Mon, May 4, 2015 at 12:19 PM
Customer asks about feasibility of performing de novo assembly using ChIP-seq data from a well studied cancer cell line: ChIP-seq experiments have been performed on this cell line many times by various research groups, producing a huge number of reads. He is interested in studying the aneuploidy of this cell line.
Mon, May 4, 2015 at 4:08 PM
AccuraScience LB: Technical challenges involved in attempting de novo assembly of ChIP-seq data from multiple sources include (1) ChIP-seq data are often single-ended and are of short read lengths, not particularly suitable for de novo assembly, (2) Data from different sources have different read lengths and other differences that require special attention when de novo assembly is attempted, and (3) De novo assembly algorithms (implicitly) assume nearly uniform coverage across genomic regions, which ChIP-seq data do not offer. Generally, ChIP-seq data are generated with the intent to be used in mapping-based, rather than assembly-based analysis techniques.
ChIP-seq experiments would naturally introduce bias in depth coverage, which could defeat the purpose of evaluating aneuploidy through read coverage analysis. This argument aside, mapping-based strategy would work equally efficiently if read coverage is to be used for measuring aneuploidy. Considering that many (or most) reads would be mappable to the reference genome, your objectives should be accomplishable without resorting to a "completely" de novo assembly-based approach.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
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