Torrent suite software v 5

Manufactured by Thermo Fisher Scientific

Sourced in United States

Torrent Suite Software v.5.0 is a software application designed for the analysis and management of data generated by Thermo Fisher Scientific's Ion Torrent sequencing platform. The software provides tools for base calling, sequence alignment, and variant calling.

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23 protocols using torrent suite software v 5

Molecular Profiling of Pancreatic Cyst Fluid

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Nucleic acids were isolated from EUS-FNA pancreatic cyst fluid specimens that were collected into a DNA/RNA preservation solution. DNA and mRNA were isolated using the MagNA Pure Compact instrument (Roche, Indianapolis, IN). Extracted DNA and RNA was quantitated on the Glomax Discover using the QuantiFluor ONE dsDNA System and the QuantiFluor RNA system, respectively (Promega, Madison, WI). NGS libraries of genes summarized within the Supplementary Data were created from 10 ng of DNA and 10 ng of RNA using the Ion AmpliSeq Library kit PLUS and Ion Xpress Barcode Adapters as previously described.^{22 (link), 23 (link)} These genes include those implicated in pancreatic cysts and associated neoplasms and include “hot-spot” alterations, copy number alterations/loss of heterozygosity, gene fusions, gene expression alterations (KRT7, KRT20, and PGK1) and CHGA RNA expression. The libraries were normalized for template preparation on the Ion Chef and sequenced on an Ion S5 System according to the manufacturer’s instructions (ThermoFisher Scientific, Waltham, MA). The Torrent Suite Software v5.12 (ThermoFisher Scientific) and an in-house developed software Variant Explorer v2 were used for data analysis and interpretation. In parallel, one-step quantitative reverse transcription PCR (RT-qPCR) for CEACAM5 was performed.

Nikiforova M.N., Wald A.I., Spagnolo D.M., Melan M.A., Grupillo M., Lai Y.T., Brand R.E., O’Broin-Lennon A.M., McGrath K., Park W.G., Pfau P.R., Polanco P.M., Kubiliun N., DeWitt J., Easler J.J., Dam A., Mok S.R., Wallace M.B., Kumbhari V., Boone B.A., Marsh W., Thakkar S., Fairley K.J., Afghani E., Bhat Y., Ramrakhiani S., Nasr J., Skef W., Thiruvengadam N.R., Khalid A., Fasanella K., Chennat J., Das R., Singh H., Sarkaria S., Slivka A., Gabbert C., Sawas T., Tielleman T., Vanderveldt H.D., Tavakkoli A., Smith L.M., Smith K., Bell P.D., Hruban R.H., Paniccia A., Zureikat A., Lee K.K., Ongchin M., Zeh H., Minter R., He J., Nikiforov Y.E, & Singhi A.D. (2023). A Combined DNA/RNA-based Next-Generation Sequencing Platform to Improve the Classification of Pancreatic Cysts and Early Detection of Pancreatic Cancer Arising From Pancreatic Cysts. Annals of Surgery, 278(4), e789-e797.

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Comprehensive Molecular Profiling of Pancreatic Cysts

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Nucleic acids were isolated from EUS-FNA pancreatic cyst fluid specimens that were collected into a DNA/RNA preservation solution. DNA and mRNA were isolated using the MagNA Pure Compact instrument (Roche). Extracted DNA and RNA were quantitated on the Glomax Discover using the QuantiFluor ONE dsDNA System and the QuantiFluor RNA system, respectively (Promega). NGS libraries of genes summarized within the Supplementary Data, Supplemental Digital Content 1, http://links.lww.com/SLA/A622 were created from 10 ng of DNA and 10 ng of RNA using the Ion AmpliSeq Library kit PLUS and Ion Xpress Barcode Adapters as previously described.^{22 (link),23 (link)} These 74 genes include those implicated in pancreatic cysts and associated neoplasms and include “hot-spot” mutations, such as KRAS, GNAS, etc., copy number alterations/loss of heterozygosity, GFs, gene expression alterations (GEAs; KRT7, KRT20, and PGK1) and CHGA RNA expression. The libraries were normalized for template preparationon the Ion Chef and sequenced on an Ion S5 System according to the manufacturer’s instructions (Thermo Fisher Scientific). The Torrent Suite Software v5.12 (Thermo Fisher Scientific) and an in-house developed software Variant Explorer v2 were used for data analysis and interpretation. In parallel, one-step quantitative reverse transcription PCR (RT-qPCR) for CEACAM5 was performed.

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Transcriptome Analysis of Megakaryocytes and Platelets

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CD41⁺ 8N and ≥ 16N MKs were collected (23 ± 3k and 81 ± 5k, respectively) for RNA isolation (RNeasy Mini kit, Qiagen) followed by library preparation using SMARTer Stranded Total RNA Low Input Sample Prep kit (Clontech) and RNA-sequencing by Illumina HiSeq1500. The reads were aligned with STAR⁴³ (link) to Ensembl mm10. Differential gene expression analyzed with EdgeR.^44,45 (link)
Platelet RNA was isolated using TriReagent (Sigma-Aldrich). Libraries were prepared using an Ion AmpliSeq Transcriptome Mouse Gene Expression Kit and sequenced on Ion Proton Sequencer (Thermo Fisher).⁴⁶ (link) The data were analyzed using Torrent Suite Software v5.6 (Thermo Fisher) and differential gene expression analyzed using DESeq2.⁴⁷ (link) Gene ontology enrichment analysis were conducted with DAVID.⁴⁸ (link)

Heazlewood S.Y., Ahmad T., Mohenska M., Guo B.B., Gangatirkar P., Josefsson E.C., Ellis S.L., Ratnadiwakara M., Cao H., Cao B., Heazlewood C.K., Williams B., Fulton M., White J.F., Ramialison M., Nilsson S.K, & Änkö M.L. (2022). The RNA-binding protein SRSF3 has an essential role in megakaryocyte maturation and platelet production. Blood, 139(9), 1359-1373.

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Identification and Characterization of Copy Number Variations

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CNVs were identified by analyzing the percentages of sequencing reads per amplicon and allele frequencies of the common SNPs, using the Torrent Suite Software v5.6 (ThermoFisher Scientific) and Microsoft Excel (2010). Allele frequencies of the SNPs were obtained from the Variant Caller Files. A deletion was identified if multiple consecutive amplicons contained a lower percentage of reads than other samples, combined with loss of heterozygosity. A gain was called if multiple consecutive amplicons contained higher read percentages combined with an allelic imbalance. The coordinates of the CNVs are based on the 3′-end and the 5′-end of the amplicons that are located upstream and downstream of the amplicons with altered read counts. The CNVs were classified according to the joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) for interpretation and reporting of CNVs. 18 If parental DNA was available, it was used for SNP array to determine on which allele the CNV arose.

Slot E., von der Thüsen J.H., van Heijst A., van Marion R., Magielsen F., Dubbink H.J., Post M., Debeer A., Tibboel D., Rottier R.J, & de Klein A. (2021). Fast detection of FOXF1 variants in patients with alveolar capillary dysplasia with misalignment of pulmonary veins using targeted sequencing. Pediatric research, 89(3).

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Somatic Mutation Analysis for Precision Oncology

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Data analysis was carried out using Torrent Suite Software v5.0.4 (Thermo Fisher Scientific). First, somatic mutations were selected using the following criteria¹⁹: (1) variant allele frequency of somatic mutations in tumor tissues >4%, (2) removal of single‐nucleotide polymorphisms with a threshold allele frequency value ≥0.01 in either the NHLBI GO Exome Sequencing Project (ESP6500; http://evs.gs.washington.edu/EVS/) or the Integrative Japanese Genome Variation Database (iJGVD, 20181105) (https://ijgvd.megabank.tohoku.ac.jp/)²⁴, and (3) registration of mutations as “pathogenic/likely pathogenic variants” in ClinVar²⁵ or “oncogenic/likely oncogenic variants” in OncoKB (http://oncokb.org) databases²⁶ using OncoKB‐annotator (accessed on 14 April 2020). In the current study, gene aberrations with evidence levels 1A‐3B registered in OncoKB were identified as candidate actionable mutations for molecular‐targeted drugs.²⁷ Finally, all selected variants were manually inspected using the Integrative Genomics Viewer (http://www.broadinstitute.org/igv/).²⁸

Kobayashi Kato M., Asami Y., Takayanagi D., Matsuda M., Shimada Y., Hiranuma K., Kuno I., Komatsu M., Hamamoto R., Matsumoto K., Ishikawa M., Kohno T., Kato T., Shiraishi K, & Yoshida H. (2022). Clinical impact of genetic alterations of CTNNB1 in patients with grade 3 endometrial endometrioid carcinoma. Cancer Science, 113(5), 1712-1721.

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Optimized Somatic Variant Calling for Ion Data

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Primary alignment of the sequencing reads to the reference genome and subsequent base calling was performed using the instrument provided Torrent Suite software v.5.0.4 (Thermo Fisher Scientific). The Torrent Suite software pipeline was adapted in view of the highly optimized analysis parameters specific to Ion instrument data. The human genome build 19 was used for the alignment, calling and annotations of potential variant changes in the specimen. Subsequently, the generated BAM files were uploaded to the Ion Reporter Cloud. Detailed annotation of the individual variant calls was generated by the Ion Reporter v.5.2 (Thermo Fisher Scientific) software as shown previously.^{7 (link)} In addition to the Ion Reporter annotation, a custom built software pipeline composed of Linux bash scripts was used to confirm the somatic variants of single nucleotide variants (SNVs) and insertion-deletion (indel) changes called by Ion Reporter. For the purposes of variant calling, we set a coverage threshold of 200X reads for SNVs and 350X reads Indels at a variant allele fraction of 5%. Anything below these limits was not called.

Martin D.R., LaBauve E., Pomo J.M., Chiu V.K., Hanson J.A, & Gullapalli R.R. (2018). Site Specific Genomic Alterations in a Well-Differentiated Pancreatic Neuroendocrine Tumor With High-Grade Progression. Pancreas, 47(4), 502-510.

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Somatic Mutation Detection in Tumor Samples

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Sequencing reads were mapped to the UCSC human reference genome (GRCh37), and data analysis was carried out using the Torrent Suite Software v5.0.4 (Thermo Fisher Scientific). First, somatic mutations were selected as follows: (1) The variant allele frequency of somatic mutations of more than 4% in tumor tissues was included; (2) single nucleotide polymorphisms were removed if they showed a threshold allele frequency of ≥ 0.01 in either the NHLBI GO Exome Sequencing Project (ESP6500) (http://evs.gs.washington.edu/EVS/) or the Integrative Japanese Genome Variation Database (iJGVD, 20181105) (https://ijgvd.megabank.tohoku.ac.jp/)^{37 (link)}; (3) the mutations were registered as “pathogenic/likely pathogenic variants” in ClinVar^{38 (link)} or as “oncogenic/likely oncogenic variants” in the OncoKB dataset (http://oncokb.org) (using oncokb-annotator ver 2.2.0, https://github.com/oncokb/oncokb-annotator/tree/v2.2.0) were included⁷. Finally, all the selected variants were manually checked using the Integrative Genomics Viewer (http://www.broadinstitute.org/igv/)^{39 (link)}.

Ito T., Takayanagi D., Sekine S., Hashimoto T., Shimada Y., Matsuda M., Yamada M., Hamamoto R., Kato T., Shida D., Kanemitsu Y., Boku N., Kohno T., Takashima A, & Shiraishi K. (2023). Comparison of clinicopathological and genomic profiles in anal squamous cell carcinoma between Japanese and Caucasian cohorts. Scientific Reports, 13, 3587.

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Variant Identification for Oncogenic Research

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Data analysis was carried out using the Torrent Suite Software v5.0.4 (Thermo Fisher Scientific). We selected mutations that met the following criteria: the frequency of variant alleles was more than 4% in tumor tissues; single nucleotide polymorphisms were excluded if they showed a threshold allele frequency ≥ 0.01 in either the National Heart, Lung, and Blood Institute (NHLBI) Grand Opportunity Exome Sequencing Project (ESP6500; http://evs.gs.washington.edu/EVS/) or the integrative Japanese Genome Variation Database (iJGVD, 20181105; https://ijgvd.megabank.tohoku.ac.jp/). The variants have been registered as “pathogenic/likely pathogenic variants” in ClinVar^{42 (link)} or “oncogenic/likely oncogenic variants” in OncoKB (http://oncokb.org) databases using the OncoKB annotator commit 8910b65 (accessed on June 29, 2019). All selected variants were validated using the Integrative Genomics Viewer (IGV; http://www.broadinstitute.org/igv/).

Kuno I., Takayanagi D., Asami Y., Murakami N., Matsuda M., Shimada Y., Hirose S., Kato M.K., Komatsu M., Hamamoto R., Okuma K., Kohno T., Itami J., Yoshida H., Shiraishi K, & Kato T. (2021). TP53 mutants and non-HPV16/18 genotypes are poor prognostic factors for concurrent chemoradiotherapy in locally advanced cervical cancer. Scientific Reports, 11, 19261.

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Identification of rG4-Enriched mRNA Transcripts

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Mouse forebrain tissues were lysed and precleared using the Protein G HP SpinTrap Kit (GE Healthcare). The precleared samples were incubated at 4°C overnight in a Protein G HP SpinTrap column that had been treated with His-tagged BG4 (or normal rabbit IgG for control) and anti-6×His. The resultant solution was washed three times with wash buffer and eluted with elution buffer. Wash buffer and elution buffer were provided with the RiboCluster Profiler RIP-Assay Kit (MBL). The BG4-bound RNA fraction was purified using the RNeasy Mini Kit (Qiagen) and subjected to RNA-sequencing library preparation using the Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific). The cDNA libraries obtained were sequenced on an Ion Proton sequencer (Thermo Fisher Scientific). Data were processed using the Torrent Suite Software v.5.2.2 (Thermo Fisher Scientific) to remove adapter sequences and low-quality ends. The Cufflinks software package was used to analyze BG4-enriched mRNAs compared with IgG-treated samples. Putative rG4-forming motifs of mRNAs were predicted using the QGRS mapper (http://bioinformatics.ramapo.edu/QGRS/index.php) and QuadBase2 (http://quadbase.igib.res.in/).

Asamitsu S., Yabuki Y., Matsuo K., Kawasaki M., Hirose Y., Kashiwazaki G., Chandran A., Bando T., Wang D.O., Sugiyama H, & Shioda N. (2023). RNA G-quadruplex organizes stress granule assembly through DNAPTP6 in neurons. Science Advances, 9(8), eade2035.

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RNA-Seq Analysis of Mouse Transcriptome

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Total RNA was extracted from the cells as previously described. RNA samples were checked using a Bioanalyzer 2100 with RNA 6000 nano kit (Agilent Technologies, Santa Clara, CA, USA), and RNA integrity number (RIN) was calculated. The high-quality RNA (RIN > 7.2) samples were subjected to RNA sequencing using the Ion Torrent NGS system (Thermo Fisher Scientific). Briefly, RNA libraries were prepared using 10.62 ng of total RNA with an Ion AmpliSeq Transcriptome Mouse Gene Expression kit, and sequenced on Ion Proton using an Ion PI Hi-Q Sequencing 200 kit and Ion PI Chip v3 (Thermo Fisher Scientific). The data were analyzed using AmpliSeqRNA plug-in v5.2.0.3 in the Torrent Suite Software v5.2.2 (Thermo Fisher Scientific), and normalized using RPM (reads per million mapped reads) method. The normalized data were further analyzed and visualized using GeneSpring v14.9.1 software (Agilent Technologies), R Studio (R Foundation for Statistical Computing, Vienna, Austria), and Microsoft Excel (Microsoft, Redmond, WA, USA).

Murakami Y., Fukui R., Tanaka R., Motoi Y., Kanno A., Sato R., Yamaguchi K., Amano H., Furukawa Y., Suzuki H., Suzuki Y., Tamura N., Yamashita N, & Miyake K. (2021). Anti-TLR7 Antibody Protects Against Lupus Nephritis in NZBWF1 Mice by Targeting B Cells and Patrolling Monocytes. Frontiers in Immunology, 12, 777197.

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