Compositional spectra (CS) analysis based on k-mer scoring of DNA sequences was employed in this study for dot-plot comparison of human and primate genomes. The detection of extended conserved synteny regions was based on continuous fuzzy similarity rather than on chains of discrete anchors (genes or highly conserved noncoding elements). In addition to the high correspondence found in the comparisons of whole-genome sequences, a good similarity was also found after masking gene sequences, indicating that CS analysis manages to reveal phylogenetic signal in the organization of noncoding part of the genome sequences, including repetitive DNA and the genome "dark matter". Obviously, the possibility to reveal parallel ordering depends on the signal of common ancestor sequence organization varying locally along the corresponding segments of the compared genomes. We explored two sources contributing to this signal: sequence composition (GC content) and sequence organization (abundances of k-mers in the usual A,T,G,C or purine-pyrimidine alphabets). Whole-genome comparisons based on GC distribution along the analyzed sequences indeed gives reasonable results, but combining it with k-mer abundances dramatically improves the ordering quality, indicating that compositional and organizational heterogeneity comprise complementary sources of information on evolutionary conserved similarity of genome sequences.
Primate specific retrotransposons, SVAs, in the evolution of networks that alter brain function
SINE VNTR Alu（SVA）是人类基因组中最年轻的转座因子。最古老的SVA A型的传播发生在大约一千三百万年前，而最年轻的SVA类型在黑猩猩分化后出现在人类基因组中。与SVA插入相关的基因的功能富集分析表明，它们与归因于脑功能和疾病的多个本体论类别有很强的联系。SVA类型，扩大其存在于人类基因组中的不同阶段的类人猿生活史也与逐步演变的行为特征，表明一个潜在的影响SVA传播的认知能力的现代人。SVA相关基因在功能网络中高度交联，表明SVA插入可能引起的功能改变的累积影响。我们的分析揭示了SVA在人类CNS进化中的潜在作用，特别是与社会和父母行为相关的功能趋势的出现。它还支持的模型，部分解释了大脑功能如何可以调制的免疫和生殖系统的基础上的基因表达模式和基因途径可能改变SVA插入。
Motivation: Protein-to-genome alignment is critical to annotating genes in non-model organisms. While there are a few tools for this purpose, all of them were developed over ten years ago and did not incorporate the latest advances in alignment algorithms. They are inefficient and could not keep up with the rapid production of new genomes and quickly growing protein databases. Results: Here we describe miniprot, a new aligner for mapping protein sequences to a complete genome. Miniprot integrates recent techniques such as k-mer sketch and SIMD-based dynamic programming. It is tens of times fas…
Identification and characterization of unique to human regulatory sequences in embryonic stem cells reveal associations with transposable elements, distal enhancers, non-coding RNA, and DNA methylation-driven mechanisms of genome editing
The Stanford University School of Medicine（斯坦福大学医学院）；Sanford-Burnham Medical Research Institute（桑福德-伯纳姆医学研究所）；Genlight Technology Corporation（Genlight科技有限公司）
Genome and metagenome comparisons based on large amounts of next-generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been …
Genomics is changing our understanding of humans, evolution, diseases, and medicines to name but a few. As sequencing technology is developed collecting DNA sequences takes less time thereby generating more genetic data every day. Today the rate of generating genetic data is outpacing the rate of computation power growth. Current sequencing machines can sequence 50 humans genome per day; however, aligning the read sequences against a reference genome and assembling the genome will take 1300 CPU hours. The main step in constructing the genome is aligning the reads against a reference genome. Nu…
Genome wide identification of regulatory networks associated with general cognitive ability using a normalized alignment free similarity measure of promoter regions
Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have…
Motivation: A pan-genome graph represents a collection of genomes and encodes sequence variations between them. It is a powerful data structure for studying multiple similar genomes. Sequence-to-graph alignment is an essential step for the construction and the analysis of pan-genome graphs. However, existing algorithms incur runtime proportional to the product of sequence length and graph size, making them inefficient for aligning long sequences against large graphs. Results: We propose the graph wavefront alignment algorithm (Gwfa), a new method for aligning a sequence to a sequence graph. Al…
The rapidly changing landscape of sequencing technologies brings new opportunities to genomics research. Longer sequence reads and higher sequence throughput coupled with ever-improving base accuracy and decreasing per-base cost is now making long reads suitable for analyzing polymorphic regions of the human genome, such as those of the human leucocyte antigen (HLA) gene complex. Here I present a simple protocol for predicting HLA signatures from whole genome shotgun (WGS) long sequencing reads, by directly streaming sequence alignments into HLAminer. The method is as simple as running minimap…
A mathematical framework for centromere-aware evaluation of human genome assemblies
ItalAI；Department of Computer Science, University of Rome “La Sapienza”（罗马第一大学计算机科学系）；Department of Biology and Biotechnologies “Charles Darwin”, University of Rome “La Sapienza”（罗马第一大学生物与生物技术系（查尔斯·达尔文））
This paper presents a novel optical processing approach for exploring genome sequences built upon optical correlator for global alignment and extended DV-curve method for local alignment. To overcome the problem of traditional DV-curve method for presenting an accurate and simplified output, we propose HAWPOD, built upon DV- curve method, to analyze genome sequences in five steps: DNA coding, alignment, noise cancellation, simplification, and modification. Moreover, all-optical implementation of the HAWPOD method is developed, while its accuracy is validated through numerical simulations in LU…
In recent years, aligning a sequence to a pangenome has become a central problem in genomics and pangenomics. A fast and accurate solution to this problem can serve as a toolkit to many crucial tasks such as read-correction, Multiple Sequences Alignment (MSA), genome assemblies, variant calling, just to name a few. In this paper we propose a new, fast and exact method to align a string to a D-string, the latter possibly representing an MSA, a pan-genome or a partial assembly. An implementation of our tool dsa is publicly available at https://github.com/urbanslug/dsa
Motivation: Next generation methods of DNA sequencing produce relatively high rate of reading errors, which interfere with de novo genome assembly of newly sequenced organisms and particularly affect the quality of SNP detection important for diagnostics of many hereditary diseases. There exists a number of programs developed for correcting errors in NGS reads. Such programs utilize various approaches and are optimized for different specific tasks, but all of them are far from being able to correct all errors, especially in sequencing reads that crossing by repeats and DNA from di/polyploid eu…
Genome sequence analysis plays a pivotal role in enabling many medical and scientific advancements in personalized medicine, outbreak tracing, and forensics. However, the analysis of genome sequencing data is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems. In this dissertation, we propose four major works, where we characterize the real-system behavior of the genome sequence analysis pipeline and its associated tools, expose the bottlenecks and tradeoffs, and co-design fast and efficient algorithms along with scalable and energy-efficient…
Motivation: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -- called short reads -- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and "candidate" locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (1) it is implemented using quadratic-time dynamic programming algorithms, and (2)…
Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. Unfortunately, it is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data. A major contributor to this bottleneck is approximate string matching (ASM). We propose GenASM, the first ASM acceleration framework for genome sequence analysis. We modify the underlying ASM algorithm (Bitap) …
BarraCUDA是一个C程序，它使用BWA算法与nVidia CUDA并行，将短的下一代DNA序列与参考基因组进行比对。遗传改良（GI）代码在来自1000个基因组项目的短配对末端读取上快了三倍，在短的BioPlanet.com GCAT比对基准上准确率提高了60%。在单个K80 Tesla GPU上运行的Barracuda可以比对短的成对端nextgen序列，速度比12核CPU上的bwa快10倍。
Purdue University（普渡大学）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Adobe Research（Adobe 研究院）
Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads;…
Analysis of evolutionary origins of genomic loci harboring 59,732 candidate human-specific regulatory sequences identifies genetic divergence patterns during evolution of Great Apes
