A goal for health care researchers and practitioners is to improve the safety, quality, and effectiveness of health care for every patient. Personalized health care is directed to achieving these goals on an individual level. For instance, “genomics” and/or “bioinformatics” are fields of study that aim to facilitate the safety, the quality, and the effectiveness of prophylactic and therapeutic treatments on a personalized, individual level. Accordingly, by employing genomics and/or bioinformatics techniques, the identity of an individual's genetic makeup, e.g., his or hers genes, may be determined and that knowledge may be used in the development of therapeutic and/or prophylactic regimens, including drug treatments, that are personalized to the individual, thus, enabling medicine to be tailored to meet each person's individual needs.
The desire to provide personalized care to individuals is transforming the health care system. This transformation of the health care system is likely to be powered by breakthrough innovations at the intersection of medical science and information technology such as is represented by the fields of genomics and bioinformatics. Accordingly, genomics and bioinformatics are key foundations upon which this future will be built. Science has evolved dramatically since the first human genome was fully sequenced in 2000 at a total cost of over $1 Billion. Today, we are on the verge of high resolution sequencing at a cost of less than $1K per genome, making it economically feasible for the first time to move out of the research lab and into widespread adoption for medical care. Genomic data, therefore, may become a vital input to diagnostic screening, therapeutic and/or prophylactic drug discovery, and/or disease treatment.
More particularly, genomics and bioinformatics are fields concerned with the application of information technology and computer science to the field of molecular biology. In particular, bioinformatics techniques can be applied to process and analyze various genomic data, such as from an individual so as to determine qualitative and quantitative information about that data that can then be used by various practitioners in the development of prophylactic and therapeutic methods for preventing or at least ameliorating diseased states, and thus, improving the safety, quality, and effectiveness of health care on an individualized level.
Because of its focus on advancing personalized healthcare, bioinformatics, therefore, promotes individualized healthcare that is proactive, instead of reactive, and this gives the patient the opportunity to become more involved in their own wellness. Typically, this can be achieved through two guiding principles. First, federal leadership can be provided to support research that addresses these individual aspects of disease and disease prevention, such as with the ultimate goal of shaping diagnostic and preventative care to match each person's unique genetic characteristics. Additionally, a “network of networks” may be created to aggregate health care data to help researchers establish patterns and identify genetic “definitions” to existing diseases.
An advantage of employing bioinformatics technologies in such instances is that the qualitative and/or quantitative analyses of molecular biological data can be performed on a broader range of sample sets at a much higher rate of speed and often times more accurately, thus expediting the emergence of a personalized healthcare system.
Accordingly, in various instances, the molecular data to be processed in a bioinformatics based platform typically concerns genomic data, such as Deoxyribonucleic acid (DNA) data. For example, a well-known method for generating DNA data involves DNA sequencing. DNA sequencing can be performed manually, such as in a lab, or may be performed by an automated sequencer, such as at a core sequencing facility, for the purpose of determining the genetic makeup of a sample of an individual's DNA. The person's genetic information may then be used in comparison to a referent, e.g., a reference genome, so as to determine its variance therefrom. Such variant information may then be subjected to further processing and used to determine or predict the occurrence of a diseased state in the individual.
For instance, manual or automated DNA sequencing may be employed to determine the sequence of nucleotide bases in a sample of DNA, such as a sample obtained from a subject. Using various different bioinformatics techniques these sequences may then be assembled together to generate the genomic sequence of the subject, and/or mapped and aligned to genomic positions relative to a reference genome. This sequence may then be compared to a reference genomic sequence to determine how the genomic sequence of the subject varies from that of the reference. Such a process involves determining the variants in the sampled sequence and presents a central challenge to bioinformatics methodologies.
For example, a central challenge in DNA sequencing is assembling full-length genomic sequences, e.g., chromosomal sequences, from a sample of genetic material and/or mapping and aligning sample sequence fragments to a reference genome, yielding sequence data in a format that can be compared to a reference genomic sequence such as to determine the variants in the sampled full-length genomic sequences. In particular, the methods employed in sequencing protocols do not produce full-length chromosomal sequences of the sample DNA.
Rather, sequence fragments, typically from 100-1,000 nucleotides in length, are produced without any indication as to where in the genome they align. Therefore, in order to generate full length chromosomal genomic constructs, or determine variants with respect to a reference genomic sequence, these fragments of DNA sequences need to be mapped, aligned, merged, and/or compared to a reference genomic sequence. Through such processes the variants of the sample genomic sequences from the reference genomic sequences may be determined.
However, as the human genome is comprised of approximately 3.1 billion base pairs, and as each sequence fragment is typically only from 100 to 500 to 1,000 nucleotides in length, the time and effort that goes into building such full length genomic sequences and determining the variants therein is quite extensive often requiring the use of several different computer resources applying several different algorithms over prolonged periods of time.
In a particular instance, thousands to millions of fragments or even billions of DNA sequences are generated, aligned, and merged in order to construct a genomic sequence that approximates a chromosome in length. A step in this process may include comparing the DNA fragments to a reference sequence to determine where in the genome the fragments align.
A number of such steps are involved in building chromosome length sequences and in determining the variants of the sampled sequence. Accordingly, a wide variety of methods have been developed for performing these steps. For instance, there exist commonly used software implementations for performing one or a series of such steps in a bioinformatics system. However, a common characteristic of such software based bioinformatics methods and systems is that they are labor intensive, take a long time to execute on general purpose processors, and are prone to errors.
A bioinformatics system, therefore, that could perform the algorithms implemented by such software in a less labor and/or processing intensive manner with a greater percentage accuracy would be useful. However, even as we approach the “$1000 Genome”, the cost of analyzing, storing and sharing this raw digital data has far outpaced the cost of producing it. This data analysis bottleneck is a key obstacle standing between these ever-growing raw data and the real medical insight we seek from it.
Accordingly, presented herein are systems, apparatuses, and methods for implementing a genomics and/or bioinformatic protocols, such as for performing one or more functions for analyzing genomic data, for instance, on an integrated circuit, such as on a hardware processing platform. For example, as set forth herein below, in various implementations, a hardware accelerator, such as an integrated circuit, may be employed in performing such bioinformatics related tasks where the integrated circuit may be formed of one or more hardwired digital logic circuits, which may be interconnected by a plurality of physical electrical interconnects, that can be arranged as a set of processing engines, wherein each processing engine is capable of being configured to perform one or more steps in a bioinformatics genetic analysis protocol. An advantage of this arrangement is that the bioinformatics related tasks may be performed in a manner that is faster than the software typically engaged for performing such tasks. Such hardware accelerator technology, however, is currently not typically employed in the genomics and/or bioinformatics space.