Bioinformatics Core
General Questions
Can you suggest any good references in preparing for a second-generation sequencing or microarray experiment?
- Applications of next-generation sequencing
- RNA-Seq: a revolutionary tool for transcriptomics
- Advancing RNA-Seq analysis
- Next-generation sequencing transforms today’s biology
- Next-Generation Sequencing: From Basic Research to Diagnostics
- Sequencing technologies — the next generation
- Characterizing and Measuring Sequence Bias
- Evaluation of next generation sequencing platforms for population targeted sequencing studies
- Expression Profiling – Best Practices for Data Generation and Interpretation in Clinical Trials
- The Use and Analysis of Microarray Data
- Multiple-laboratory comparison of microarray platforms
- Independence and reproducibility across microarray platforms
- Standardizing global gene expression analysis between laboratories and across platforms
What experimental variables may affect my second generation sequencing or microarray experiments?
- Sample collection and nucleic acid isolation. All variables that you do not want to study should remain the same. In other words, samples should be collected at the same time of day, the same amount of time after watering or feeding.
- DNA and RNA concentration and quality should be checked on a reliable spec. We have a NanoDrop Spectrophotometer available to read low volumes. Make sure the amount of starting material is the same for every library or chip in the experiment. We also have a Qubit fluorometer for quantifying low concentrations of nucleic acids. For next generation sequencing library quantification we recommend Q-PCR quantification with the KAPA Library Quant Kit.
- Using different technicians, equipment and a variety of reagent lots may also affect your results.
How many replicates should I use in my microarry experiment?
- You should have at least 3 replicates per each experimental group. The more replicates you have the better statistical power.
About Analysis
What software do I need to align Illumina reads to reference sequences in my own lab?
How do I get an account on UCI's High Performance Community Computing Cluster (HPC3)?
- If you are affiliated with UCI, you can get an account on HPC3 for free. See OIT's HPC3 webpage for details.
What software do I need to de novo assemble illumina reads in my own lab?
- For genome assembly, you can use commercial software such as CLCbio Workbench or open source software such as ABySS, Velvet and ALLPATHS (all available on HPC). For de novo transcriptome assembly, Trans-AByss, Velvet-Oases and Trinity can be used. For reference-based transcriptome assembly, StringTie is recommended.
What software do I need to analyze illumina reads from RNA-Seq in my own lab?
- You can use commercial software such as CLCbio Workbench or open source software such as Hisat2 and STAR to align your reads and StringTie or Salmon, (all available on HPC3) to do transcript assembly, featureCounts and Salmon for abundance quantification and DESeq2 differential expression. Differential expression statistical analysis can also be done with R packages such as edgeR, DESeq
Where can I find the shared genome index on HPC?
- You can find the shared prebuilt genome index files for mouse and human on HPC3 at /data/apps/commondata/.
How do I interpret the columns in the differential expression analysis in DESeq2?
- col2: log2 fold change (MAP): condition treated vs untreated
- col3: standard error: condition treated vs untreated
- col4: Wald statistic: condition treated vs untreated
- col5: ald test p-value: condition treated vs untreated
- col6: BH adjusted p-values that controls FDR(false discovery rate)
What free software do you recommend viewing my alignment data?
- You can use Integrative Genomics Viewer (IGV), Tablet, Eagleview as stand-alone application. You can also visualize your data online with UCSC Genome Browser.
Can I reanalyze my data using other software programs?
- Yes – Our data are in standard format (BAM/SAM, FASTA/FASTAQ, BED, VCF, WIG etc.) and can be viewed and analyzed using a variety of software that accept standard input.
Can I view my data in the UCSC genome browser?
- Yes, as long as it is in one of the supported format.
How do I get gene annotation information?
- You can download gene annotation from UCSC genome browser or EMBL.
What other software programs can be used for downstream statistical data analysis?
- R and Python (available on HPC3).
Do you have information about NIH guidelines for data sharing and security?
- Please visit https://sharing.nih.gov/genomic-data-sharing-policy
What commercial software do you recommend for further analysis of gene expression data such as pathway analysis?
- GeneSpring, Partek, Qiagen IPA and GeneGO MetaCore. CLCbio (available on HPC3).
About IPA
Where can I learn more about Ingenuity Pathways Analysis (IPA)?
- Get to Know IPA
- A series of live webinars geared for new users. This link also provides recorded webinars covering:
- Discover IPA Introduction
- Uploading Data in IPA
- Interpreting the Results of your Core Analysis in IPA
- Tips and Tricks for doing RNA-Seq Analysis in IPA
- IPA Tutorials: step by step instructions for specific tasks within IPA
- IPA Training Videos: 2-5 min training videos on specific features in IPA
- Regulator Effects Analysis: Provides insights into your data by integrating Upstream Regulator results with Downstream Effects results to create causal hypotheses that explain what may be occurring upstream to cause particular phenotypic or functional outcomes downstream.
- Molecule Activity Predictor: simulate directional consequences of downstream molecules and the inferred activity upstream in the network or pathway
- BioProfiler: Quickly profile a disease or phenotype by understanding its associated genes and compounds. Identify genes known to be causally relevant as potential targets or identify targets of toxicity, associated known drugs, biomarkers and pathways.
- Causal Network Analysis & Upstream Regulators: identifying upstream molecules that control the expression of the genes in your datasets.
- Pathway Activity Analysis: determine if Canonical Pathways are increased or decreased based on your data
- Comparison Analysis : Quickly visualize trends and similarities across analyses and datasets
- Isoform Content and Features for processing of RNA-Seq Data
Licensed IPA Users have access to customer Support team (PhD Scientists) via phone and email M-F 6am to 5pm PST.
Customer Support: (650) 381-5111 | support@ingenuity.com
Quality Control
How much sample volume do I need to bring for QC?
For typical QC including nanodrop/ qubit and bioanalyzer, it would be best to bring at least 5 uL. However, if you require additional QC services, such as Kapa qPCR, then you would need to provide more sample volume. We can always return the leftover samples when we are done with QC.
Can I get my samples back after QC?
Yes, just write on the order form that you would like us to keep the leftover samples. However, unless otherwise noted, samples will need to be picked up within 1 month after project completion.
What if I need Bioanalyzer results as soon as possible?
If you need results as soon as possible, you have the option of paying to run a full chip, which means that in addition to paying for your samples you will also have to pay for all the empty wells on that chip. Each chip has 11 wells.
For example, if you have 4 DNA samples to run on a DNA high sensitivity chip, burning a chip means that you will have to pay for 11 samples (4 DNA samples + 7 empty wells).
How are the samples run on the Bioanalyzer?
We normally dilute the samples to the appropriate loading concentration range for the type of bioanalyzer run:
- DNA HS – up to 0.7 ng/uL by qubit reading
- RNA Nano – up to 500 ng/uL by nanodrop reading
- RNA Pico – up to 5 ng/uL by nanodrop reading
What is the difference between RNA Pico Bioanalzyer and RNA Nano Bioanalyzer?
The major difference between the Pico and Nano assay is the quantitative range. RNA Pico has a quantitative range of 50-5000 pg/uL. While RNA Nano has a quantitative range of 5 – 500 ng/uL. Also, the RNA Pico chip processes 11 samples at time, while the RNA Nano processes 12 samples at a time.