Helping Whole Biome Advance Microbiome Research

A main concern of any science team in the microbiome is being left behind technologically. In order to stay ahead of the competition, teams need to constantly be on the lookout for, and integrating, the newest tech. A person who understands the power of new tech more than most is Whole Biome’s founder and CEO, Dr. Colleen Cutcliffe. Loop sat down with Colleen to go over some of the things that her team are working on and how she has found Loop’s kits to be helpful.

Whole Biome is in many ways an original microbiome company (despite only being a 4 year old startup). Their founders were scientists from UC Berkeley, Harvard and Johns Hopkins who worked together at PacBio and saw an opportunity to make a platform technology to analyze human gut microbiome samples to find clinically relevant insights. “The mission of the company is to improve people’s health through microbiome intervention,” said Colleen. By all means, the Whole Biome team is onto some amazing insights with products currently in clinical trial with hopes of selling products directly to patients.

However, they understand the shortcomings of the traditional characterization methods for microbiome - short read, single variable region 16S data. “The problem is that it turns out [the 16S variable regions] are actually not so variable,” she said. “You can get a sequence of that which actually matches to a whole bunch of different strains. People are starting to realize, you can’t actually get a very accurate classification.”

What Colleen appreciates about LoopSeq is the departure from dependency on just one variable region and entering an era of full length 16S. “Full length gives you a better ability to classify because now if just that V1 region looks the same [as another microbe], the chances of the V1 region and the V4 region looking exactly the same is much less.” Given that Loop provides all the variable regions, the probability of having V1-V9 identical and being different species is right next to zero.

Accurate classification can mean the difference between having a pipeline that will lead to a clinical insight and one that doesn’t. “When you’re trying to really understand what are the small differences in somebody’s microbiome, you need tools in place that help you get clarity at a high resolution, and also high specificity of what those different microbes are.” So if the data leads to mischaracterization of the species present, then it can lead a team in the wrong direction. Having the right analysis tools can mean greater success. “My hope is that the quality of [Loop’s] data will enable us to be very precise, and specific about the kinds of interventions that we’d all develop.”

Some companies might be a bit hesitant to bring on new technology, given they have an established pipeline. But at Whole Biome, Colleen and her team understand that staying ahead is what makes them different. “LoopSeq and Whole Biome see a world in which the next step is going to be wanting more comprehensive information. And there are no other sample prep / analytic products out there today that give you better data quality than LoopSeq.”

Whole Biome and Loop are excited about the future of microbiome research and the potential of full length 16S. The newest technology will push their team to more meaningful insights and success.

Learn More About LoopSeq

Ask Us About LoopSeq

Which Are You Interested In?

By submitting this form you agree to our Privacy Policy and Terms and Conditions.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Related Applications

Fungal read lengths with text PNG

Mycobiome Metagenomics with Element LoopSeq™

LoopSeq’s longer read-length directly translates into more accurate taxonomic assignment.
Learn More
16S Loop Long-reads vs Sanger Sequencing For Bacterial Isolate Identification

Element LoopSeq™ 16S vs Sanger Sequencing for Bacterial Isolate Identification

If your lab, like many other microbiology labs, has a backlog of isolates sitting at -80°C waiting to be analyzed because the funds and/or time for 16S Sanger sequencing is unavailable, you may be able to clear up space with the Element LoopSeq 16S long-read sequencing.
Learn More
Loop Seq metastatic Cancer1

Discover Element LoopSeq™ Long-Reads' Power in Transcriptomics

Dr. Luo used Loop's long-read technology to examine the transcriptome in matched sets of tumor tissue, neighboring normal tissue, and metastatic tissue from lymph nodes.
Learn More
16S/18S versus shotgun metagenomics: LoopSeq changes how you choose

Element LoopSeq™ 16S vs Shotgun Metagenomics

Highly accurate Element LoopSeq synthetic long-read sequencing technology changes the approach to shotgun metagenomics approaches, increasing the depth of information researchers can uncover.
Learn More
Enabling reproducible microbiome results.

Enabling Reproducible Microbiome Results with Element LoopSeq™

The Element LoopSeq sample prep kit generates robust and reliable data on relative species abundance for microbial metagenomics studies by utilizing single-molecule barcoding and de novo sequence assembly.
Learn More
Jellyfish moving through water

How GC Bias Can Skew Species Quantification and How to Help Stop It

Comparison of the ten most abundant microbes in the sample show close agreement of measured abundance to expected. In addition, we see no GC bias, as shown in the close agreement of measured versus expected across species with different levels of GC content.
Learn More
Quantification without bias.

Quantification Without Bias - Element LoopSeq™

NGS that relies exclusively on short-read length may not provide reliable, reproducible measurements of relative species abundance. To address this, our team has developed the Element LoopSeq™ sample prep kit, delivering a precise quantitation of relative microbial abundance in a complex sample.
Learn More
16S Loop Long-reads vs Sanger Sequencing For Bacterial Isolate Identification

Full-Length 16S Microbiome Classification with Element LoopSeq™

The Element LoopSeq kit uses synthetic long-read sequencing, utilizing barcode technology to computationally sequence a single molecule assembled from a cluster of short reads. This enables comprehensive phylogenetic classification all while having a lower error rate and coverage of all nine variable regions.
Learn More