scholarly journals The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced cancer patients

2017 ◽  
Author(s):  
Oliver A. Zill ◽  
Kimberly C. Banks ◽  
Stephen R. Fairclough ◽  
Stefanie A. Mortimer ◽  
James V. Vowles ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Clonal Evolution ◽  
Patient Treatment ◽  
Large Set ◽  
Sequencing Analysis ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Mutual Exclusivity ◽  
Advanced Cancer Patients ◽  
Driver Genes

AbstractCell-free DNA (cfDNA) sequencing provides a non-invasive method for obtaining actionable genomic information to guide personalized cancer treatment, but the presence of multiple alterations in circulation related to treatment and tumor heterogeneity pose analytical challenges. We present the somatic mutation landscape of 70 cancer genes from cfDNA deep-sequencing analysis of 21,807 patients with treated, late-stage cancers across >50 cancer types. Patterns and prevalence of cfDNA alterations in major driver genes for non-small cell lung, breast, and colorectal cancer largely recapitulated those from tumor tissue sequencing compendia (TCGA and COSMIC), with the principle differences in alteration prevalence being due to patient treatment. This highly sensitive cfDNA sequencing assay revealed numerous subclonal tumor-derived alterations, expected as a result of clonal evolution, but leading to an apparent departure from mutual exclusivity in treatment-naïve tumors. To facilitate interpretation of this added complexity, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality. Upon applying these methods, robust mutual exclusivity was observed among predicted truncal driver cfDNA alterations, in effect distinguishing tumor-initiating alterations from secondary alterations. Treatment-associated resistance, including both novel alterations and parallel evolution, was common in the cfDNA cohort and was enriched in patients with targetable driver alterations. Together these retrospective analyses of a large set of cfDNA deep-sequencing data reveal subclonal structures and emerging resistance in advanced solid tumors.

Impact of Eltrombopag on Expansion of Clones with Somatic Mutations in Refractory Aplastic Anemia

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 300-300 ◽  
Author(s):  
Bhumika J. Patel ◽  
Bartlomiej Przychodzen ◽  
Michael J. Clemente ◽  
Cassandra M. Hirsch ◽  
Tomas Radivoyevitch ◽  
...  
Keyword(s):  
Aplastic Anemia ◽  
Board Of Directors ◽  
Deep Sequencing ◽  
Somatic Mutations ◽  
Clonal Evolution ◽  
Clonal Expansion ◽  
Quantitative Detection ◽  
Sequencing Analysis ◽  
Advisory Committees ◽  
Before And After

Abstract Despite documented success of immunosuppressive therapy (IST) in the treatment of aplastic anemia (AA), a significant minority of patients remain refractory, most responses are incomplete, and allogeneic stem cell transplantation is not available for older patients or those with significant comorbidities. Until introduction of the cMpl agonist eltrombopag, anabolic steroids were the most commonly used salvage drugs. At least theoretically, engaging growth factor receptors with eltrombopag has the potential to promote the evolution or expansion of mutant clones and thereby increase the rate of progression to secondary MDS, a feared complication of AA occurring in 10-20% of patients. Recently we and others reported detection of clonogenic somatic mutations typical of MDS in patients with AA and PNH. Subsequent study demonstrated that mutations characteristic of sMDS can be found in some patients at presentation of AA and may constitute risk for subsequent progression to MDS. As the risk of MDS evolution was a prominent concern when filgrastim was more widely used in management of AA, now similar questions have been raised regarding use of eltrombopag, be it as salvage therapy or to complement IST. Recently, one of our primary refractory patients receiving eltrombopag progressed to AML. This clinical observation led to investigation of the impact of eltrombopag on evolution and clonal expansion using deep sequencing of a cohort of patients with AA. DNA from bone marrow cells was sequenced before and after initiation of eltrombopag to evaluate clonal expansion or evolution using a targeted multi-amplicon deep sequencing panel of the top 60 most commonly mutated genes in MDS. Among 208 AA patients treated at Cleveland Clinic, we identified 13 patients (median age 68 yrs.) who were treated with eltrombopag for IST-refractory AA; median duration of treatment was 85 wks. The overall response rate, defined as sustained improvement in blood counts and transfusion independence after 12 weeks of therapy, was 46% (6/13), while 38% (5/13) of patients showed stable disease with intermittent transfusions (one of whom underwent HSCT). Among the two non-responders, one patient developed a PNH clone and another progressed to AML (see below). Expansion of PNH granulocytes after eltrombopag treatment was observed in 2 patients. Two patients had chromosomal abnormalities at initial diagnosis, one with t (10; 18) in 2 metaphases, and one with an extranumeral Y chromosome. Use of next generation sequencing (NGS) allows for the quantitative detection of clonal events. We hypothesized that serial analysis by NGS before and after eltrombopag therapy may provide clues as to potential effects of this drug on clonal evolution. Sequencing analysis before eltrombopag treatment revealed the presence of a sole clonal mutational event in 3/13 cases, including CEBPA, EZH2, and BCOR. In the patient with a CEBPA mutation, the mutation persisted during treatment with minimal clonal expansion evidenced by a change in VAF from 53% to 65%. In the second patient, NGS results revealed the initial presence of an EZH2 mutation. A post eltrombopag sample clearly identified acquisition of additional clonal events in genes highly associated with advanced disease and clonal evolution (RUNX1 and U2AF1), as well as slight expansion of a persistent EZH2 clone from 2 to 8%. The third patient harbored a BCOR mutation which expanded markedly, increasing from 8% to 21%, and was accompanied by a hematological response. Sequencing results after eltrombopag treatment revealed the acquisition of new somatic mutations in 5/13 (38%) cases: 2 new CEBPA mutations, 1 new BCOR mutation, and, as discussed, one case with an initial EZH2 mutation in which RUNX1 and U2AF1 mutations were later discovered. In the 5th patient, evolution to AML was observed and accompanied by a large DNMT3A and U2AF1 clone that was absent on initial evaluation. In conclusion, we did observe occasional expansion of clones with potentially leukemogenic mutations during treatment with eltrombopag. At our institution a case control study of patients with refractory aplastic anemia without treatment with eltrombopag is ongoing; ideally a prospective trial would be needed to confirm results. Our results suggest that the initial detection of certain somatic mutations (CBL, SETBP1 and RUNX1) associated with post-AA MDS may contraindicate use of eltrombopag in AA. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees.

Whole-Exome Sequencing and Targeted Deep Sequencing of cfDNA Enables a Comprehensive Mutational Profiling of Multiple Myeloma

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 197-197 ◽  
Author(s):  
Salomon Manier ◽  
Jihye Park ◽  
Samuel Freeman ◽  
Gavin Ha ◽  
Marzia Capelletti ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Research Funding ◽  
Clonal Evolution ◽  
Target Coverage ◽  
Whole Genome ◽  
Driver Genes ◽  
Whole Exome ◽  
Low Pass ◽  
Targeted Deep Sequencing ◽  
Tumor Dna

Abstract Background . Cell-free DNA (cfDNA) sequencing enables serial temporal sampling, which offers the possibility of following the dynamics of biomarkers and clonal evolution in Multiple Myeloma (MM) over time. The use of cfDNA in clinical practice as a molecular biomarker and for monitoring response/resistance is dependent on a comprehensive profile of matched cfDNA and tumor DNA (tDNA) samples. Here we performed Ultra-Low Pass Whole Genome Sequencing (ULP-WGS) followed by whole-exome sequencing (WES) and targeted deep sequencing of matched cfDNA/tDNA samples from MM patients. Methods. We performed next generation sequencing of matched cfDNA/tDNA samples for 63 patients with newly diagnosed or relapsed MM, SMM, or MGUS. Libraries were constructed using the Kappa Hyper kit and sequenced by ultra-low-pass whole-genome sequencing (ULP-WGS, 0.1x coverage) to quantify tumor fraction within cfDNA. WES was performed on 30 matched samples cfDNA/tDNA/germline DNA from 10 patients with more than 5% of tumor fraction. Libraries were hybridized to the Nextera Rapid Capture Exome kit (Illumina) and then sequenced on HiSeq 4000 (Illumina). Targeted deep sequencing was performed on 32 matched cfDNA/tDNA samples from 16 patients using the HaloPlex HS technology (Agilent), allowing for molecular barcoding. Libraries were constructed according to the manufacturer's instructions and sequenced on HiSeq 2500 (Illumina). Sequencing data were analyzed using the Firehose pipelines, including MuTect, ABSOLUTE, ReCapSeg, GISTIC and MutSig. Results. We first used a cost-effective approach to establish the tumor content of cfDNA in a large-scale manner by ULP-WGS. Among 63 tested samples (53 MM, 6 SMM and 4 MGUS patient samples), the tumor fraction within cfDNA ranged from 0 to 81% with a mean of 10%. About 43% of these samples had tumor fraction greater than 5% within cfDNA. To assess whether cfDNA can capture the genetic diversity of MM and inform clinical management, we performed WES of matched cfDNA/tDNA/germline DNA samples for 10 patients (mean target coverage 194x). Copy number alterations (CNAs) assessed by WES (ReCapSeg) were consistent between cfDNA and tumor DNA. Similarly, focal CNAs assessed by GISTIC were consistent between tDNA and cfDNA. We then examined the overlap of somatic single nucleotide variants (SSNVs) between WES of cfDNA and matched tDNA. We found, on average, 100% of the clonal and 96% of the subclonal (range 54-100%) SSNVs that were detected in the tumor were confirmed to be present in cfDNA. Similarly, for mutations detected in the cfDNA, we found, on average, 100% of the clonal and 99% of the subclonal (range 98-100%) SSNVs were confirmed in the tumor. To assess whether targeted deep sequencing of cfDNA could be a good proxy for tumor biopsy we used a targeted deep sequencing approach of known MM driver genes. Libraries were prepared using unique molecular barcodes to avoid duplication rates, for 32 matched cfDNA/tDNA samples from 16 patients with MM. The mean target coverage was 596x. We found similar frequencies of altered MM driver genes in both cfDNA and tDNA, including KRAS, NRAS, and TP53, indicating that cfDNA can be used for precision medicine. Conclusions. Our study demonstrates that both WES and targeted deep sequencing of cfDNA are consistently representative of tumor DNA alterations in terms of CNAs, focal CNAs and SSNVs. This approach could therefore be used to longitudinally follow clonal evolution across the course of the disease and precision medicine in patients with MM. Disclosures Palumbo: Takeda: Employment, Honoraria; Janssen Cilag: Honoraria. Kumar:Noxxon Pharma: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Kesios: Consultancy; Glycomimetics: Consultancy; BMS: Consultancy; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; AbbVie: Research Funding; Onyx: Consultancy, Research Funding. Roccaro:Takeda Pharmaceutical Company Limited: Honoraria. Facon:Amgen: Consultancy, Speakers Bureau; Novartis: Consultancy; Janssen: Consultancy, Speakers Bureau; Bristol: Consultancy; Millenium/Takeda: Consultancy; Celgene: Consultancy, Speakers Bureau; Karyopharm: Consultancy. Ghobrial:Celgene: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Noxxon: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Amgen: Honoraria.

Profiling the Genomic Landscape at the Early Stages of CLL: Low Genomic Complexity and Paucity of Driver Mutations in MBL and Indolent CLL

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3214-3214 ◽  
Author(s):  
Andreas Agathangelidis ◽  
Viktor Ljungström ◽  
Lydia Scarfò ◽  
Claudia Fazi ◽  
Maria Gounari ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Research Funding ◽  
Somatic Mutations ◽  
Driver Mutations ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Driver Genes ◽  
Hematopoietic Stem ◽  
Genomic Complexity ◽  
Targeted Deep Sequencing

Abstract Chronic lymphocytic leukemia (CLL) is preceded by monoclonal B cell lymphocytosis (MBL), characterized by the presence of monoclonal CLL-like B cells in the peripheral blood, yet at lower numbers than those required for the diagnosis of CLL. MBL is distinguished into low-count (LC-MBL) and high-count (HC-MBL), based on the number of circulating CLL-like cells. While the former does not virtually progress into a clinically relevant disease, the latter may evolve into CLL at a rate of 1% per year. In CLL, genomic studies have led to the discovery of recurrent gene mutations that drive disease progression. These driver mutations may be detected in HC-MBL and even in multipotent hematopoietic progenitor cells from CLL patients, suggesting that they may be essential for CLL onset. Using whole-genome sequencing (WGS) we profiled LC-MBL and HC-MBL cases but also CLL patients with stable lymphocytosis (range: 39.8-81.8*109 CLL cells/l) for >10 years (hereafter termed indolent CLL). This would refine our understanding of the type of genetic aberrations that may be involved in the initial transformation rather than linked to clinical progression as is the case for most, if not all, CLL driver mutations. To this end, we whole-genome sequenced CD19+CD5+CD20dim cells from 6 LC-MBL, 5 HC-MBL and 5 indolent CLL cases; buccal control DNA and polymorphonuclear (PMN) cells were analysed in all cases. We also performed targeted deep-sequencing on 11 known driver genes (ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, FBXW7) in 8 LC-MBL, 13 HC-MBL and 7 indolent CLL cases and paired PMN samples. Overall similar mutation signatures/frequencies were observed for LC/HC-MBL and CLL concerning i) the entire genome; with an average of 2040 somatic mutations observed for LC-MBL, 2558 for HC-MBL and 2400 for CLL (186 for PMN samples), as well as ii) in the exome; with an average of non-synonymous mutations of 8.9 for LC-MBL, 14.6 for HC-MBL, 11.6 for indolent CLL (0.9 for PMN samples). Regarding putative CLL driver genes, WGS analysis revealed only 2 somatic mutations within NOTCH1, and FBXW7 in one HC-MBL case each. After stringent filtering, 106 non-coding variants (NCVs) of potential relevance to CLL were identified in all MBL/CLL samples and 4 NCVs in 2/24 PMN samples. Seventy-two of 110 NCVs (65.5%) caused a potential breaking event in transcription factor binding motifs (TFBM). Of these, 29 concerned cancer-associated genes, including BTG2, BCL6 and BIRC3 (4, 2 and 2 samples, respectively), while 16 concerned genes implicated in pathways critical for CLL e.g. the NF-κB and spliceosome pathways. Shared mutations between MBL/CLL and their paired PMN samples were identified in all cases: 2 mutations were located within exons, whereas an average of 15.8 mutations/case for LC-MBL, 8.2 for HC-MBL and 9 for CLL, respectively, concerned the non-coding part. Finally, 16 sCNAs were identified in 9 MBL/CLL samples; of the Döhner model aberrations, only del(13q) was detected in 7/9 cases bearing sCNAs (2 LC-MBL, 3 HC-MBL, 2 indolent CLL). Targeted deep-sequencing analysis (coverage 3000x) confirmed the 2 variants detected by WGS, i.e. in NOTCH1 (n=1) and FBXW7 (n=1), while 4 subclonal likely damaging variants were detected with a VAF <10% in POT1 (n=2), TP53 (n=1), and SF3B1 (n=1) in 4 HC-MBL samples. In conclusion, LC-MBL and CLL with stable lymphocytosis for >10 years display similar low genomic complexity and absence of exonic driver mutations, assessed both with WGS and deep-sequencing, underscoring their common low propensity to progress. On the other hand, HC-MBL comprising cases that may ultimately evolve into clinically relevant CLL can acquire exonic driver mutations associated with more dismal prognosis, as exemplified by subclonal driver mutations detected by deep-sequenicng. The existence of NCVs in TFBMs targeting pathways critical for CLL prompts further investigation into their actual relevance to the clinical behavior. Shared mutations between CLL and PMN cells indicate that some somatic mutations may occur before CLL onset, likely at the hematopoietic stem-cell level. Their potential oncogenic role likely depends on the cellular context and/or microenvironmental stimuli to which the affected cells are exposed. Disclosures Stamatopoulos: Novartis: Honoraria, Research Funding; Janssen: Honoraria, Other: Travel expenses, Research Funding; Gilead: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria, Other: Travel expenses. Ghia:Adaptive: Consultancy; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Roche: Honoraria, Research Funding.

scholarly journals SSA.ME Detection of cancer mutual exclusivity patterns by small subnetwork analysis

2015 ◽  
Author(s):  
Sergio Pulido-Tamayo ◽  
Bram Weytjens ◽  
Dries De Maeyer ◽  
Kathleen Marchal
Keyword(s):  
Clonal Evolution ◽  
Complex Problem ◽  
Added Value ◽  
Breast Cancer Dataset ◽  
Mutual Exclusivity ◽  
Driver Genes ◽  
Cancer Dataset ◽  
Fitness Advantage ◽  
Mutational Frequency ◽  
Subnetwork Analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway, as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result mutated genes display patterns of mutual exclusivity across tumors. The identification of such patterns have been exploited to detect cancer drivers. The complex problem of searching for mutual exclusivity across individuals has previously been solved by filtering the input data upfront, analyzing only genes mutated in numerous samples. These stringent filtering criteria come at the expense of missing rarely mutated driver genes. To overcome this problem, we present SSA.ME, a network-based method to detect mutually exclusive genes across tumors that does not depend on stringent filtering. Analyzing the TCGA breast cancer dataset illustrates the added value of SSA.ME: despite not using mutational frequency based-prefiltering, well-known recurrently mutated drivers could still be highly prioritized. In addition, we prioritized several genes that displayed mutual exclusivity and pathway connectivity with well-known drivers, but that were rarely mutated. We expect the proposed framework to be applicable to other complex biological problems because of its capability to process large datasets in polynomial time and its intuitive implementation.

scholarly journals SuperFreq: Integrated mutation detection and clonal tracking in cancer

2018 ◽  
Author(s):  
Christoffer Flensburg ◽  
Tobias Sargeant ◽  
Alicia Oshlack ◽  
Ian Majewski
Keyword(s):  
Clonal Evolution ◽  
The Cancer Genome Atlas ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Scope Of Application ◽  
Cancer Types ◽  
Multiple Samples

AbstractAnalysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/SuperFreq.

scholarly journals A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

2015 ◽  
Author(s):  
Heiko Horn ◽  
Michael S. Lawrence ◽  
Jessica Xin Hu ◽  
Elizabeth Worstell ◽  
Nina Ilic ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Cancer Biology ◽  
Low Frequency ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Driver Genes ◽  
Significance Level ◽  
Cancer Genomes ◽  
Tumor Types

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.

scholarly journals MERIT: a Mutation Error Rate Identification Toolkit for Ultra-deep Sequencing Applications

2017 ◽  
Author(s):  
Mohammad Hadigol ◽  
Hossein Khiabanian
Keyword(s):  
Deep Sequencing ◽  
High Throughput Sequencing ◽  
Clonal Evolution ◽  
False Negative ◽  
Error Rates ◽  
Sequencing Data ◽  
Genomic Context ◽  
Nucleotide Incorporation ◽  
Double Base

AbstractRapid progress in high-throughput sequencing (HTS) has enabled the molecular characterization of mutational landscapes in heterogeneous populations and has improved our understanding of clonal evolution processes. Analyzing the sensitivity of detecting genomic mutations in HTS requires comprehensive profiling of sequencing artifacts. To this end, we introduce MERIT, designed for in-depth quantification of erroneous substitutions and small insertions and deletions, specifically for ultra-deep applications. MERIT incorporates an all-inclusive variant caller and considers genomic context, including the nucleotides immediately at 5′ and 3′, thereby establishing error rates for 96 possible substitutions as well as four singlebase and 16 double-base indels. We apply MERIT to ultra-deep sequencing data (1,300,000×) and show a significant relationship between error rates and genomic contexts. We devise an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of true mutations. Finally, we assess nucleotide-incorporation fidelity of four high-fidelity DNA polymerases in clinically relevant loci, and demonstrate how fixed detection thresholds may result in substantial false positive as well as false negative calls.

scholarly journals Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 81
Author(s):  
Florian PICHOT ◽  
Virginie MARCHAND ◽  
Mark HELM ◽  
Yuri MOTORIN
Keyword(s):  
Deep Sequencing ◽  
Model Organisms ◽  
Trna Genes ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Rna Modifications ◽  
Modern Biology ◽  
Widespread Application ◽  
Reference Sequences ◽  
Trna Sequencing

Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecules are among the frequent targets of such investigation, since they contain multiple modified residues. However, the major challenge in tRNA examination is related to a large number of duplicated and point-mutated genes encoding those RNA molecules. Moreover, the existence of multiple isoacceptors/isodecoders complicates both the analysis and read mapping. Existing databases for tRNA sequencing provide near exhaustive listings of tRNA genes, but the use of such highly redundant reference sequences in RNA-seq analyses leads to a large number of ambiguously mapped sequencing reads. Here we describe a relatively simple computational strategy for semi-automatic collapsing of highly redundant tRNA datasets into a non-redundant collection of reference tRNA sequences. The relevance of the approach was validated by analysis of experimentally obtained tRNA-sequencing datasets for different prokaryotic and eukaryotic model organisms. The data demonstrate that non-redundant tRNA reference sequences allow improving unambiguous mapping of deep sequencing data.

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