Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Genomic analysis of cancer tissues is an essential aspect of personalized oncology treatment. Though it has been suggested that formalin fixation of patient tissues may be suboptimal for molecular studies, this tissue processing approach remains the industry standard. Therefore clinical molecular laboratories must be able to work with formalin fixed, paraffin embedded (FFPE) material. This study examines the effects of pre-analytic variables introduced by routine pathology processing on specimens used for clinical reports produced by next-generation sequencing technology. Tissue resected from three colorectal cancer patients was subjected to 2, 15, 24, and 48 hour fixation times in neutral buffered formalin. DNA was extracted from all tissues twice, once with uracil-N-glycosylase (UNG) treatment to counter deamination effects, and once without. Of note, deamination events at methylated cytosine, as found at CpG sites, remains unaffected by UNG. After extraction a two-step PCR targeted sequencing method was performed using the Illumina MiSeq and the data was analyzed via a custom-built bioinformatics pipeline, including filtration of reads with mapping quality <30. A larger baseline group of samples (n = 20) was examined to establish if there was a sample performance difference between the two DNA extraction methods, with/without UNG treatment. There was no statistical difference between sequencing performance of the two extraction methods when comparing read counts (raw, mapped, and filtered) and read quality (% mapped, % filtered). Analyzing mutation type, there was no significant difference between mutation calls until the 48 hour fixation treatment. At 48 hours there is a significant increase in C/G->T/A mutations that is not represented in DNA treated with UNG. This suggests these errors may be due to deamination events triggered by a longer fixation time. However the allelic frequency of these events remained below the limit of detection for reportable mutations in this assay (<2%). We do however recommend that suspected intratumoral heterogeneity events be verified by re-sequencing the same FFPE block.
Pubmed ID: 29698444
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics. The source code is made available under the Biopython License, which is extremely liberal and compatible with almost every license in the world. It works along with the Open Bioinformatics Foundation, who generously host it''s website, bug tracker, and mailing lists. Sponsor: This resource is supported by the Open Bioinformatics Foundation. Keywords: Tool, Software, Python, Biological, Computation, Bioinformatics,
View all literature mentionsA Python-based environment of open-source software for mathematics, science, and engineering. The core packages of SciPy include: NumPy, a base N-dimensional array package; SciPy Library, a fundamental library for scientific computing; and IPython, an enhanced interactive console.
View all literature mentionsA software suite using feature-based classifiers for somatic mutation prediction from paired tumour/normal next-generation sequencing data. mutationSeq has the advantages of integrating different features (e.g., base qualities, mapping qualities, strand bias, and tailed distance features), and validated somatic mutations to make predictions. Given paired normal/tumour bam files, mutationSeq will output the probability of each candidate site being somatic.
View all literature mentionsPython 2D plotting library which produces publication quality figures in variety of hardcopy formats and interactive environments across platforms. Used in python scripts, web application servers, and six graphical user interface toolkits. Used to generate plots, histograms, power spectra, bar charts, error charts, scatter plots.
View all literature mentions