SeqCAT: A bioconductor R-package for variant analysis of high throughput sequencing data [version 1; peer review: 1 approved with reservations, 1 not approved]

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Fasterius, Erik,1987-KTH,Systembiologi (author)

SeqCAT : A bioconductor R-package for variant analysis of high throughput sequencing data [version 1; peer review: 1 approved with reservations, 1 not approved]

Article/chapterEnglish2018

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F1000 Research Ltd,2018
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LIBRIS-ID:oai:DiVA.org:kth-276495
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276495URI
https://doi.org/10.12688/F1000RESEARCH.16083.1DOI

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Language:English
Summary in:English

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Subject category:art swepub-publicationtype

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QC 20200617
High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, demonstrating that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%.

Subject headings and genre

NATURVETENSKAP Biologi Bioinformatik och systembiologi hsv//swe
NATURAL SCIENCES Biological Sciences Bioinformatics and Systems Biology hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Medicinska och farmaceutiska grundvetenskaper Medicinsk genetik hsv//swe
MEDICAL AND HEALTH SCIENCES Basic Medicine Medical Genetics hsv//eng
Bioconductor
High throughput sequencing
R
RNA sequencing
Single nucleotide variant
Variant analysis
Whole exome sequencing
Article
bioinformatics
controlled study
data analysis
gene ontology
genetic difference
genetic heterogeneity
genetic variation
human
Kyoto Encyclopedia of Genes and Genomes
liver cancer
organoid
single nucleotide polymorphism

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Al-Khalili Szigyarto, CristinaKTH,Science for Life Laboratory, SciLifeLab,Systembiologi(Swepub:kth)u1c02mh9 (author)
KTHSystembiologi (creator_code:org_t)

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In:F1000 Research: F1000 Research Ltd72046-1402

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NATURAL SCIENCES: NATURAL SCIENCES; and Biological Scien ...; and Bioinformatics a ...

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Basic Medicine; and Medical Genetics

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By the university: Royal Institute of Technology

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