Posts Tagged ‘from_yz’

Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic a nd microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” La ncet Oncology 15(13):1521-1532 (PMID: 25456371)

May 17, 2016

Genomic & microenvironmental heterogeneity as integrated predictors for prostate #cancer recurrence
http://www.ncbi.nlm.nih.gov/pubmed/25456371 CNVs & hypoxia

* Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” Lancet Oncology 15(13):1521-1532 (PMID: 25456371)

The novelty of the paper is that it is the first study integrating DNA-based signatures and microenviroment-based signature for cancer prognosis. The authors found four prognostic indices, i.e. cancer genomic subtype (generated from clusters of CNV profiles), genomic instability (represented by the percentage of genome alteration), DNA signature (276 genes identified from random forests), and tumor hypoxia (the microenvironment signature), to be effective in predicting patient survival in different groups. Standard clinical univariate and multivariate analyses were performed.

Bias from removing read duplication in ultra-deep sequencing experiments

December 25, 2015

Bias from removing read duplication [eg from PCR amplification] in ultra-deep #sequencing
http://bioinformatics.oxfordjournals.org/content/early/2014/01/02/bioinformatics.btt771 pot. overcorrection issues

Zhou et al.

Bias from removing read duplication in ultra-deep sequencing experiments

Estimating variant allele frequency and copy number variations can be approached by counting reads. In practice, read counting is
complicated by bias from PCR amplification and from sampling coincidence. This paper assessed the overcorrection introduced while removing read duplicates. The overcorrection is a particular concern when the sequencing is ultra-deep and the insert size is short and non-variant.