The development of next-generation sequencing (NGS) technology allows to sequence whole exomes or genome. However, data analysis is still the biggest bottleneck for its wide implementation. Most laboratories still depend on manual procedures for data handling and analyses, which translates into a delay and decreased efficiency in the delivery of NGS results to doctors and patients. Thus, there is high demand for developing an automatic and an easy-to-use NGS data analyses system. We developed comprehensive, automatic genetic analyses controller named Mobile Genome Express (MGE) that works in smartphones or other mobile devices. MGE can handle all the steps for genetic analyses, such as: sample information submission, sequencing run quality check from the sequencer, secured data transfer and results review. We sequenced an Actrometrix control DNA containing multiple proven human mutations using a targeted sequencing panel, and the whole analysis was managed by MGE, and its data reviewing program called ELECTRO. All steps were processed automatically except for the final sequencing review procedure with ELECTRO to confirm mutations. The data analysis process was completed within several hours. We confirmed the mutations that we have identified were consistent with our previous results obtained by using multi-step, manual pipelines.
Citation: Yoon J-H, Kim TW, Mendez P, Jablons DM, Kim I-J (2017) Mobile Genome Express (MGE): A comprehensive automatic genetic analyses pipeline with a mobile device. PLoS ONE 12(4): e0174696. https://doi.org/10.1371/journal.pone.0174696
Editor: Jian-Hua Mao, Lawrence Berkeley National Laboratory, University of California, Berkeley, UNITED STATES
Received: January 24, 2017; Accepted: March 13, 2017; Published: April 12, 2017
Copyright: © 2017 Yoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: A sequencing data file of the Acrometrix Oncology Hotspot Control DNA is deposited and available at the Sequence Read Archive (SRA) website (http://www.ncbi.nlm.nih.gov/Traces/sra/?view=run_browser&run=SRR3406184).
Funding: This study was supported by funds from CureSeq Inc. and Thoracic Oncology Laboratory at University of California San Francisco (UCSF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: DMJ and IJK are equity holders and consultants of CureSeq Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
There were several breakthroughs for genetic and genomic analyses since the first description of DNA double helix structure in 1953 [1,2]. A development of the polymerase chain reaction (PCR) technology opened an era for different types of genetic analyses with limited amount of DNA from various biological samples [3–5]. PCR amplification technology is still a foundation of up-to-date genome analysis technologies. Sanger sequencing was then developed [6,7], and enabled a discovery of human genome sequences and completed the human genome sequence map (The Human Genome Project) in 2003 (https://www.genome.gov/10001772). The next revolution of genetic and genomic analyses would be the development of microarray or DNA chip technology . With this hybridization-based high throughput expression or genotype technologies, all known genes could be analyzed at one time for their expression. Microarray technology contributed to the identification of new subtypes and biomarkers of diseases such as cancer [8–12].
Next-generation sequencing (NGS) is the state-of-the-art technology widely used for genetic and genome analyses in many life science fields. NGS was initially developed in mid-2000’s [13–15], and aimed to sequence the genome in one experiment. Later, the same technology was adapted to allow the sequencing of the whole transcriptome, also called RNA-seq [16–19] and the epigenome . The previous technologies mentioned above also had their own ‘paradigm-shifting’ and ‘revolutionary’ points. But, NGS is the first technology enabling us to sequence the whole exome or genome in one experiment [13–15]. Despite technical superiority by NGS, it was not easy to use NGS technology in a regular laboratory at the beginning for several reasons. First, it was strictly too complex. It required multiple challenging steps and relatively higher quality and quantity of DNA or RNA. Second, the cost was tremendously high. Although NGS can generate great amount of genome or exome data, the costs for reagents, chips or sequencing cells, and machines were extremely high, which prevented a frequent and regular use of NGS for various applications. Lastly, NGS data analyses or bioinformatics procedures were notoriously difficult for most biologists. As one whole exome or genome sequencing can produce up to gigabytes of data, it is overwhelming to handle by most molecular biologists or geneticists.
However, situations for NGS applications had changed dramatically for last several years. The sequencing cost has been significantly reduced from more than several thousands of dollars per exome to near one thousand dollars . The size of sequencing data has also been dramatically reduced because of the increasing demand of a targeted sequencing focusing on 5–300 genes rather than exome sequencing [22–25]. Exome sequencing strategy is still invaluable for identifying new biomarkers by big research consortiums such as TCGA [26,27]. However, a targeted sequencing approach focusing only selected genes such as cancer panels or hereditary disease panels seems more dominant for precision or personalized medicine applications [22–25]. In fact, a lot of Clinical Laboratory Improvement Amendments (CLIA) labs provide a targeted sequencing panel rather than an exome or whole genome sequencing due to data accuracy, cost, and turn-around-time (TAT) [22–25]. Thus, a reduction of sequencing cost, data size, TAT, and an increase of competition among many commercial companies and CLIA labs would expedite the processes to the era of having personal sequencing information. Currently, only family members under screening programs of hereditary cancer or some already diagnosed cancer patients are genetically tested by NGS with appropriate biological samples (i.e. blood or tumor). Nevertheless, we believe that more and more people will need or want to know their genetic profiles in a near future. A successful application of circulating tumor DNA sequencing for the early detection of cancer or prognosis surveillance [28–30], supports the idea that sequencing information will be available to people who want to know it. Though, the cyber-security must be improved and there should be clear legal regulations and consensus for handling, analyzing, interpreting, and delivering the genome information as it is very massive and delicate personal information.
Although it seems very promising to see the era for a regular NGS sequencing screening testing (like X-ray), in a regular clinical laboratory, there are some significant hurdles to overcome. Those are NGS data analyses, interpretation, and reporting. There should be a robust automatic data processor from the sequencer through bioinformatics data analysis pipelines, clinical interpretation, and genetic information delivery. Many CLIA labs use different bioinformatics pipelines to analyze NGS data [22–25,31–33]. And yet, each step tends to be separated and controlled manually, possibly by different persons. After a laboratory technician complete the sequencing, the data quality may be checked first and then downloaded to the bioinformatics pipelines. Then Bioinformatics team starts converting the raw sequencing data into their appropriate analysis programs going through several steps such as sequence alignment, filtering, variant calling, annotation, and the final review. The selected genetic variants will be passed to the interpretation or analytics team for making a report and providing clinical or biological meanings. The finalized genetic report will then be delivered to patients or doctors via mail, e-mail, or fax. All these multiple and complex steps would be feasible only for analyzing a small scale of samples. However, if we want to handle a large number of samples accurately and robustly, this kind of currently used bioinformatics methods will be a problem.
Thus, we decide to develop an automatic NGS data analysis program which works in a mobile device (i.e. cellular phone) that controls the whole process, from the sequencing run in a sequencer through NGS data analyses to the genetic information delivery. One of challenging points for making the whole automatic process from the ‘wet’ laboratory sequencing works to the ‘dry’ bioinformatics works may be an efficient communication between wet and dry laboratories. We aim to make a simple but very useful program working in a mobile device to check and control all the processes of NGS. Though it is still preliminary to deliver the genome information directly to patient or individual due to legal issues, it may be eventually possible to get their genome information through a simple and easy way like mobile phone, not though on-site hospital visit or a regular mail delivery. Thus, it will be required to develop a comprehensive system delivering personal genome information by satisfying and keeping the rule and regulation by authorized organizations.
In this work, we present an automatic NGS data analysis processor, MGE, to show that full steps for the automatic NGS data analyses are available through a simple pipeline with mobile application and server application.
Materials and methods
An overview of the Mobile Genome Express (MGE)
(a) Users access to the Sequencing instrument (e.g. Ion Torrent PGM) by using MGE smartphone application, to select a sequencing run/s to analyze. Then users’ server accesses to the Sequencer and downloads the corresponding data as selected on the smartphone. (b) Automatically, the Sequencing machine sends the data to MGE database. Then, the MGE variant-calling tool runs. Next, the variant calling output data is ready for user can check the sample result with MGE review page, including the sample’s status and comments. After the reviewer curation, result is submitted. (c) Users can download the result in spreadsheet file from users’ server to their smartphone.
MGE is executed in a mobile device, connected to a main server (controlled by the user) and to the sequencer. To ensure the security of the data and access of the user, an authentication system has been implemented. After logging in, MGE requires to input the internal internet protocol (IP) address of the corresponding sequencer. Once the user access the sequencer, the sequencing run to be analyzed is selectable from the run list. The user can also check some of the sequencing run features (e.g. the number of samples included in the sequencing run and their barcode number).
After the user access the sequencer with MGE smartphone application and selects the samples to be analyzed, the app will guide the user through to verify the data imported into the submission form. The data can be modified into MGE database if needed. (a) First page of the submission form. Because the users use their own panel and chip size, there are sections where they can fill-in the information about the samples, which gets inserted into MGE database. (b) The second page of the submission form is managed to get detailed experiment information such as DNA extraction method as well as DNA quantity and quality control method used (Nanodrop, Picogreen or qPCR). (c) Third page. The sample type information can also be recorded for further analysis and evaluation of sequencing data quality.
Data calculation can be customized with different plugins such as: sequencing depth calculator, low quality sequencing reads filter, variant caller, and many more. MGE manage the server’s calculation process by running PHP5 and Python codes, and can be applied in with samples automatically, since MGE passes to calculating codes file paths and essential information. After getting results in this step, server inserts data to MGE database. MGE sends notification to MGE administrator by email each time when the data calculation starts and when it ends. The recipient list of the MGE alert email, can include as many users as needed, under administrator supervision.
MGE has a separate web-based application to review the sample-related output sequencing data, called ELECTRO (Fig 3). The application is designed in a table-like interface, where the users can easily browse the data by user’s sequencing name, submission date, and user ID. All the tabs are sortable, which makes the data reviewing process more efficient and convenient. In addition, ELECTRO integrates an Integrative Genomic Viewer (IGV) interface, that allows the review of each called mutation, by clicking the IGV button. Once the review is finished, ELECTRO incorporates the results editor and generator, which exports the final mutation data to an independent spreadsheet file. With these features users can edit sample’s mutation status details and export the final result. ELECTRO allows the simultaneous online connection of all the data-review team members. This feature allows a real-time review of any sequencing result (Fig 3).
(a) ELECTRO’s web interface, the MGE original sequencing data review web page. (b) Variant curation page. Important information is displayed per each variant. MGE reflects this information on final result screen on user’s smartphone and spreadsheet file. (c) Review of raw sequencing data on IGV plugin within the ELECTRO-MGE interface. Csm id: MGE own ID for a mutation; css id: MGE own ID for a sample; cosmic id: COSMIC ID; gene: Gene symbol (HGVS); CDS: CDS nomenclature of each variant; Amino Acid Change: amino acidic nomenclature of each variant; # of COSMIC samples: number of positive records of the variant in COSMIC database; # of mutation Reads: number of sequencing reads with variant allele; # of Wildtype reads: number of sequencing reads with reference allele; VAF: variant allele frequency; Report flag (true = 1): Present decision of a mutation, 1 = true mutation, 0 = false mutation; Homopolymer status: region within a homopolymer region (Y = yes, N = no); Location: genomic coordinates (chr#:start_nt) of the variant.
MGE has its own database and automatically stores all the data into it, such as: sequencing file, information from sequencing data, mutation result, and final result.
Data can be stored at any step, even if the calculation is being performed. Upon data submission and calculation, MGE inserts the information to its database and user can access to this database by ELECTRO. We built this database scheme to optimize all MGE procedure in practice.
Data distribution and security
Data distribution, the final step of MGE process, is similar to ELECTRO. The only difference is that it runs in a smartphone. MGE smartphone application can show final reviewed data such as: Catalogue of Somatic Mutations in Cancer (COSMIC) mutation ID, amino acid change and mutation’s allele frequency (Fig 4). Every information gets processed and reviewed by the bioinformatics and data reviewing specialists, and the MGE results include direct comments from the data curation team. This information can be displayed both in the smartphone and in the output spreadsheet, which is very important for a proper contextualization of each mutation and variant annotation, when needed. Moreover, read-depth report for each amplicons and quality PDF from sequencing machine are provided to users for checking sample’s quality. For information security, we used Virtual Private Network (VPN) to secure our all information and MGE can be install in VPN environment.
MGE shows the sample results on its smartphone application. Users can check (a) comments from bioinformatics analyst, (b-c) mutation and coverage reports of the sample, and (d) quality matric PDF from PGM server. All these reports get generated automatically by a smartphone and MGE server. Users can also check the status of their samples analyzing progress on a smartphone. The server will update the messages depending on the status of the progress, such as: “Running”, “Coverage completed”, “Reviewing”, and “Analysis done”.
Targeted cancer panel sequencing
The Acrometrix Oncology Hotspot Control DNA (Thermo Fisher, Waltham, MA; Cat. No. 969056) was used for generating sequencing data for testing and evaluating MGE and ELECTRO as previously described . This control DNA contains more than 500 COSMIC mutations from 53 genes (Thermo Fisher, Waltham, MA; Cat. No. 969056). A targeted cancer panel with selected 25 genes (NextDay Seq-Pan Cancer HotSpot Panel kit, CureSeq Inc.) was used for a deep sequencing of the The Acrometrix Oncology Hotspot Control DNA as previously described [22,34]. In brief, 10 ng of control DNA was amplified and ligated with barcoded and adaptors with the library prep kit (NextDay Seq-Pan Cancer HotSpot Panel kit, CureSeq Inc.). The prepared library was cleaned up using magnetic beads and resuspended in 1x low-TE buffer, followed by qualitative and quantitative electrophoretic analysis by High Sensitivity DNA chip (Agilent Technologies, Santa Clara, CA; Cat. No. 5067–4626). After the emulsion PCR and library enrichment, the prepared library was sequenced using an Ion 314 v2 Chip (Thermo Fisher), and sequenced on an Ion Torrent™ PGM sequencer. The generated sequencing data was processed with MGE from data submission to data distribution as described above.
Access to MGE and ELECTRO
MGE and ELECTRO are available for purchase at CureSeq Inc. (www.cureseq.com, Cat #: SKU#: CS-MGE-01). The register is allowed through the administrator and the administrator will create an account upon request. The account credentials will be given for the user to access the application. Also, the application is protected by virtual private network (VPN), and the address for VPN access will also be provided by administrator. MGE is a smartphone application so a mobile device is used for MGE, and ELECTRO can be used by PHP path installed in a personal computer. Additionally, the user ID and password for ELECTRO is embedded in the system.
Results and discussion
Actual data process with sequencing data
Once the sequencing reaction was completed, the MGE smartphone application notifies it by email. Next, MGE saves the data into its database, and execute DanPA (CureSeq Inc.), a variant caller specifically coded to call variants from targeted sequencing experiments, previously annotated in the COSMIC database . MGE transfer the.bam and.bai files from the PGM sequencer server to the MGE’s main server and send an alert to the bioinformaticians. The mutation data was curated by using ELECTRO.
We used a 25-gene targeted cancer panel aiming to detect clinically relevant mutations . Thus, we designed our variant detecting algorithm for detecting known and reported mutations rather than un-validated (both technically and clinically) variants. That is why we decided to focus on mutations registered in the COSMIC database, one of the most extensive and robust cancer-related, somatic mutation repository with approximately 3.5 million mutations reported and registered at COSMIC database. If users want to use other variant callers such as GATK or MuTect [31–33], MGE also supports them.
Data review from submitted data
By checking the data under ELECTRO, bioinformaticians can review the sequencing results easily and make a comment for communicating with researchers, medical doctors, or other bioinformaticians.
First, although our variant caller algorithm filters out most of variants located within homopolymer regions (to avoid false positives in those regions, caused by the difficulties of the Ion Torrent PGM chemistry to resolve accurately the insertion/deletions (indels) at homopolymer regions), special attention was paid while reviewing such variants located in a tandem repeat or a homopolymer region . Second, MGE database includes artificial intelligence (AI) features that allows the identification of false positive variants specific for a given sequencing panel. If during the variant call, MGE detects a match between a detected variant and any variant record in the mentioned database, a warning message is delivered by MGE into ELECTRO. The history of false positive variants could be optionally used as a filtering factor. As NGS tends to detect the same false positives variants in a recurrent fashion in the same genomic regions, a record of false positives especially at the homopolymer regions resulted extremely helpful to increase the accuracy of variant calling. Third, we also considered the number of times that a single mutation has been previously reported in the COSMIC database as a deciding factor of robust mutation call. The output format of the ELECTRO-MGE mutation file can be customized according to the user’s preferences. The whole analysis was completed within an hour after receiving the raw sequencing result data from the sequencer.
We used all the variants registered in COSMIC (version 76) database and filtered out germline polymorphisms or sequencing error or false positives in the final validation analyses. We checked all the detected variants using IGV to confirm the true positive results. If necessary, additional validation sequencing was done and analyzed to confirm the results. The library construction panel targeted 83 out of the 500 COSMIC mutations included in the Acrometrix control DNA (Table 1). All 83 mutations were detected, including 7 synonymous and 76 non-synonymous. Among the non-synonymous, 2 were insertions, 1 was a deletion, and 73 were point mutations. Additionally, 74 amplicons out of 75 amplicons in this sample had a sequencing depth of >500x. The results analyzed by MGE were 100% consistent with previous sequencing data of Acrometrix control DNA which was manually analyzed and reviewed .
The suspicious false positives variant calls were filtered out by ELECTRO according to the reviewing policy described above. In addition, MGE supports IGV launcher and searching mechanism, and it would boost the sharing of each decision making process of multiple users.
It is highly expected that NGS sequencing information will be available to more and more people who want to know their genetic information, thanks to the significant reduction of a cost and TAT of NGS sequencing. We show that MGE can be an efficient mobile device or cell phone-based genetic controller enabling personalized genome analyses using targeted NGS panel feasible to most of regular laboratories. Current workflows in most laboratories require technicians or bioinformaticians to stay in a laboratory to check the sequencing run quality from the sequencing machine. Then data should be transferred to different types of bioinformatics pipelines used for each laboratory and analyzed by skilled bioinformaticians. All these manual-based sequencing quality check, data transfer, analyses, and review steps prevent the fast and robust genetic analyses. MGE allows us to check all the processes mentioned above into one application of a cellular phone or other mobile device. We used TMAP (Torrent Mapping Alignment Program) for mapping the raw sequencing reads and used DanPA program for variant calling and annotation. DanPA was previously tested in different sequencing projects [22, 37]. We plan to install and use another variant caller in MGE in a near future.
We believe that the automated MGE software, streamlines the genetic data flow from sequencer to clinical report, increasing robustness and efficiency. MGE is a very helpful tool that can help to increase the adoption of NGS technology in CLIA certified laboratories, as well as increase productivity of research, medical, and other life science sequencing laboratories.
This study was supported by funds from CureSeq Inc. and Thoracic Oncology Laboratory at University of California San Francisco (UCSF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. D.M.J. and I.J.K. are equity holders and consultants of CureSeq Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
- Conceptualization: JHY DMJ IJK.
- Data curation: JHY TWK PM DMJ IJK.
- Formal analysis: JHY TWK PM IJK.
- Funding acquisition: DMJ IJK.
- Investigation: JHY TWK PM.
- Methodology: JHY TWK PM DMJ IJK.
- Project administration: DMJ IJK.
- Resources: JHY TWK PM DMJ IJK.
- Software: JHY TWK IJK.
- Supervision: DMJ IJK.
- Validation: JHY TWK PM.
- Visualization: JHY TWK PM.
- Writing – original draft: JHY TWK IJK.
- Writing – review & editing: JHY TWK PM DMJ IJK.
- 1. Watson JD, Crick FHC. Molecular structure of nucleic acids. Nature. 1953. pp. 737–738. pmid:13054692
- 2. Watson JD, Crick FH. The structure of DNA. Cold Spring Harb Symp Quant Biol. 1953;18: 123–131. pmid:13168976
- 3. Saiki RK, Bugawan TL, Horn GT, Mullis KB, Erlich H a. Analysis of enzymatically amplified beta-globin and HLA-DQ alpha DNA with allele-specific oligonucleotide probes. Nature. 1986;324: 163–166. pmid:3785382
- 4. Lee MS, Chang KS, Cabanillas F, Freireich EJ, Trujillo JM, Stass SA. Detection of minimal residual cells carrying the t(14;18) by DNA sequence amplification. Science (80-). 1987;237: 175–178.
- 5. Wong C, Dowling CE, Saiki RK, Higuchi RG, Erlich HA, Kazazian HH Jr. Characterization of beta-thalassaemia mutations using direct genomic sequencing of amplified single copy DNA. Nature. 1987;330: 384–6 ST–Characterization of beta–thalassaemia. pmid:3683554
- 6. Sanger F, Coulson AR. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J Mol Biol. 1975;94.
- 7. Sanger F, Nicklen S, Coulson a R. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74: 5463–7. pmid:271968
- 8. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (80-). 1995;270: 467–470.
- 9. Favis R, Day JP, Gerry NP, Phelan C, Narod S, Barany F. Universal DNA array detection of small insertions and deletions in BRCA1 and BRCA2. Nat Biotechnol. 2000;18: 561–564. pmid:10802632
- 10. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002;415: 436–42. pmid:11807556
- 11. Shipp M a, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RCT, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine. 2002. pp. 68–74.
- 12. van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415: 530–536. pmid:11823860
- 13. Porreca GJ, Zhang K, Li JB, Xie B, Austin D, Vassallo SL, et al. Multiplex amplification of large sets of human exons. Nat Methods. 2007;4: 931–936. pmid:17934468
- 14. Wheeler DA, Srinivasan M, Egholm M, Shen Y, Chen L, McGuire A, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature. 2008;452: 872–6. pmid:18421352
- 15. Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Eichler EE, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461: 272–6. pmid:19684571
- 16. Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008;453: 1239–1243. pmid:18488015
- 17. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320: 1344–9. pmid:18451266
- 18. Cloonan N, Forrest ARR, Kolle G, Gardiner BB a, Faulkner GJ, Brown MK, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008;5: 613–9. pmid:18516046
- 19. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5: 621–628. pmid:18516045
- 20. Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell. 2008;133: 523–536. pmid:18423832
- 21. Hayden EC. The $1,000 genome. Nature. 2014;507: 295.
- 22. Mendez P, Dang J, Kim JW, Lee S, Yoon J, Kim T, et al. Comprehensive evaluation and validation of targeted next ‑ generation sequencing performance in two clinical laboratories. 2016; 235–242. pmid:27121194
- 23. Han SW, Kim HP, Shin JY, Jeong EG, Lee WC, Lee KH, et al. Targeted Sequencing of Cancer-Related Genes in Colorectal Cancer Using Next-Generation Sequencing. PLoS One. 2013;8.
- 24. Nikiforova MN, Wald AI, Roy S, Durso MB, Nikiforov YE. Targeted next-generation sequencing panel (ThyroSeq) for detection of mutations in thyroid cancer. J Clin Endocrinol Metab. 2013;98.
- 25. Ross JS, Wang K, Gay L, Otto GA, White E, Iwanik K, et al. Comprehensive Genomic Profiling of Carcinoma of Unknown Primary Site: New Routes to Targeted Therapies. JAMA Oncol. 2015;1: 40–9. pmid:26182302
- 26. Weinstein JN, Collisson E a, Mills GB, Shaw KRM, Ozenberger B a, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45: 1113–20. pmid:24071849
- 27. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502: 333–9. pmid:24132290
- 28. Forshew T, Murtaza M, Parkinson C, Gale D, Tsui DWY, Kaper F, et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med. 2012;4: 136ra68–136ra68. pmid:22649089
- 29. Dawson S-J, Tsui DWY, Murtaza M, Biggs H, Rueda OM, Chin S-F, et al. Analysis of Circulating Tumor DNA to Monitor Metastatic Breast Cancer. N Engl J Med. 2013;368: 1199–1209. pmid:23484797
- 30. Jovelet C, Ileana E, Le Deley MC, Motté N, Rosellini S, Romero A, et al. Circulating cell-free tumor DNA analysis of 50 genes by next-generation sequencing in the prospective MOSCATO trial. Clin Cancer Res. 2016;22: 2960–2968. pmid:26758560
- 31. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20: 1297–1303. pmid:20644199
- 32. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31: 213–219. pmid:23396013
- 33. Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: Accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics. 2012;28: 1811–1817. pmid:22581179
- 34. Kang HC, Kim HK, Lee S, Mendez P, Kim W, Woodard G, et al. Whole exome and targeted deep sequencing identify genome-wide allelic loss and frequent. Oncotarget. 2016;7: 8321–8331. pmid:26824986
- 35. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, et al. COSMIC: Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43: D805–D811. pmid:25355519
- 36. Bragg LM, Stone G, Butler MK, Hugenholtz P, Tyson GW. Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data. PLoS Comput Biol. 2013;9.
- 37. Shi X, Wu H, Lu J, Duan H, Liu X, Liang Z. Screening for major driver oncogene alterations in adenosquamous lung carcinoma using PCR coupled with next-generation and Sanger sequencing methods. Sci Rep. 2016;6: 22297. pmid:26923333