How Many Replicates are Sufficient for Differential Gene Expression?

In a nicely done, convincing 2 condition, 48 replicate RNA-Seq experiment, researchers from the University of Dundee aimed to answer a frequently asked question in the field and on Genohub.com, ‘How many replicates are necessary for differential gene expression (DGE)?’. In their study they examined three statistical models to see which best represented read-count distribution of genes from commonly used DGE tools.

Using the statistical power of 48 replicates they determined that inter-lane variability does not play a large role in DGE results. Assuming even loading and amplification their results showed a Poisson distribution of counts from individual genes. The authors also determined that read count distribution was consistent with a negative binomial model, an assumption in widely used tools such as edgR, DESeq, cuffdiff and baySeq. Performing a goodness-of-fit test for log-normal, negative binomial and normal distributions the authors demonstrated that inclusion of ‘bad replicates’ made results inconsistent with the statistical models they tested, complicating the interpretation of differential expression results. A bad replicate was defined as 1) one that poorly correlated with other replicates, 2) a replicate with atypical read counts, 3) one having non-uniform read depth profiles. 

So how many replicates are sufficient for differential gene expression? The authors sequenced 96 mRNA samples in 7 1×50 HiSesq lanes. The cost for this on Genohub today is $28k USD. When the authors removed 6-8 bad replicates from their pool of 48 samples, their data became consistent with a negative binomial distribution. Assuming experimental variability similar to the authors, this indicates at least 6 replicates in a DGE experiment is good practice. The cost for the preparation of 6 RNA-seq libraries and sequencing is $2,500 USD.  

Doing a literature search and observing client behavior on Genohub, we estimate ~80% of those studying DGE use 3 replicates in their experiments, which with dropouts and variation is unsatisfactory. A final point we’d like to make is the authors used ~11M, 1×50 reads/sample, which goes to show that with DGE, replicates can be more important than read depth. This is further discussed in our Coverage and Read Depth Guide

At Genohub, we help consult and design sequencing experiments with users. Determining the replicates needed for a study is a common question that needs to be answered. Unfortunately, too few studies examine these fundamental elements in sequencing design. We hope this article gets the recognition it deserves. 

Reference: 

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment Marek Gierliński, Christian Cole, Pietà Schofield, Nicholas J. Schurch, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon Simpson, Tom Owen-Hughes, Mark Blaxter, Geoffrey J. Barton

 

AGBT 2015 Summary of Day 3

Advances in Genome Biology and Technology Conference 2015

Day 3 of the Advances in Genome Biology and Technology meeting in Marco Island began with an announcement that next year the meeting would be held in Orlando due to hotel renovations, eliciting a groan from the audience. The meeting will come back to Marco Island in 2017.

Today’s plenary session speakers all presented work with a clinical focus, acknowledgement by the conference organizers about the direction of genome sequencing. The first speaker, Gail Jarvik, head of medical genetics at the University of Washington Medical Center presented on lessons learned from the Clinical Sequencing Exploratory Research (CSER) Consortium, marketed as ‘Hail CSER’. CSER is a national consortium of projects aimed at sharing innovations and best practices in the integration of genomic sequencing into clinical care. CSER has established a list of 112 actionable genes, some overlapping with the American College of Medical Genetics (ACMG) list. The CSER group annotated pathogenic and novel variants of the Exome Variant Server (EVS) to estimate rates in individuals of European and African ancestry.

The next talk was by Euan Ashley on moving toward clinical grade whole genome sequencing. He started by describing the genome as complex, full of repeats, duplications and paralogous sequences, giving him ‘a cold sweat at night’. He gave an example of a study with 12 adult participants who underwent WGS and described how clinical grade sequencing demands consistency in reporting. Most variants annotated as pathogenic were downgraded after manual review, but this takes lots of time. 12 individuals with 1,000 variants took around 1 hour per variant. In this case the use of WGS was associated with incomplete coverage of inherited disease genes, low reproducibility of detectable genetic variation and uncertainty about clinically reportable findings. He commented that new algorithms would be needed to address these problems and that ‘we’re at the beginning of genomics medicine’. Parts of his talk can be seen in his presentation at PMWC last month.

The last presentation before the break was by Levi Garraway who discussed the goal of cancer precision medicine to develop new therapeutics and combinations against molecular defined tumors. He mentioned that there are many discovery opportunities in clinical cancer genomics especially in terms of response and resistance to new therapies. Garraway sequenced the genomes of 57 prostate tumors and matched normal tissues to study somatic alterations. His model suggests that chormoplexy induces considerable genomic derangement over a relatively few number of events in prostate cancer supporting a model of punctuated cancer evolution. He introduced a 10X Genomics approach for phasing of large – 100 kb regions with exonic baits to obtain rearrangement information for chromoplexy. In the end he emphasized the importance of RNA-Seq profiling in conjunction with DNA sequencing for translational medicine to be relevant.

After the break, Stephen Kingsmore gave a presentation on rapid genome sequencing for genetic disease diagnostics in neonatal intensive care units. Kingsmore began the talk by describing how newborn screening (NBS) and early diagnosis reduces morbidity and mortality. NGS of 60 genetic diseases identifies ~5,000 affected newborns each year. He described how rapid genome sequencing (RGS) has the potential to improve NBS to most genetic diseases in newborns admitted to level II-IV NICUs. He mentioned a ‘ultra rapid’ sequencing pipeline he developed along with Illumina that takes 28 hours to go from sample to variant annotation (not publically available). He also discussed NSIGHT, a consortium for newborn sequencing sponsored by the NIH to understand the role of genome sequencing. More details can be found on the NHGRI page.

The last two plenary talks were by Christian Matranga and Malachi Griffith. Matranga described the clinical sequencing of viral genomes as important to understanding the evolution and transmission of the pathogen and the ability to inform on surveillance and therapeutic development. They developed a sequencing approach that combines RNAse H based depletion of rRNA with random primed cDNA RNA-seq to detect and assemble genomes from divergent lineages. They sequenced ~300 Lassa (LASV) and ~100 Ebola (EBOV) genomes. We describe some of their efforts in an earlier post called, Sequencing Suggests the Ebola Virus Genome is Changing. Be sure to read the New Yorker reference, it’s compelling!

Griffith’s talk was on optimizing genome sequencing and analysis. He makes the point that while most tumors are sequenced by exome sequencing at 75-100x mean coverage or by whole genome sequencing (WGS) to 30-50x mean coverage, detection of low frequency mutations require greater depth. He performed deep sequencing of an acute myeloid leukemia (AML) by WGS up to 350X, whole exome to 300X and using a capture panel of ~260 recurrently mutated AML genes to ~10,000x coverage. He found that deeper sequencing revealed more driver variants and improved the assignment of variants to clonal clusters. Checkout his animation of WGS depth down-sampling.

After lunch began the ‘Bronze sponsor workshops’, essentially the talks you pay >$40K to give. The most interesting was the last by 10X Genomics, mainly because as @bioinformer put it, “10X Genomics is the new princess of the AGBT ball”. First, check out the video that received a round of applause from the AGBT crowd: Changing the Definition of Sequencing. They announced their instrument would be available in Q2 this year, cost ~$75K and $500 / sample. This brings the question whether 10X Genomic’s microfluidic platform offers greater potential than Molecule. What are the implications for Illumina or PacBio? To learn more check out Keith Robison’s insightful post detailing all there is currently known about 10X Genomics.

After dinner began concurrent sessions on technology, genomic medicine and transcriptomics. Hopefully someone else will post details about the genomic medicine and transcriptomics sessions. The technology session began with Iain Macaulay describing G&T-seq, separation and parallel sequencing of genomes and transcriptomes of single cells. This was the first talk this year at AGBT with an embargo, no tweets were allowed. So rather than go into details, we did find this lecture online. The next talk was by Alexandre Melnikov on MITE-Seq, an approach to site directed mutagenesis referred to as Mutagenesis by Integrated TiLEs. MITE facilitates structure-function studies of proteins at higher resolution than typical site directed approaches. To read more check out their paper published last year in Nucleic Acid Research. Andrea Kohn, then described single-cell methylome profiling of Aplysia neurons. Using methyl-dip and bisulfite sequencing she achieved >20x coverage for each neuron and then added RNA-seq providing the first methylome and transcriptome from a single neuron. Next up was Sara Goodwin who gave an in depth analysis of the Oxford MinION Device for de novo and cDNA sequencing. She sequenced the yeast strain W303 to over 120x coverage and was able to achieve up to 80% aligned reads. She mentioned that identifying the right aligner was still a work in progress but overall found promise in the technology for long read sequencing, de novo assembly and splice site id.

Tomorrow’s plenary talks are the second installment of genomics, ‘Genomics II’ with presentations by Michael Fischback, Rob Knight, Chris Mason, and Gene Myers, excellent lineup to close the final day of AGBT. Checkout our earlier posts if you’ve missed day 1 or day 2

AGBT 2015 – Summary of Day 1

AGBT 2015

Highlights of Day 1 at AGBT

‘Welcome to paradise’, first words by Rick Wilson kicking off the annual Advances in Genome Biology and Technology (AGBT) meeting.  The plenary session began with David Goldstein from Columbia University presenting, “Toward Precision Medicine in Neurological Disease”.  David’s talk began with a discussion of clinical sequencing for neurological diseases, specifically large scale gene discoveries in epileptic encephalopathies. In epilepsy, 12% of patients are ‘genetically explained’ by a casual de novo mutation, which allows for the application of precision medicine. He discussed how a K+ channel plays a key role in at least two different epilepsies and how Quinidine which has never been used for epilepsy, was being used as a targeted treatment. He cautioned that in the literature there are too may correlation studies that don’t really amount to much and as we use genetics to target diseases, it’s critical to perform proper genetics driven precision medicine and not put patients on wrong treatment plans. To better characterize the effects of mutations, he emphasized the need for solid model systems. He also mentioned that he believes truly complex diseases can be tackled with enough patients, numbers matter. To illustrate his point, he described the sequencing of over 3,000 ALS patients to get a clear picture of what genes/proteins have therapeutic importance. At the end of his talk he was asked the old whole genome sequencing (WGS) vs. whole exome sequencing (WES) question and replied that WES was sufficient, as WGS added little due to lack of interpretability. This touched off some debate in the audience and Twitter with regards to Exome-seq and WGS. Highlighted are the advantages of each approach here: https://blog.genohub.com/whole-genome-sequencing-wgs-vs-whole-exome-sequencing-wes/.

The second talk in the plenary session was by Richard Lifton, from Yale and it was titled, “Genes, Genomes and the Future of Medicine”. Richard cautioned the audience, describing the rush to sequence whole genomes as more industry driven than good science, essentially reiterating the point that WGS is hard to interpret. This began a side discussion on Twitter about those who agree and disagree with this sentiment. Most notably, Gholson Lyon referenced two recent papers that demonstrated new ways to make processing of WGS data easier: and that the accuracy of INDEL detection was greater in WGS compared to WES, even in targeted regions. On the cost front, WGS at 35x coverage currently costs $1,750: https://genohub.com/shop-by-next-gen-sequencing-project/#query=ef95a222ca23fc310eedf6de661e4b22 or $3,500 for 70X coverage, while whole exome sequencing costs at 100x coverage are around $1,314: https://genohub.com/shop-by-next-gen-sequencing-project/#query=0d4231a1d12425085f4e284373605acd.  Richard remarked at the end of his talk that not much had been found in non-coding regions, several in the audience challenged him on this assessment.

The third talk in the plenary session was by Yaniv Erlich titled, “Dissecting the Genetic Architecture of Longevity Using Massive-Scale Crowd Sourced Genealogy”. We’ve had the pleasure to hear Yaniv give several lectures in the past, all have been engaging, this was no different. His talk was on using social media to dissect the genetic architecture of complex traits, specifically whether they work independently (additive) or together (epistatic). Predictions of epistasis suggest an exponential increase with added risk alleles. He used geni.com to dissect complex traits in large family trees and validated publicly submitted trees using genetic markers. He encoded birthplace as GPS coordinates using Yahoo Maps and showed migration from the Middle Ages through the early 20th Century. The video he played was amazing, check it out: https://www.youtube.com/watch?v=fNY_oZaH3Yo#t=19. His take home message was that longevity is an additive trait, which is good for personalized medicine. The Geni data he described is open to the public for use.

The fourth and final talk of the night was by Steven McCarroll titled, “A Common Pre-Malignant State, Detectable by Sequencing Blood DNA”. He started by posing the questions, what happens in the years before a disease becomes apparent; cancer genomes are usually studied when there are enough mutations to drive malignancy, do they happen in a particular order? He examined 12,000 exomes for somatic variants at low allele frequency and uncovered 3,111 mutations. Blood derived schizophrenia clustered in four genes: DNMT3A, TET2, ASXL1 and PPM1D, all disruptive. He postulates that driver mutations give cells an advantage, over several years clonal progeny takes over creating pre-cancerous cells. Therefore clonal hematopoiesis with somatic mutations can be readily detected by DNA sequencing and should become more common as we age. Patients with clonal mutations have a 12 fold higher rate of blood cancer, meaning there is a window for early detection, possibly 3 years. This work was recently published in the New England Journal of Medicine: Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.

Today’s sessions were impressive and set expectations high for tomorrow’s talks. McCarroll’s last comment nicely captured this sentiment, setting the tone for the rest of the meeting, “Medicine thinks of health and illness, there is a lot in between that is ascertainable via genome sequencing”. 

Whole Genome Sequencing (WGS) vs. Whole Exome Sequencing (WES)

Gene, exon, intron, sequencing

 

“Should I choose whole genome sequencing (WGS) or whole exome sequencing (WES) for my project?” is such a frequently posed question during consultation on Genohub, we thought it would be useful to address it here. With unlimited resources and time, WGS is a clear winner as it allows you to interrogate single-nucleotide variants (SNVs), indels, structural variants (SVs) and copy number variants (CNVs) in both the ~1% part of the genome that encodes protein sequences and the ~99% of remaining non-coding sequences. WES still costs a lot less than WGS, allowing researchers to increase sample number, an important factor for large population studies. WES does however have its limitations. Below we’ve highlighted the advantages of WGS vs. WES and described a real case example of someone ordering these services using Genohub.

Advantages of Whole Genome Sequencing

  1. Allows examination of SNVs, indels, SV and CNVs in coding and non-coding regions of the genome. WES omits regulatory regions such as promoters and enhancers.
  2. WGS has more reliable sequence coverage. Differences in the hybridization efficiency of WES capture probes can result in regions of the genome with little or no coverage.
  3. Coverage uniformity with WGS is superior to WES. Regions of the genome with low sequence complexity restrict the ability to design useful WES capture baits, resulting in off target capture effects.
  4. PCR amplification isn’t required during library preparation reducing the potential of GC bias. WES frequently requires PCR amplification as the bulk input amount needed to capture is generally ~1 ug of DNA.
  5. Sequencing read length isn’t a limitation with WGS. Most target probes for exome-seq are designed to be less than 120 nt long, making it meaningless to sequence using a greater read length.
  6. A lower average read depth is required to achieve the same breath of coverage as WES.
  7. WGS doesn’t suffer from reference bias. WES capture probes tend to preferentially enrich reference alleles at heterozygous sites producing false negative SNV calls.
  8. WGS is more universal. If you’re sequencing a species other than human your choices for exome sequencing are pretty limited.

Advantages of Whole Exome Sequencing

  1. WES is targeted to protein coding regions, so reads represent less than 2% of the genome. This reduces the cost to sequence a targeted region at a high depth and reduces storage and analysis costs.
  2. Reduced costs make it feasible to increase the number of samples to be sequenced, enabling large population based comparisons.

Most functional related disease variants can be detected at a depth of between 100-120x (1) which definitely makes the cost case for exome sequencing. Today on Genohub if you want to perform whole human genome sequencing at a depth of ~35X, the cost is roughly $1700/sample. If you were to request human exome-sequencing services with 100x coverage, using a 62 Mb target region, your cost would be $550/sample. Both of these prices include library preparation. So in terms of producing data WES is still significantly cheaper than WGS. It’s important to note that this doesn’t include your data storage and analysis costs which can also be quite a bit higher with whole genome sequencing.

It’s also important to remember that depth isn’t everything. The better your uniformity of reads and breath of coverage, the higher the likelihood you’ll actually find de novo mutations and call them. And that’s the main goal, if you can’t call SNPs or INDELs with high sensitivity and accuracy, then the most high depth sequencing runs are worthless.

To conclude, whole genome sequencing typically offers better uniformity and balanced allele ratio calls. While greater exome-seq depth can match this, sufficient mapped depth or variant detection in specific regions may never reach the quality of WGS due to probe design failures or protocol shortcomings. These are important considerations when examining tissues like primary tumors where copy number changes and heterogeneity are confounding factors.

If you’re ready to start an exome project, spend a few minutes determining the coverage you’ll need for your experiment. We have an exome-seq guide with examples to help you determine the number of sequencing reads you need to achieve a certain coverage of your exome. If you’re planning to embark on whole genome sequencing, use our NGS Matching Engine which automatically calculates the amount of sequencing capacity on various platforms to meet the coverage requirements for your project.

Reference:

1) Effect of Next-Generation Exome Sequencing Depth for Discovery of Diagnostic Variants

Illumina’s Latest Release: HiSeq 3000, 4000, NextSeq 550 and HiSeq X5

HiSeq 3000, HiSeq 4000, HiSeq X Five, HiSeq X Ten

Illumina’s latest instrument release essentially comes down to more data/day.  Using the same patterned flow cell technology already in use with the HiSeq X Ten, The HiSeq 3000 has an output of 750 Gb or 2.5B PE150 reads in 3.5 days. The HiSeq 4000 has two flow cells, so twice the output: 1.5 Tb, 5B PE150 reads in 3.5 days. The NextSeq 550 combines the current NextSeq 500 with a microarray scanning system that fits right into the flow cell holder. The HiSeq X Five is less exciting; just half the number of instruments as the HiSeq X Ten.

If you don’t have the $10M budget for a HiSeq X Ten, you can purchase a HiSeq X Five and scale to the X Ten at a lower price/instrument: $1M/unit

Price Price/unit $/Genome* Consumables $/Gb
HiSeq X Five $6M $1.2M $1,425 $1,200 $10.6
HiSeq X Ten $10M $1M $1,000 $800 $7

*Price per 30X human genome according to Illumina. We’re not aware of any sequencing facility currently offering 30 human genomes for $1,000. On Genohub today, you can order a single whole human genome at 35X for $1,750.

Both the HiSeq X Five and Ten are still only “licensed” for human whole genomes [Update: Since this post was published in January 2015, Illumina now allows the sequencing of other large species on the HiSeq X Ten. For an up to date status on what is and what isn’t allowed on a HiSeq X, follow our HiSeq X Guide Page]. That basically means that while they can technically be used on non-human samples or transcriptomes, Illumina wants these focused on the WGS market (probably thinking about the BGI / Complete Genomic’s WGS instrument release this year).  Plus it gives them an excuse to release patterned flowed cells on more models, hence the HiSeq 3000/4000. Interestingly, Illumina is going to start bundling the TruSeq PCR-free and TruSeq Nano library prep kits (the only chemistry currently compatible with the X Five and X Ten) with X Five/Ten cluster reagents. At least for now, they don’t intend on doing this with the HiSeq series. Other news from this release:

HiSeq 3000/4000 do not have a rapid mode, high-output only. However PE150 reads only take 3.5 days

You can’t upgrade from a HiSeq 2500 (non-patterned flow cell) to a HiSeq 3000 or 4000 (patterned flow cells)

You can upgrade from the single flow cell HiSeq 3000 to dual flow cell HiSeq 4000

HiSeq 3000 yields >200 Gb/day, a 28% increase vs. HiSeq 2500 v4, yet the cost to purchase a HiSeq 3000 is the same as a HiSeq 2500. With two flow cells, the HiSeq 4000 yields twice as much data.

Sequencing Applications and Turnaround Time 

Exomes Transcriptomes 30X Genomes
HiSeq 3000 90 (2×75, <2 days) 50 (2×75, <2 days) 6 (2×150, 3.5 days)
HiSeq 4000 180 (2×75, <2 days) 100 (2×75, <2 days) 12 (2×150, 3.5 days)

So in the end, assuming sequencing facilities aren’t fed up with this break neck upgrade cycle and actually purchase these instruments, researchers can expect more data with faster turnaround times. We’ve already spoken to a few of our service providers who are considering upgrades to their HiSeq 1500/2000 instruments. As soon as these new instruments are available on Genohub, we’ll make an announcement [Update: they are all available, use our NGS Matching Engine for access to the latest Illumina instruments]. If you’d like to be the first to know send us an email at support@genohub.com. In the meantime, our providers offer services on the HiSeq 2500 v4, HiSeq X Ten, NextSeq 500 and HiSeq instruments (amongst many others).  You can order these services immediately and expect data delivery within the listed guaranteed turn around times. If you’re not sure what technology / instrument is right for you, just enter the number of reads or coverage you need and let our NGS Matching Engine identify the best service for you.  So what’s next? A little bird has told us patterned flow cells on the MiSeq !

TCR-Repertoire Sequencing Services

TCR sequencing

The immune repertoire reflects the sum total of diverse B and T-cells in the circulatory system. The adaptive immune system drives immune response by these hypervariable molecules. The antigen specificity of each T-cell receptor (TCR) is determined by the complementarity-determining region: CDR3 of the beta receptor chain, formed by V, D and J gene regions. Examination of TCR diversity is important for understanding adaptive immunity and it’s function in diseases.  Next generation sequencing has become a powerful tool for measuring TCR diversity. Before samples can be sequenced a unique library preparation method must be performed to allow for reproducible and reliable results.

Girihlet, a newly formed biotech company in Brooklyn, NY is one of the first companies to offer TCR repertoire sequencing services and is the first to offer it on Genohub.com. We got in touch with Girihlet to learn more about this service offering and have posted our conversation with one of it’s co-founders, Dr. Ravi Sachidanandam. Ravi also holds a position as Assistant Professor on the faculty of  the Icahn School of Medicine at Mount Sinai, department of Oncological Sciences. He has published over 85 papers in the latest and most interesting areas of genomics, including small RNA, mRNA splicing, methylation and virology.

Genohub: Hi Ravi, we’re excited that you’ve joined Genohub.com and listed your services. We’re particularly interested in the TCR-repertoire sequencing services you have on Genohub.com. Not many service providers currently offer this service, how come?

Ravi: There are very few companies that offer this currently, and this is mostly because it’s a very challenging problem both experimentally and computationally. It may be easier to count all the dollar bills in circulation than to profile the diversity of the T Cell Receptors.

Genohub: Can you comment briefly on the ‘library prep’ approach to TCR profiling?

Ravi: Our library prep method is very unique, it is based on quantifying RNA, and in particular just the CDR3 regions while most of the other companies quantify DNA. This allows us to only quantify functional rearranged TCR locus. We also use universal primers for amplification and do not depend on previously known TCR regions, thereby accelerating discovery.  We have also compared our data to flow results and demonstrated good concordance.

Genohub: Inefficiencies during library prep and and sequencing can lead to severe bias generating artificial TCR diversity. Does your approach address this?

Ravi: The beauty of our approach is we use common primers to amplify the T cell receptor regions.  This ensures there is no bias during PCR, allowing for accurate sequencing. And since the accuracy and enrichment for the TCR mRNAs is >98%, we need very little total RNA and less sequencing depth reducing the overall cost of sequencing.

Genohub: How many sequencing reads or TCR sequences do you recommend for a single human sample? Our readers can use your recommendation directly on our project search page: https://genohub.com/shop-by-next-gen-sequencing-project/.

Ravi: Currently 10 million sequences of 150bp PE reads is enough to accurately and quantitatively capture most of the TCR diversity

Genohub: How do you handle under-expression?

Ravi: We keep track of low -expressed TCR transcripts as they are needed to understand the statistics of the distribution of the TCR repertoire. We provide these to the researchers, in case they might need to look for rare transcripts.

Genohub: Why is diversity of the immune repertoire important for health?

Ravi: The diversity is the key to the effectiveness of the TCR-repertoire.  The diversity reflects the ability of the immune system to fight infections.  

Genohub: Any comments on its use for vaccine development, autoimmune study, biomarker detection?

Ravi: We believe the TCR sequence can be easily monitored over time, thereby serving as a powerful biomarker to study the effects of vaccination, to determine if the vaccination was effective. It will also be useful in understanding the underlying cause of autoimmune reactions.

Genohub: Thanks for taking the time to discuss this exciting new method. Is there anything that you’d like to add?

Ravi: Girihlet is very excited to take this approach to the rest of scientific community and make a significant difference on how the TCR is sequenced currently and eventually have an impact on the practice of “precision medicine”. 

Genohub Projects Now Support Multiple Collaborators

Most researchers using Genohub typically work in a team with other investigators and administrators. However, so far every Genohub user has been able to view and manage only the projects they directly started on Genohub.

We’re pleased to announce that effective immediately, you can add one or more collaborators to any of your Genohub projects, all the way from the project request stage until after your project is complete and the results are ready.

By quickly adding a collaborator to your project you can allow another member of your team to view the quotes and detailed project information. You may also give them permission to manage the project (e.g. post messages, accept quotes, attach files, etc.).

In addition to setting per-member permissions, you can also choose whether each member receives email notifications when there is activity on your project.

To add a collaborator, all you need is their email address:

Genohub Collaboration Tool

For instance you may want to share the instant Genohub quote on a particular service with the primary investigator on your team in order to get their approval. You may also want to give someone at your purchasing department access to the detailed pricing information on your project. Or you may want a colleague to manage the project and handle the communication with the service provider while you’re on vacation. This can all be done by simply adding these individuals as collaborators.

Please give it a try and feel free to reach out to us at support@genohub.com if you have any questions or feedback.

 

Genohub Opens Access to Latest Project Management Tool

We’re pleased to announce the launch of a new project management tool called PIP (Provider Initiated Projects) on Genohub! Up to now, Genohub has been a successful marketplace for connecting researchers with next-generation sequencing service providers. Service providers on Genohub have used Genohub’s project management tools to manage hundreds of incoming researcher requests. We’re instituting two new BIG changes:

  1. We’ve opened up our project management tools to all service providers and CROs. These tools are no longer limited to providers offering sequencing services. If you offer any type of scientific service, you can now use Genohub software to initiate projects, write quotes and manage back and forth project communication at no charge.
  2. In the past, a researcher would have to inquire about your services before you could start a project. Providers can now start projects and quotes for researchers anywhere in the world.

Here are examples of how this service could be useful to you:

Example 1 

You’re a service provider who handles internal service projects from researchers within your University. You’re tired of using email threads to manage these projects and are looking for a way to send quotes, have researchers upload project specs and have all back and forth communication saved as part of a unique project. Genohub’s PIP feature handles all of that and is available free of charge! 

Example 2

A new researcher or someone you’ve worked with in the past contacts you to start a service project. Using PIP you can initiate a project, write a quote and manage communication with anyone in the world. We’ll only charge you if you elect Genohub to handle invoicing and billing, otherwise it’s free. 

 

To get started, use the blue ‘Start New Project and Create Quote’ button on your Project Dashboard to initiate a project. 

 

Genohub Project Dashboard Continue reading

How Much Sequencing is Needed For ChIP-Seq ?

ChIP-Seq Peaks

Adapted from: ChIP–seq: advantages and challenges of a maturing technology
Peter J. Park
Nature Reviews Genetics 10, 669-680 (October 2009)

One of the most common sequencing applications searched for and ordered on Genohub is ChIP-Seq. A frequent question we’re asked is how much sequencing do I need for my ChIP-Seq experiment?

ChIP-Seq is the most widely used technique for measuring protein – DNA interactions on a genome wide scale. Before starting a ChIP-Seq experiment it’s important to have some information about specificity. Assume that you’ve tested your protein’s (nucleosomes, histones, chaperone) specificity and it enriches 10x over background.  If you’re fragmenting a human genome and there are ~3,000 places in the genome that your protein binds, you’ll need approximately 1 sample/fragment:

Your background (human) is : 3 Gb / 300 bp, or 1×107 fragments. 

Signal enrichment is 10x x 3000 locations = 3×104

So you need 1×107 + 3×104 ~= 1×107 sample hits for a 10x signal, with 10 fold enrichment. 

Here are some services on Genohub that would meet these ChIP-Seq metrics: https://genohub.com/shop-by-next-gen-sequencing-technology/#query=cac50ae846ca1cf773a39716b66f7142.

ChIP Signal Strength

The relationship between ChIP signal strength and regulatory activity is an area of active research. Some very active transcriptional enhancers often display moderate ChIP signal.  As a result it can be difficult to set a threshold for Chip Signal strength that will be inclusive of all functional sites. A rough guide is ~ 20M unique mapped reads / mammalian sample.

ChIP-Seq Control

Designing a good control is essential for every ChIP-Seq experiment. A separate control should be run for every sample, cell type, condition or treatment. For a useful control, perform ChIP with an antibody that reacts with an unrelated antigen. Make sure you’re able to make a library that’s as complex as your experimental samples. We typically recommend that users dedicate at least the same if not more reads to their control versus actual samples.

Making highly ‘complex’ libraries is important for ChIP-Seq. We’ve outlined several library prep kit options here: https://genohub.com/chip-seq-library-preparation/.

Finally, if you’re new to ChIP-Seq and need more project advice, contact us for complimentary project consultation

Sequencing Suggests the Ebola Virus Genome is Changing

Genome of the Ebola Virus is Changing Rapidly

Using high throughput sequencing, researchers from MIT, Harvard and the Sierra Leone Ministry of Health and Sanitation have recently reported rapid changes in the Ebola’s genetic code. The Ebola virus genome, a single stranded RNA comprised of ~19,000 nucleotides encodes several structural proteins: RNA Polymerase,  nucleoprotein, polymerase co-factors and transcription activators. The researchers used Illumina HiSeq 2500 platforms to achieve 2000x coverage of the Ebola genome. Using Genohub, we estimate the cost to sequence 100 such genomes at 2000x to be under $1,500: https://genohub.com/shop-by-next-gen-sequencing-project/#query=0929767cd66b8ec8a9fb209c99d75b27.

Sequencing 99 Ebola genomes from 78 patients, they found greater than 300 genetic changes that make the genomes sequenced from the current outbreak distinct from previous outbreaks. In fact, they found that the substitution rate was twice as high with this year’s outbreak compared to all other Ebola virus outbreaks. They also determined that mutations during this year’s outbreak were frequently nonsynonymous (mutation that alters the amino acid sequence of a protein). 50 mutational events and 29 new viral lineages were observed in this outbreak alone, suggesting potential for viral adaptation. To determine whether Ebola could be evolving away from defenses against it or whether it could become more contagious and spread faster, will require functional analysis. For their part, Gire et al., have published the full length Ebola genomes in the NCBI database.  Tragically, the authors note that 5 co-authors died from the disease before the manuscript could be published. Last week The New Yorker, published The Ebola Wars, an excellent in depth story of the work involved to actually sequence the Ebola genome and track its mutations.

While basic PCR tests are sufficient for giving you a yes/no answer about infection, this new study highlights the important role of sequencing in characterizing patterns of viral transmission and mutations in an epidemic. We expect sequencing to play a greater role in development of diagnostics and treatments for this and other viral outbreaks.