The document discusses the advantages of long read sequencing technology over short read sequencing, specifically in applications like genome assembly and gene expression measurement. It highlights challenges associated with single cell genomics, particularly the limited number of molecules and the difficulty in handling single cells. Additionally, it introduces a new method called SAD-seq that measures DNA interactions and reveals its potential applications in cancer research.

1. Long vs short read sequencing. Long read sequencing technology is poised to replace short read
sequencing (e.g. Illumina).
a. What is the typical read length in short read sequencing, and what is the read length range for
long range sequencing?
b. What are the downsides for using short read sequencing for genome assembly/SNP calling,
and RNA isoform discovery/quantification?
c. Which genomic protocols and/or applications are expected to benefit the most from long read
sequencing?
1. Sequencing DNA that are accessible (e.g. ATAC-seq/DNASE-seq).
2. Measuring histone modifications (e.g. for ChIP-seq).
3. Measuring gene expression levels.
4. Measuring messenger RNA isoform diversity.
5. DNA sequencing for genome assembly.
6. DNA sequencing for variant calling (SNPs? Large structural variants?).
d. Bulk versus single cell. Most if not all genomics protocols described have been or can be
adapted to probe gene regulation in a single cell. What are some of the difficulties that make
measuring DNA, RNA, and/or protein more difficult in single cells? Select the answer that
applies.
1. The small number of molecules (DNA, RNA, protein) in a single cell is the general limiting
factor for single cell genomics.
2. Handling single cells is a lot more difficult because they tend to undergo apoptosis. c. Making
sure that you actually sequence one cell instead of multiple cells.
3. Finding a good way to dissociate cells is difficult for many tissue-types.
e. You attend a seminar and hear about a super cool approach called SUPER AWESOME DNA-
seq (SAD-seq). SAD-seq is able to measure, at nucleotide resolution, DNA bases that interact
with other DNA bases (DNA-DNA interactions). The speaker shows data that demonstrate SAD
seqs ability to recapitulate enhancer and promoter activity in two cancer-relevant cell lines (e.g.
HeLa cells and K562). The speaker further shows that SAD-seq can be used to identify
differentially expressed genes by comparing promoter activation. SAD-seq can do it all!
Naturally, you start to wonder whether you should be using SAD-seq in your project, or whether
you should start developing methods for analyzing SAD-seq. What are some important
questions you should ask yourself before jumping head in?