Acceleration platform used for biological information sequence analysis

Abstract

The present invention provides an acceleration platform used for biological information sequence analysis and belongs to the field of bioinformatics and high performance computation. The acceleration platform consists of a cloud computing platform, an information sequence analysis hardware acceleration platform, a local task scheduling and reconstructing control interface and a bottom driver, wherein the cloud computing platform receives a compressed or non-compressed target biological information sequence task, stores the target task in a target task database, and waits to carry out analysis processing individually or cooperatively together with the hardware acceleration platform. The acceleration platform refers to a biological information sequence reference library to carry out analysis on the target task database; and when the platform is adopted to carry out analysis on a biological information sequence, an analysis speed can be greatly improved and meanwhile, computing cost is greatly reduced.

Description

technical field [0001] The invention belongs to the field of biological information computing and high-performance computing, in particular to an acceleration platform for sequence analysis of biological information. Background technique [0002] Biological information sequences are usually DNA, RNA sequences, etc. that contain genetic information. These sequences with genetic information will mutate under the influence of some factors, and mutations often correspond to various diseases, such as cancer. Therefore, it is of great significance to analyze and process biological information sequences to find out the mutation sites. At present, biological information sequences are mainly obtained through gene sequencing. [0003] In recent years, with the rapid development of gene sequencing technology, the throughput of sequencing data has been greatly improved. In early 2014, Illumina, a leading company in gene sequencers, released the HiSeqXTen sequencer, whose uninterrupte...

AI Technical Summary

Problems solved by technology

However, the realization of ASIC requires huge R&D investment and time cost, making it the most difficult to realize

Typically, implementing an ASIC requires thorough design verification, testing, and multiple trial runs, a process that often takes years and typically costs millions of dollars

Conventional CPU computing platforms process data through software programs, which is easier to implement than other platforms, but has the lowest performance

For the GPU platform, it has high-speed I / O, but is limited by limited storage resources

Although it is easier to implement than FPGA and ASIC, its performance is lower than both FPGA and ASIC

Although the clock frequency of FPGA is an order of magnitude lower than that of CPU, it provides a large number of available parallel computing resources.

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