By role
By subject area
By Q&A topic
Charles D Warden
Summary
I am currently a Bioinformatics Specialist in the Integrative Genomics Core at City of Hope. I assist scientists with next-generation sequencing (and sometimes microarray) analysis. As needed, I also develop algorithms for genomic analysis.
My work mostly currently focuses on cancer research, but I also assist with basic biology and virology research. I also have published evolutionary genomics papers.
Bioinformatics Biotechnology Computational Biology Genetics Genomics Oncology Translational Medicine Virology
Institution affiliations
Work details
Bioinformatics Specialist
Websites
Articles published in PeerJ
Detailed comparison of two popular variant calling packages for exome and targeted exon studies
BD-Func: a streamlined algorithm for predicting activation and inhibition of pathways
Preprints
Detailed comparison of two popular variant calling packages for exome and targeted exon studies
Provided feedback on
Interesting editorial! It looks like this article doesn't currently mention standard high-throughput databases. Although most scientists should be aware of these, data depositi...
Interesting paper - I've noticed that exon coverage has been low in exosome RNA-Seq experiments, and it was interesting to see how much non-coding reads specifically aligned to rRN...
dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms
Interesting paper! 1) My understanding was that the population frequencies (75%, 90%, and 99%) were used to try and estimate the specificity of the SNP calls (so, variants fou...
This sounds like an interesting package. A Bioconductor package for alternative splicing analysis would certainly be useful to the community. However, I think it would help to...
A simple scaling normalization for comparing ChIP-Seq samples
This paper looks like it has the potential to be quite useful. However, it seems a bit short and I think it would help to expand on the following details: 1) Do you provide a...
Interesting paper. In table 1, maybe it would help to show the same answer frequencies for before and after taking class? At first, I was confused how 82/116 could answer "YES"...
Cool paper! Perhaps it would help to provide a link to the software (e.g. https://github.com/scikit-image/scikit-image) in the abstract? I eventually figured it out from link...