Read alignment using deep neural networks

2019 Spring.Includes bibliographical references.Read alignment is the process of mapping short DNA sequences into the reference genome. With the advent of consecutively evolving "next generation" sequencing technologies, the need for sequence alignment tools appeared. Many scientific communities and the companies marketing the sequencing technologies developed a whole spectrum of read aligners/mappers for different error profiles and read length characteristics. Among the most recent successfully marketed sequencing technologies are Oxford Nanopore and PacBio SMRT sequencing, which are considered top players because of their extremely long reads and low cost. However, the reads may contain error up to 20% that are not generally uniformly distributed. To deal with that level of error rate and read length, proximity preserving hashing techniques, such as Minhash and Minimizers, were utilized to quickly map a read to the target region of the reference sequence. Subsequently, a variant of global or local alignment dynamic programming is then used to give the final alignment. In this research work, we train a Deep Neural Network (DNN) to yield a hashing scheme for the highly erroneous long reads, which is deemed superior to Minhash for mapping the reads. We implemented that idea to build a read alignment tool: DNNAligner. We evaluated the performance of our aligner against the popular read aligners in the bioinformatics community currently — minimap2, bwa-mem and graphmap. Our results show that the performance of DNNAligner is comparable to other tools without any code optimization or integration of other advanced features. Moreover, DNN exhibits superior performance in comparison with Minhashon neighborhood classification

Analyzing large-scale DNA Sequences on Multi-core Architectures

Rapid analysis of DNA sequences is important in preventing the evolution of different viruses and bacteria during an early phase, early diagnosis of genetic predispositions to certain diseases (cancer, cardiovascular diseases), and in DNA forensics. However, real-world DNA sequences may comprise several Gigabytes and the process of DNA analysis demands adequate computational resources to be completed within a reasonable time. In this paper we present a scalable approach for parallel DNA analysis that is based on Finite Automata, and which is suitable for analyzing very large DNA segments. We evaluate our approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog (2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results on a dual-socket shared-memory system with 24 physical cores show speed-ups of up to 17.6x. Our approach is up to 3x faster than a pattern-based parallel approach that uses the RE2 library.Comment: The 18th IEEE International Conference on Computational Science and Engineering (CSE 2015), Porto, Portugal, 20 - 23 October 201