Recommended
More Related Content
Similar to BioniformRep
Similar to BioniformRep (20)
BioniformRep
- 1. Market Report: The Bioinformatics Services Industry October 15th 2016
- 2. Table of Contents I. Introduction i. General overview II. Industry Overview i. Table of bioinformatics companies III. Categories of Bioinformatics Companies i. Drug Discovery ii. Personalized Medicine iii. Preventative Medicine iv. Data Management IV. Macroscopic Trends i. Summary of market reports V. Current Adhesive Customer Profile i. Overview of customer types VI. Top Company Overviews i. Illumina ii. Accelrys iii Schrödinger VII. Preliminary Recommendations i. Summary ii. Recommendation on how resources should be distributed
- 3. I. Introduction: In the world economy, there has been an increase in bioinformatics companies. This has most obviously occurred due to the need for improve research methods, improving drug development efficiency and, generally, the advancement human knowledge. Part of this growth is due to the ability for the human genome to be sequenced using Next Generation Sequencing (NGS). With the abundance of information such as billions of base pairs, it is no wonder that bioinformatics is a growing industry both in size and complexity. And base pair sequences are not just the input to this algorithm— we have recently seen a shift from genomics to proteomics. This is exemplified by the ENCODE project which seems to find out the regulatory proteins that exist in a living organism, which is equal or of greater importance than just coming to understand simple sequences. And there are of course countless other inputs that are used in research today that have informational outputs. For example: protein sequence and conformation, chemical formulas and structure; regulatory proteins and binding sites, and differential genomes and differently expressed genes. In short, there is an increase in the complexity in this bioinformatics sector. It is therefore the purpose of this paper to give an overview of the highly diverse bioinformatics sector in the hopes that some patterns can be elucidated and suggestions to current issues can be addressed. In effect, this report should give someone who has relatively no knowledge of the industry enough information for him or her to construct a very good picture of the past, current, a future face of bioinformatics. First of all, let us define bioinformatics. The most basic definition is the use of computers to manage or manipulate biological information. We are not talking about simple calculations in this context, but the use of computers that have unique algorithms with unique inputs and unique outputs to typically deal with information that cannot be handled by a single person. And so we see that bioinformatics is not well defined by this definition— there are infinite processes that fall under the described categories. And so, we tend to talk about bioinformatics in a general sense, not in the particular. But we must remember at the same time the particular is important as well. The particular is where innovation comes from and it is therefore very important. This paper cannot talk about all particulars, but it will mention those that are important. The general trends, although connected to the particular, are what this paper will focus on. The different general group of bioinformatics classifications are: personalized medicine, drug discovery, preventative medicine, and agronomics. Within these categories are sequence analysis, genome analysis, pharmacodynamics, structure activity analysis, clinical trials management, and diagnostics. There are a variety of categories that exist. Below is a diagram of the major categories to the biotechnology sector adapted from Markets and Markets. Figure 1: Bioinformatics Industry Breakup
- 4. The obvious benefits to bioinformatics is a decrease in the cost to do research and development. By optimizing processes and the algorithms that form he basis of bioinformatics, cost to research and development can be greatly reduced. Many processes can be streamlined. Though, the degree of efficiency that is obtained is completely dependent on the validity of the algorithms. This puts a high amount of dependency on new, efficient, and accurate algorithms. There has been a steady increase in the type of algorithms and the number of algorithms in the bioinformatics sector, as evidences by the increase of the number of products and the increase in the number of tracks these products can perform. In the context of the drug development industry, we see that there are massive research and development costs for a single drug. This is due to the elimination of a high degree of variables to find the specific small molecule that exists to perform a function. Money is spent on target identification and target validation. After that, a molecule needs to be found that binds directly to the target. And the that drug need to be safe and effective. And so we see that the main functions of the drug discovery process is to cut down the variation that exist in an organism— to take the discrete out of the continuous. And this is very much done via trail and error and via empirical methods. There really is no rational method to drug design. Even in using the current bioinformatics tools for structure analysis, the algorithms are not accurate to a high degree. And so, once again the effectiveness is dependent on the validity of the algorithms. And so there must be a more accurate algorithm that could furthermore increase the efficiency. Another issue that comes up in the drug companies is the development of novel therapeutics that bring a substantial value to the market. It seems at though the number of drugs that are approved currently are for diseases that are already well managed. And it seems that the “new” meaning in the term “new drugs” referred merely to the drug itself, not the effect of the Bioinformatics Industry Breakup NGS Cheminformatics Scientific Software MicroArray Biological Data Others
- 5. drug on the patient. And so there must be a method in which we can use bioinformatics to help this dilemma. There must be a way that new, novel, and revolutionary drugs are discovered verses the traditional methods. The answer may depend on organizing signaling cascades, finding new regulatory proteins, or using bioinformatics to obtain information from raw sequences. In these contexts, accomplishing these goal just comes down to the input or the correct data efficiency of the algorithms. Below is a diagram from ResearchGate. There is also a bubble that does exist in the market— an astonishing overvalue of some companies that have little to know value. This in turn, multiplied by a variety of companies, gives the illusion that the market is growing. This overvaluation is quite necessary though for development as investors would not be able to put their money down if there were not optimistic about the company’s ability. And progress would not be made. A simple way to measure the ability for pharmaceutical and medical device company’s ability to perform would be a year after year look at the fraction of money invested and the revenue generated. In so much we can do this and as we see below, that this ratio tends to remain constant. There is therefore little growth of the industry in this context. The diagram is adapted from Statista.com.
- 6. The objective of this article is to once again to give an overview of the bioinformatics industry and pinpoint certain pressure points that needs to be considered. The alleviation of stress at these pressure point should make the system more efficient, or at least give a foundation for new innovation. The industry is like an artist who paints a painting. In order to sell the painting certain requirements must be made such as sufficient quality an innovation. The only way that the paintings can reach this quality is through either experience or knowledge. And so this paper seeks to give the painter knowledge so he can know what to paint and now how to do it. This way, the artist fulfills his role as an artist and a valid painting is created. Similarly, the bioinformatics industry has many projects that need to be completed and certain projects hold more weight than others. II. The bioinformatics industry overview There are a variety of bioinformatics companies that exist. This paper focuses on companies who offer at lead on bioinformatics service to a customer. That means that we are not focusing on internal bioinformatics processes that occur out of a company’s own research and development budget. Open source bioinformatics resources are not considered part of the biotech industry. They are, however, of importance. The makeup of this sector will be mentioned later in the article in the context of discussion innovations in bioinformatics. The bioinformatics companies are organized alphabetically. Certain categories that exist are: database management and maintenance, biobank maintenance, NGS diagnostics, lab management systems, sequence analysis, work flow development, comparative genomics, pathway analysis, life science research software, proteomics, modeling of complex systems, cheminformatics, drug discovery, SAR, clinical trials management, and imaging software. Company Name Year Established Main Competencies Estimated Revenue/ Public or Private Notes 0 10 20 30 40 50 60 70 Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 Phizer Revenue vs. R & D Costs Revenue M USD Research and Development in M USD
- 7. Valuation Accelrys 2001 Workflow Integration/ Automation/ Data Sharing/Enterprise Management 155 m/ 750 m Private Acquired by Dassault in 2014 Advaita 2014 Genetic Analysis, Pathway Analysis, Variant Analysis Unreported/ Unreported Private National Cancer Institute Adapts Pathway Program Advanced Brain Monitoring 2001 Monitoring Brain Function Unreported/ Unreported Private Heavily funded by NIH Agile Bio 2002 Laboratory Management and Collaboration Platform Unreported/ Unreported Private Annai Systems 2010 NGS analysis Unreported/ Unreported Private Applied Maths 1992 Analyses of a laboratory assays Unreported/ Unreported Private BioNumerics Suite ArcherDx 2013 NGS for cancer treatment Unreported/ Unreported Private Astrid Bioscience 2014 Research Data Management, NGS, Banking Unreported/ Unreported Private Atomwise 2012 AI to improve drug discovery Unreported/ Unreported Private Ebola treatment BaseHealth 2011 Predictive analytics based on compiled scientific data Unreported/ Unreported Private Berg 2014 Healthcare optimization through AI Unreported/ Unreported Private Biobam 2011 Functional analysis of genomes Unreported/ Unreported Private Biobase 1997 Molecular Biology Databases Unreported/ Unreported Private Knowledge process outsourcing Biodiscovery 1997 Genomic Analysis Unreported/ Unreported Private BioMax 1997 Tissue Arrays Unreported/ Unreported Private Bioxing 2013 Library for DNA and protein Microarrays Unreported/ Unreported Private
- 8. Cellcion 2016 Flo Cytometry Software Unreported/ Unreported Private Certara 2008 PK/PD Analysis Unreported/ Unreported Private CLC BIO 2005 Microbial genetics and sequence analysis Unreported/ Unreported Private Clondiag Chip Technologies 2006 Microarrys Unreported/ Unreported Private Cognuse Neuroscience 2013 Cognitive rehab program Unreported/ Unreported Private Compugen 1993 Drug discovery through data analytics Unreported/ Unreported Public Focus on cancer and autoimmunity Cytel 1987 Statistical software Unreported/ Unreported Private Data MATRIX 2009 Clinical Research Management Software Unreported/ Unreported Private Definiens 1994 Tissue Image Analysis Unreported/ Unreported Private Desktop Genetics 2012 CRISPR Design and Analysis Unreported/ Unreported Private DNA Star 1984 Sequence analysis Unreported/ Unreported Private Eidogen- Sertanty 2003 Software and database for drug discovery Unreported/ Unreported Private eneData 1997 Life Science Research Unreported/ Unreported Private Envisagenics 2014 Predictive Analytics Unreported/ Unreported Private ePitope Informatics 2001 Antibody epitope discovery Unreported/ Unreported Private FreeThink Technologies 2011 Stability Studies Unreported/ Unreported Private Genamics 2003 Online journals Unreported/ Unreported Private Largest free journal database GenCAD 1997 Compilation of annotated genomes Unreported/ Unreported Private Gene Codes 1988 Sequence and microarray Unreported/ Unreported Private Gene Data 1997 Automated workflows Unreported/ Unreported Private Gene Talk 2011 Sequence analysis Unreported/ Unreported Private Genestack 2011 Bioinformatics organizer Unreported/ Unreported Private
- 9. Geneva Bioinformatics (GeneBio) 1997 Life Science Software Unreported/ Unreported Private GenoLogics (Illumina) 2002 Laboratory Management System 20m/Unrep orted Private Genomatica, Inc. 2000 Manufacting process Unreported/ Unreported Private Gene BDO licensed to BASF Genomatix Software GmbH 1997 Literature and genome editing Unreported/ Unreported Private Genome Life Sciences 2004 Biomarker Discovery Unreported/ Unreported Private Genostarr 2004 Microbial Research Unreported/ Unreported Public-private Geospiza, Inc. 1997 Genetic Analysis Workflow 4.5m/ Unreported Private goBalto 2008 Clinical Trials Mangemnt Unreported/ Unreported Private Health Discovery Corporation Home Page 2004 Diagnostics vis Artificial Intelligence 10.5m /Unreported Public HeartVista 2006 Imaging Technologies Unreported/ Unreported Private Illumia 1998 NGS Systems 2.2 B /15 B Public INCOGEN, Inc. 2000 Workflows and Data Management Unreported/ Unreported Private Informagen 1992 Sequence Analysis Unreported/ Unreported Private Ingenuity Pathways Analysis 1998 Pathway analysis Unreported/ Unreported Private IPA System Inte:Ligand 2003 Drug Discovery Unreported/ Unreported Private Integrated Proteomics Applications 2008 Analysis of proteins Unreported/ Unreported Private Integromics 2002 Data management for proteomics or genomics Unreported/ Unreported Private inviCRO 1964 Diagnostics through MRI Unreported/ Unreported Public InVitrogen 1987 Drug discovery and genomics Unreported/ Unreported Public IO Informatics 2003 Data Management Unreported/ Unreported Private Kelaroo 2000 Inventory Management Unreported/ Unreported Private
- 10. Lab7 2012 Enterprise System for NGS Unreported/ Unreported Private LabVantage Solutions 1981 Laboratory Management Unreported/ Unreported Private MAcVector 2007 Sequence design and analysis Unreported/ Unreported Private MasterControl 1993 Enterprise management Unreported/ Unreported Private Metabolic Explorer 2007 Bacteria manufacturing 53m/Unrep orted Public MetaHelix Life Sciences 2001 Agricultural/GMO Unreported/ Unreported Private Metalife AG 2000 Compilation of data Unreported/ Unreported Private MicroDiscover y 2000 Personalized medicine and research Unreported/ Unreported Private Molecular Connections 2001 Drug discovery via database Unreported/ Unreported Private Over 1000 employees NextBio 2005 Knowledge sharing Unreported/ Unreported Private NimbleGen Systems, Inc. 1999 Next gen optimization Unreported/ Unreported Private NorayBio 2002 Bioscience software Unreported/ Unreported Private Novocraft 2008 NGS Analysis Unreported/ Unreported Private Ocimum Bio SOlutions 2000 Genomics and laboratory management Unreported/ Unreported Private Optibrium 2009 Small Molecule Design Unreported/ Unreported Private Origent Data Sciences 2013 Data Analytics Unreported/ Unreported Private Premeir Biosoft International 1994 Life science research solutions Unreported/ Unreported Private Qlucore 2007 Data Analysis Unreported/ Unreported Private Quaigen 1984 Molecular Diagnostics 210m/Unre ported Private Quantiom 2003 Statistical data analysis Unreported/ Unreported Private QuantumBio 2004 Data analysis Unreported/ Unreported Private Quartzy 2009 Lab Inventory Unreported/ Unreported Private QuesGen Systems, Inc. 2004 Clinical Trails Management Unreported/ Unreported Private
- 11. Sapio Sciences 2004 Laboratory Management System Unreported/ Unreported Private Schrödinger 1990 Molecular Simulation Unreported/ Unreported Private Seascape Learning 1997 Molecular modeling Unreported/ Unreported Private Sequencing.co m 2014 Storage of genetic data and analysis Unreported/ Unreported Private SilcsBio 2012 Protein Modeling and Drug Discovery Unreported/ Unreported Private SimBioSys 2001 3D Modeling Unreported/ Unreported Private SoftGenetics 2001 Genetic Analysis Unreported/ Unreported Private SOLABS 1999 Quality Management System Unreported/ Unreported Private SolveBio 2013 Precision genetics, personalized medicine Unreported/ Unreported Private SRA International 1978 IT for government Unreported/ Unreported Public Station X 2010 Enterprise software Unreported/ Unreported Private Strand Life Sciences 2000 Sequence analysis Unreported/ Unreported Private Synamatix Sdn Bhd 2001 Sequence analysis Unreported/ Unreported Private Synergistix 1997 Customer relationship management Unreported/ Unreported Private TimeLogic 1981 Sequence Analysis Unreported/ Unreported Private twoXAR 2014 Drug Discovery Unreported/ Unreported Private Veeva Systems 2007 Cloud computing Unreported/ 5B Public VeraChem 2000 Drug Discovery Via Modeling Unreported/ Unreported Private Verily (Google) 2015 Big Data for Life Sciences Unreported/ Unreported Public Veritomyx 2009 Spectral Analysis Unreported/ Unreported Private The majority of bioinformatic companies are privately held, have less than twenty employees, and offer consulting services. There are a couple stand out companies that are substantially larger such as Certara or Schrodinger. The larger companies offer a much more
- 12. comprehensive software options and their software is considered an industry standard. The smaller companies tend to deal with small niche product lines, versus the general product lines. There is therefore a very real chance for acquisition for the smaller companies depending how it relates to larger company’s product lines. In summary, the bioinformatics industry is moderately difficult to navigate. There have been considerable shifts in the industry in recent years due to the decrease cost of NGS. This has allowed companies to be formed that solely deal with NGS analysis and the storing of NGS data. Additionally, a trend that is occurring is the use for artificial intelligence or machine learning in a multitude of contexts. In these types of systems, quality results can be ensured by a positive or negative feedback mechanism. Atomwise, a recent company that uses this process and has rapidly found a possible treatment for Ebola. There are also macroscopic trends that exist such as the transition to more personalized medicine or preventive medicine. Bioinformatics and the use of these technologies are applicable to these fields as well. For any company that enters this race to find a valuable product, there is competitiveness is only as good as their algorithms or minimum viable product. In so much, many companies that obtain funding by selling this idea are ultimately dependent on developing their technology. Let us, in the nest section, examine what barriers these companies face. III. Bioinformatics Company Categories i. Drug Discovery The drug discovery industry faces many barriers to discovering viable drugs. The traditional method to rational drug discovery depends on how well a target can be validated and how well a small molecule can be used to inhibit the target. In the lab, this is extremely costly in terms of money and time. The assays vary from reporter assays, to microarrays, to comparative genomics, to gene knockouts or add-ins. High Throughput Screening (HTS) is also very costly, but can overlook target identification to find an effective drug. There has been a substantial increase in the use of in silico methods to find targets. This however, is not an easy undertaking. Comparative genomics really fails to find targets. Comparative proteomics is much more effective. The reason is because comparative genomics lacks crucial information about how the genome is expressed. Gene expression is one of the most complex and highly regulated process in particular and in a general sense. And so differential proteomics is much better because knowledge requires to know what genes are expressed can be overlooked. The only way for comparative genomics to surpass comparative proteomics would be to accurately model the intense regulatory processes for genes. And these processes may have variability due to the genetic diversity that exist between humans. And so, for this barrier to be overcome, there would need to be a model or formula that exist that could readily express what genes are transcribed. On top of this, the interactions between proteins and signaling cascades would have to be modeled. This is currently being done, but not by rational methods, rather, but empirical
- 13. methods. For example, Advaita uses compiled research articles to form models for important biological pathways. And after a target has been discovered, the assays that follow to find the most effective drug that binds to a target is very much a process. In traditional methods, this is done via reported assays or some variation of high throughput screening. In the context of in silico methods, this process is difficult because of the sheer number of combinations of active compounds. There are a variety that will theoretically bind. The difficulty lies in the finding the select few that have the proper effectiveness, ADME profile, and the proper cytotoxicity level when applied to in vivo systems. These process can also be modeled in silico. And so the inefficiency seems to come from the most technologically advanced molecular modeling software is not necessarily finding the right molecule, but it is narrowing it down to find the best molecule. Validation reports should exist to show how well the software selects a potential drug candidate, but are quite hard to find. Theoretically, an approved compound should be traced back to the SAR relationship with the target and the lack of SAR with other important targets. The combination of these two relationships would create compound that is both effective and has proper ADME and cytotoxicity profiles. Currently, in the market, there is no software that can accurately predict the SAR relationship with the essential proteins of the human genome. The data required to do this would be immense and there would have to be a mathematical or logical approach to overcoming this barrier in the future. The current drug modeling software used integrated Newtonian dynamics along with quantum mechanics. Ultimately, what is mapped out in the context of molecular modeling software is the energy states and energy profiles of a molecule. The equations that exist here, However, are not accurate. They are at best, a “best guess” and require significant modification to adhere to accuracy. One would suggest that the code that does exist for this software doe have an empirical aspect to it in sol much that the codes is not completely rational. The proper question to ask is for the way to overcome this discrepancy would be: So why do the equations not work? The reason is that the mathematical basis for the equations are not accurate of course. There would need to be a technical advancement in how molecules are modeled— there would have to be an advancement in molecular modeling. This is very much the barrier to technical innovation that many drug discovery companies do face. And there are extremely clever ways that the companies try to adjust their models to be more accurate. For example, Atomwise used artificial intelligence to narrow down SAR relationships. In this company, we can assume that the software has a build in trial and error system that allows for the algorithm to be continuously modified and improved while different compounds are tested. This is the cleverest way that drug development can be improved and explains why the company is backed by such solid investors. In summary, the limitations that companies have that can be overcome via technological advantage are 1) Understanding and modeling the regulation of gene expression, 2) Using a molecular dynamic theory that works with a high degree of efficiency in silico. 3) Being able to
- 14. understand and create novel pathways 4) Modeling the interactions between drugs and proteins to accurately predict cytotoxic effects and ADME profiles. Ultimately, all of these points come down to the fact that molecular dynamics needs to be revolutionized. This would accurately predict all microscopic interactions that exist in the cell and allow biologist to understand everything that exists between genotype and phenotype that is not currently known. A possible way to overcome this barrier is to reevaluate the model of an atom, for that is the general input for the molecular dynamic theory. If these barriers are overcome, there is potential for enormous value, not just to the pharmaceutical industries mass inefficiencies, but the efficacy of healthcare to treat disease. II. Personalized Medicine The concept of personalized medicine has existed for many years. But until recently has it only begun to merge with the field of bioinformatics. This is of course due to the existence of NGS and the huge amount of data that follows. Personalized medicine is therefore very much based on sequence analyses of whole genomes. By comparing and contrasting genomes of different people, each patient can be placed into a certain group that requires a certain treatment. The idea of personalized medicine involves numerous applications. For example, in cancer, a patient’s genome or even the cancer genomes can be analyzed to optimize treatments. But even in this context, the benefit of personalized medicine is not completely lucid. It is hard to prove that is works in all contexts. There are considerable challenges that personalized medicine faces sue to the inability to organize all the genetic variations in humans and the treatments that these variations suggest. And so there is the technique of using genetic analysis in a case by case context and dependent ton disease types. And through time the compilation of this data should theoretically lead to personalized medicine that is highly accurate and effective. However, let’s reiterate the fact that personalized medicine is hard to create. The variations that exists between genomes is quite difficult to understand. One would assume that one genetic marker would be all that is required to have an implication. This is not necessarily the case for some diseases as there can be multiple markers. In addition, there are environmental factors that cause disease that have to be integrated for proper diagnosis. And so personalized medicine becomes convoluted— it is merely a statistic that has a probability of being correct. Despite its shortcomings, personalized medicine has progressed to a point where is does offer significant value to patients. However personalized medicine can be optimized to a different level if new algorithms and more data is collected. III. Preventative Medicine Preventative medicine also has existed for a very long time, even before bioinformatics became relevant. Currently, privative medicine seems to identify patients who are at risk and alter their lifestyle to mitigate their risk. In modern day medicine, the at risk patients tend to be at risk for cardiovascular disease or heart attack. Obesity is one of the facts that can cause this
- 15. complication and many other health issues. Therefore, changing diet and increasing exercise is the most common prescription for preventative medicine. Bioinformatics can and does apply to this regimen of weight loss, but bioinformatics seeks to go further. Cancer is one of those things that is much easier to be treated in its early stages. One of the risk factors is of course age, but there are a variety of genetic and ontological risk that can be observed. Bioinformatics can be used to home in on these slight changes and increase the change of diagnosing cancer in the early stages. Traditional methods in those field used for preventative medicine are annual checkups and they work quite well. However, current bioinformatic companies are trying to use sequence analysis and biosensors to detect cancer even earlier. One example of these companies is Verily. The certain barriers to having this type of technology implemented are of course obtaining the hard data that is necessary to make a prediction and of course the difficulty in developing biosensors that can continuously monitor patients. In so much, the idea dog this type of preventative medicine is very similar to personalized medicine in that there seems to be a gap between the known data and the requires data to make a significant contribution to science or healthcare. IV. Data Management Data management is a general term that can apply many industries. In the context of the biological sciences, there is a need to accurately store and communicate data between people. Science is become increasingly collaborative and this certainly plays a role in the needs for bioinformatics software that is easy to use and allows for easy communication. The large about of data that can be obtained by new assays such as NGS poses problems that some companies face. There is, respectively, bioinformatics companies that specialize in these aspects of the industry. There are companies that can store large amount of data in a cloud-based form and there are companies that have a platform for communication of both private and public data. The competitive barriers that exist to improving data management are, in line with all aspects of the bioinformatics industry, are novel algorithms, adherence to customer needs, and optimization of architecture. Data management is such a general term that it is a prerequisite that this form of bioinformatics has many forms. And in fact, there are a lot of bioinformatic companies in this niche that are competing with each other. In order to gain a competitive advantage, these companies must adhere to the competitive barriers mentioned above. VIII: Customer profile The customer profile of the bioinformatics industry is heterogeneous. The largest customers of course are large biotech companies whose mission is drug discovery. Secondarily, there is a great deal of customer basis in the academic research arena. Thirdly, there is the healthcare sector in where products are sold directly to clinicians from bioinformatic companies. There are of course customers that exist in the private sector are also less known. For example, in the agricultural context of biotechnology, there is a great need of bioinformatics to produce genetically modified organisms. Consequently, Monsanto would be a good customer for the bioinformatics industry. However, a variety of the bioinformatics processes are done internally in a research and
- 16. development context. For this particular customer, certain value could be found if they outsourced their informatics to a professional company that had strategic industry knowledge. Below is a graph of the varied customer profiles in the industry. Below is an industry report from Grandview research. In summary, the bioinformatic industry is quite heterogeneous and highly specialized. There exist not companies that have highly distinguished products, but rather a high number of companies with similar products. The size of the company is not necessarily due to the effectiveness of the product, but the relationships that exist between customer and the company. What forms the basis of this relationship is tough to describe in particular, but there must be some social aspect that make one company have a high affinity for a bioinformatics company. In the largest companies, there is a year after year rapport that must develop. In addition, companies that re larger and have more services also tend to have more customers and larger revenue, which can explain the different in the size of the company. VII. Growth data and methodology There is limited publish data on the growth of the bioinformatics industry. Most of the data deal with the macroscopic aspects of the industry and does not go onto detail about the small minutia that exists in the industry. In this way, the small niche markets are overlooked and the larger market for well-known processes are focused on. For example, the grow of NGS sequencing as seem by Illumina appears to be the basis of most articles. And other things such as the use of artificial intelligence is overlooked. The consensus is that bioinformatics is set to grow rapidly in the coming years. There is growth in in the genomic context over proteomics or other industry services. Though, the market report is very vague. There is too much heterogeneity in the industry to talk about each niche market in detail. In the context methodology, it would be improbable to collect all this data on privately held companies due to contacts with vendors and contact with the government. Therefore, as
- 17. expected. We only have categorical data that is somewhat hard to interpret. We will therefore go into specific detail about the industry leaders in the categories mentions in the industry report. IV. Illumina Illumina is the industry leader in the process of Next Generation Sequencing and the development of NGS machines. HiSeq is the most technologically advanced line of sequencing machines that allows for the sequencing of a human genomes in one to two days for the cost of about USD $1,000. This technological development has occurred due to improvements in computations, development of better algorithms and the optimization of biological processes. It can be assumed that the biggest advancements have been in computation. In the context of bioinformatics, there are many calculations that are necessary to construct a genome after information is obtained from the machines. The full genome can be constructed and stored after appropriate statistics are performed. As of 2015, Illumina’s revenue is 2.2 Billion dollars. The price of the Hiseq machine is around 700,000, which suggest that only 3,000 machines are sold annually. Acceleys(BioVia) Accelrys has a variety of products that provide value to a wide number of customers. It’s main product is an enterprise platform. Other products include software that assists in research analyses and software that assists in drug discovery and modeling. In 2015, it’s revenue was 150 Million dollars. Schrödinger Schrodinger offers molecular modeling for the purpose of drug discovery and material science. The basis of the models are a combination of Newtonian Mechanics and Quantum mechanics. Schrödinger ha s a variety of competitors and the aspect that make their software unique is ambiguous. A validation report again other products does not exist. However, it is estimated that their revenue is 25 million dollars a year. X. Recommendations and summary The bioinformatics industry is quite a fascinating industry. There is a lot of projected growth and a lot of potential for technological disruption. The diversity among companies is astonishing. In so much, the industry appears to be less competitive as other industries. The
- 18. barriers to entry are somewhat obscure. Technological expertise and a relatively low amount of financial backing are necessary for starting up a bioinformatics company. There seems to be a bias for the human health sector for bioinformatics. There are relatively few bioinformatics companies that assist in research in the agricultural or environmental science industries. These industries are the least competitive out of all bioinformatics companies, but the revenues of both sectors are comparable. As we head into the future, it would be expected that there is considerable emphasis on the use of NGS to gather more information on human health. In the long term, there will be companies that deal with protein expression as well. Molecular drug discovery will also evolve into something more technologically advance with the devolvement of novel algorithms with artificial intelligence. Ultimately, the industry will continue to change and reach deeper into the development of technologies that can do very powerful things. References http://www.marketsandmarkets.com/PressReleases/bioinformatics-market.asp https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702932/ https://www.researchgate.net/figure/267634664_fig1_New-drugs-approved-by-the-FDA- BLAstherapeutic-biologics-filed-under-Original https://www.statista.com/statistics/266171/revenue-of-pfizer-since-2006/ https://www.glassdoor.com/Overview/Working-at-Schrodinger-EI_IE428690.11,22.htm https://www.grandviewresearch.com/press-release/global-bioinformatics-market