World’s Leading NGS Matching Engine Just Got Better

Posted on July 19, 2016 by Pouya Razavi

Every week thousands of researchers from around the world rely on Genohub’s free next-generation sequencing matching engine to explore sequencing solutions that match their project requirements. This typically involves finding out the right amount of capacity (e.g. number of sequencing lanes) on different sequencing platforms to meet a coverage or read count requirement for various applications such as DNA-Seq, RNA-Seq, Exome, Amplicon-Seq, etc.

We are very excited to release a brand new version of our NGS matching engine, that packs a lot of major improvements. Here are a few highlights:

Redesigned Interface

We have completely redesigned the interface to make it even faster and easier to find matching services. It’s now also easier to quickly send a request to get confirmed quotes or to get help from our scientists

Detailed Quote View

You can now view detailed quotes right from the results. This makes it a lot easier to evaluate different options based on additional service details.

Instrument and Library Preparation Kit Filters

You can now filter the results by instrument or library preparation kit. This helps with situations where for whatever reason (e.g. consistency with a previous sequencing run) you’d like to stick with a particular sequencing instrument.

Expanded Range of Services and Lower Prices

We’ve been working very hard with our network of partnering service providers to expand the range of sequencing services. Here are just a few examples:

New instruments such as 10X Genomics, HiSeq X, HiSeq 3000/4000, NextSeq 500
New applications such as Ribo-Seq, targeted amplicon, mtDNA, HLA and TCR-repertoire sequencing
Gene panels such as Qiagen’s amplicon panels, Agilent and Nimblegen’s Target Hybrid capture panels and IDT’s xGEN Lockdown panels.

Whether you are just exploring options for a future project, looking to get a few quotes for a grant application, or have an immediate sequencing project with samples ready to ship, there’s no better way to find and order the right NGS services. As always, we’d love to hear your feedback on what you like, and more importantly what else you’d like to see improved. Leave a comment here or email us at support@genohub.com.

To use the service visit Genohub.com.

New Short, Long and High Throughput Sequencing Reads in 2016

Posted on January 1, 2016 by genohub

Nanopore sequencing

An exciting wave of newly released DNA sequencing instruments and technology will soon be available to researchers. From DNA sequencers the size of a cell phone to platforms that turn short reads into long-range information, these new sequencing technologies will be available on Genohub as services that can be ordered. Below is a summary of the technology you can expect in Q1 of 2016:

10X Genomics GemCode Platform

The GemCode platform from 10X Genomics partitions long DNA fragments up to 100 kb with a pool of ~750K molecular barcodes, indexing the genome during library construction. Barcoded DNA fragments are made such that all fragments share the same barcode. After several cycling and pooling steps, >100K barcode containing partitions are created. GemCode software then maps short Illumina read pairs back to the original long DNA molecules using the barcodes added during library preparation. With long range information, haplotype phasing and improved structural variant detection are possible. Gene fusions, deletions and duplications can be detected from exome data.

Ion Torrent S5, S5 XL

The S5 system was developed by Ion to focus on the clinical amplicon-seq market. While the wait for delivery of Proton PII chips continues, Ion delivered a machine with chip configurations very much similar to past PGM and Proton chips. 520/530 chips offer 200-400 bp runs with 80M reads and 2-4 hour run times. Using Ion’s fixed amplicon panels, data analysis can be completed within 5 hours. The Ion chef is required to reduce hands on library prep time, otherwise libraries and chip loading needs to be performed manually. Ion looks to have positioned their platform toward clinical applications. With stiff competition from Illumina and their inability to deliver similar read lengths and throughput, this is a smart decision by Ion. Focusing their platform on a particular application likely means future development (longer and higher throughput reads) has been paused indefinitely.

Pacific Biosciences Sequel System

Announced in September 2015, the Sequel System uses the same Single Molecule, Real Time (SMRT) technology as the RSII, but boasts several tech advancements. At around one third the cost of a RSII, the Sequel offers 7x more reads with 1M zero –mode waveguides (ZMWs) per SMRT cell versus the previous standard of 150K. The application of Iso-Seq or full length transcript sequencing is especially promising as 1M reads crosses into the threshold where discovery and quantitation of transcripts becomes interesting. By providing full length transcript isoforms, it’s no longer necessary to reconstruct transcripts or infer isoforms based on short read information. Of course, the Sequel is ideal for generating whole genome de novo assemblies. We’l follow how the Oxford Nanopore’s ONT MinIon competes with the Sequel system in 2016.

Oxford Nanopore’s (ONT) MinIon

In 2014, Oxford Nanopore started it’s MinIon Access Program (MAP) delivering over 1,000 MinIons to users who wanted to test the technology. These users have gone on to publish whole E. Coli and Yeast genome assemblies. Accuracy of the device is up to 85% per raw base and there are difficulties in dealing with high G+C content sequences. There remains a lot of work left to improve the technology before widespread adoption. The workflow is simple and uses typical library construction steps of end-repair and ligation. Once the sample is added to the flow cell, users can generate long reads >100 kb and can analyze data in real time. Median reads are currently in the 1-2 kb length. Combined alongside with MiSeq reads, publications have shown MinIon output can enhance contiguity of de novo assembly. Lower error rates generated by Two Direction reads produced with recent updated MinIon chemistry does give cause for optimism that greatly reduced error rates can be achieved in the near future. This along with a low unit cost and the ability to deploy the USB sized device in the field make this a very exciting technology.

Illumina HiSeq X

While HiSeq X services have been available on Genohub for over a year, Illumina’s announcement of its expansion to non-human whole genomes was well received. However there are still several unanswered questions. Illumina states,

“The updated rights of use will allow for market expansion and population-scale sequencing of non-human species in a variety of markets, including plants and livestock in agricultural research and model organisms in pharmaceutical research. Previously, it has been cost prohibitive to sequence non-human genomes at high coverage.”

You can now sequence mouse, rat and other relatively large sized genomes economically on the HiSeq X. This makes the most sense for high coverage applications, e.g. 30x or above. While smaller sized and medium sized genomes can be sequenced on a HiSeq X, the low level of barcoding and high coverage you’d obtain makes these applications less attractive. According to Illumina, as of 12/20/2015, metagenomic whole genome sequencing was not a compatible application on the HiSeq X. The instrument is still restricted to WGS only. RNA-Seq, Exome-seq and ChIP-Seq applications will have to wait. Perhaps by the time the HiSeq X One is released access will be opened to these non-WGS applications.

While these new instruments make their way onto Genohub’s Shop by Project page, you can make inquiries and even order services by placing a request on our consultation page.

Key Considerations for Whole Exome Sequencing

Posted on September 23, 2015 by genohub

exome sequencing and library preparation

Exome, UTR, non-coding regions, CDS

Whole exome sequencing is a powerful technique for sequencing protein coding genes in the genome (known as the exome). It’s a useful tool for applications where detecting variants is important, including population genetics, association and linkage, and oncology studies.

As the main hub for searching and ordering next generation sequencing services, most researchers about to embark on an exome sequencing project start their search on Genohub.com. It’s our responsibility to make sure the researcher is informed and prepared before placing an order for an exome sequencing service.

Working toward achieving this goal, we’ve established a series of guides for anyone about to start a whole exome sequencing project. We’ve described each of these guides here.

Should I choose Whole Genome Sequencing or Whole Exome Sequencing

This guide describes what you can get with WGS that you won’t with WES and compares pricing on a per sample basis. It also provides an overview of sequence coverage, coverage uniformity, off-target effects and bias due to PCR amplification.

How to choose a Exome Sequencing Kit for capture and sequencing

This guide breaks down each commercial exome capture kit, comparing Agilent SureSelect, Nimblegen SeqCap and Illumina Nextera Rapid Capture. Numbers of probes used for capture, DNA input required, adapter addition strategy, probe length and design, hybridization time and cost per capture are all compared. This comparison is followed by a description of each kit’s protocol.

How to calculate the number of sequencing reads needed for exome sequencing

In the same guide that compares library preparation kits (above), we go through an example on how to determine the amount of sequencing and read length required for your exome study. This is especially important when you start comparing the cost for exome sequencing services (see the next guide).

How to choose an exome sequencing and library preparation service

Are you looking for 100x sequencing coverage, what many in the industry call standard exome sequencing or 200x coverage, considered ‘high depth’? Or are you interested in a CLIA grade, clinical whole exome sequencing service? This exome guide breaks each down into searches that can be performed on Genohub. The search buttons allow for real time comparison of available exome services, their prices, turnaround times and kits being used. Once you’ve identified a service that looks like a good match, you can send questions to the provider or immediately order the exome-seq service.

Find a service provider to perform exome-seq data analysis only

Do you already have an exome-seq dataset? Do you need a bioinformatician to perform variant calling or SNP ID? Are you interested in studying somatic or germline mutations? Use this guide to identify providers who have experienced bioinformaticians on staff that regularly perform this type of data analysis service. Simply click on a contact button to immediately send a message or question to a provider. If you’re looking for a quote, they will respond within the same or next business day.

If you still need help, feel free to take advantage of Genohub’s complimentary consultation services. We’re happy to help make recommendations for your whole exome sequencing project.

Benchmarking Differential Gene Expression Tools

Posted on June 3, 2015 by genohub

In a recent study, Schurch et al., 2015 closely examine 9 differential gene expression (DGE) tools (baySeq , cuffdiff , DESeq , edgeR , limma , NOISeq , PoissonSeq , SAMSeq, DEGSeq) and rate their performance as a function of replicates in an RNA-Seq experiment. The group highlights edgeR and DESeq as the most widely used tools in the field and conclude that they along with limma perform the best in studies with high and low numbers of biological replicates. The study goes further, making the specific recommendation that experiments with greater than 12 replicates should use DESeq, while those with fewer than 12 replicates should use edgeR. As for the number of replicates needed, Schurch et al recommend at least 6 replicates/condition in an RNA-seq experiment, and up to 12 in studies where identifying the majority of differentially expressed genes is critical.

With each technical replicate having only 0.8-2.8M reads, this paper and others (Rapaport et al., 2013) continue to suggest that more replicates in an RNA-seq experiment are preferred over simply increasing the number of sequencing reads. Several other papers, including differential expression profiling recommendations in our Sequencing Coverage Guide recommend at least 10M reads per sample, but do not make recommendations on the numbers of replicates needed. The read/sample number disparity is related to the relatively small and well annotated S. cerevisiae genome in this study and the more complex, multiple transcript isoforms in mammalian tissue. By highlighting studies that carefully examine the number of replicates that should be used, we hope to improve RNA-seq experimental design on Genohub.

So why don’t researchers use an adequate number of replicates? 1) Sequencing cost, 2) Inexperience in differential gene expression analysis. We compare the costs between 6 and 12 replicates in yeast and human RNA-Seq experiments using 1 and 10M reads/sample to show that in many cases adding more replicates in an experiment can be affordable.

	6 replicates	12 replicates
Human (10M reads/sample)	$2,660	$4,470
Yeast (1M reads/sample)	$2,810	$4,470

*Prices are in USD and are inclusive of both sequencing and library prep cost. Click on prices in the table to see more project specific detail.

The table shows that the main factor in the price difference is related to library preparation costs. Sequencing on the Illumina Miseq or Hiseq at the listed sequencing depths do not play as significant a role in cost, due to the sequencing capacity of those instruments.

To accurately determine the sequencing output required for your RNA-seq study, simply change the number of reads/sample in our interactive Project Page.

References:

Evaluation of tools for differential gene expression analysis by RNA-seq on a 48 biological replicate experiment. Nicholas J. Schurch, Pieta Schofield, Marek Gierliński, Christian Cole, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon G. Simpson, Tom Owen-Hughes, Mark Blaxter, Geoffrey J. Barton

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Franck Rapaport, Raya Khanin, Yupu Liang, Mono Pirun, Azra Krek, Paul Zumbo,Christopher E Mason, Nicholas D Socci and Doron Betel

Genohub Registered with SAM – System for Award Management

Posted on March 29, 2015 by genohub

Genohub is now registered with SAM, (System for Award Management) making it even easier for organizations within the federal government to get quotes and order sequencing services through Genohub. SAM is the official government system that collects data from suppliers, validates, stores and disseminates this information to government acquisition agencies. These include the FDA, NIH, USGS, DOD and USDA. The registration for Genohub, Inc. / 079294466 / 7ARM5 is now active in the U.S. federal government’s System for Award Management (SAM).

To get a quote for sequencing, library prep or data analysis services, start by entering your requirements using the Shop by Project or Shop by Technology page. You’ll get an instant quote and be able to immediately place your order.

AGBT 2015 Summary of Day 3

Posted on February 27, 2015 by genohub

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

Posted on February 26, 2015 by genohub

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 20^th 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)

Posted on February 21, 2015 by genohub

“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

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.
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.
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.
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.
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.
A lower average read depth is required to achieve the same breath of coverage as WES.
WGS doesn’t suffer from reference bias. WES capture probes tend to preferentially enrich reference alleles at heterozygous sites producing false negative SNV calls.
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

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.
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

Posted on January 27, 2015 by genohub

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

Posted on January 26, 2015 by genohub

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”.

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