POSTER: BioSEAL: In-Memory Biological Sequence Alignment Accelerator for Large-Scale Genomic Data

2019 ◽  
Author(s):  
Roman Kaplan ◽  
Leonid Yavits ◽  
Ran Ginosar
Keyword(s):  
Sequence Alignment ◽  
Large Scale ◽  
Genomic Data ◽  
Biological Sequence

scholarly journals Distributed hybrid-indexing of compressed pan-genomes for scalable and fast sequence alignment

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255260
Author(s):  
Altti Ilari Maarala ◽  
Ossi Arasalo ◽  
Daniel Valenzuela ◽  
Veli Mäkinen ◽  
Keijo Heljanko
Keyword(s):  
Data Compression ◽  
Sequence Alignment ◽  
Large Scale ◽  
Genomic Data ◽  
Hybrid Index ◽  
Sequence Alignments ◽  
Single Node ◽  
Indexing Methods ◽  
Pan Genome ◽  
Compressed Index

Computational pan-genomics utilizes information from multiple individual genomes in large-scale comparative analysis. Genetic variation between case-controls, ethnic groups, or species can be discovered thoroughly using pan-genomes of such subpopulations. Whole-genome sequencing (WGS) data volumes are growing rapidly, making genomic data compression and indexing methods very important. Despite current space-efficient repetitive sequence compression and indexing methods, the deployed compression methods are often sequential, computationally time-consuming, and do not provide efficient sequence alignment performance on vast collections of genomes such as pan-genomes. For performing rapid analytics with the ever-growing genomics data, data compression and indexing methods have to exploit distributed and parallel computing more efficiently. Instead of strict genome data compression methods, we will focus on the efficient construction of a compressed index for pan-genomes. Compressed hybrid-index enables fast sequence alignments to several genomes at once while shrinking the index size significantly compared to traditional indexes. We propose a scalable distributed compressed hybrid-indexing method for large genomic data sets enabling pan-genome-based sequence search and read alignment capabilities. We show the scalability of our tool, DHPGIndex, by executing experiments in a distributed Apache Spark-based computing cluster comprising 448 cores distributed over 26 nodes. The experiments have been performed both with human and bacterial genomes. DHPGIndex built a BLAST index for n = 250 human pan-genome with an 870:1 compression ratio (CR) in 342 minutes and a Bowtie2 index with 157:1 CR in 397 minutes. For n = 1,000 human pan-genome, the BLAST index was built in 1520 minutes with 532:1 CR and the Bowtie2 index in 1938 minutes with 76:1 CR. Bowtie2 aligned 14.6 GB of paired-end reads to the compressed (n = 1,000) index in 31.7 minutes on a single node. Compressing n = 13,375,031 (488 GB) GenBank database to BLAST index resulted in CR of 62:1 in 575 minutes. BLASTing 189,864 Crispr-Cas9 gRNA target sequences (23 MB in total) to the compressed index of human pan-genome (n = 1,000) finished in 45 minutes on a single node. 30 MB mixed bacterial sequences were (n = 599) were blasted to the compressed index of 488 GB GenBank database (n = 13,375,031) in 26 minutes on 25 nodes. 78 MB mixed sequences (n = 4,167) were blasted to the compressed index of 18 GB E. coli sequence database (n = 745,409) in 5.4 minutes on a single node.

scholarly journals Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters

2016 ◽  
Vol 17 (S9) ◽  
Author(s):  
Haidong Lan ◽  
Yuandong Chan ◽  
Kai Xu ◽  
Bertil Schmidt ◽  
Shaoliang Peng ◽  
...  
Keyword(s):  
Parallel Algorithms ◽  
Sequence Alignment ◽  
Large Scale ◽  
Xeon Phi ◽  
Biological Sequence

Aligning Multiple Sequences Using an Improved Tabu Search Algorithm

2016 ◽  
Vol 26 (04) ◽  
pp. 1750066 ◽  
Author(s):  
Lamiche Chaabane ◽  
Moussaoui Abdelouahab
Keyword(s):  
Tabu Search ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Large Scale ◽  
Search Algorithm ◽  
Protein Structures ◽  
Biological Sequence ◽  
Multiple Sequence ◽  
Alignment Problem

One of the most essential operations in biological sequence analysis is multiple sequence alignment (MSA), where it is used for constructing evolutionary trees for DNA sequences and for analyzing the protein structures to help design new proteins. In this research study, a new method for solving sequence alignment problem is proposed, which is named improved tabu search (ITS). This algorithm is based on the classical tabu search (TS) optimizing technique. ITS is implemented in order to obtain results of multiple sequence alignment. Several variants concerning neighborhood generation and intensification/diversification strategies for our proposed ITS are investigated. Simulation results on a large scale of datasets have shown the efficacy of the developed approach and its capacity to achieve good quality solutions in terms of scores comparing to those given by other existing methods.

scholarly journals Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

2008 ◽  
Vol 40 (7) ◽  
pp. 854-861 ◽  
Author(s):  
Jun Zhu ◽  
Bin Zhang ◽  
Erin N Smith ◽  
Becky Drees ◽  
Rachel B Brem ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Genomic Data ◽  
Functional Genomic ◽  
Functional Genomic Data

scholarly journals The theory on and software simulating large-scale genomic data for genotype-by-environment interactions

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiujin Li ◽  
Hailiang Song ◽  
Zhe Zhang ◽  
Yunmao Huang ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Simulated Data ◽  
Genomic Data ◽  
Efficient Tool ◽  
Phenotypic Data ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Threshold Trait ◽  
Genome Wide ◽  
Increasing Demand

Abstract Background With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. Results We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. Conclusions This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

scholarly journals Frequent birth-and-death events throughout perforin-1 evolution

2020 ◽  
Author(s):  
Miguel Araujo-Voces ◽  
Victor Quesada
Keyword(s):  
Preliminary Analysis ◽  
Large Scale ◽  
Mammalian Species ◽  
Genomic Data ◽  
Start Codon ◽  
High Similarity ◽  
Gene Encoding ◽  
Coding Sequences ◽  
Copy Numbers ◽  
Multiple Species

Abstract Background Through its ability to open pores in cell membranes, perforin-1 plays a key role in the immune system. Consistent with this role, the gene encoding perforin shows hallmarks of complex evolutionary events, including amplification and pseudogenization, in multiple species. A large proportion of these events occurred in phyla for which scarce genomic data were available. However, recent large-scale genomics projects have added a wealth of information on those phyla. Using this input, we annotated perforin-1 homologs in more than eighty species including mammals, reptiles, birds, amphibians and fishes. Results We have annotated more than 400 perforin genes in all groups studied. Most mammalian species only have one perforin locus, which may contain a related pseudogene. However, we found four independent small expansions in unrelated members of this class. We could reconstruct the full-length coding sequences of only a few avian perforin genes, although we found incomplete and truncated forms of these gene in other birds. In the rest of reptilia, perforin-like genes can be found in at least three different loci containing up to twelve copies. Notably, mammals, non-avian reptiles, amphibians, and possibly teleosts share at least one perforin-1 locus as assessed by flanking genes. Finally, fish genomes contain multiple perforin loci with varying copy numbers and diverse exon/intron patterns. We have also found evidence for shorter genes with high similarity to the C2 domain of perforin in several teleosts. A preliminary analysis suggests that these genes arose at least twice during evolution from perforin-1 homologs. Conclusions The assisted annotation of new genomic assemblies shows complex patterns of birth-and-death events in the evolution of perforin. These events include duplication/pseudogenization in mammals, multiple amplifications and losses in reptiles and fishes and at least one case of partial duplication with a novel start codon in fishes.

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