Applications

Cofactor can produce a large variety of libraries for multiple applications and from a wide range of sample types. Several examples are outlined in greater detail below. Additional libraries may be possible upon request.

Fragment View »



Whole genome characterization by single-pass shotgun sequencing of fragments from total DNA, PCR products, etc.

Paired-end View »



Whole genome characterization by shotgun sequencing from both ends of DNA fragments with ~200bp inserts. Specialty large inserts libraries are available upon request for an additional charge.

ChIP-Seq View »



Discovery & quantitation of protein-DNA interactions by sequencing DNA from immunoprecipitations.

MicroRNA View »



Discovery & quantitation of novel microRNAs and isoforms by sequencing cDNAs of microRNAs isolated from total RNA.

TranscriptomeView»



Quantitative whole transcriptome profiling (RNA-seq) by sequencing cDNAs constructed from messenger RNA isolated from total RNA.

Bisulfite View »



Genome methylation profiling by sequencing DNA fragments bisulfite treated to convert non-methylated C’s into U’s.



Capture View »


Single-nucleotide polymorphism and insertion/deletion detection by targeted selection and sequencing of discreet genomic regions of interest.



Whole Transcriptome

Quantitative transcriptome profiling by sequencing cDNAs constructed from messenger RNA isolated from total RNA

RNA-Seq is the most popular application at Cofactor. By sampling randomly from total RNA, RNA-Seq yields counts that are as quantitative as qRT-PCR but across a space more broad than a microarray - the entire transcriptome! RNA-Seq is also quantitative over more than 5 orders of magnitude giving it a much wider dynamic range than microarrays, which are compressed at both the top and bottom ends of detection.

To avoid a large number of counts going to highly-expressed ribosomal genes, Cofactor will also perform a PolyA selection or Ribo-Depletion on your sample, depending on the application and your sample.

RNA-Seq can be combined with Paired-Ends for sequencing transcriptomes from non-model organisms for which no reference is available.  Even if you do not need the expression data, RNA-Seq can also be a more cost effective strategy for sequencing the gene space of an organism not previously
sequenced.

RNA-Seq profiles are also more versatile than microarray expression profiles as they are amazingly reproducible and can represent an absolute rather than relative quantitation. In this way, data sets produced over many runs can be easily compared. Further, it is possible to make gene-to-gene comparisons instead of only treatment-to-treatment
comparisons of the same gene given by a microarray.

Just submit 10 micrograms of total RNA to Cofactor and let us do the rest.