It has been two decades since the publication of the much-heralded first draft of a composite human genome reference sequence. Earlier this year, human genome sequencing achieved the next level in terms of diversity and accuracy when scientists announced the first comprehensive reference dataset of 64 assembled human genomes representing 25 different human populations from across the globe.
And it’s not just human genome sequences. Researchers now have access to complete genome sequence reference data for hundreds of animal and plant species.
As remarkable as all that may be, we are still essentially talking about raw data. All this sequence data is only as useful as the ability of academic, corporate and healthcare researchers to pursue novel research pathways and propose and test ground-breaking hypotheses to create a sustainable working framework for applied genomics intelligence that enables practical insight and real-world value.
We have written a lot about the widening gap between data generation and data analysis and the general inability of current analytical technologies, frameworks and processes to facilitate the extraction of biologically and practically relevant insights from the exponentially expanding treasury of raw sequence data. And this situation is untenable to any form of genomic research be it academic, commercial or medical.
Today, I want to focus on PhD research, one of the best sources of significant and original research in any field including genomics. And I want to take a more provocative view on the limitations imposed on this valuable form of research by proposing that almost every process downstream to raw sequence data, including multi-omics analysis and bioinformatics, is a complete mess.
Even at the best of times, the experience of a new PhD researcher can be a rude awakening. On the one hand, there’s the excitement of setting out in pursuit of answers to a brand new question – and the opportunities for discovery that a world of knowledge potentially has to offer. On the other, there’s the grim reality of the current data landscape which fuels that research.
The truth is that new PhD researchers are often dropped into the wild with little more than a burning curiosity, a fresh pair of eyes and an open mind to get by. However, when the time comes to start wading through current genomics research and analysis toolsets that are supposed to support those enquiring minds, it’s surprising that any research gets done at all.
There are literally thousands of tools and databases out there, and the one thing they have in common is a tendency toward information silos and data fragmentation. As a researcher, you may have no idea where to start looking for the data you need, what tools to use to analyse it, or how to use these tools to glue everything together.
Data analytics and information management tools are evolving at an alarming pace, but this in itself introduces problems for the novice or inexperienced user. The steep learning curves associated with computing and data science can generate frustration, in addition to slowing down or hampering your research efforts.
These problems are especially acute in the field of genetics research. In the current state of the discipline, genomic data analysis consists of a complex series of operations, which often require the skills of highly trained professionals to perform. Information analysis also demands an enormous amount of computing power and data storage – all of which can cost you significant amounts of money.
The past year precipitated radical shifts in the way we live, work, and study – changes that may remain in force for the foreseeable future. Even as the first wave of coronavirus vaccines begins to roll out, there’s still universal concern about when – if ever – the COVID-19 situation will be brought under control, and life can return to some semblance of normalcy.
In both work and education, lockdowns and movement restrictions have made it necessary to conduct much of our business online. For universities, this presents the challenge of making academic resources quickly and readily available, while still maintaining some semblance of the social interaction and culture that are equally vital to the personal development of individual students.
Meanwhile, for every PhD researcher, doubts and questions persist concerning the lectures you have to take, the research projects you have to complete to strict deadlines, and the vital academic work that has to be done if you’re to stand a chance in a highly competitive job market.
According to some estimates, the amount of information generated by genomic research will surpass the combined data volumes from astronomy, YouTube and Twitter by 2025, with human genomics data alone clocking in at 40 exabytes. Even so, an incredible number of errors can and do occur in its analysis, leaving researchers to settle only for best approximations.
With the evolution of sequencing technologies and the proliferation of multi-omics databases, tools, and other resources, sequencing a genome is becoming cheaper. However, making sense of the resulting data remains a significant challenge.
Though bioinformatics algorithms for specific jobs or areas of research do exist, the size of the analytics dataset must be restricted to make analysis computationally feasible. This requires ‘pre-filtering’ and narrow search spaces, which limit the scope of visibility of any such research.
In order to galvanise innovation and accelerate value creation across a range of industries, including biopharma, life sciences, healthcare etc., PhD researchers require a digital platform that can help them overcome the limitations of current methods of data aggregation and analysis.
If you’re looking to complete your PhD in a timely and successful fashion, the last thing you need is an omics data analysis platform that’s complex, time-consuming, and requires you to become a data scientist in order to use it. As we explore in our recent eBook, A Better Way to Analyse Genomic Data, the need of the hour, therefore, is a radical new approach that addresses the limitations of current solutions and equips genomics data analysis with the digital computational capabilities required for the Big Data age.
BioStrand R&R (Retrieve and Relate) provides an all-in-one platform that reduces the complexity of data analysis and improves ease of use. R&R presents as a single Google-style interface for you to search all the databases relevant to your research. From its simple one source interface, there’s no need to select your sources and go through them one at a time, which significantly decreases the number of steps which would otherwise impede a global overview of your data.
You don’t have to become a data scientist to use it, either. R&R provides one tool to “search as you go” for the data and answers you need. Your primary search can be as simple as pasting a protein sequence into the search bar and hitting Enter to get the Quick Filter View – and you can even start with a text-based search. The BioStrand eBook delves deeper into the options available, with helpful illustrations.
The BioStrand R&R platform provides Data Management and Integration features that enable you to quickly compare your own data with existing datasets, combine sequence data with textual data, discover associations between DNA, RNA, and protein levels – and maintain global oversight of your results. With its single pane overview and ease of use, R&R is your ultimate research companion. This single source view allows analysis on all available data at once, for an integrated view of results which can be drilled down to the finer details for unlocking true and complete knowledge.
And as a student on a tight budget, Retrieve and Relate’s single point global access to a world of information won’t cost you the Earth. The R&R Academic Edition offers affordable yearly rates for PhD students.
BioStrand R&R is your ideal research and analysis solution for a number of scenarios, such as identifying similar sequences, searching by RNA sequence, and broad text-based searches. To take a deeper dive into these options, access your free trial today.