrome, present in a sub-set of individuals with MODY5, who present with additional clinical features. Historically, these large scale deletions are not identified using Sanger or targeted next generation sequencing (NGS) panels, and require other tools, such as microarray-based genomic hybridization or multiplex ligation probe amplification to detect. Incorporating such tests to targeted sequencing adds significantly to cost and labor. Recent advances in bioinformatics allow for identification of large-scale copy number variation (CNV) from NGS output data without requiring additional laboratory methods. The NGS data from 57 patients suspected to have MODY by their physicians but who were negative for pathogenic mutations using a targeted NGS approach were thus re-examined using a CNV calling tool (CNV Caller, VarSeq v1.4.3). Three unrelated patients were found to have large scale deletions spanning HNF1B. These deletions were verified using whole exome sequencing data as well as microarray analysis (CytoScan, Affymetrix), which confirmed the presence of 17q12 deletion in all three individuals. The deletions varied in size from 1.46 to 1.85 million base pairs. Clinical correlation revealed a history of genitourinary abnormalities in all three probands. These results confirm the utility of the CNV Caller tool in identifying large-scale deletions causing MODY5, as well as 17q12 deletion syndrome. This may represent an easier, more cost-effective approach to making this diagnosis, ultimately improving case detection and allowing for more personalized care for affected individuals. Disclosure A.J. Berberich: None. J. Wang: None. H. Cao: None. A.D. McIntyre: None. J.F. Robinson: None. P. Yang: None. J. Knoll: Stock/Shareholder; Self; Cytognomix Inc. R. Hegele: Consultant; Self; Aegerion Pharmaceuticals, Akcea Therapeutics, Amgen Inc., Regeneron Pharmaceuticals, Sanofi.

Genomic coordinates and continental distribution of 120 blood group variants reported by the 1000 Genomes Project.

The 1000 Genomes Project provides a database of genomic variants from whole genome sequencing of 2504 individuals across five continental superpopulations. This database can enrich our background knowledge of worldwide blood group variant geographic distribution and identify novel variants of potential clinical significance. The 1000 Genomes database was analyzed to 1) expand knowledge about continental distributions of known blood group variants, 2) identify novel variants with antigenic potential and their geographic association, and 3) establish a baseline scaffold of chromosomal coordinates to translate next-generation sequencing output files into a predicted red blood cell (RBC) phenotype. Forty-two genes were investigated. A total of 604 known variants were mapped to the GRCh37 assembly; 120 of these were reported by 1000 Genomes in at least one superpopulation. All queried variants, including the ACKR1 promoter silencing mutation, are located within exon pull-down boundaries. The analysis yielded 41 novel population distributions for 34 known variants, as well as 12 novel blood group variants that warrant further validation and study. Four prediction algorithms collectively flagged 79 of 109 (72%) known antigenic or enzymatically detrimental blood group variants, while 4 of 12 variants that do not result in an altered RBC phenotype were flagged as deleterious. Next-generation sequencing has known potential for high-throughput and extended RBC phenotype prediction; a database of GRCh37 and GRCh38 chromosomal coordinates for 120 worldwide blood group variants is provided as a basis for this clinical application.

Monitoring biodiversity in libraries: a pilot study and perspectives for indoor air quality.

Indoor Air Quality (IAQ) in libraries is influenced by the presence of specific factors which can impact on both paper storage as well as people health. Microclimatic conditions induce and support a biodiversity pattern involving environmental and anthropic microorganisms. We used a multidisciplinary monitoring model to characterize microflora biodiversity by Next Generation Sequencing (NGS). Biodiversity indexes were adapted to evaluate anthropic vs environmental pollution by combining Shannon mean index (H), species representativeness (EH), human/environmental pollution ratio (SA) to better characterize the NGS output and acquire synthetic information on Indoor Air Microbial Biodiversity (IAMB). Results indicate a frequently low microbial load (IGCM/m3 < 1000) characterized by different species (n = 102), including several cellulose metabolizing bacteria. Workers and visitors appeared a relevant source of microbial contamination. Air biodiversity assayed by NGS seems a promising marker for studying IAQ.

Abstract 3960: Combined circulating tumour cell (CTC) and circulating tumor DNA (ctDNA) analysis of blood from patients with pancreatic cancer

Abstract Background The challenge to improve outcomes for patients diagnosed with advanced pancreatic cancer remains very real with only small improvements in median survival gained by the use of systemic chemotherapy and little improvement in 5-year survival over the past decades. The advent of next generation sequencing (NGS) of tumor nucleic acids has opened up the possibility of improving outcomes through personalized therapies selected on the basis of tumor genetics. Whilst NGS biomarkers can be measured in tumor biopsies sampled shortly before and after treatment this is often not practical for ethical, logistical and simple lack of availability. To circumvent problems associated with tumor sampling we have developed and evaluated blood-borne nucleic acids biomarkers for patients diagnosed with advanced pancreatic cancer. Approach We developed a NGS circulating free DNA (cfDNA) analysis pipeline based on the generation of whole genome NGS libraries followed by sequencing of over 600 cancer-associated genes using Agilent SureSelect. Using whole blood collected with Streck cell free DNA blood collection tubes (cfDNA BCT) we optimised combined circulating tumor cell (CTC) enrichment and cfDNA isolation. Quantitative measurements of KRAS mutations using both NGS and droplet digital PCR (ddPCR) were used to compare the tumor component present in both CTCs and cfDNA. We evaluated the overall approach using External Quality Assessment (EQA) controls and over 50 advanced pancreatic cancer patient samples. For CTC analysis we also compared epitope dependent (CellSearch) and independent (Parsortix) enrichment. Results After applying our NGS pipeline to 5 EQA genomic controls we identified all 14 known mutations correctly indicating high sensitivity and specificity. Both ddPCR and NGS identified KRAS mutations in patient cfDNA with a higher success rate seen for ddPCR consistent with the higher sensitivity of this methodology. From the NGS output additional mutations were detected in samples which either harboured or lacked detectable KRAS mutations. Consistent with previous observations CellSearch CTCs were detected at low levels in around 20% of patients with a similar frequency seen in initial analysis of CTCs obtained by epitope independent enrichment (Parsortix). In general KRAS mutations were detected at a higher level in patient cfDNA compared to enriched CTCs although some patients showed detectable CTC KRAS mutations with no KRAS mutations detected in their cfDNA by either ddPCR or NGS. Ongoing analysis is aimed at establishing if the molecular observations correlate with clinical outcome. Conclusion Combined CTC enrichment and cfDNA isolation is readily achievable using a single Streck cfDNA BCT. Results indicate that combined CTC and cfDNA analysis is more sensitive than either approach alone. Citation Format: Ged Brady, Dominic Rothwell, Mahmood Ayub, Nigel Smith, Sumitra Mohan, Jakub Chudziak, Kyaw Aung, Richard Hubner, Crispin Miller, Alison Backen, Hui Sun Leong, Sakshi Gulati, Chang Sik Kim, Angela Lamarca, Mairéad McNamara, Juan Valle, Caroline Dive. Combined circulating tumour cell (CTC) and circulating tumor DNA (ctDNA) analysis of blood from patients with pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3960.

SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.

Motivation: Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs.Results: We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5′-end processing and 3′-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain.Availability and Implementation: The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502.Contact: yasu@bio.keio.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

Evaluation of the evenness score in next-generation sequencing.

The evenness score (E) in next-generation sequencing (NGS) quantifies the homogeneity in coverage of the NGS targets. Here I clarify the mathematical description of E, which is 1 minus the integral from 0 to 1 over the cumulative distribution function F(x) of the normalized coverage x, where normalization means division by the mean, and derive a computationally more efficient formula; that is, 1 minus the integral from 0 to 1 over the probability density distribution f(x) times 1-x. An analogous formula for empirical coverage data is provided as well as fast R command line scripts. This new formula allows for a general comparison of E with the coefficient of variation (=standard deviation σ of normalized data) which is the conventional measure of the relative width of a distribution. For symmetrical distributions, including the Gaussian, E can be predicted closely as 1-σ(2)/2⩾E⩾1-σ/2 with σ⩽1 owing to normalization and symmetry. In case of the log-normal distribution as a typical representative of positively skewed biological data, the analysis yields E≈exp(-σ*/2) with σ*(2)=ln(σ(2)+1) up to large σ (⩽3), and E≈1-F(exp(-1)) for very large σ (⩾2.5). In the latter kind of rather uneven coverage, E can provide direct information on the fraction of well-covered targets that is not immediately delivered by the normalized σ. Otherwise, E does not appear to have major advantages over σ or over a simple score exp(-σ) based on it. Actually, exp(-σ) exploits a much larger part of its range for the evaluation of realistic NGS outputs.

Abstract 628: Determinants of quality of next-generation sequencing output from the strand-specific TruSight Tumor Sequencing Panel in a clinical diagnostic setting

Abstract The use of Next-Generation Sequencing (NGS) technologies is increasingly prevalent within diagnostic labs. As genomic regions are sequenced to greater depth in cancer diagnostics, it is critical to differentiate clinically actionable variants from artifacts arising from sequencing-errors, sample- processing or sample-age, and to identify samples that will be difficult to evaluate. We sought to determine whether strand-specific sequencing approaches, such as the TruSight Tumor Sequencing Panel (Illumina) could enable sample and variant triage in a clinical diagnostic settingTruSight Tumor Sequencing Panel allows for paired-end sequencing of individual strands of DNA and analyzing them either together (Paired) or separately (Pool A and Pool B). Variants identified in one pool, but not the other, are putative artifacts; variants identified in both pools are considered true calls. Combined analysis of both pools was performed in two ways: By summing variant calls across pools, and by informatically determining overlapping variant calls between pools. In a test cohort of 44 FFPE samples of varying age and tumor type, we assessed whether age of sample, strand bias, and fixation impacted the detection of high confidence variants using the TruSight Tumor Sequencing panel. Data were compared to the results of analysis of the same samples using the established Illumina TruSeq Amplicon Cancer Panel and/or Sanger Sequencing.Sample age, tumor cellularity, tumor type and template DNA quality were not found to be associated with quality of NGS output in our study. We also evaluated the overall transition/transversion (Ti/Tv) ratios for variants detected either uniquely in one pool or in combined analysis. Interestingly, for variants detected in both pools, the Ti/Tv ratio was 1.97, compared to 0.52-0.60 for those detected in only 1 pool (p &amp;lt; 0.001). Strikingly, samples that sequenced successfully but gave inconclusive and difficult to interpret variant lists were associated with%G&amp;gt;A:C&amp;gt;T transition &amp;gt; 62.5% and Ti/Tv ratios of &amp;gt; 4.0 (p &amp;lt; 0.001). G&amp;gt;A:C&amp;gt;T transitions were significantly over-represented in these samples. The overall%G&amp;gt;A:C&amp;gt;T transitions were equivalent (44-52%) in individual pools or in paired analysis. However, when inconclusive samples were accounted for, the%G&amp;gt;A:C&amp;gt;T transitions differed between the two analyses: 49.7% (paired) vs. 30.1-32.1% (individual pools). In summary, the Ti/Tv ratio can act as a critical determinant of variant call quality - Ti/Tv ratios ∼0.5 represent sequencing artifacts, while Ti/Tv ratios &amp;gt; 4.0 are indicative of inconclusive sequencing output.We conclude that variant Ti/Tv ratio as well as%G&amp;gt;A:C&amp;gt;T transition in variants detected by the TruSight Tumor Sequencing Panel may be helpful evaluators of quality and clinical utility of sequencing output for FFPE tumor samples tested in a clinical diagnostic setting. Citation Format: Swati Garg, Mahadeo A. Sukhai, Mariam Thomas, Michelle Mah, Tong Zhang, Trevor Pugh, Suzanne Kamel-Reid, Tracey L. Stockley. Determinants of quality of next-generation sequencing output from the strand-specific TruSight Tumor Sequencing Panel in a clinical diagnostic setting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 628. doi:10.1158/1538-7445.AM2015-628

RNA-Seq: revelation of the messengers

Next-generation RNA-sequencing (RNA-Seq) is rapidly outcompeting microarrays as the technology of choice for whole-transcriptome studies. However, the bioinformatics skills required for RNA-Seq data analysis often pose a significant hurdle for many biologists. Here, we put forward the concepts and considerations that are critical for RNA-Seq data analysis and provide a generic tutorial with example data that outlines the whole pipeline from next-generation sequencing output to quantification of differential gene expression.

Integrating whole transcriptome sequence data and public databases for analysis of somatic mutations in tumors

The annotation of pathologically relevant somatic variations has gained importance with the wide use of next-generation sequencing in biomedical studies. At present, this evaluation is performed using public tools such as SAMtools and ANNOVAR by comparing predicted mutations and small nucleotide variations (SNVs) with databases such as 1000 Genomes and dbSNP, as well as with paired normal data if available. However, these analytical methods lack the ability to integrate information from the different analyses into a single output. Additionally, many approaches are filter based and remove data that does not match specific criteria, thus leading to the removal of variations that would otherwise be reconsidered later. To this end, we have developed a Perl wrapper script that utilizes standard next-generation sequencing output files along with SAMtools and ANNOVAR to produce an annotated tumor variant file with sequence calls from related tumor and matched normal samples. We performed SOLiD paired-end sequencing of the whole transcriptome of one lung adenocarcinoma and seven normal lung samples (including one matched normal). BioScope 1.3 was used to map the reads, and the SNVs were identified by the diBayes package. The map files in binary-sequence alignment format (BAM) and SNV files in generic feature format (GFF) were used to annotate the tumor SNVs with matched normal sequence information at each position (diBayes and SAMtools), as well as other normal samples (both position and gene based). Furthermore, SNVs were annotated with positional information, including whether intronic, exonic, or synonymous versus nonsynonymous, as well as with data from the 1000 Genomes Project (allele frequency), the dbSNP database (rs identifiers) and the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Of the 1,804 SNVs initially identified in the tumor sample, 138 SNVs were found in non-coding RNA, and 75 did not appear in the normal samples according to diBayes or in the specific matched normal sample according to SAMtools. Because the capacity to sequence the whole transcriptome is subject to the expression level, the possibility of failure to detect variations in normal lung samples cannot be ignored. To address this concern, we analyzed 1000 Genomes data and found that only 23 of the 75 potential tumor-specific SNVs exhibited allele frequencies <1%, and 6 of these exist in dbSNP. All of these steps can be rapidly performed by a researcher, and modifying the approach to identify other types of SNVs is easily achievable. The use of a single script that tracks input file names and locations is expected to improve data handling and reporting. Notably, all variant data are present in a single file, allowing straightforward modification of criteria and instant hypothesis testing and therefore reducing the need for an informed end user to re-engage a bioinformatician to address another biological question.