FAQ Does the Core Make Libraries?

FAQ Does the Core Make Libraries?

We offer library preparation services for:

1. RNA-Seq using
1. Illumina truSeq kits 200ng+ of total RNA
2. 
   
2. Agilent SureSelect Exome Capture

We can train you to make other libraries, but do not offer these services.

Why You Should Make Your Own Libraries

We are planning on purchasing a robot to automate this process, but for now library prep is a time-consuming task that can take a while for us to complete.

FAQ How to Make Libraries

FAQ How to Make Libraries

Wow that's a big question!  It's too much to handle in one FAQ, so here are links to individual pages for various library types.

FAQ Getting Started

FAQ - How do I Get Started?

The basic things you need to do are:

1. Read the other FAQs about experiment design and library prep
2. If you still have questions, check the Consultation Calendar and propose a time to meet to discuss your questions.
3. Have your PI create an account (this only needs to be done once.)
4. Create an account for yourself (this only needs to be done once.)
5. Submit an experiment request.
6. The next morning we will review experiment requests and contact you to resolve any questions.
7. We will notify you that we can accept samples.
8. Bring samples by at either 11-12 or 2-3 Monday to Friday.

FAQ Which Core To Use

There are a few Cores at Penn that do DNA sequencing.  Here is what we offer

NGSC

The NGSC has 3 Illumina hiSeq2000s and a miSeq. Here is what these machines are good for:

hiSeq2000

The hiSeq2000 is good for these techniques (and their many variations) RNA-Seq, ChIP-Seq, miR-Seq, HITS-CLIP, exome capture, BIS-Seq, and whole genome sequencing in mammals.

There are two aspects of ultra-high throughput sequencing that are important counts and coverage.  Counts are important for RNA-Seq, ChIP-Seq, miR-Seq, and HITS-CLIP.  Coverage is important for exome capture, BIS-Seq, and whole genome sequencing.   The hiSeq2000 generates sequence for about 200 million fragments per lane.  For each fragment the hiSeq can produce single or paired-end 50bp or 100bp sequences.  Using 100bp pair-end sequencing, you can get up to 40Gb per lane.

In many cases a single lane can generate more counts or coverage than a sample needs.  In that case, it is important to use multiplexed adapters so we can sequence multiple samples per lane.  Multiplexed adapters are generally a good idea is they allow samples to be test sequenced for quality, then sequenced deeper as needed.

Technique	Typical Volume	Samples per Lane
RNA-Seq	30 to 200 million reads	1 to 6
ChIP-Seq	30 to 100 million reads	2 to 6
miR-Seq	10 million reads	20
HITS-CLIP	30 million reads	6
Exome capture	20-30x coverage	5 to 20
BIS-Seq	20-30x coverage	1/3
Genome Sequencing	20-30x coverage	1/3

miSeq

The miSeq uses the same libraries as the hiSeq2000. It generates only about 15 million fragments per lane, but runs very quickly, and can generate reads as long as 150bp (or longer).

It is good for sample testing, miR-Seq, amplicon sequencing, or the techniques above applied to small, e.g., bacterial genomes.

Analysis - ChIP-Seq - Regional Enrichment

Introduction

This tool is used to identify statistically significant enrichment on regions that are defined relative to annotated regions of the genome, rather than to regions defined by the ChIP-Seq data itself.  It is typically used when the pattern of the ChIP-Seq target is so diffuse that standard peak callers have a difficult time identifying regions of enrichment.  In this case we will use regions that are of a priori interest, such as promoters, gene bodies, CpG islands , etc. that are likely regions to contain enrichment for the ChIP target.  The ngsc-chipseq-RegionalEnrichment tool counts reads on these pre-defined regions of interest for both a ChIP and a control (usually input) sample, then uses a Fisher exact test and Benjamini-Hochberg correction to assess the enrichment of the ChIP signal on the region.  A new track will be loaded with the enrichment ratio as the score and the p-value and FDR.

Details

A pseudocount of 1 is added to each region when computing the Fisher test.

FGC or NGSC - which core to use?

The Functional Genomics Core (FGC) and the Next-Generation Sequencing Core (NGSC) provide similar services, but with some important differences.
Here is a summary to help you decide which core is right for your project.

FGC

• high-throughput sequencing for IDOM/DRC members
• downstream data analysis for IDOM/DRC members as capacity allows
• Agilent microarrays for IDOM, UPenn, and academic clients
• limited RNA-Seq library prep for IDOM/DRC members

NGSC

• high-throughput sequencing for UPenn and academic clients
• standardized basic preliminary data analysis for UPenn and academic clients
• limited RNA-Seq library prep for UPenn, and academic clients

For the NGSC and FGC, prices are higher for external clients.

To good news is that you talk to the same people no matter which core you use.

Analysis - Tools - RUM-MultipleComparisons

Introduction

We routinely run the pipeline RUM-MultipleComparisons to assess RNA-Seq data.  Although the tool includes the work 'RUM' in the title, it can work with gene expression values from a variety of RNA-Seq tools.

We are still expanding what analyses RUM-MultipleComparisons performs but at the moment, it includes these basic steps.

1. Assemble a table of the raw data
2. Filter to consider just transcripts
3. Performs quantile normalization of the values
4. Does a series of k-means clustering of the data and displays results as heatmaps
5. Generates MvA plots of averages for all conditions
6. Generates MvA plots of replicates within a condition
7. Tabulates fold-changes between average values for all conditions

What Files Should I Look At?

 First, take a look at the plot, Replicates and Kmeans-heatmap.pdf files so that you can see if the samples have good intra-condition consistency.  In addition, the heatmap file will help you see if the changes between conditions are consistent across samples, and roughly how many sets of expression patterns there are in the set.

Once you can see that the data is ok, turn to the Averages.tab file or the appropriate Kmeans-*-clusters.tab file to see gene IDs.  All of the tab files can be opened from within Excel which can be used to further filter the genes.  Gene lists can also be created for use with functional analysis.

How Do we Usually Run It?

 We usually focus on well-characterized RefSeqs, i.e., those with IDs like NM_* or NR_*.

What Does the Output Look Like?

 Plots

• AllPairs-mva.png - a comparison of all samples in the data set.
• Kmeans-heatmap.pdf - series of heatmaps using different numbers of clusters.  Yellow/white is high expression, red is low.
• Pairs.pdf - MvA plots of all condition comparisons
• Replicates-mva.pdf - MvA plots of replicates within a condition

Tables of Data

• AllTranscriptReadCounts-sql.tab - initial raw data
• AllTranscriptReadCounts.tab - data filtered to just transcripts
• Averages.tab - averages over conditions with fold-changes for all comparison
• Details-Lg2-Qn.tab - quantile normalized values for individual samples
• Kmeans-04-clusters.tab - details of genes in each cluster.
• Kmeans-05-clusters.tab
• Kmeans-06-clusters.tab
• ...
• Kmeans-28-clusters.tab
• Kmeans-29-clusters.tab
• Kmeans-30-clusters.tab