Uploading Data Files to Terra v1

2021 ◽  
Author(s):  
Francis J J. Ambrosio
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Genomic Sequencing ◽  
Sequencing Data ◽  
Essential Step ◽  
Metadata File ◽  
Data Files

Uploading data to Terra.bio is an essential step in the protocol for analyzing locally stored genomic sequencing data. The Terra.bio uploads page allows users to easily organize their data files using an associated metadata file via a browser-based graphical user interface. This protocol explains the process to prepare the data files and the associated metadata file for upload, and provides the link to the Terra.bio uploads page.

scholarly journals Afirma Genomic Sequencing Classifier & Xpression Atlas Molecular Findings in Consecutive Bethesda III-VI Thyroid Nodules

2021 ◽  
Author(s):  
Mimi I Hu ◽  
Steven G Waguespack ◽  
Chrysoula Dosiou ◽  
Paul W Ladenson ◽  
Masha J Livhits ◽  
...  
Keyword(s):  
Needle Aspiration ◽  
Braf V600e ◽  
Genomic Alteration ◽  
Genomic Sequencing ◽  
Test Results ◽  
Sequencing Data ◽  
Non Invasive ◽  
Thyroid Fine Needle Aspiration ◽  
Data Files ◽  
Genomic Variant

Abstract Context Broad genomic analyses among thyroid histologies have been described from relatively small cohorts. Objective Investigate the molecular findings across a large, real-world cohort of thyroid fine needle aspiration (FNA) samples. Design Retrospective analysis of RNA sequencing data files. Setting CLIA laboratory performing Afirma Genomic Sequencing Classifier (GSC) and Xpression Atlas (XA) testing. Participants 50,644 consecutive Bethesda III-VI nodules. Intervention none. Main Outcome Measures Molecular test results. Results Of 48,952 Bethesda III/IV FNAs studied, 66% were benign by Afirma GSC. The prevalence of BRAF V600E was 2% among all Bethesda III/IV FNAs and 76% among Bethesda VI FNAs. Fusions involving NTRK, RET, BRAF, and ALK were most prevalent in Bethesda V (10%), and 130 different gene partners were identified. Among small consecutive Bethesda III/IV sample cohorts with one of these fusions and available surgical pathology excision data, the positive predictive value of an NTRK or RET fusion for carcinoma or non-invasive follicular thyroid neoplasm with papillary-like nuclear features was >95%, while for BRAF and ALK fusions it was 81% and 67%, respectively. At least one genomic alteration was identified by the expanded Afirma XA panel in 70% of Medullary Thyroid Carcinoma Classifier positive FNAs, 44% of Bethesda III or IV Afirma GSC suspicious FNAs, 64% of Bethesda V FNAs, and 87% of Bethesda VI FNAs. Conclusions This large study demonstrates that almost half of Bethesda III/IV Afirma GSC suspicious and most Bethesda V/VI nodules had at least 1 genomic variant or fusion identified, which may optimize personalized treatment decisions.

scholarly journals omicplotR: visualizing omic datasets as compositions

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel J. Giguere ◽  
Jean M. Macklaim ◽  
Brandon Y. Lieng ◽  
Gregory B. Gloor
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Visual Exploration ◽  
Command Line ◽  
Sequencing Data ◽  
Software Packages ◽  
High Throughput Sequencing Data ◽  
Differential Abundance Analysis

Abstract Background Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method. Results omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset. Conclusions omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

scholarly journals High-Throughput Identification of Adapters in Single-Read Sequencing Data

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 878
Author(s):  
Asan M.S.H. Mohideen ◽  
Steinar D. Johansen ◽  
Igor Babiak
Keyword(s):  
High Throughput ◽  
Automated Detection ◽  
Research Articles ◽  
Substantial Portion ◽  
Sequencing Data ◽  
Essential Step ◽  
Sequencing Technologies ◽  
Data Files ◽  
Public Repositories ◽  
Tool Set

Sequencing datasets available in public repositories are already high in number, and their growth is exponential. Raw sequencing data files constitute a substantial portion of these data, and they need to be pre-processed for any downstream analyses. The removal of adapter sequences is the first essential step. Tools available for the automated detection of adapters in single-read sequencing protocol datasets have certain limitations. To explore these datasets, one needs to retrieve the information on adapter sequences from the methods sections of appropriate research articles. This can be time-consuming in metadata analyses. Moreover, not all research articles provide the information on adapter sequences. We have developed adapt_find, a tool that automates the process of adapter sequences identification in raw single-read sequencing datasets. We have verified adapt_find through testing a number of publicly available datasets. adapt_find secures a robust, reliable and high-throughput process across different sequencing technologies and various adapter designs. It does not need prior knowledge of the adapter sequences. We also produced associated tools: random_mer, for the detection of random N bases either on one or both termini of the reads, and fastqc_parser, for consolidating the results from FASTQC outputs. Together, this is a valuable tool set for metadata analyses on multiple sequencing datasets.

scholarly journals artMAP: a user-friendly tool for mapping EMS-induced mutations in Arabidopsis

2018 ◽  
Author(s):  
Peter Javorka ◽  
Vivek Raxwal ◽  
Jan Najvarek ◽  
Karel Riha
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Source Code ◽  
Ease Of Use ◽  
Sequencing Data ◽  
Induced Mutations ◽  
Mouse Click ◽  
User Friendly ◽  
Paired End Sequencing ◽  
Mapping By Sequencing

AbstractMapping-by-sequencing is a rapid method for identifying both natural as well as induced variations in the genome. However, it requires extensive bioinformatics expertise along with the computational infrastructure to analyze the sequencing data and these requirements have limited its widespread adoption. In the current study, we develop an easy to use tool, artMAP, to discover ethyl methanesulfonate (EMS) induced mutations in the Arabidopsis genome. The artMAP pipeline consists of well-established tools including TrimGalore, BWA, BEDTools, SAMtools, and SnpEff which were integrated in a Docker container. artMAP provides a graphical user interface and can be run on a regular laptop and desktop, thereby limiting the bioinformatics expertise required. artMAP can process input sequencing files generated from single or paired-end sequencing. The results of the analysis are presented in interactive graphs which display the annotation details of each mutation. Due to its ease of use, artMAP made the identification of EMS-induced mutations in Arabidopsis possible with only a few mouse click. The source code of artMAP is available on Github (https://github.com/RihaLab/artMAP).

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