From 3d05c2fff08884d1f3680c4b4d409f1eb5da9d5b Mon Sep 17 00:00:00 2001 From: Hangjia Zhao <55445423+hangjiaz@users.noreply.github.com> Date: Fri, 2 Feb 2024 14:33:08 +0100 Subject: [PATCH] Update and rename 2023-05-19-labelseg.md to 2023-12-28-labelseg.md --- docs/publications/2023-05-19-labelseg.md | 22 ---------------- docs/publications/2023-12-28-labelseg.md | 32 ++++++++++++++++++++++++ 2 files changed, 32 insertions(+), 22 deletions(-) delete mode 100644 docs/publications/2023-05-19-labelseg.md create mode 100644 docs/publications/2023-12-28-labelseg.md diff --git a/docs/publications/2023-05-19-labelseg.md b/docs/publications/2023-05-19-labelseg.md deleted file mode 100644 index c9c95d95..00000000 --- a/docs/publications/2023-05-19-labelseg.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: "LabelSeg: segment annotation for tumor copy number alteration profiles" -description: A tool to assign relative SCNA levels to segments -template: post.html -authors: - - '@hangjiaz' - - '@mbaudis' - -date: 2023-05-19 -pdf_file_name: 2023-05-19___Zhao-and-Baudis__LabelSeg-segment-annotation-for-tumor-copy-number-alteration-profiles__bioRxiv.pdf -links: - - '[preprint at bioRxiv](https://www.biorxiv.org/content/10.1101/2023.05.17.541097v1)' - ---- - -#### Zhao H and Baudis M -##### doi: [https://doi.org/10.1101/2023.05.17.541097](https://doi.org/10.1101/2023.05.17.541097) - - -![biorXiv logo](/img/logo_biorXiv.jpg){: style="float: right; width: 200px; margin-left: 20px; margin-bottom: 10px; margin-top: -10px;"} -**Abstract** Somatic copy number alterations (SCNA) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating a large number of SCNA profiles. However, annotation and integration of these profiles are challenging due to the presence of multiple sources of noise and heterogeneity in measurement platforms. In this study, we present LabelSeg, an algorithm for fast and accurate annotation of CNA segments, to improve the interpretation of tumor SCNA profiles. LabelSeg uses segment files as input to estimate calling thresholds for identifying different relative copy number states and is compatible with most CNA measurement platforms. We confirmed its performance on simulated data and sample-derived data from The Cancer Genome Atlas (TCGA) reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and mRNA expression, promoting the investigation of the role of SCNA in cancer development. - diff --git a/docs/publications/2023-12-28-labelseg.md b/docs/publications/2023-12-28-labelseg.md new file mode 100644 index 00000000..269b4575 --- /dev/null +++ b/docs/publications/2023-12-28-labelseg.md @@ -0,0 +1,32 @@ +--- +title: "labelSeg: segment annotation for tumor copy number alteration profiles" +description: A tool to assign relative SCNA levels to segments +template: post.html +authors: + - '@hangjiaz' + - '@mbaudis' + +date: 2023-12-28 +pdf_file_name: 2023-12-28___Zhao-and-Baudis__labelSeg-segment-annotation-for-tumor-copy-number-alteration-profiles__Briefings-in-Bioinformatics.pdf +links: + - '[article at Briefings in Bioinformatics](https://academic.oup.com/bib/article/25/2/bbad541/7595616)' + - '[preprint at bioRxiv](https://www.biorxiv.org/content/10.1101/2023.05.17.541097v2)' + +--- + +#### Hangjia Zhao and Michael Baudis +##### Briefings in Bioinformatics (Oxford). 2024 Jan 31;2024:bbad541. +* doi: 10.1093/bib/bbad541 +* PMID: 38300514 +* bioRxiv. doi: https://doi.org/10.1101/2023.05.17.541097 + + +**Abstract** Somatic copy number alterations (SCNAs) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg, an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length–amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and messenger RNA expression, promoting the investigation into the role of SCNA in cancer development. + +Package: https://github.com/baudisgroup/labelSeg + +#### Notes + +The first version of the article had been posted at biorXiv on 2023-05-19. + +The second version of the article had been posted at biorXiv on 2023-09-02.